energy storage system scheduling in wind-diesel...

Energy Storage System Scheduling in

Wind-Diesel Microgrids

Michael Ross

Department of Electrical & Computer EngineeringMcGill UniversityMontreal, Canada

July 2010

A thesis submitted to McGill University in partial fulfillment of the requirements for thedegree of Master of Engineering.

c� 2010 Michael Ross

i

Abstract

This thesis proposes a knowledge based expert system tool that can be used as an on-

line controller for the charging/discharging of an energy storage system in a wind-diesel

microgrid. The wind-diesel microgrid is modelled, and a typical energy storage system

is implemented to test the functionality of the controller using hourly-discrete power val-

ues. The results are compared against an offline optimization that was provided 24-hour

lookahead wind values, as well as a controller that was implemented using artificial neural

networks. The knowledge based expert system is then used to analyze the cost of energy,

by means of a parametric analysis, consisting of varying the wind penetration, energy stor-

age system power rating and energy rating to determine for which wind penetration values

a storage system implementation would be technically and economically viable. Differ-

ent storage technologies are tested in a one-year time frame to determine which would be

best suited for this particular application. The energy storage systems are implemented

as single-layer and dual-layer, in which the knowledge based expert system is modified for

the latter analysis, in order to determine whether or not there are advantages to having

a dual-layer storage system. Throughout these analyses, the flexibility of the knowledge

based expert system controller to various energy storage systems and microgrid models

is verified. It also demonstrates that, in a context of high base generation costs, energy

storage can be a viable solution to managing wind power variations.

ii

Resume

Cette these propose un systeme expert avec une base de connaissance qui peut etre utilise

comme un contoleur lors de la charge et de la decharge d’un systeme de stockage d’energie

dans un micro-reseau eolien-diesel. Un micro-reseau eolien-diesel modele est etabli, et un

stockage est installe pour tester les fonctionnalites du controleur en utilisant des valeurs

de la puissance horaire. Les resultats sont compares avec une optimisation utilisant 24

heures de valeurs en avance pour la vitesse du vent, et aussi avec un controleur base

sur un reseau de neurones artificiels. Le controleur systeme expert est ensuite utilise pour

analyser les couts d’energie d’une analyse parametrique, en variant la penetration du vent, la

puissance nominale du stockage, et la capacite nominale du stockage. Cette analyse indique

pour quelles valeurs de penetration eolienne une mise en œvre d’un stockage serait viable

economiquement et techniquement. Differentes technologies de stockage sont testees afin de

determiner laquelle serait le mieux adapte pour cette application particuliere. Les systemes

de stockage sont realises a l’aide d’un ou de plusieurs types de systemes, et le controleur

systeme expert est modifie en consequence, afin de determiner s’il y a des avantages a avoir

ce type de stockage. Ces analyses montrent aussi que le controleur systeme expert a la

capacite et la flexibilite de s’adapter a des technologies ainsi qu’a des micro-reseaux de

differents types.

iii

Acknowledgments

I would like to sincerely thank my supervisor, Professor Geza Joos, for his guidance and

support throughout my Master’s studies. I have learned so much in my education at McGill

University, and I owe a large part of that to Professor Joos. He challenged me with high

objectives and pushed me to always strive to be better, and he was always able to reward

the hard work. I am extremely grateful for the opportunities that he has provided me and

allowed me to pursue. I am excited to continue my studies under his supervision.

I would also like to thank my colleagues in the Electric Energy Systems Laboratory

group. In particular, I would like to sincerely thank Rodrigo Hidalgo for his help through-

out my Master’s program. He has co-authored most of my publications and has helped

in teaching me many things about practical issues of power systems. He has been a close

friend that I can always trust and rely on. My gratitude also goes out to Dr. Chad Abbey

for his help and guidance throughout my research endeavours, by taking the time to help

and explain many things to me, and for making his LATEX thesis template available to

me. Thanks to Mohamed El Chehaly, Carlos Martinez, Jonathan Robinson, Amir Kalan-

tari, Etienne Veilleux, and Hamed Golestani Far, and other people in the Electric Energy

Systems Laboratory for being helpful and giving advice on how to improve my research.

I would like to thank my family and friends that have helped me keep a balanced life.

I know that my family is there for me whenever I need it, and just that knowledge has

enabled me to take chances in life and strive to be a better person. My parents are the

most selfless people I know, and their constant support has enabled us to grow into a strong

and close family. I can always count on my siblings, Stephanie, Nicole, Katie, Bryan, and

John, to be there for me if ever I need to relax, have fun, or just joke around. I would

particularly like to thank Bryan, who was my housemate throughout my time in Montreal.

It’s always a great pleasure spending time with my family.

Finally, I would like to thank the friends I made through the McGill Rowing Team,

McGill Nordic Skiing Team, and the McGill Triathlon Team. Because of everyone men-

tioned here, I feel I have been able to keep my mind, body and soul in proper balance.

iv

Contents

1 Introduction 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.1 Microgrids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.2 Energy Storage Systems . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.3 Expert Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.3.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 System Model and Knowledge Based Expert System Controller 11

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Microgrid Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2.1 Diesel Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2.2 Wind Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2.3 Load Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.2.4 Energy Storage System . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3 Knowledge Based Expert System Controller . . . . . . . . . . . . . . . . . 16

2.3.1 Inferencing Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.3.2 Knowledge Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3 Validation of the KBES Controller 21

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Contents v

3.2 Case Study Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.3.1 Comparison to ANN Controller . . . . . . . . . . . . . . . . . . . . 26

3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4 Parametric Analysis of Varying Wind Penetration and ESS Sizing 33

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.3.1 Power Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.3.2 Cost Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5 ESS Technology Analysis Using KBES Tool 47

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.2 ESS Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.4 Single-Layer ESS Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

5.5 Dual-Layer ESS Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.5.1 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.5.2 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

6 Conclusions 67

6.1 Thesis Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

6.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

6.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

References 73

vi

List of Figures

1.1 Typical ESS power (x-axis) and energy (y-axis) capabilities [26]. . . . . . . 5

2.1 Wind-diesel power system with an energy storage system. . . . . . . . . . . 12

2.2 Plot of WTG power curve per turbine. . . . . . . . . . . . . . . . . . . . . 15

2.3 Diagram of the inputs and outputs of the KBES Controller. . . . . . . . . 17

3.1 Power Profiles of the continuous diesel system in per unit between hours

2050-2450 of the five-year analysis. . . . . . . . . . . . . . . . . . . . . . . 25

3.2 Plot of ESS power versus energy states in pu for all hours in year 2 for the

KBES Controller. Figures on the left represent the offline optimization and

figures on the right represent the online KBES Controller, with and without

diesel shutdown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.3 Plot of ESS power versus energy states in pu for all hours in a given year

for the ANN Controller. Figures on the left represent the offline optimiza-

tion and figures on the right represent the online ANN controller, with and

without diesel shutdown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.4 Plot of ESS discrete probability functions for all hours in year 2 for the

KBES Controller. Figures on the left represent the offline optimization and


diesel shutdown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.5 Plot of ESS discrete probability functions for all hours in a given year for the

ANN Controller. Figures on the left represent the offline optimization and

figures on the right represent the online ANN controller, with and without

diesel shutdown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

List of Figures vii

3.6 Plot of diesel power discrete probability functions for all hours in year 2 for

the KBES Controller. Figures on the left represent the offline optimization

and figures on the right represent the online KBES Controller, with and


3.7 Plot of diesel power discrete probability functions for all hours in a given

year for the ANN Controller. Figures on the left represent the offline opti-

mization and figures on the right represent the online ANN controller, with

and without diesel shutdown. . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.8 Plot of ESS power versus diesel generator power in pu for all hours in year 2

for the KBES Controller. Figures on the left represent the offline optimiza-

tion and figures on the right represent the online KBES Controller, with and


3.9 Plot of ESS power versus diesel generator power in pu for all hours in a

given year for the ANN Controller. Figures on the left represent the offline

optimization and figures on the right represent the online ANN controller,

with and without diesel shutdown. . . . . . . . . . . . . . . . . . . . . . . 30

4.1 Power Profiles of the system in per unit for two weeks for both the continuous

(left) and discontinuous (right) diesel operation using 15 WTG (rwl = 0.75),

and a small ESS (PESS = 60 kW, EESS = 600 kWh). . . . . . . . . . . . . . 35



and a large ESS (PESS = 240 kW, EESS = 1600 kWh). . . . . . . . . . . . . 37



and a medium-sized ESS (PESS = 120 kW, EESS = 800 kWh). . . . . . . . . 38

4.4 The cost per kilowatt hour of the system in continuous diesel operation,

varying the ESS sizes and number of WTG. Lower values are better. . . . . 39

4.5 The cost per kilowatt hour of the system in discontinuous diesel operation,

varying the ESS sizes and number of WTG. Lower values are better. . . . . 40

4.6 The cost difference between having a fully rated ESS and no ESS for the con-

tinuous diesel operation for various wind power penetration levels. Positive

values indicate that it is more expensive to implement an ESS. . . . . . . . 42

viii List of Figures

4.7 The cost difference between having a fully rated ESS and no ESS for the

discontinuous diesel operation for various wind power penetration levels.

Positive values indicate that it is more expensive to implement an ESS. . . 42

5.1 Flow chart of the methodology for sizing the ESS and cost evaluation. . . . 52

5.2 The optimized power and energy ratings of the ESS. . . . . . . . . . . . . . 55

5.3 The cost of energy for the system, ckWh, after implementing the optimized

respective ESS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.4 Cost with a Lead-Acid ESS for Varying Power and Energy Ratings for Con-

tinuous Diesel Operation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.5 Cost with a Lead-Acid ESS for Varying Power and Energy Ratings for Dis-

continuous Diesel Operation. . . . . . . . . . . . . . . . . . . . . . . . . . . 64

ix

List of Tables

2.1 Rules within KB for the Continuous Diesel Case. . . . . . . . . . . . . . . 18

2.2 Rules within KB for the Discontinuous Diesel Case. . . . . . . . . . . . . 19

3.1 Range of Community Load Values . . . . . . . . . . . . . . . . . . . . . . . 22

3.2 Diesel Generator Technical Specifications . . . . . . . . . . . . . . . . . . 22

3.3 ESS Technical Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.4 Wind Farm Technical Specifications . . . . . . . . . . . . . . . . . . . . . 23

3.5 KBES Controller and offline optimization energy performance for different

time periods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.6 KBES Controller and offline optimization cost performance for different time

periods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.1 The lowest cost of energy for each wind penetration and the respective power

and energy ratings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.1 ESS technical parameter specifications and continuing costs based on [62]. 50

5.2 ESS initial cost specifications based on [62]. . . . . . . . . . . . . . . . . . 51

5.3 The sizes for the most inexpensive results of the dual-layer ESS technologies

for the continuous diesel operation. . . . . . . . . . . . . . . . . . . . . . . 60

5.4 The sizes for the most inexpensive results of the dual-layer ESS technologies

for the discontinuous diesel operation. . . . . . . . . . . . . . . . . . . . . . 61

x

List of Acronyms

AI Artificial IntelligenceANN Artificial Neural NetworkCAES Compressed Air Energy StorageDER Distributed Energy ResourceES Expert SystemESS Energy Storage SystemGAMS General Algebraic Modelling SystemIEEE-RTS Reliability Test System-1996KB Knowledge BaseKBES Knowledge Based Expert SystemMLC Minimum Loading ConstraintO&M Operation and MaintenanceRE Renewable EnergySMES Superconducting Magnetic Energy StorageSOC State of ChargeVRB Vanadium Redox Flow BatteryWTG Wind Turbine Generator

xi

List of Symbols

The following lists the most important symbols used in the thesis. Where relevant, boldface

is used to denote vectors or matrices.

Indices

h Index of total time periods

t Index of time periods from 1 to T

Functions

Cy(·) Yearly cost of operating the system [$]

ckWh(·) Average minimized cost of energy per kilowatt-hour [$/kWh]

Parameters

PESS Power rating of ESS [kW]

PESS,m Power rating of medium-term ESS [kW]

PESS,s Power rating of short-term ESS [kW]

Pmin Diesel minimum loading constraint [kW]

Pd,max Maximum diesel generating power [kW]

PW Power rating of wind farm [kW]

PL Base load power rating [kW]

xii List of Symbols

EESS Energy rating of ESS [kWh]

EESS,m Energy rating of medium-term ESS [kWh]

EESS,s Energy rating of short-term ESS [kWh]

Et−1 Energy state of ESS at the beginning of hour, t-1 [kWh]

NWTG Number of wind turbine generators

πd Cost of energy supplied by the diesel generator [$/kWh]

πw Cost of energy supplied by the wind farm [$/kWh]

πOMfFixed O&M Costs of ESS [$/kW/year]

πOMv Variable O&M Costs of ESS [$/kW/year]

πPCS ESS Power Conversion System initial cost [$/kW]

πdis ESS disposal cost [$/kW]

πess,p Incremental cost of ESS storage power rating [$/kW]

πess,e Incremental cost of ESS storage energy rating [$/kWh]

πESS Total annual average cost of ESS [$/year]

ηch Charging efficiency of ESS [%]

ηdis Discharging efficiency of ESS [%]

η Round-trip efficiency of ESS [%]

rwl Wind power penetration [%]

L Average lifespan of ESS [years]

T Time period that is being analyzed [h]

Variables

PL,t Community load power demand for hour t [kW]

Pw,t Wind power production for hour t [kW]

Pres,t Residual power of load minus wind for hour t [kW]

Pres,max Maximum excess power that the ESS would have to absorb or deliver

[kW]

pess,t Power of ESS during hour t [kW]

pess,m,t Power of medium-term ESS during hour t [kW]

pess,s,t Power of short-term ESS during hour t [kW]

pdiesel,t Diesel generator power during hour t [kW]

List of Symbols xiii

pdump,t Dump load power during hour t [kW]

Eloss Total lost energy due to inefficiencies of charging and discharging the

ESS [pu]

Edump Total lost energy wasted through the dump load [pu]

Eres,max Maximum excess energy that the ESS would have to store [kWh]

ESStech ESS technology used

eess,t Energy in ESS after hour t [kWh]

eess,m,t Energy in medium-term ESS after hour t [kWh]

eess,s,t Energy in short-term ESS after hour t [kWh]

udiesel,t Binary variable associated with diesel plant dispatch at hour t (0 = off,

1 = on)

x Vector of free variables in the minimization function

1

Chapter 1

Introduction

1.1 Background

Remote communities are typically isolated from large power systems (off-grid), and there-

fore must supply their load through their own means. Some of these remote communities

depend mainly on fossil fuel power plants for their energy supply. Many microgrids use

diesel generation to feed the local grid, however this results in inherently high operation

costs and emissions. These negative effects can be reduced with the integration of Renew-

able Energy (RE) sources at a high penetration level [1].

In recent years, in Canada as well as in other countries, the use of RE has grown as a

source of electricity in many power systems. This is largely due to government incentives

and public pressures to find alternatives to carbon-emitting generators. The aforementioned

remote communities may benefit greatly from the integration of RE generation to their

micro systems. Such integration would reduce the costs of fossil fuel consumption while

enhancing the sustainable development of the area, thus providing both economical and

environmental advantages [2]. For instance, some remote communities are located in an

area of high winds, thus making it economically justifiable to incorporate wind generation

into their microgrid. However, power systems are not designed to operate completely on

RE generation due to the inconsistent and intermittent source of power, and so it is still

dependent on fossil-fuel based power generation [3].

Planning and operating power systems with medium to high RE penetration levels im-

plies some difficulties due to the stochastic and intermittent characteristics of the sources

[4–6]. Exact power production of wind generators is irregular and difficult to accurately

2 Introduction

predict. Furthermore, it is necessary for isolated grids with fossil-fuel based power genera-

tion that have been integrated with RE (primarily wind farms) to implement dump loads

or generation curtailment [3]. This practice is in place to adequately meet the demand and

keep the power balance while operating within the technical constraints of the diesel plant,

as the diesel generator must operate above a Minimum Loading Constraint (MLC). The

use of dump loads or generation curtailment wastes this excess generation and reduces the

benefits of RE, which in turn affects the investment in such technologies.

The implementation of an energy storage system (ESS) may be beneficial, as it may

help to increase the RE penetration in the system [7]. An ESS is able to store the excess

generation during times of high wind/low load conditions, and deliver the power during

low wind/high load conditions. An ESS with a large storage capacity may offer a suitable

alternative to wasting energy from intermittent sources, and they increase the penetration

of RE [2, 5, 7]. In addition, an ESS that includes power conversion equipment and control

also offers operational advantages, such as power fluctuation suppression (power smoothing)

and voltage and frequency regulation [8,9]. Energy that would have otherwise been wasted

can be used later to provide power instead of increasing the diesel output. In this sense,

the total diesel consumption, carbon emissions, and fuel costs will decrease, thus increasing

the value of the RE investment and improving the output controllability [7, 10]. Another

advantage to implementing an ESS is that the system can operate in discontinuous diesel

mode, which may reduce the amount of diesel that is consumed.

Despite these advantages, there are also some disadvantages to ESSs. Primarily, they

are associated with a very high capital cost, which may not make them a financially vi-

able solution to the microgrid. These high implementation costs associated with the ESS

makes it necessary to perform an economic analysis in conjunction with any initial techni-

cal analysis considering the implementation of an ESS to determine the feasibility of the

project. An appropriate estimate on the return on investment is based on the advantages

that an ESS could provide to the community in terms of reducing fuel consumption and

optimization of the use of the wind power [2]. Another issue involved with ESSs is its

technical feasibility. Some ESSs fare better with longer-term storage, but do not handle

fast charging well, while others can do fast charging but may not be able to provide a high

energy capacity [11]. Further analysis is also required for initial estimates of the sizing and

costs of ESS implementation [12,13].

Scheduling of the available resources is required for optimal grid performance to meet

1.2 Literature Review 3

the load and reduce costs and emissions. Several studies have been conducted to optimize

the operation and scheduling of power systems with an integrated ESS [4–7]. This thesis

presents a controller that is implemented as a Knowledge Based Expert System (KBES),

with an hourly-discrete scheduling algorithm developed for an isolated power system with

diesel-wind-ESS resources. The objective of this KBES is to optimize the cost of opera-

tion by minimizing the use of dump loads (wasted energy), and therefore reducing diesel

consumption, assuring that the demand and all other constraints are met. The KBES tool

is able to control the diesel generation and the charging/discharging cycles of the storage

system from the wind and load profiles one hour in advance.

1.2 Literature Review

1.2.1 Microgrids

Microgrids are self-contained power systems that are isolated from large power systems.

Therefore, they must provide energy to their local loads themselves through at least one

distributed energy resource (DER), but they cannot provide power to other loads on a

different grid. Typically, they are located too far from a large grid to enable them to be

connected.

Distributed generators can be of many different types. Synchronous generators and

induction generators are very typical, and they act as the interface between the power

source and the rest of the microgrid [14]. Power electronic interfaced DERs, however, can

also provide voltage and frequency control. The control of these units depends on whether

or not their power source can be dispatched; e.g., wind farms are typically controlled for

peak power output. If the microgrids cannot be connected to another grid, load/generation

shedding must be used to maintain the power balance. The control of the power system

can either be centralized by one actor, or by using a decentralized approach [15].

When using more than one generator of different ratings or technologies, the scheduling

of the power output can be controlled to minimize fuel consumption [16]. The load can be

shared by one or both generators (if they are rated appropriately to do so), and the cost of

energy can be minimized. A communication link between the two power sources will facili-

tate the optimization process. The selection of the different DERs, as well as implementing

an energy storage system, will have an effect on the operation of the microgrid, and some

4 Introduction

technologies can provide operational advantages such as power response time and system

stability improvements [17].

In [18], the management issues of integrating RE generation is assessed based on its in-

tegration in a university microgrid. This paper uses Information and Communication Tech-

nology to help manage the operation of the system. A small variable speed wind turbine is

modelled as its RE generator, and it was shown that the information and communication

technology aided the human-interfaced system operation to maintain the power balance

and stability throughout its operation. Another control method, implemented in [19], uses

real power droop to regulate the power output of the DER in a microgrid. The objec-

tive of the control is to adjust its generating output to minimize fuel consumption while

maintaining stable operation.

In some situations, the power system operation can be evaluated with a microgrid that

is able to connect to a larger power system. In this case, the DER can be used to supply

the load in the microgrid by itself or with the help from the grid, or provide additional

power to the grid; it also has the advantage of providing local power reliability [20]. While

connected to the power grid, the microgrid must control its system taking into consideration

economic scheduling, load forecasting, security of the system and demand side management

functions.

1.2.2 Energy Storage Systems

There are many advantages to incorporating an energy storage system into a microgrid.

For instance, ESSs can be used to optimize the energy from RE sources and reduce fuel

consumption, offering economic and environmental advantages. When implementing wind

generation, the random nature of this DER can lead to issues with both probabilistic and

deterministic criteria of the planning and design of microgrids [21]. An ESS can help

alleviate these issues, by providing control to such uncontrollable resources.

Energy storage systems can ameliorate microgrid operations by improving the relia-

bility and quality of generated power, support other DER, and time shift generation of

uncontrollable sources [22]. When integrated with wind generation, it can also provide

the system reserve, increase the overall system operation efficiency, enhance wind power

absorption, provide fuel cost savings, and reduce CO2 emissions [23]. In [24], an ESS is

used to alleviate the intermittency of the wind generation in a microgrid and reduce the


Fig. 1.1 Typical ESS power (x-axis) and energy (y-axis) capabilities [26].

amount of wind curtailment. It was shown that an ESS can reduce the power loss and

improve the voltage profile in a system with a high wind power penetration, to the extent

that an ESS incentive program should be recommended to the regulators.

There are many different ESS technologies available [22, 25], and Fig. 1.1 shows the

typical ESS ratings of different technologies. Battery technologies have been shown to pro-

vide frequency control and stability for longer duration requirements [25, 27]. In [28], a

superconducting magnetic energy storage system is used to perform both power flow and

damping enhancements of a large wind farm. It was found that the system successfully sta-

bilizes the power output and reduces the power fluctuations inherent with wind generation.

In [29], Direct Methanol Fuel Cells are modelled based on statistical design of experiment

methodology to detail their complex nonlinear functionality. Other examples of particular

ESS technology uses include, but are not limited to, flywheel energy storage and Vanadium

Redox Flow Batteries [30, 31].

Many papers address the economic issues involved with an ESS. A nonlinear optimal

6 Introduction

ESS scheduling algorithm is presented in [32] that optimizes the charging and discharging of

the ESS using time-of-use rates with wind energy in the system. This controller determines

the optimal operating values for the next time step, and it can be used in real time and

for offline analyses. In [33], an analysis based on a Dutch generation system with large

wind generation is simulated for an implemented ESS. The operational cost savings from

the ESS increases with an increase in installed wind capacity, however it was deemed that

the large initial investment may not be worth the cost savings. In [34], attractive value

propositions for modular electricity storage are outlined. Among these propositions include

the improvement of local power quality, and electric utility end-user cost management

during times of peak or critical peak pricing.

1.2.3 Expert Systems

Many papers describe methods of using Expert Systems for control purposes. Some meth-

ods include Data Mining, Genetic Algorithms, Artificial Neural Networks, Fuzzy Logic,

and Knowledge Based Expert Systems.

Data mining is used to discover knowledge within a large amount of data [35]. In [36],

data mining is used to predict the power output of WTGs based on wind speeds that are

evaluated at different time intervals. It was found that although the wind power curves

may not be completely reliable in determining the total wind farm power output, the data

mining technique predicts the wind power very accurately when provided a multi-period

lookahead.

Genetic algorithms use populations of results, and by combining them and keeping the

best solutions for that generation, a solution may be achieved after several iterations. This

has been used for scheduling and planning in short-term and long-term time intervals.

In [37], genetic algorithms are used to plan the future expansion of an electric distribution

system over the following seven years. The algorithm uses a dynamic programming model,

whose design actions are interdependent and complex, that was tested on a 100-bus dis-

tribution system. Although it yielded higher initial costs than other expansion scheduling

approaches, the final costs were much lower after several generation iterations. In [38],

a genetic algorithm with multiplier updating is implemented to determine the optimized

power for an economic load dispatch of complex systems. The benefit of using genetic

algorithms is that mathematical models may not be applicable to solve this problem. This


has been found to obtain a minimum cost and requiring only a small amount of populations

for real-world economic dispatch problems.

Artificial Neural Networks (ANN) employ adaptable nodes that can modify their firing

threshold to output desired results. ANNs have been successfully applied to power systems

in [39], which used them for load forecasting, fault classification, voltage stability, economic

dispatch, and power system stabilizer design. In [40], a fast-learning ANN is used as a real-

time fault detector for distance protection. Another use in power systems is to predict

different transformer oil parameters, which was implemented using a three-stage neural

network cascade in [41].

Fuzzy logic implements certainty weightings associated with logic values, such that

they can have a value between 0 and 1 instead of being strictly binary. Fuzzy logic is

used to control the coordination of a circuit breaker with an implemented superconducting

magnetic energy storage system (SMES) in [42]. It was found that the fuzzy logic controller

performed much better in terms of system stability than by simply auto-reclosing the circuit

breaker, and the control system also had a better transient stability performance than a

static nonlinear controlled SMES. In [43], a fuzzy logic controller is used to control the power

output and voltage stabilization of a four-machine power system. This paper also uses a

systematic analytical method so that there is no need of prior knowledge of the system.

Results show that the controller is able to provide good system stability and sufficient

oscillatory damping without compromising voltage regulation. In [44], fuzzy optimization

models are used to determine day-ahead unit commitment of generators in power systems

with wind generators. Fuzzy set theory helps to find an optimized solution to imprecise or

conflicting objectives.

Conversely, this thesis focuses primarily on a Knowledge Based Expert System (KBES).

The purpose of developing a KBES is to create an engine that attempts to perform tasks

at the level of experts in the respective domain (i.e. a system that imitates the problem-

solving behaviour used by human experts [45, 46]). The general structure of an expert

system includes the user inputs, the Knowledge Base (KB), an inference engine, and the

outputs. The inference engine combines the user inputs with the knowledge base to generate

the outputs [47].

The facts and the rules that constitute the expert system are derived from one or

several human experts in the problem domain [48]. In order for the KBES to perform as

an expert, it is necessary that both declarative and procedural knowledge possessed by

8 Introduction

experts are implemented in the KB. Declarative knowledge is concerned about the facts

and concepts, as well as the relationships between them within in a particular domain.

Procedural knowledge describes how to reason or handle the information within the KB

[45,46].

The interface of the expert system should be developed so that it could be used to solve

problems even by users without experience in the knowledge domain [49]. The Knowledge

Engineer (designer) must to attempt to develop a user-friendly system in order to avoid

wasting the user’s time and effort struggling with how to use it. In the design process, all

the user requirements should be considered for a successful system design [46].

1.3 Research Objectives

1.3.1 Problem Definition

The purpose of this thesis is to develop a controller that will optimally schedule the charg-

ing/discharging cycle of the ESS and the diesel generation to cover the power balance

within the system’s constraints. This controller will be used to determine under which

circumstances an ESS is technically and economically justifiable, and which technologies

are best suited for this application.

1.3.2 Contributions

This thesis provides new research in the area of scheduling and operation of a wind-diesel-

ESS microgrid. In particular, it provides insights into the implementation phase of incor-

porating an ESS through the use of a Knowledge Based Expert System Controller. Several

contributions of this thesis include:

• The creation of an online controller, implemented as a Knowledge Based Expert Sys-

tem, that is able to dispatch the diesel generation and the ESS charging/discharging

that can yield optimized results for minimizing the dump load consumption and the

cost of energy. These results have been presented and published in [13].

• A parametric analysis of the wind penetration and ESS ratings in the microgrid to

determine for which penetration values and for which ESS sizes would yield the lowest

1.4 Summary 9

cost of energy and best technical operation of the system. These results have been

presented and published in [50].

• An analysis of microgrid operation under both continuous and discontinuous diesel

modes of operation. These results have been presented and published in [51].

• An analysis of various ESS technologies in single-layer and dual-layer configurations.

These results have been presented and published in [52].

Work performed in Chapters 2 and 3 have been done in collaboration with fellow grad-

uate student Rodrigo Hidalgo Anfossi. He has provided much of the research data into the

system parameters and he helped develop the KBES Controller. He has also been consulted

on further work to verify the logic of the methodology and knowledge base.

1.4 Summary

This thesis is divided into the following chapters:

Chapter 1: Introduction

In this chapter, an introduction to the problem area is presented. Some generic, key

problems are addressed, and a literature review section details what research has been

done in this area in an attempt to solve the problem as well as overviewing other AI

techniques for control. The purpose of the thesis, in terms of the research goals and the

contributions, are given in the Research Objectives.

Chapter 2: System Model and Knowledge Based Expert System Controller

This chapter will detail the base model within which the Knowledge Based Expert System

tool will control. The basic parameters, the technical constraints, and the mathematical

models of each component are outlined. Once this is established, a description of the KBES

Controller is given with the rule base that is derived from the model system.

Chapter 3: Validation of the KBES Controller

In this chapter, the KBES Controller is tested online, and the results of the testing are

provided. The success of the controller is based upon the amount of diesel fuel that is

10 Introduction

saved, measured by the reduction in diesel generation, and the reduced cost of energy to

the community. These results are compared against an offline optimization function, as

well as to an Artificial Neural Network controller that is attempting to achieve the same

goal.

Chapter 4: Parametric Analysis of Varying Wind Penetration and ESS Sizing

This chapter modifies the microgrid model developed in Chapter 2 to analyze the effects of

varying the level of relative wind generation and ESS power rating and energy rating. The

purpose of this chapter is twofold. First, it demonstrates the flexibility and adaptability of

the KBES tool. Second, it shows the effect of implementing various ESS sizes in microgrids

of different wind penetrations on the cost of energy. This chapter also determines at which

penetration level the implementation of an ESS is financially justified for both continuous

and discontinuous diesel operation.

Chapter 5: ESS Technology Analysis Using KBES Tool

In this chapter, different ESS sizes of different ratings are implemented in the base micro-

grid model from Chapter 2. At first, a single-layer ESS is implemented for ten different

technologies for continuous and discontinuous diesel operation. Then, the KBES is mod-

ified to enable a dual-layer ESS, where medium-term ESS technologies are coupled with

short-term ESS technologies. The technical and economic analyses for these results are

provided.

Chapter 6: Conclusions

This chapter concludes the thesis by reviewing the work that has been done and explicitly

details the conclusions that are drawn from these analyses. This chapter is divided into

subsections to summarize these results for Chapters 2-5. Future work is then suggested in

order to guide the advancement of research in this area.

11

Chapter 2

System Model and Knowledge Based

Expert System Controller

2.1 Introduction

This chapter will cover the system model for which the Knowledge Based Expert System

Tool will control the energy generation, dump, and charge/discharge of the ESS. This model

will be used as a base model, and studies detailed in further chapters will use this model

or a modification. The KBES Controller is also detailed, and is based on the parameters

of the microgrid.

2.2 Microgrid Model

The isolated power system, shown in Fig. 2.1, comprises a diesel generator, the community

load, a wind farm, an ESS, and a dump load. Transmission losses and reactive power flow

are neglected since a small remote system is considered, although one method of how it

can be implemented is presented in [53]. Community load and wind data are assumed to

be deterministic for the following hour; that is, the forecasting of these values are assumed

to be known without errors. The impact of the energy storage sizing, and the wind and

load forecasting accuracy, which is discussed in [6], is out of the scope of this thesis.

The main goal of the KBES Controller is to minimize the cost of the operation of the

system over a given period. The wind and load data are independent and are treated

as uncontrolled inputs. It is assumed that the ESS has been previously sized using [13].

12 System Model and Knowledge Based Expert System Controller

Fig. 2.1 Wind-diesel power system with an energy storage system.

The controller must then schedule the diesel generation and the charging and discharging

of the ESS in order to minimize the cost of operation, hour by hour, based on the rules

implemented in its Knowledge Base (KB).

The yearly cost of operating the system can be calculated as:

Cy(x) = πess,e · EESS + πess,p · PESS +T�

t=1

(πd · pdiesel,t + πw · Pw,t) (2.1)

which is then divided by the total annual load to yield the cost per kilowatt-hour.

The costs associated with the ESS implementation and Operation & Maintenance

(O&M), πess,e and πess,p, include the incremental costs, fixed costs and variable costs of

O&M of the ESS throughout the projected lifetime.

2.2 Microgrid Model 13

The optimization problem is defined as:

minx

Cy(x) (2.2)

where the vector of free variables x is:

x =�

pdiesel pdump pess udiesel

�(2.3)

subject to the constraints of the power system characteristics as described in Sections 2.2.1

through 2.2.4 and the power balance.

The optimal results are found through an optimization function implemented in GAMS

(General Algebraic Modelling System) for (2.2), with the constraints listed in Sections

2.2.1-2.2.4. Note that this is minimizing the generation from the diesel generator since the

costs associated with the ESS in (2.1) are fixed for this analysis, and the wind generation

is also pre-defined.

The GAMS optimization is used as a benchmark against which the KBES Controller

is compared. The twenty-four future hourly values for wind and load are provided to

the GAMS optimization in order to appropriately schedule the power for the given hour.

Conversely, the KBES scheduling implementation is only given the values for the current

hour. The KBES is implemented in this manner to justify whether it is necessary to provide

forecasting values for ESS scheduling systems, as used in [6]. Since it is not provided with

future data, the KBES attempts to minimize the diesel generation by minimizing the power

wasted through the dump load for every hour. Formally written, the KBES attempts to

minimize:

minx

pdump,t ∀t ∈ T (2.4)

with the same constraints as (2.2). Section 2.3 details the implementation of the KBES.


2.2.1 Diesel Generation

The technical characteristics of the diesel generator used in this analysis are extracted

from [54], and is given in Table 3.2. One important factor in the operation of diesel-fuelled

equipment is their Minimum Loading Constraint (MLC). This should be considered in order

to avoid premature aging and engine failure [3]. In this case, a 30% MLC of the generator

rating was defined:

Pmin = 0.3 · Pd,max (2.5)

Two operating cases are considered in this thesis: one with the diesel generator operating

continuously, and the other that allows for the diesel to be turned off. For both cases, a

binary variable will be defined as:

udiesel,t =

�1 diesel generator turned on

0 diesel generator turned off(2.6)

where udiesel,t = 1 ∀t ∈ T for the continuous case. Although there are both time and

financial costs associated with inefficiencies of turning on and off the diesel generator, these

are not factored into this optimization problem. Ramping constraints of diesel generators

are also neglected, so the generator can instantly increase or decrease its power output to

any desired level within its operation constraints, which are defined as:

udiesel,t · Pmin ≤ udiesel,t · pdiesel,t ≤ udiesel,t · Pd,max (2.7)

The maximum constraint is not considered since all generating power is assured to meet

the load for all t ∈ T without load shedding.

2.2.2 Wind Generation

The local grid includes a wind farm, modelled from [13]. The technical specifications of the

wind farm are extracted from [55], and the WTG power curve is shown in Fig. 2.2. Wind

conditions are assumed to be the same for all turbines; that is, all the turbines will have

the same power output for any given hour [6].

The wind power profiles are acquired by the KBES from external files, allowing the

2.2 Microgrid Model 15

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Net

Pow

er O

utpu

t (kW

)

Wind Speed (m/s)

WTG Power Curve

Fig. 2.2 Plot of WTG power curve per turbine.

Expert System to be adaptable to any community.

2.2.3 Load Model

The community load for the system was modelled using the IEEE Reliability Test System-

1996 (IEEE-RTS) [56], which takes into consideration the typical variation of a system’s

demand. Such variations are due to the time of day, the day of the week, and the different

seasons. In addition, these variations are defined as a percentage of a community peak load,

allowing the Expert System to be adapted to any microgrid. The minimum and maximum

values of the community load are shown in Table 3.1. For the twenty-year simulation, the

same one-year load data is used and repeated for every year.

Since the load data and the wind data are both given in the problem and are not

affected by the controllable vector x in (2.3), a new variable that represents the residual

power between these two vectors is defined as:

Pres = PL −Pw (2.8)

This is used as a simplification for other equations since the power balance can be met by

either absorbing or producing the required residual power in the system.


It is necessary to include a dump load in the system in order to maintain power balance

while generating at the MLC during low load and/or high wind conditions. The dump load

power rating is defined to be equal to the maximum rated output of the wind farm plus

the MLC, so it is able to handle all possible dumped energy. In the present case, wind

generation curtailment is considered to be synonymous with the use of a dump load.

2.2.4 Energy Storage System

The ESS is used to absorb excess electrical energy when generation is too high, and deliver

electrical energy to the system to help meet the power demand. This, along with the dump

load, are used to balance the power in the system. The purpose of adding an ESS to

a microgrid would be to shift the generation, when applicable, to better match the load

profile. This can help reduce the amount of diesel generation and the amount of wasted

energy in the system. The methodology used for sizing the ESS is detailed in [12]. The

cost of an ESS comprises the capital costs, and fixed and variable O&M costs [7].

Similar to the load and wind profiles, the expert system could be adapted to different

energy storage technologies, provided that the ESS is defined by its energy capacity, power

rating, and charging/discharging efficiencies [8, 57]. This will be shown by the parametric

analysis in Chapter 4. The optimization can also be expanded to incorporate an imposed

minimum and maximum State of Charge (SOC) of the ESS, since the lifetime of some ESS

(such as VRB) will be reduced if it operates frequently in low or high SOC [58]. Another

approach would be to appropriately monitor and manage the battery SOC, as presented

in [59].

In the power balance equation, the ESS will be considered as a source, and so pess,t is

positive when the ESS is discharging, and negative when it is charging. A VRB was used

in the base case instead of other technologies because of its long life, high stability, and the

energy and power ratings are modular and easily scalable [60].

2.3 Knowledge Based Expert System Controller

An Expert System (ES) is a computer program with a knowledge based component that

has the ability to reason as a human expert in the problem domain [13]. In the case of a

KBES, the Knowledge Base is typically a set of rules that is able to infer new knowledge

in order to allow the ES to make intelligent decisions.

2.3 Knowledge Based Expert System Controller 17

2.3.1 Inferencing Engine

Fig. 2.3 Diagram of the inputs and outputs of the KBES Controller.

The first step in implementing the KBES was to appropriately implement the inferencing

engine into Matlab. Most programming languages, however, are run as a sequential set of

instructions in an algorithm. Unlike in a program, new knowledge is inferred from rules

within the KB, which is then fed back into the ES. One of the benefits of using this approach

is that that each constraint can be implemented as a separate rule, and an algorithm does

not need to be formulated.

In a KBES, the rules are fired once the conditions have been met, and the rules do

not fire in any particular order. The rules are implemented using if statements (the KB

component) within a while loop (the search for a solution or inference engine) [61]. The

loop continues until all the constraints are met, including the power balance.


2.3.2 Knowledge Base

Table 2.1 Rules within KB for the Continuous Diesel

Case.

Rule # Description1 Default - The ESS can handle the excess load/generation

pdiesel,t = Pmin, pdump,t = 0

2 Power balance must be metpdiesel,t + pess,t = Pres,t + pdump,t

3 Dumped energy must be greater than or equal to zeropdump,t ≥ 0

4 Generator level must be higher than the MLCpdiesel,t ≥ Pmin

5 ESS Power cannot exceed its maximum rated levels�pess,t� ≤ PESS

6 Final energy in ESS is the initial energy minus the powereess,t = Et−1 − ηch · pess,t

1

eess,t = Et−1 − pess,t/ηdis2

7 ESS Energy cannot exceed its maximum rated levels0 ≤ eess,t ≤ EESS

8 ESS Power is the difference in energy levels for the hour3

pess,t = (Et−1 − eess,t)/ηch1

pess,t = ηdis · (Et−1 − eess,t) 2

1. Charging.

2. Discharging.

3. This is the same equation as in Rule #6, except re-arranged for the Power.

For the proposed problem, only the information for the next hour is considered. The

rules are implemented to minimize the use of the dump load, which reduces the diesel

generator power for that hour. The list of rules for the continuous diesel operation is given

in Table 2.1. The first rule is to assume the optimal case for each hour: the diesel is

operating at its MLC, and the excess generation or load is then handled by the ESS. This

is the only rule that is outside the while loop. The other rules are used to modify the

2.3 Knowledge Based Expert System Controller 19

scheduling if any of the constraints are being violated. A diagram of the KBES Controller

is shown in Fig. 2.3.

Table 2.2 Rules within KB for the Discontinuous Diesel

Case.

Rule # Description1 Default - The ESS can handle the excess load/generation

without the use of the diesel generatorudiesel,t = 0, pdump,t = 0

2 Power balance must be metudiesel,t · pdiesel,t + pess,t = Pres,t + pdump,t

3 Dumped energy must be greater than or equal to zeropdump,t ≥ 0

4 If on, generator level must be higher than the MLCpdiesel,t ≥ Pmin

5 ESS Power cannot exceed its maximum rated levels�pess,t� ≤ PESS

6 Final energy in ESS is the initial energy minus the powereess,t = Et−1 − ηch · pess,t

1

eess,t = Et−1 − pess,t/ηdis2

7 ESS Energy cannot exceed its maximum rated levels0 ≤ eess,t ≤ EESS

8 ESS Power is the difference in energy levels for the hour3

pess,t = (Et−1 − eess,t)/ηch1

pess,t = ηdis · (Et−1 − eess,t) 2

9 If power balance cannot be met with given constraints,turn on the generator 4

udiesel,t = 1, pdiesel,t = Pmin, pdump,t = 0

1. Charging.

2. Discharging.

3. This is the same equation as in Rule #6, except re-arranged for the Power.

4. This is the same condition as in Rule #1 in Table 2.1.


For the case of discontinuous diesel operation, the main differences in the rules are that

the optimal case assumes that the diesel is turned off and the excess generation or load is

then handled solely by the ESS, as shown in Table 2.2. This will minimize the dumped

power, which is the objective defined in (2.4). There is also a ninth rule that states that

if the diesel is turned off and no power balance can be found given the constraints, the

generator is turned on. Note that Rules 3-8 in Tables 2.1 and 2.2 are identical.

The variable inputs for each hour are simply the initial energy state of the ESS, Et−1,

and the residual power, Pres,t. The outputs are the scheduled powers for the diesel generator,

pdiesel,t, and the ESS, pess,t. From this, the dump load power, pdump,t, can easily be calculated

through the power balance equation.

The optimal results, to be used as a benchmark, are obtained by a minimizing function

over the entire time interval that minimizes (2.1). This optimizing function is given a 24-

hour lookahead to the residual values in order to appropriately schedule the powers for the

given hour.

2.4 Conclusions

The microgrid that will be used for further analyses is modelled in this chapter. The

microgrid consists of a diesel generator and a wind farm to power the local demand, and an

ESS and dump load to help balance the power in the system. The system constraints are

outlined, based on the equipment that are modelled, and a mathematical objective function

is defined to determine the cost of energy for the remote community.

A Knowledge Based Expert System Controller is detailed, including the rules that are

stored in the Knowledge Base which are based on the system constraints. The KBES

Controller attempts to minimize the amount of energy wasted through the dump load in

order to minimize the cost of energy. This controller is responsible for scheduling the diesel

generation, the ESS charging/discharging powers and the dump load for the next hour,

since it is only provided a one-hour lookahead of wind and load values. This system can

then be tested with specific parameters in order to validate the functionality of the KBES

Controller.

21

Chapter 3

Validation of the KBES Controller

3.1 Introduction

The purpose of this chapter is to test the KBES system within the modelled microgrid to

determine how well the KBES performs online. Its performance is based on whether it is

able to schedule the hourly powers of the generator, ESS, and dump load accordingly such

that the overall cost of energy is minimized. Specific values are given to the elements within

the microgrid to enable test cases to be simulated. Both continuous and discontinuous diesel

operation are considered for the test cases.

The results of the simulation are compared against an offline optimization function

that is given precise 24-hour lookahead values for each hour. The offline optimization

function also attempts to minimize the cost of energy through the scheduling of the diesel

generator, ESS, and dump load. In addition, the KBES Controller is also compared against

an Artificial Neural Network (ANN) Controller to analyze how the two different Artificial

Intelligence (AI) techniques operate.

3.2 Case Study Parameters

The microgrid model developed in Chapter 2 is now used in a particular case to test how

the KBES Controller compares against the optimized GAMS results. For this test case,

the parameters in Tables 3.2-3.4 are used for the system.

The load values were taken from the IEEE-RTS, providing a base value of 1 MW.

From this, the resulting community load values are given in Table 3.1. These values are

22 Validation of the KBES Controller

considered to be uncontrollable parameters, since load shedding is never used in this system

to balance the power.

Table 3.1 Range of Com-

munity Load Values

Minimum Maximum Average(kW) (kW) (kW)338.8 1000 614.8

In order to ensure that the diesel generator can generate enough power to supply the

community load without any wind generation, the diesel generator is modelled after a

generator with a nominal power output similar to, but greater than, that of the base load

value.

Table 3.2 Diesel Generator

Technical Specifications

Power Rating (kW) MLC (kW)925 227.5

The ESS installed in the system is a single layer VRB whose technical characteristics

and costs were taken from [62] and are provided in Table 3.3. For this test case, the VRB

has an energy capacity of EESS = 900 kWh and a power rating of PESS = 150 kW.

Table 3.3 ESS Technical Specifications

PESS EESS ηch ηdisType(kW) (kWh) (%) (%)

Vanadium RedoxFlow Battery

150 900 85 85

The wind farm consists of twelve 50 kW wind turbine generators (WTG), whose specific

ratings are shown in Table 3.4. The wind profiles used in this simulation were taken

from [63] for the five year simulations, and from [64] for the twenty-year simulation. The

wind power for each hour of the simulation was calculated with the power curve of the

WTG equipment from Fig. 2.2.

3.3 Results and Analysis 23

Table 3.4 Wind Farm Technical Specifications

WTG Rated WTG Maximum Number Total Capacity Capacity FactorPower (kW) Power (kW) of WTG (kW) (%)

50 65 12 600 33.1

The costs of operating the diesel generator that are taken into consideration are primar-

ily associated with the cost of diesel. The cost of diesel used in this analysis is 1.25 $/L [65],

and the conversion factor for the diesel generator is 0.368 L/kWh [54]. This results in an

energy price of diesel of:

πd = 0.368× 1.25 (3.1)

= 0.46 $/kWh (3.2)

Note that this value does not take into consideration the cost of transporting the diesel to

the remote community, nor the O&M costs of the diesel generator.

3.3 Results and Analysis

Both the offline minimizing function and the KBES optimization are simulated for con-

tinuous and discontinuous diesel operation. The analysis is performed over time intervals

of five years and twenty years. The results are shown in Table 3.5 and Table 3.6. The

cost columns refer to the yearly costs of energy per kilowatt hour, and is calculated by

dividing (2.1) by the total load energy. Similarly, the energy columns refer to the sum of

the respective energies divided by the total load energy.

The error between the Expected Cost and the Simulated Cost for the continuous diesel

case in Table 3.6 is very close to zero. This validates the simplification made with the

one hour lookahead values, namely between (2.2) and (2.4), and so reducing the energy

lost in the dump load will achieve a minimization of the cost during continuous diesel

operation [51].

For the discontinuous case, however, there is a larger margin of error between the

optimal and KBES cost results. Although Table 3.5 shows that the KBES Controller does

minimize the energy lost through the dump load, the total wasted energy in the system is

higher due to the charging and discharging efficiencies of the ESS. Since more generation


Table 3.5 KBES Controller and offline optimization energy

performance for different time periods.

Operating Period1 Expected Edump Simulated Edump Simulated Eloss2

Approach [pu] [pu] [pu]

1 to 5 0.0446 0.0440 0.0056

6 to 10 0.0412 0.0405 0.0052Continuous

11 to 15 0.0422 0.0416 0.0052

16 to 20 0.0428 0.0423 0.0053

1 to 20 0.0427 0.0421 0.0053

1 to 5 0.0060 0.0026 0.0063

6 to 10 0.0058 0.0025 0.0058Shut-down

11 to 15 0.0060 0.0028 0.0059

16 to 20 0.0066 0.0030 0.0061

1 to 20 0.0061 0.0027 0.0060

1. Period refers to the year or range of years over which the controller’s performance

was evaluated.

2. Simulated loss refers to the lost energy due to inefficiencies of charging and dis-

charging the ESS.

Table 3.6 KBES Controller and offline optimization cost

performance for different time periods.

Operating Period1 Expected Cost2 Simulated Cost3 Percent Error

Approach [$/kWh] [$/kWh] [%]

1 to 5 0.5655 0.5652 0.05

6 to 10 0.5671 0.5668 0.05Continuous

11 to 15 0.5673 0.5670 0.05

16 to 20 0.5670 0.5668 0.04

1 to 20 0.5667 0.5665 0.04

1 to 5 0.5394 0.5408 -0.26

6 to 10 0.5429 0.5444 -0.28Shut-down

11 to 15 0.5427 0.5442 -0.28

16 to 20 0.5424 0.5437 -0.24

1 to 20 0.5419 0.5433 -0.26

1. Period refers to the year or range of years over which the controller’s perfor-

mance was evaluated.

2. Expected cost refers to the cost of energy obtained using offline optimization.

3. Simulated cost refers to the cost of energy resulting from simulation of the

system using the KBES Controller.


Fig. 3.1 Power Profiles of the continuous diesel system in per unit between

hours 2050-2450 of the five-year analysis.

is required to cover the losses, the cost obtained for the KBES Controller is greater than

the optimal minimization. Despite this, the results for the 5 year and 20 year periods

are within 0.3% error of each other. Therefore, one cannot neglect the efficiency of energy

storage systems when performing analysis for discontinuous diesel operation, and the KBES

may be improved with a 24-hour lookahead implementation.

For some hours, it may be more efficient to keep the generator running than using the

ESS, but this would require a longer lookahead period. The lookahead values would be used

to predict whether or not it would be best to keep the generator running or to turn it off


for the hour in question. For example, it would be beneficial to keep the generator running

if the energy lost through the dump load is less than the energy lost in the efficiency of the

ESS and if the ESS can be discharged in the near future. Without this lookahead, however,

the small error values indicate that the minimization simplification in (2.4) is valid for the

given conditions.

With a longer lookahead period of the residual power, it may be possible to obtain

better results for the discontinuous diesel case. The lookahead is also useful when taking

into consideration other imperfections in the system such as the lifetime of ESS, optimal

SOC, efficiency of ESS, maximum diesel ramp rates, cost and time penalty associated with

turning on and off the diesel plant, and optimal diesel plant operating point.

As is shown in Fig. 3.1, the dump load is in use only when the ESS is at full capacity,

or if Pres,t > PESS. This occurs in most cases during periods of high wind. During periods

of low wind, the ESS is able to absorb the excess generation. By delivering the power as

soon as there is an opportunity to do so, a greater storage capacity becomes available to

the system if more residual energy is required to be stored.

3.3.1 Comparison to ANN Controller

The dual-layer ANN Controller, whose performance is being compared against, was devel-

oped in [66]. Fig. 3.2 and Fig. 3.3 show plots of the charging/discharging power versus the

energy stored in the ESS at the end of each hour for the KBES Controller and the ANN

Controller, respectively. The graphs show a rhombus shape, as the regions in the bottom

left and top right are invalid regions; it is impossible to have those power values and end

up with a final energy within that region. For both the continuous and discontinuous diesel

case, the KBES ESS power vs energy graphs show that the controller uses the whole range

of power and energy values within the constraints. When compared to the offline results,

the graphs are very similar. For the ANN Controller, however, the graphs do not appear

similar to the offline optimization. Although it does take advantage of the energy capacity,

the power capacities are very close to 0. This could be seen as beneficial since an ESS with

a smaller power rating could be required, but since it does not yield optimal results, this

is not ideal.


Fig. 3.2 Plot of ESS power versus energy states in pu for all hours in year

2 for the KBES Controller. Figures on the left represent the offline optimiza-

tion and figures on the right represent the online KBES Controller, with and

without diesel shutdown.

Fig. 3.3 Plot of ESS power versus energy states in pu for all hours in a

given year for the ANN Controller. Figures on the left represent the offline


with and without diesel shutdown.


Fig. 3.4 Plot of ESS discrete probability functions for all hours in year 2 for

the KBES Controller. Figures on the left represent the offline optimization and


diesel shutdown.

Fig. 3.5 Plot of ESS discrete probability functions for all hours in a given

year for the ANN Controller. Figures on the left represent the offline opti-

mization and figures on the right represent the online ANN controller, with

and without diesel shutdown.


Fig. 3.6 Plot of diesel power discrete probability functions for all hours

in year 2 for the KBES Controller. Figures on the left represent the offline

optimization and figures on the right represent the online KBES Controller,


Fig. 3.7 Plot of diesel power discrete probability functions for all hours in

a given year for the ANN Controller. Figures on the left represent the offline




Fig. 3.8 Plot of ESS power versus diesel generator power in pu for all hours

in year 2 for the KBES Controller. Figures on the left represent the offline

optimization and figures on the right represent the online KBES Controller,


Fig. 3.9 Plot of ESS power versus diesel generator power in pu for all hours

in a given year for the ANN Controller. Figures on the left represent the offline

optimization and figures on the right represent the online ANN controller, with

and without diesel shutdown.


The KBES controller behaviour is to discharge the ESS whenever possible. This mini-

mizes the energy stored in the ESS, shown in Fig. 3.4, which operates at a minimal SOC

over 50% of the time. The ESS will then be able to absorb more of the excess generation,

thus minimizing the energy lost in the dump load. As mentioned previously, the control

system can be implemented differently if there was a penalty associated with extreme SOC.

When comparing the ANN Controller’s discrete probability functions to the KBES and the

optimized solution, it can be seen that the ANN Controller leaves some charge in the ESS

more often. Since this does not yield optimal results, it may not be a desirable attribute

for a non-lookahead controller. Otherwise, the behaviour of both the KBES and ANN is

similar to their optimized results.

Fig. 3.6 and Fig. 3.7 show the frequency of operation of the various diesel generator

power values for the KBES and ANN controllers, respectively. For the KBES in the contin-

uous case, the diesel generator operates at its minimum loading constraint more often than

in other power regions, which is a by-product from minimizing (2.4). For the discontinuous

case, the diesel is turned off for a portion of the time of operation, thus further reducing

fuel consumption. Therefore, the diesel generator power levels are minimized for both the

continuous and discontinuous operation cases. Similar points can be made for the ANN

Controller since the results were also similar to the offline optimization.

There are clear differences in the diesel power discrete probability functions for the

KBES and ANN controllers, as shown in Fig. 3.8 and Fig. 3.9, respectively. For the KBES

Controller, the figures are almost identical to the offline optimized results, which means

that it optimizes the charging/discharging power levels appropriately to reduce the amount

of diesel generation. The “F” shape of the graph occurs since in the offline and KBES

situation, the diesel generation is increased mostly when the ESS power is zero (which can

mean that there is no more energy stored in the ESS, so it cannot be discharged), or when

the ESS is being discharged at its maximum power rating (which means that additional

generation is required to balance the load). Fig. 3.9 shows that this shape is not present

for the ANN controller, which could mean that the diesel is being used even though the

ESS power rating has not reached its limit. Since there is no lookahead, this would result

in excess diesel generation, thus increasing the cost of the generation.


3.4 Conclusions

This chapter performed two case studies (one using continuous and one using discontinuous

diesel operation) of the KBES Controller and microgrid model developed in Chapter 2.

Specific parameters were used, based on equipment chosen for the microgrid model, to

create a mathematical model of the microgrid. The KBES Controller is responsible for

scheduling the power production or absorption of the diesel generator, ESS, and dump load

respectively. The controller is given only one lookahead value of the hourly uncontrollable

parameters, i.e., the wind and load data. The results are compared against an offline

minimization function and an ANN Controller.

The KBES Controller was found to have very similar results when compared to the min-

imizing function. For the continuous diesel case, the KBES Controller yielded a lower cost

of energy than the optimization function by approximately 0.05%, which was attributed to

roundoff errors. For the discontinuous diesel case, the results were also very similar, with

the cost of energy being less than 0.3% more expensive when using the KBES Controller.

This cost difference is attributed to the fact that there is much more energy being lost

through the efficiency of the ESS when charging and discharging, and with a longer looka-

head, the KBES Controller may yield a lower cost of energy. However, it was deemed to

yield optimized results when given only lookahead values for the next time step.

The KBES Controller is then compared against an ANN Controller for the system.

Although they both display similar behaviour in some analyses, the ANN does differ from

the optimal behaviour more so than the KBES. The KBES utilizes the ESS’s power and

energy capacities better than the ANN Controller. It also operates the diesel generator

less often, yielding less diesel consumption. For these reasons, the KBES is deemed to be

a better controlling method than using Artificial Neural Networks.

This chapter did not determine whether or not an ESS would reduce the cost of energy,

but instead proved that the KBES Controller can yield a minimized cost of energy when an

ESS is used in a wind-diesel microgrid. Now that this tool has been shown to yield optimized

results for the cost of energy, subsequent chapters will analyze whether incorporating an

ESS is technically and economically viable by using the KBES Controller.

33

Chapter 4

Parametric Analysis of Varying Wind

Penetration and ESS Sizing

4.1 Introduction

The purpose of this chapter is to demonstrate the effects of implementing different wind

penetration values and ESS sizes on the microgrid detailed in Chapter 2. By performing

parametric analyses, it can be shown which wind power penetration levels an ESS would

be best suited. In addition, it can be shown for which ESS power and energy ratings

are appropriate. In this chapter, three parameters will be varied in order to perform the

sensitivity analysis, namely the number of WTG in the system (thus, the wind power

penetration), the power rating of the ESS, and the energy rating of the ESS.

To perform this sensitivity analysis, the KBES Controller is used on the various wind-

diesel-ESS microgrids. The only features within the KBES Controller that are modified

are the rules within the KB that reflect the system constraints. Both the continuous and

discontinuous diesel modes of operation are considered.

4.2 Methodology

For the wind profile, a one-year analysis is performed using the wind data from [63]. It is

assumed that each WTG receives the same amount of wind for each hour regardless of the

number of turbines. From this, the power produced from the wind farm is the same as the

power produced for one turbine multiplied by the number of turbines. Also, the efficiency

34 Parametric Analysis of Varying Wind Penetration and ESS Sizing

of the diesel generator is taken to be 90% from diesel fuel to electrical power.

Wind generation is modified by increasing or decreasing the number of WTG in the

system, NWTG, thus increasing or decreasing the wind penetration. Each WTG has a base

rating of 50 kW, and so the wind power base value is the number of WTG in the system

times 50. Since this model should be generic to other microgrids, a wind penetration value,

rwl, is defined which relates the amount of rated wind power installed (PW) to the base

load value (PL):

rwl =PW

PL

(4.1)

Since the base load power of the community is defined as 1000 kW, as in Chapter 3, and

the wind power base value is dependant on the number of turbines, (4.1) can be simplified

to:

rwl =NWTG × 50kW

1000kW(4.2)

In this analysis, the wind penetration will range from 0 to 1.0 in increments of 0.05.

A VRB is modelled as the ESS, and the power and energy ratings will vary independently

of each other [60]. In this analysis, the power rating ranges from

0 ≤ PESS ≤ 300

and increases in intervals of 30 kW. The energy capacity of the ESS ranges from

0 ≤ EESS ≤ 2000

and increases in intervals of 200 kWh. It is important to note that it is not feasible to

have the power rating higher than the energy rating when using the hourly discrete values

since the intra-hour variations are not modelled. For instance, although PESS and EESS

range from 0 to 300 kW and 0 to 2000 kWh, respectively, the ESS parameters for which

PESS > EESS are not taken into consideration.


4.3 Results and Analysis

4.3.1 Power Profiles

This section details the analysis of varying the wind penetration and ESS sizing on the

system, as well as the effects on the system operation. To demonstrate how the system

operates, the power profiles of the various sources and loads are shown for various wind

penetrations and ESS sizes.

0

0.5

1 Load Consumption

0

0.5

1Wind Generation

0

0.5

1Diesel Generation

0

0.5

1Dump Load Consumption

Week 1 Week 20

0.5

1ESS Energy

Continuous

0

0.5

1Load Consumption

0

0.5

1Wind Generation

0

0.5

1Diesel Generation

0

0.5


Week 1 Week 20

0.5

1ESS Energy

Discontinuous

Fig. 4.1 Power Profiles of the system in per unit for two weeks for both

the continuous (left) and discontinuous (right) diesel operation using 15 WTG

(rwl = 0.75), and a small ESS (PESS = 60 kW, EESS = 600 kWh).

Fig. 4.1 shows the power profiles for two weeks of a system with a high wind power


penetration (rwl = 0.75) and a small ESS (PESS = 60 kW, EESS = 600 kWh) for both

the continuous and discontinuous diesel modes of operation. The wind and load graphs,

shown in the first two rows, are uncontrollable variables in the analysis. The last three are

controlled by the KBES Controller, which must operate within the system constraints.

The continuous and discontinuous diesel operations yield very different profiles for the

controllable power graphs. Although the diesel peaks are very similar, there are many

situations in which the diesel would be turned off for even a day at a time (during times of

high wind). For this day, the ESS, wind generation and dump load are sufficient to power

the community and balance the microgrid without requiring the use of the diesel generator.

Of course, this is assuming that the ESS and wind farm are able to handle voltage and

frequency control. The discontinuous mode also decreases the amount of dumped load,

even if the ESS seems to be charging and discharging in a similar manner to the continuous

mode. The same two-week period is shown for this graph as well as the graphs in Fig.

4.2 and Fig. 4.3 in order to directly compare their profiles and how the KBES Controller

manages the diesel generator, ESS, and dump load.

Fig. 4.2 shows the power profiles with the same wind penetration, but with a larger ESS.

These profiles, although very similar in shape to those from Fig. 4.1, do have some notable

differences in terms of the generation and ESS utilization. For example, there are fewer

dump load fluctuations and less dumped energy for both the continuous and discontinuous

diesel modes of operation, which is to be expected. In terms of diesel generation for the

discontinuous mode, the diesel can be turned off for longer periods of time, as there are fewer

periods of frequent on/off operation. This would be important to take into consideration

if the diesel ramping rates were constrained. For both modes of diesel operation, the diesel

generator peaks are shorter in duration. This means that the generator operates at its MLC

value or is turned-off more often, which reduces the fuel consumption. However, the larger

ESS does not significantly reduce the amount of dumped energy. Thus, the initial cost of

the increased ESS size may not be economically justified, and a smaller ESS may suffice.

This is a result of the KBES Controller, which discharges the ESS whenever possible; it

does not hold energy for long periods of time.

From Fig. 4.3, it can be seen that the dump load is rather small, and the ESS is not

used as often as with a high wind power penetration, as previously discussed. The shape of

the diesel power profile resembles the load more closely than in previous examples since less

energy is being stored in, and retrieved from, the ESS. This is expected since, with lower


0

0.5

1Load Consumption

0

0.5

1Wind Generation

0

0.5

1Diesel Generation

0

0.5


Week 1 Week 20

0.5

1ESS Energy

Continuous

0

0.5

1Load Consumption

0

0.5

1Wind Generation

0

0.5

1Diesel Generation

0

0.5


Week 1 Week 20

0.5

1ESS Energy

Discontinuous



(rwl = 0.75), and a large ESS (PESS = 240 kW, EESS = 1600 kWh).

wind generation, there is less energy to be stored during high wind/low load conditions.

Therefore, its implementation may not be economically or technically justified; the dumped

energy would be low in either case. Also, during these weeks, the diesel does not operate in

discontinuous mode, even though the controller allows it to do so. This is because there are

few periods of time when there is sufficient energy stored in the ESS to cover the demand,

especially with very little aid from the wind generation. The difference in the cost of energy

for the two operating cases is 0.4454 $/kWh for the continuous case, and 0.4438 $/kWh

for the discontinuous case. These numbers are strikingly similar, comparing to results from

Chapter 3. Thus, with little wind power penetration, a medium-sized ESS is not justifiable,

and it will not be able to take advantage of the discontinuous diesel operation.


0

0.5

1Load Consumption

0

0.5

1Wind Generation

0

0.5

1Diesel Generation

0

0.02

0.04Dump Load Consumption

Week 1 Week 20

0.5

1ESS Energy

Continuous

0

0.5

1Load Consumption

0

0.5

1Wind Generation

0

0.5

1Diesel Generation

0

0.02

0.04Dump Load Consumption

Week 1 Week 20

0.5

1ESS Energy

Discontinuous



(rwl = 0.2), and a medium-sized ESS (PESS = 120 kW, EESS = 800 kWh).

4.3.2 Cost Analysis

This section details the analysis of varying the wind penetration and the ESS sizes on the

system, and specifically how this affects the cost of energy of the system. For each wind

penetration value within the range provided in the previous section, the ESS sizes are varied

to determine the sensitivity of each parameter in this analysis to the cost of energy of the

system. The minimized cost of energy of the system for each ESS power and energy rating

are shown in Fig. 4.4 and Fig. 4.5 for the continuous and discontinuous diesel operation,

respectively. These figures are then compared for different wind penetration values so that

the effect of varying this parameter can also be analyzed.


0 600 1000 1400 1800

0

150

3000.55

0.56

0.57

0.58

Energy Rating [kWh]

0 WTG

Power Rating [kW]

Cos

t [$/

kWh]

0 600 1000 1400 1800

0

150

3000.385

0.39

0.395

0.4

0.405

Energy Rating [kWh]

7 WTG

Power Rating [kW]

Cos

t [$/

kWh]

0 600 1000 1400 1800

0

150

3000.325

0.33

0.335

0.34

0.345

Energy Rating [kWh]

13 WTG

Power Rating [kW]

Cos

t [$/

kWh]

0 600 1000 1400 1800

0

150

300

0.29

0.3

0.31

Energy Rating [kWh]

20 WTG

Power Rating [kW]

Cos

t [$/

kWh]

Fig. 4.4 The cost per kilowatt hour of the system in continuous diesel op-

eration, varying the ESS sizes and number of WTG. Lower values are better.

Fig. 4.4 shows the effect of varying the ESS sizes for different numbers of turbines, hence

different wind penetrations. To analyze the effect of varying the wind power penetration,

only the relative shapes of the curves are used since the range of the cost of energy will

vary for different wind penetration values. For the case with no turbines (rwl = 0), the

effect of varying the ESS power and energy ratings is linear since there would be no excess

wind energy to be stored. Therefore, the additional cost is simply the cost of implementing

the ESS with no reduced diesel savings, which is a function of the ESS power and energy

ratings. Thus, the graph appears to be a slanted, but relatively flat plane.

For the cases of 7 WTG and 13 WTG (rwl = 0.35 and rwl = 0.65, respectively), the


graphs are relatively more curved, with a shape resembling a valley. The lowest cost of

energy in both cases are with a smaller ESS, and the “dip” to these minima are surrounded

by high energy costs for higher ESS ratings. For the VRB model, there is a higher sensitivity

of the ESS energy rating than the power rating to the cost of energy. For the case with

20 WTG (rwl = 1.0), the graph becomes flatter, and the sensitivity of the ESS ratings to

the cost of energy is much less. The reason for this is because a lot more of the generation

comes from the wind farm. In this sense, there is no substantial difference in the cost of

energy without an ESS, and with an ESS that can store a lot of wind energy, but with a

high initial cost.

0 600

1000

0

150

3000.55

0.56

0.57

0.58

0.59

Energy Rating [kWh]

0 WTG

Power Rating [kW]

Cos

t [$/

kWh]

0 600

1000

0

150

3000.35

0.36

0.37

0.38

0.39

Energy Rating [kWh]

7 WTG

Power Rating [kW]

Cos

t [$/

kWh]

0 600

1000

0

150

3000.24

0.25

0.26

0.27

0.28

Energy Rating [kWh]

13 WTG

Power Rating [kW]

Cos

t [$/

kWh]

0 600

1000

0

150

3000.16

0.18

0.2

0.22

Energy Rating [kWh]

20 WTG

Power Rating [kW]

Cos

t [$/

kWh]

Fig. 4.5 The cost per kilowatt hour of the system in discontinuous diesel

operation, varying the ESS sizes and number of WTG. Lower values are better.


Fig. 4.5 shows the effect of varying the parameters in this study on the cost of energy

for the discontinuous diesel mode. For the case with no WTG, this is the same as for the

continuous case, which is to be expected since no excess wind energy is able to be stored,

and so the ESS would operate in the same manner. For the cases with 7 WTG and 13

WTG, a valley-type shape is still observed, but instead of the valley minimized around a

smaller power and energy rating, here, it is minimized around a power rating of around 150

kW. For the case with 7 WTG, there is a higher sensitivity to the energy rating than in the

case with 13 WTG. Since more wind energy can be stored with a higher wind penetration,

a higher ESS can be economically more viable. The cost of energy is highest with a lower

power rating, which means that for discontinuous diesel operation, a higher power rating

would be preferred over one with a lower power rating. Similar to the continuous case, there

is very little sensitivity of the cost of energy to the ESS rating for a high wind penetration

for higher ESS power ratings, although this lowered sensitivity is more pronounced in the

discontinuous case.

The cost of energy in the system decreases with an increase in wind penetration. This

is intuitive as more energy is produced from a less expensive resource. For this analysis,

the WTG system is considered to be already installed, and so the incremental cost of the

wind power is considered to be zero. Therefore, in order to analyze the effect of the ESS

on the cost of the system with different wind power penetrations, the cost of the system

with an ESS must be compared with the cost of the system without an ESS as a base case.

Although the optimized results are not shown, the difference in cost of having an ESS with

high power and energy ratings in the system (PESS = 240 kW, EESS = 1600) minus the

cost when not implementing one at all, i.e.,

ckWh(ESS) − ckWh(no ESS)

is shown in Fig. 4.6 and Fig. 4.7 for the continuous and discontinuous diesel cases, respec-

tively.

For the continuous case, one can see that there is a point in the wind power penetration

at which the cost difference of implementing a large ESS versus not implementing one at all

reaches a minimum. For smaller wind penetration values, there may not be enough wind

energy to store to economically validate the implementation of an ESS; this is why there


0 10 20 30 40 50 60 70 80 90 100 110−0.025

−0.02

−0.015

−0.01

−0.005

0

0.005

0.01

0.015

0.02

0.025

Wind Power Penetration [%]

Cos

t Diff

eren

ce [$

/kW

h]

Fig. 4.6 The cost difference between having a fully rated ESS and no ESS

for the continuous diesel operation for various wind power penetration levels.

Positive values indicate that it is more expensive to implement an ESS.

0 10 20 30 40 50 60 70 80 90 100 110−0.025

−0.02

−0.015

−0.01

−0.005

0

0.005

0.01

0.015

0.02

0.025

Wind Power Penetration [%]

Cos

t Diff

eren

ce [$

/kW

h]

Fig. 4.7 The cost difference between having a fully rated ESS and no ESS for

the discontinuous diesel operation for various wind power penetration levels.

Positive values indicate that it is more expensive to implement an ESS.


is a relatively large difference in cost. Although there is substantial of wind energy to store

for systems with very large wind penetration values, there may not be as many opportunities

to discharge the ESS. In such cases, it may not be economically justifiable to implement

an ESS. Around the minimum point, the wind penetration value is reached where there is

enough wind energy to be stored and there are sufficient periods of time to discharge the

ESS. For the continuous diesel case, the wind penetration value at this minimum was found

to be between rwl ≈ 0.7-0.8. The prices of energy in this figure are not optimized; and so

although it is not shown in this figure, a smaller ESS may be economically more feasible

than not implementing an ESS for certain wind penetration levels.

For the discontinuous diesel case, shown in Fig. 4.7, the graph has a very different

shape; no minimum value is obtained. For low wind penetration values, the cost differences

are identical to that for the continuous diesel mode. This is expected since there will not be

enough energy stored in the ESS to operate in discontinuous mode. However, as the wind

penetration increases, a minimum is not reached because, unlike the continuous case, there

are still opportunities to discharge the ESS with a high wind penetration when turning off

the diesel generator. The cost difference does taper somewhat with higher wind penetration

values, with the steepest slope at rwl ≈ 0.3. It is also worth noting that although the cost

differences for the continuous case were all above 0, the discontinuous case went below zero

for high wind penetrations, which means that it is more economically viable to install that

ESS than to not install one at all.

These results can also be seen in Table 4.1, which shows the lowest cost of energy

achieved for each wind power penetration along with the respective power and energy

ratings. For the continuous diesel operation, the minimum observed in Fig. 4.6 is reflected

in the size of the ESS. As the wind penetration increases from rwl = 0.3-0.55, the size of the

ESS increases. However, it decreases in both the power and energy ratings at rwl = 0.95,

once again due to the fact that the stored energy does not have an opportunity to discharge.

The cost of energy is significantly less for the discontinuous diesel operation than for the

continuous diesel operation. An ESS implementaiton starts to become more economically

viable at lower wind penetrations for the discontinuous diesel mode rather than for the

continuous diesel mode, and the sizes of the ESSs continue to increase for increasing wind

penetrations. Table 4.1 shows that the KBES Controller can also be used for ESS sizing

purposes through a parametric analysis, since an optimized ESS rating that yields the

lowest cost of energy per wind penetration can be specifically determined.


Table 4.1 The lowest cost of energy for each wind penetration and

the respective power and energy ratings.

Continuous Discontinuous

Wind Minimized Power Energy Minimized Power Energy

Penetration Cost of Energy Rating Rating Cost of Energy Rating Rating

rwl ckWh(x) PESS EESS ckWh(x) PESS EESS

[$/kWh] [kW] [kWh] [$/kWh] [kW] [kWh]

0.00 0.5513 0 0 0.5513 0 0

0.05 0.5216 0 0 0.5216 0 0

0.10 0.4922 0 0 0.4922 0 0

0.15 0.4647 0 0 0.4647 0 0

0.20 0.4401 0 0 0.4401 0 0

0.25 0.4190 0 0 0.4125 180 200

0.30 0.4011 0 0 0.3840 180 400

0.35 0.3860 30 200 0.3570 180 400

0.40 0.3726 30 200 0.3322 180 400

0.45 0.3608 60 400 0.3101 180 400

0.50 0.3505 60 400 0.2905 180 400

0.55 0.3414 90 600 0.2731 180 400

0.60 0.3333 90 600 0.2571 180 400

0.65 0.3263 90 600 0.2419 180 400

0.70 0.3201 90 600 0.2278 180 600

0.75 0.3145 90 600 0.2150 180 600

0.80 0.3096 90 600 0.2029 180 600

0.85 0.3052 90 600 0.1919 180 800

0.90 0.3013 90 600 0.1816 180 800

0.95 0.2979 60 400 0.1719 180 800

1.00 0.2948 60 400 0.1631 210 800

4.4 Conclusions 45

4.4 Conclusions

This chapter detailed the effects of varying the number of WTG, and the ESS sizes in the

microgrid model from Chapter 2 through a parametric analysis. Since this analysis is meant

for a generic microgrid, the analysis is performed based on the wind power penetration and

the ESS power and energy ratings. All three parameters are varied independently, and a

sensitivity analysis is performed to determine its effect on the technical operation of the

system and the cost of energy.

In terms of power profiles for the controllable parameters (i.e., diesel generation, ESS

charging/discharging, and dump load consumption), the operation of the system is found

to be very different for different wind penetrations, but more similar for different ESS sizes

for the same wind penetration. The discontinuous diesel operation allowed for less diesel

to be consumed and less energy to be wasted through the dump load. For a lower wind

penetration (rwl = 0.2), an ESS may not be technically justifiable in either the continuous

or discontinuous diesel cases since an insignificant amount of excess energy is created.

By varying the wind penetration, a minimum of the difference in cost of energy between

implementing an ESS and not implementing one can be observed. There is a minimum

cost difference when operating in continuous diesel mode, occurring at rwl ≈ 0.7-0.8. This

is because at a lower wind penetration, there is not enough energy to be stored; at a higher

wind penetration, there are not sufficient opportunities to discharge the stored energy. For

the discontinuous diesel operation, however, the difference continued to decrease until it

became more economical to implement an ESS than to not implement an ESS as the wind

penetration increased. Throughout the various parametric analyses, it was evident that

the diesel generator mode of operation has a large effect on the appropriate sizing of an

ESS and the overall average cost of energy.

Since the excess energy generated from the wind farm is stored in the ESS and is not

wasted, the incorporation of an ESS will increase the RE penetration in the system without

having to increase the RE generating capacity. This chapter also showed that the KBES

Controller can be used as a sizing tool by performing such parametric analyses.

47

Chapter 5

ESS Technology Analysis Using

KBES Tool

5.1 Introduction

The purpose of this chapter is to compare different ESS technologies for use in the microgrid

system. These technologies will be implemented in two manners: the first will use each

technology as a single-layer ESS, as in the previous chapters; and the second approach

will be to implement a dual-layer ESS [67] that will take advantage of fast charging ESS

coupled with an ESS with a high storage capacity. The final cost of the energy in dollars

per kilowatt-hour will be compared for the various sizes and technologies used, and cost-

optimized technologies and configurations will be proposed. In order to accomplish this

analysis, the KBES tool is modified slightly to take into consideration the dual-layer ESS.

The same KBES Controller is used for all ESS technologies and sizes in order to maintain

consistency in the results.

This chapter details the sensitivity analysis of varying ESS sizes and technologies with

the main goal of identifying, from an economic point of view, the optimal ratings and

technologies for reducing the total cost of the operation of the system. There are many

different technologies available for use in power systems [68], and each one is subject to

its specific costs and operating constraints that provide advantages and disadvantages in

certain applications. By modifying the ESS technologies and their respective power and

energy ratings used, an analysis can be performed to determine the technology and ratings

48 ESS Technology Analysis Using KBES Tool

that are best suited for a particular microgrid.

The modelled community is similar to that used in previous chapters, in that it has a

diesel generator as well as an installed wind farm that power the local loads. Although many

analyses have been performed on the technical and economical feasibility of incorporating

an ESS in general, this chapter focuses instead on the effects of varying ESS technologies

and sizes to analyze the effect on the overall cost of operating the system. The cost of

operating the system is reflected in the cost of energy per kilowatt-hour to the community,

ckWh. By performing a sensitivity analysis, the optimized power and energy ratings of each

technology can be compared. The economic analysis will include the diesel costs as well as

the ESS capital costs and O&M costs, all normalized for a one-year analysis.

The ESS technologies used for the analysis vary from High Speed Flywheels and Super

Capacitors to different battery chemistries. Both the power and energy ratings of the ESS

are modified through a parametric analysis. The different ESS ratings are analyzed and the

optimal sizes are compared for each technology. Since the largest hurdle of implementing

an ESS is the high initial cost, it may not be justified by the diesel savings throughout the

ESS lifetime. Thus, the optimal technology and the optimal sizes of the ESS are determined

for each technology.

5.2 ESS Technologies

The analysis is performed on different energy storage sizes of various storage technolo-

gies to determine its comparative impact on the systems performance and overall oper-

ation costs. There are many different ESS technologies available, each offering different

advantages to various applications. Although not every technology in this analysis is

typically used for wind-diesel microgrids, none of them are neglected. The various ESS

technologies considered in this chapter include: Lead-Acid Batteries, Nickel-Cadmium Bat-

teries, Sodium-Sulfur Batteries, Zinc-Bromine Batteries, Vanadium Redox Flow Batteries

(VRB), Sodium-Polysulfide Batteries, Superconducting Magnetic Energy Storage (SMES),

Flywheel Energy Storage, Electrochemical Capacitors and Large Compressed Air Energy

Storage (CAES) [62].

The ESS technologies that are typically best suited for this application are the long-term

storage application, such as the battery technologies listed above. The SMES, flywheel and

capacitor storage systems are typically used for short duration power requirements due to

5.3 Methodology 49

their very high initial energy rating cost; their application is typically for shorter duration,

high power conditions, where battery technologies are not well suited. Although the CAES

is initially considered in this analysis, the most efficient application of CAES to date has

been in association with a thermal combustion power plant.

The technical parameters used in this analysis are the average lifetime, L, the charg-

ing/discharging round trip efficiency, η, the power rating, PESS, and energy rating, EESS,

of each respective technology. These and other parameters used for the ESS technologies

are given in Table 5.1 and Table 5.2.

Since the power and energy ratings can be changed by the configuration of the ESS, they

are left as variables that are modified throughout the analysis. The parameters used in this

analysis are taken from a single source to ensure consistency of the relative parameters; it

may not be justified to compare technologies taken from different years as ESS parameters

may change drastically. Although there are inefficiencies and the lifespan diminishes when

operating some of these technologies at minimum and maximum state of charge [58], these

are not taken into consideration. Instead the lifespan and efficiencies are averaged over the

minimum and maximum values.

5.3 Methodology

This section will outline the methodology of the single-layer ESS analysis, although it is

very similar to the dual-layer ESS analysis. The simulation is performed on different energy

storage sizes of various storage technologies. This will demonstrate the impact of resizing

the ESS on the cost of energy in order to determine the most cost-effective system for

the community. A flow chart of the methodology is shown in Fig. 5.1. The analysis is

performed on the basis that the local utility operator wants to minimize the cost of energy,

and they would like to know which ESS technology and ratings will enable them to best

achieve this goal.

A parametric analysis is performed for each ESS technology, varying the power rating

and energy rating, and a one-year simulation is performed to determine the average price

of energy. Once all the simulations are performed, the size of the ESS that yields the

minimum cost of energy is compared for each technology. This analysis neglects the cases

where PESS > EESS since only hourly values are considered.

The power and energy ratings of the ESS will vary between the case where no ESS is


Table 5.1 ESS technical parameter specifications and con-

tinuing costs based on [62].

ESS Lifespan Round-Trip Fixed Variable

Technology Efficiency O&M Costs O&M Costs

L η πOMf πOMv

[years] [%] [$/kW/year] [$/kW/year]

Lead-Acid

Batteries15-30 75-85 17.6 6.5

Nickel-Cadmium

Batteries10-15 65-85 26.5 1

Sodium-Polysulfide

Batteries15 60-65 54.6 6.4

Electrochemical

Capacitors50 100 13.1 6.8

SMES 20 95 22.2 12.4

Flywheel

Energy Storage50 70-80 18.4 9.1

Sodium-Sulfur

Batteries15 85-90 19.2 3.9

Zinc-Bromine

Batteries10 70-80 30 8.8

VRB 10 75 28.1 4.1

CAES 30 85 13 58.8

5.3 Methodology 51

Table 5.2 ESS initial cost specifications based on [62].

ESS Power Conversion Power Rating Energy Rating Disposal

Technology System Initial Cost Initial Cost Initial Cost Cost

πPCS πess,p πess,e πdis

[$/kW] [$/kW] [$/kWh] [$/kW]

Lead-Acid

Batteries173 315 325 1.4

Nickel-Cadmium

Batteries153 640 1197 1.2

Sodium-Polysulfide

Batteries120 808 269 1.2

Electrochemical

Capacitors153 203 370000 1.5

SMES 150 309 560000 0

Flywheel

Energy Storage153 206 370000 0

Sodium-Sulfur

Batteries202 508 508 11.2

Zinc-Bromine

Batteries173 366 467 1.8

VRB 311 883 442 0

CAES 0 120 40 0


Perform a one-year analysis with the installed ESS

Determine ckWh

Determine which PESS and EESS ratings will yield the

optimized ckWh for that Technology

Choose new ESS Technology; set

PESS = 0EESS= 0

Start

A) EESS Eres,max?B) PESS Pres,max?C) More technologies to analyze?

Increase PESSEESS = 0

Increase EESS

A

B

Yes

No

Yes

No

CYes

No

End

Fig. 5.1 Flow chart of the methodology for sizing the ESS and cost evalua-

tion.

implemented (i.e. PESS = 0 and EESS = 0), and the case where all the excess power and

energy can be handled by the ESS, in increments of 10% of the respective residual values.

The maximum excess power, Pres,max, that the ESS would have to absorb or deliver is given

by the absolute value of the maximum residual power over the entire year, T . This is the

difference in the community load for each hour t, PL,t, with the generation of the diesel’s

5.3 Methodology 53

MLC, Pmin, and the wind generation, Pw,t. This value is found to be:

Pres,max = max∀t∈T

|Pmin + Pw,t − PL,t| (5.1)

= 616 kW (5.2)

The maximum residual energy, Eres,max, that the ESS would have to store is the sum-

mation of the individual residual powers over a certain timespan, which is given as:

Eres,max = max∀h∈T

��

h�

t=1

(Pmin + Pw,t − PL,t)

�� (5.3)

= 2440 kWh (5.4)

Wind data for the one-year analysis are taken from [63].

In order to determine the price of energy, the cost of the ESS must be determined. The

cost of the ESS is dependant on many factors, and is primarily broken down into capital

costs and O&M costs, as shown in Table 5.1. The capital costs include the power rating

initial cost, πess,p, the energy rating initial cost, πess,e, the power conversion system initial

cost, πPCS, and finally the disposal cost, πdis. In order to integrate this with the O&M cost,

a one-year analysis is performed while distributing the initial costs over the lifespan of the

respective ESS technologies, L.

The recurring O&M costs can be broken down into fixed costs, πOMf, and variable

costs, πOMv . Fixed costs refer to O&M costs related to a planned maintenance program,

whereas variable costs incorporate primarily costs due to power conversion inefficiencies

and standby losses [62]. Once the capital and O&M costs are normalized on a per-year

basis, they can be combined to create the cost of the ESS per year.

Therefore, the cost of the ESS per year would average to:

πESS =(πess,p + πPCS + πdis) · PESS + πess,e · EESS

L+πOMf

+ πOMv (5.5)

Note that once the ESS has been installed, any energy stored or provided by the ESS

during the operation is considered to be free since the marginal cost per kilowatt-hour is

zero. The average price per kilowatt-hour of energy to the community over the one-year


analysis, ckWh, is then calculated as:

ckWh(ESStech, PESS, EESS) =πESS +

�Tt=1

(πd · pdiesel,t)�Tt=1

PL,t

(5.6)

where pdiesel,t represents the diesel generation power for hour t. Note that the amount of

diesel that is consumed will vary for different ESS technologies and sizes. (5.6) neglects

the cost of the wind farm since it is assumed that it has already been installed with an

incremental cost of producing power of zero.

5.4 Single-Layer ESS Analysis

Fig. 5.2 shows the energy and power ratings of the various ESSs when the operation costs

were at a minimum for the respective technology, and are ranked from biggest to lowest

resulting sizes. The first bar represents the power rating of the ESS, and the second bar

represents the energy rating.

When comparing the different ESS technologies, the long-term ESSs perform very dif-

ferently from the short term ESSs. The technologies that fared the best, in terms of

highest power and energy ratings, were (in order): CAES, Lead-Acid Batteries, Sodium

Polysulfide Batteries, Sodium-Sulfur Batteries and Zinc-Bromine Batteries. These are all

considered to be long-term storage technologies. Conversely, it was found that the ESS size

that would produce the most inexpensive cost of energy for the Nickel-Cadmium Batteries,

Electrochemical Capacitors, SMES, Flywheel Energy Storage, and VRB (most of which

are considered to be short-term ESSs) is PESS = 0 kW, and EESS = 0 kWh. In this sense,

it is cheaper to not install an ESS for these technologies than to install one, since the cost

of their respective implementation does not justify the diesel savings. Therefore, these

technologies are not deemed appropriate for this application in the model microgrid. Also,

the CAES is a special case because it is mostly associated with improving the efficiency of

thermal plants, and since most microgrids may not have one available, this technology is

not included in the economic analysis.

It is interesting to note that both the power ratings and the energy ratings decreased

with each technology in Fig. 5.2, as opposed to one parameter decreasing while the other

remained constant or increased. This makes it easier to compare the ESS technologies in

5.4 Single-Layer ESS Analysis 55

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Fig. 5.2 The optimized power and energy ratings of the ESS.

terms of sizes.

Once the appropriate sizes of the ESSs are determined, an economic comparison between

the technologies can be achieved. Fig. 5.3 shows the average price of energy per kilowatt-

hour for the one-year analysis. This graph is ranked with the lowest cost on the left and

increases until it reaches the case when no ESS is implemented. Other technologies are

not included in this graph since the minimum cost was found to be equal to the case of no

implemented ESS, and it would be more expensive to implement those technologies than

to not implement an ESS at all.

The running cost of operating the system depends on the cost of maintaining the equip-

ment as well as providing power to the local community. Not all parameters are used in

the analysis since they are consistent for every case study. For example, the O&M costs of

the diesel generator and wind farm are not included. This would increase the total cost of

operation, but it does not affect the comparison since only these constant parameters are

taken out of the minimization function. As the size of the ESS is increased, the fuel savings


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Fig. 5.3 The cost of energy for the system, ckWh, after implementing the

optimized respective ESS.

are also increased, but the incremental cost of the ESS may not be worth the investment.

The ESS technology that yields the lowest cost of energy is the Lead-Acid Battery,

followed by Sodium Polysulfide Batteries, Sodium-Sulfur Batteries, and finally the Zinc-

Bromine Batteries. This order of the most economic technology is the same as the order

from Fig. 5.2. One can see that the actual prices of energy are within one cent of each

other per kilowatt-hour, but the small changes can add to significant savings in the long

term. This analysis does not take into consideration carbon credits or any environmental

advantages to reducing diesel consumption.

These results can vary for different remote communities. For example, the cost of

diesel can be much higher in communities that are difficult to access, and so there are

additional costs related to diesel transportation to the community. If diesel prices are

higher, the cost savings of an ESS could be much higher, and some ESS technologies may

no longer be considered infeasible. As diesel prices are expected to rise in future years,

5.5 Dual-Layer ESS Analysis 57

and taking a higher value for the future cost of diesel will increase the potential savings of

implementing an ESS. The difference in cost would also be more pronounced with a higher

wind penetration. These results are conservative, but attempt to give a current view to

implementing an ESS. As research into ESS technologies progresses, the savings associated

with implementing an ESS could become much greater.

5.5 Dual-Layer ESS Analysis

There are other ways in which ESS implementation may be financially and technically

viable. For example, by creating a dual-layer ESS [9], a short-term storage with a high

power rating can be used in conjunction with a medium-term storage device. This may

reduce the cost of the entire system by reducing the initial energy cost of the short-term

device and decreasing the power rating of the medium-term device. By taking advantage

of the strengths of the technologies, they can be paired to reduce their weaknesses and

complement one another to provide a better storage solution.

The purpose of this section is to compare single-layer ESS to dual-layer ESS and propose

the best technologies and configuration. Both the continuous and discontinuous diesel

operation are considered in this analysis to demonstrate how each ESS is used for the

different operating modes.

5.5.1 Implementation

For this study, the short-term ESSs included the Electrochemical Capacitor, the Flywheel

Energy Storage, and the SMES; the medium-term ESSs included Sodium-Polysulfide Bat-

teries, Sodium-Sulfur Batteries, Nickel-Cadmium Batteries, Lead-Acid Batteries, VRB,

and Zinc-Bromine Batteries. CAES was not included in this analysis due to the reasons

mentioned in Section 5.4. The short-term ESS technologies were identified in [62] due to

the high cost of their energy ratings and high power capacities, as seen in Table 5.2. The

medium-term technologies were also chosen due to their designed usage in [62], and because

battery technologies do not handle fast charging/discharging very well [69].

The microgrid model that is used is similar to that used for the single-layer ESS analysis.

However, to emphasize the role of the short-term ESSs, the microgrid is modified to have

a higher wind penetration. The equivalent of two extra turbines are added, and so from


(4.2), this gives a new power penetration of

rwl = 0.4 (5.7)

Since the wind power output is proportional to the number of turbines, the hourly fluctu-

ations in the wind will be greater. It is also worthwhile to note that with a higher wind

penetration, the cost of energy will also be reduced for this analysis since more energy will

come from a “free” source.

In this analysis, the power rating of the short-term ESS ranges from

0 ≤ PESS,s ≤ 10

and increases in intervals of 5 kW. The energy capacity of the short-term ESS ranges from

0 ≤ EESS,s ≤ 10

and also increases in intervals of 5 kWh.

The power rating of the medium-term ESS ranges from

0 ≤ PESS,m ≤ 260

and increases in intervals of 20 kW. The energy capacity of the short-term ESS ranges from

0 ≤ EESS,m ≤ 1500

and also increases in intervals of 100 kWh. These values were chosen to provide the best

distribution that shows differences between each technology and their respective results.

There are many ways to connect the two ESSs [9,70], but here they are treated as two

ESSs that are separately connected to the grid. A different configuration can also be used,

where the short-term ESS interfaces the medium-term ESS to the grid, as in [66]. However,

since it is still possible for one ESS to charge or discharge the other ESS by displacing the

stored energy in the current configuration, it is deemed to be equivalent for the current

implementation and useage.

With the inclusion of the dual-layer ESS, the KBES must be modified to accommodate


both storage systems in the same microgrid. For the default rule, the KBES divides the

residual power for each hour equally between the short-term and medium-term ESS, based

on their energy ratings. That is, the short-term ESS will attempt to deliver/absorb the

equivalent power of

pess,s,t =EESS,s

EESS,s + EESS,m× (Pres,t − udiesel,t · Pmin) (5.8)

and the medium-term ESS will attempt to deliver/absorb

pess,m,t =EESS,m

EESS,s + EESS,m× (Pres,t − udiesel,t · Pmin) (5.9)

From this default scenario, the rules within the KBES are defined within a while loop as

follows. First, Rules #4-8 from Table 2.1 are used on the medium-term ESS, and the excess

power will be added to the short-term ESS. Then, the same Rules #4-8 from Table 2.1 are

used on the short-term ESS. Finally, Rules #2-3 are used to determine the power balance

and any dumped wind generation or increase in diesel generation. A similar strategy is

used for the discontinuous diesel operation, based on the rules in Table 2.2.

5.5.2 Results and Analysis

Both the continuous and discontinuous diesel modes of operation were simulated with the

different combinations of short-term ESSs paired with the medium-term ESSs. As can be

seen from Table 5.3 and Table 5.4, which rank the technologies from least expensive to most

expensive, even with the higher wind power penetration and discontinuous diesel operating

mode, in no situation was the single-layer ESS economically viable for this analysis.

From Table 3.6, it was shown that there were more cost savings associated with the

discontinuous diesel operation. However, these savings were still insufficient to allow for

the short-term ESS to become economically viable. For this study, even when the Electro-

chemical Capacitors, Flywheel, or SMES are used for their intended short-term purpose,

their high initial cost does not justify their implementation, and so the optimal costs and

medium-term ESS ratings are the same for all three short-term ESS technologies. However,

one must acknowledge the fact that intra-hour diesel ramp rates or wind fluctuations were

not considered, and so there may yet be a suitable implementation for these technologies;


Table 5.3 The sizes for the most inexpensive results of the dual-layer

ESS technologies for the continuous diesel operation.

Short-Term Medium-Term ckWh(x) PESS,s1 EESS,s

1 PESS,m1 EESS,m

1

ESS ESS [$/kWh] [kW] [kWh] [kW] [kWh]

Lead-Acid 0.38643 0 0 20 600

Electrochemical Sodium-Polysulfide 0.39121 0 0 20 600

Capacitor Sodium-Sulfur 0.39121 0 0 20 600

Zinc-Bromine 0.39247 0 0 20 800

VRB 0.39334 0 0 20 800

Nickel-Cadmium 0.39937 0 0 20 500

Lead-Acid 0.38643 0 0 20 600

Flywheel Energy Sodium-Polysulfide 0.39121 0 0 20 600

Storage Sodium-Sulfur 0.39121 0 0 20 600

Zinc-Bromine 0.39247 0 0 20 800

VRB 0.39334 0 0 20 800

Nickel-Cadmium 0.39937 0 0 20 500

Lead-Acid 0.38643 0 0 20 600

Sodium-Polysulfide 0.39121 0 0 20 600

SMES Sodium-Sulfur 0.39121 0 0 20 600

Zinc-Bromine 0.39247 0 0 20 800

VRB 0.39334 0 0 20 800

Nickel-Cadmium 0.39937 0 0 20 500

1. Values in these columns refer to the respective ratings at the minimum cost for the short-

term and medium-term ESSs.


Table 5.4 The sizes for the most inexpensive results of the dual-layer

ESS technologies for the discontinuous diesel operation.

Short-Term Medium-Term ckWh(x) PESS,s1 EESS,s

1 PESS,m1 EESS,m

1

ESS ESS [$/kWh] [kW] [kWh] [kW] [kWh]

Lead-Acid 0.37328 0 0 60 300

Electrochemical Sodium-Sulfur 0.37731 0 0 100 300

Capacitor Zinc-Bromine 0.37975 0 0 100 300

VRB 0.38082 0 0 100 300


Nickel-Cadmium 0.38266 0 0 100 300

Lead-Acid 0.37328 0 0 60 300

Flywheel Energy Sodium-Sulfur 0.37731 0 0 100 300

Storage Zinc-Bromine 0.37975 0 0 100 300

VRB 0.38082 0 0 100 300


Nickel-Cadmium 0.38266 0 0 100 300

Lead-Acid 0.37328 0 0 60 300

Sodium-Sulfur 0.37731 0 0 100 300

SMES Zinc-Bromine 0.37975 0 0 100 300

VRB 0.38082 0 0 100 300


Nickel-Cadmium 0.38266 0 0 100 300

1. Values in these columns refer to the respective ratings at the minimum cost for the short-

term and medium-term ESSs.


for the purposes of hourly scheduling, a dual-layer ESS may not be the best option.

Although the inclusion of a short-term ESS was found to not yield optimal results in

all cases, the power and energy values of the medium-term ESS differed for the continuous

and discontinuous diesel operations. For the continuous case, the optimal power ratings

were typically small, being 20 kW for all technologies, as shown in Table 5.3. The energy

ratings, however, were found to be optimal in the 600-800 kWh range. This shows that the

ESS ratings that give the best financial return on investment is a lower power rating and

a relatively high energy rating for the continuous diesel case. In this manner, the ESS will

not be able to handle high power fluctuations from the wind generation, but it will be able

to compensate during times of high wind/low load or low wind/high load. On average, the

cost savings associated with the discontinuous diesel operation are 0.01332/ $kWh.

For the discontinuous case, the power ratings were much higher than for the continuous

diesel operation, but the energy ratings were much lower. Table 5.4 shows that power

ratings were typically between 60-100 kW, while energy ratings were mostly 300 kWh.

Comparing these to the continuous diesel case, there is a significant difference in the op-

timized ratings, and therefore the ESSs are used very differently in both cases. This is

because during times of moderate wind, when there is energy stored in the ESS, the diesel

can be turned off and a higher power rating is required to discharge enough energy to meet

the demand. Also, the ESS discharges whenever it has the opportunity to do so, and so

it rarely gets a chance to store a large amount of energy. During the continuous diesel

operation, there would be times when the generation is too great during high wind/low

load conditions, but there must not have been many periods of very high excess generation

that would justify a higher power rating for the ESS.

In terms of the specific technologies, the Lead-Acid Batteries yielded the lowest cost

per kilowatt-hour for both diesel operating modes. For the continuous case, the optimized

Lead-Acid energy rating was 200 kWh less than that found for the VRB or Zinc-Bromine

technologies, which provided a slightly higher minimized cost of energy. The technologies

that yielded the highest costs were the Nickel-Cadmium batteries, the VRB, and the Zinc-

Bromine Batteries. For instance, the optimized system with Nickel-Cadmium Batteries are

$0.015/kWh more expensive than the system with the Lead Acid Batteries, even with a

smaller battery size.

For the discontinuous case, again the Lead-Acid Batteries proved to be the most inex-

pensive technology. It is interesting to note that the order of the ESSs, in terms of overall


200400

600800

10001200

1400

2060

100140

180220

260

0.385

0.39

0.395

0.4

Energy Rating [kWh]Power Rating [kW]

Cos

t [$/

kWh]

0.388

0.39

0.392

0.394

0.396

0.398

Fig. 5.4 Cost with a Lead-Acid ESS for Varying Power and Energy Ratings

for Continuous Diesel Operation.

cost of of energy, is different for continuous and discontinuous diesel operation. This is

due to the fact that the ESSs are used in different ways, as previously mentioned. The

respective ESSs with a higher power rating and a lower energy rating, such as Sodium-

Polysulfide, fare better with the continuous diesel operation than with the discontinuous

diesel operation. Therefore, one must take into consideration the mode of operation before

determining the best technologies to implement in the system.

The costs per kilowatt-hour of the system for different Lead-Acid ESS power and energy

ratings for the continuous case are shown in Fig. 5.4. This figure demonstrates that the

cost of energy of the system is very sensitive to the power rating of the ESS, especially at

low values. There is a sharp increase at low power ratings, and it tapers as it increases

past 160 kW. It makes sense that low power ratings would be more economical for the


200400

600800

10001200

1400

20601001401802202600.37

0.375

0.38

0.385

0.39

Energy Rating [kWh]Power Rating [kW]

Cos

t [$/

kWh]

0.374

0.376

0.378

0.38

0.382

0.384

Fig. 5.5 Cost with a Lead-Acid ESS for Varying Power and Energy Ratings

for Discontinuous Diesel Operation.

continuous diesel mode since the residual power of the wind, load, and MLC would be

smaller than that for the discontinuous case. Therefore, a higher power rating would not

be necessary, and the added initial cost would not be justified. The cost of the energy is

less affected by the energy ratings, as its value remains relatively steady along that axis for

most power values. However, the cost does tend to saddle around EESS,s = 700 kW, and

increases slightly as the energy rating either increases or decreases. This can be attributed

to the fact that at a certain point, the diesel savings from the increase of the energy rating

does not justify the initial cost. This graph is fairly smooth, with a shape that can show a

relative gradient, unlike that shown for the discontinuous case.

For the discontinuous diesel case, shown in Fig. 5.5, the cost profile has a very different

shape. Instead of steadily increasing as the ESS ratings increase, the cost peaks near the

5.6 Conclusions 65

middle of the analyzed power rating and tapers at high and low values. Although the

optimized energy rating was found to be relatively small, it appears as though there is

not a large sensitivity to the final cost of energy with a higher energy rating. The most

interesting part of the graph is that the cost is highest at the power ratings between 80-100

kW, while the cost decreases as the power rating either increases or decreases. This can be

attributed to the fact that at lower power ratings, the ESS is technically unable to cover

the load if the diesel were to turn off. In this sense, the diesel is in continuous operation

more often. Once the power reaches this critical point, the diesel is turned off more often,

and the decrease in the cost of energy as the power rating increases is attributed to the

diesel savings.

It should be noted that in both Fig. 5.4 and Fig. 5.5, the case with no ESS (i.e.,

PESS,s = 0, EESS,s = 0, PESS,m = 0, EESS,m = 0) is not shown on this graph. This means

that for all Lead-Acid ESS ratings, it was found to be more economical to implement an

ESS than to not implement an ESS. The cost of energy when not implementing an ESS was

found to be ckWh(x) = 0.4011 $/kWh for the continuous diesel case, and ckWh(x) = 0.3949

$/kWh for the discontinuous diesel case. Also, for the given wind penetration in this

microgrid, all medium-term ESSs were deemed to be economically and technically viable

options for certain power and energy ratings.

5.6 Conclusions

This chapter performed a parametric analysis of various ESS technologies to determine the

best technologies to use in the provided microgrid and in the best configuration. Firstly, a

single-layer ESS was simulated for the various ESS technologies to determine the optimized

cost of energy while varying the ESS power and energy ratings. The methodology of

determining the best technologies involved finding the least expensive price of energy for

each technology through the technical analysis, and comparing the ratings and the cost for

each technology. Secondly, a dual-layer ESS was simulated with all combinations of short-

term and medium-term ESSs. Both continuous and discontinuous diesel operating modes

were tested, and a higher wind penetration was used for these simulations to increase the

wind power fluctuations.

Even with the increased wind penetration and the use of discontinuous diesel operation,

the sinlge-layer ESS was found to be the best configuration for the microgrid based on hourly


discrete values. For the continuous diesel operation, a lower energy rating and a higher

power rating yielded the optimized cost of energy, whereas it was the opposite situation for

the discontinuous diesel operation. This is attributed to the fact that the ESS will discharge

a large amount of energy whenever possible, and so it will not have an opportunity to store

large amounts of energy.

For continuous diesel operation, the ranking of the ESS technologies from lowest to

highest cost of energy are: Lead Acid Batteries, Sodium Polysulfide Batteries, Sodium-

Sulfur Batteries, Zinc Bromine Batteries, VRB, and Nickel-Cadmium Batteries. For the

case when the wind penetration was rwl = 0.3, the VRB and Nickel-Cadmium batteries

were not found to be economically viable for any rating. For the discontinuous diesel oper-

ation, the ranking of the ESS technologies from lowest to highest cost of energy are: Lead

Acid Batteries, Sodium-Sulfur Batteries, Zinc Bromine Batteries, VRB, Sodium Polysul-

fide Batteries, and Nickel-Cadmium Batteries. Note that the Sodium Polysulfide Batteries

performed much worse for the discontinuous diesel operation, due to the high initial power

rating cost. This demonstrates that the best technologies to use does depend somewhat on

the diesel operating mode. In all cases, the Lead Acid Batteries performed the best and

the Nickel-Cadmium Batteries performed the worst in both the technical and economic

analyses.

This chapter also shows that the KBES can be used to perform parametric analyses

of various ESS technologies and configurations. It can therefore also be used as a sizing

method to find the best ESS technology and sizes for including it in a wind-diesel microgrid.

67

Chapter 6

Conclusions

6.1 Thesis Summary

A knowledge based expert system controller is used to schedule the diesel generation and the

energy storage system charging and discharging in a wind-diesel-ESS microgrid to minimize

the cost of energy. The KBES Controller comprises a set of rules within its knowledge

base, which are constructed from the system constraints. These rules can be fired at any

time, and once all the constraints are met (including maintaining the power balance in

the system) the dispatch for the next hour is determined. The KBES Controller aims to

minimize the use of the dump load that is normally associated with diesel operation. The

results are compared to an offline optimization algorithm applied to the same power system

and ESS size that has a 24-hour lookahead. The results obtained show that by minimizing

the energy wasted through the dump load with the use of the ESS and KBES Controller,

the required diesel generation is reduced, therefore reducing operation costs and emissions.

Another benefit of the KBES Controller s that only near-term wind forecasting is required,

not medium- or long-term. This is beneficial since long-term wind data forecasting may

not be available or accurate.

This KBES tool has been shown to be very flexible, as it was used to perform various

analyses of wind-diesel-ESS microgrids. At first, a parametric analysis is performed where

the wind penetration and ESS ratings were modified to determine its effect on the system

operation and cost of energy. The purpose of this study was to determine for which wind

penetrations an ESS implementation would be most beneficial to the microgrid. Both

continuous and discontinuous diesel operations are analyzed with the result that, in general,

68 Conclusions

an ESS would yield a larger return on investment (and hence, a lower cost of energy) if

discontinuous diesel operation were used.

Different ESS technologies and configurations are also analyzed as there are many avail-

able that can offer financial or technological advantages. In this analysis, the system is sim-

ulated for one year for each technology, with the implementation costs and lifetime taken

into consideration. The overall results are analyzed for the single-layer ESS and the dual-

layer ESS, with fast-charging, costly ESSs used as the short-term ESS, and other (mostly

battery) technologies used as the medium-term ESS. Some ESS technologies have been

shown to be a technically and economically viable solution to wind-diesel microgrids. Ad-

ditional benefits of using an ESS is that it may increase the lifecycle of the diesel generator

since it is used less frequently at a higher operating point.

These analyses verify that the KBES Controller can be used for different microgrid

systems and for different ESS technologies, as long as the rules are implemented such that

they account for the constraints of the system. By using this tool in various test systems,

it was found that an implementation of an ESS can not only help with balancing the power

and increasing the amount of RE penetration in the system, but can furthermore decrease

the overall cost of energy.

6.2 Conclusions

The results and conclusions of the various chapters in this thesis are summarized below.

Chapter 2

In this chapter, a wind-diesel microgrid is modelled. The mathematical models of the sys-

tem are defined and implemented so that a KBES Controller can be integrated online. The

KBES Controller controls the scheduling of the diesel generator, ESS charging/discharging,

and dump load consumption using one-hour lookahead values. It comprises rules within its

knowledge base that are derived simply from the system constraints to minimize the cost

of energy in the system.

6.2 Conclusions 69

Chapter 3

This chapter demonstrates that the proposed Knowledge Based Expert System Controller

yields optimized results when attempting to minimize fuel costs in the isolated wind-diesel-

ESS power system. By minimizing the power lost through the dump load, the diesel

generator power is reduced, and it thus minimizes the generation cost during the projected

lifetime for the chosen system. Both the continuous and discontinuous diesel generation

modes are considered, and the respective results are compared to those obtained by using an

offline optimization function which is allowed a 24-hour lookahead window for each hour.

Both modes of operation are shown to have similar results to the offline optimization,

obtaining results that differ by less than 0.3%. Therefore, it is sufficient to have only a

one-hour lookahead and obtain near-optimal results using a KBES Controller for energy

storage for the proposed system.

Chapter 4

In this chapter, the KBES Controller is used to perform a parametric analysis to determine

the effects of varying the wind penetration, ESS power rating and ESS energy rating. The

discontinuous diesel mode has a large effect on the optimized size of the ESS as well as the

manner in which the system dispatches its controllable generators and loads. A large ESS

size will allow for more energy to be stored, but it has a high initial cost. For continuous

diesel operation, there is a minimum cost difference between implementing an ESS and

not implementing an ESS around rwl ≈ 0.7-0.8. For the discontinuous case, however, the

cost difference continued to decrease with an increased wind penetration, implying that it

is more economical to implement an ESS in discontinuous mode as the wind penetration

increases. A sizing methodology can be derived from the parametric analysis to determine

which ESS ratings will yield the minimum cost of energy for a particular microgrid.

Chapter 5

A technical and economical analysis of varying the ESS technology is performed in this chap-

ter to determine which technology is best suited for the microgrid model. The methodology

is established to perform a parametric analysis of the different ESS sizes, and from the one-

year analyses, determine the lowest cost of energy. For the single-layer ESS analysis, it was

found that the Lead Acid Batteries performed the best, yielding a higher power and energy

70 Conclusions

rating and the minimum cost of energy when compared to the other technologies. Other

technologies that performed well were (in order from best to worst) Sodium-Polysulfide Bat-

teries, Sodium-Sulfur Batteries and Zinc-Bromine Batteries. For all other ESSs analyzed, it

was determined that in order to obtain a minimized cost of energy, the best solution would

be to not implement those technologies. For the dual-layer ESS analysis, it was determined

that it is not economically viable to implement a short-term ESS in this hourly discrete

analysis. When analyzing the cost of energy for the various power and energy ratings of

the Lead-Acid ESS, differences became clear between the continuous and discontinuous

diesel mode of operation. For the continuous case, a gradient can be observed in the cost

of energy when increasing both the power and energy ratings, with a higher sensitivity to

the power rating. For the discontinuous case, the cost peaks at a certain power rating, and

then decreases and settles for higher power ratings. This is due to the fact that at this

point, the diesel generator can take advantage of its discontinuous operation. There is also

relatively little sensitivity to the energy rating on the cost of energy for the discontinuous

diesel mode.

6.3 Future Work

The next step in the implementation of the KBES Controller would be to perform a Hard-

ware in the Loop (HIL) simulation [71]. This would justify its functionality in real time

with an emulated microgrid as opposed to simply using discrete, hourly values. In real-time

operation, special considerations must be given to stand-alone operation of wind-ESS with

regards to power and voltage control of the system, as presented in [72]. Or, if the wind

data is not available, even taking discrete time variables of smaller periods of time would

allow for the short-term wind power fluctuations to be modelled, which could have a drastic

effect on how the ESS is utilized.

In order to not rely on a user creating the rule base for the microgrid, data mining can be

used as a front end of creating the rules [73]. Data mining will allow the Controller to learn

the specific constraints of the system and schedule the generation and ESS appropriately

through the generated boundary conditions. One benefit of using this method is that it

can learn from a specific system without using any model simplifications, and it can be

modified easily by re-creating boundary points as the system changes.

Further validation of the KBES Controller could be performed by implementing the

6.3 Future Work 71

controller on a system with fewer simplifications. For example, by imposing a state-of-

charge constraint on the ESS, or by taking into consideration diesel ramp rates, the KBES

can be tested on a more accurate model of the system. Another means would be to

incorporate different RE sources into the microgrid in addition to, or in place of, the

WTG, and observe whether or not the KBES Controller can handle such situations. In

terms of an economical analysis, a financial value for carbon credits [74] can be added to

the evaluation to incorporate the environmental benefits of an ESS. As detailed in [75],

government policies that address socio-economic effects such as social cost, pollution, and

investor return may change the energy dispatch in a complimentary or conflicting manner,

or it may have no effect at all.

As with all aspects of research, there is no ending point to the project as there are many

ways in which the KBES Controller can continue to be ameliorated or be used in many

different microgrid situations.

73

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