project: activate

This project has received funding from the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 229)

Document Properties

Dissemination level Public

Author(s) G. Kryonidis, L. Kontis, A. Nousdilis, K. Pippi, A.

Boubaris

Reviewed by T. Papadopoulos, N. Papanikolaou

Checked by PI 17/06/2020

Submission due date 17/06/2020

Actual submission date 17/06/2020

Project: ACTIVATE

Deliverable Number: 1.1

Deliverable Name: Review of state-of-the-art

and technical solutions

Project Number: 229

D1.1 Review of state-of-the-art and technical solutions of 70

Document History

Version Date Contributor(s) Description

1.0 01/03/2020 Eleftherios Kontis First Draft

1.1 10/03/2020 Georgios Kryonidis Second Draft

1.2 16/03/2020 Angelos Nousdilis Third Draft

1.4 14/06/2020 Kalliopi Piipi Fourth Draft

1.5 15/06/2020 Alexandros Boubaris Final Draft

1.6 16/06/2020 Dimosthenis Peftitsis Comments to Final Draft

2.0 17/06/2020 Nick Papanikolaou Comments to Final Draft

3.0 17/06/2020 Theofilos Papadopoulos Final

List of Acronyms

Acronym Meaning

ADC Analog to Digital Converters

ADN Active Distribution Network

ANN Artificial Neural Network

APC Active Power Curtailment

ARMA Autoregressive Moving Average Model

ARMAX Autoregressive–moving-average Model with Exogenous Inputs

Model

AS Ancillary Services

BESS Battery Energy Storage Systems

CHB Cascade Half Bridge Inverter

COI Center of Inertia

DG Distributed generation

DRES Distributed Renewable Energy Sources

DMD Dynamic Mode Decomposition

DSC Digital Signal Processor-Based Controllers

DSP Digital signal processor

D-STATCOMs Distribution Static Compensators

DSM Demand Side Management

DSO Distribution System Operator

EFCC Enhanced Frequency Control Capability

EKF Extended Kalman Filter

ERA Eigenvalue Realization Algorithm

ES Exponential Smoothing

ESS Energy Storage System

ECKF Extended Complex KF

FACTS Flexible AC Transmission Systems

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FBEP Forward and Backward Extended Prony

FD Frquency-Domain

FFT Fast Fourier Transform

FLL Frequency locked-loop

FPGA Field Programmable Gate Arrays

GUI Graphical User Interface

HMI Human Machine Interface

HV High Voltage

IoT Internet of Things

LPF Low Pass Filter

LV Low Voltage

MCU Micro controller unit

MFLOPS Million Floating Point Operations

MIPS Million Instruction per Second

MP Matrix Pencil

MPC Model Predictive Control

MPP Maximum Power Point

MPPT Maximum Power Point Tracking

MV Medium Voltage

N4SID Subspace State Space System Identification

OLEC Oveload Emergency State Control

OLTC On Load Tap Changers

PCC Point of Common Coupling

PDC Phasor Data Concentrators

PEM Prediction Error Method

PEV Plug-in Electric Vehicles

PLC Programmable Logic Controller

PMU Phasor Measurement Unit

PR Public Report

PSO Particle Swarm Optimization

PLL Phase-Locked Loop

PV Photovoltaic

PWM Pulse Width Modulation

RES Renewable Energy Sources

RGA Real Coded Genetic Algorithm

RoCoF Rate of Change of Frequency

RPC Reactive Power Control

RT Real-time

RT-DTLR Real-Time Dynamic Thermal Line Rating

SiC Silicon carbite

SMD Surface Mounted Device

SOC State-of-charge

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STS Static Transfer Switches

SVD Singular Value Decomposition

TCSC Thyristor Controlled Series Capacitor

TD Time-Domain

TSO Transmission System Operator

UPFC Unified Power Flow Controller

VF Vector Fitting

VISMA Virtual Synchronous Machine

VPP Virtual Power Plant

VSG Virtual Synchronous Generators

VSM Virtual Synchronous Machines

WAMS Wide-Area Monitoring Systems

WoC Web of Cells

WP Work Package

ZCS Zero Current Switch

ZVS Zero Voltage Switch

ZIP Constant impedance, current and power load model

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Disclaimer: “This document has been prepared in the context of ACTIVATE project, funded by

the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I.

Research Projects to support Faculty members and Researchers and the procurement of high-

cost research equipment grant” (Project Number: 229). This document reflects only the

authors’ views and H.F.R.I. are not responsible for any use that may be made of the

information it contains.”

Project Number: 229


TABLE OF CONTENTS

EXECUTIVE SUMMARY ................................................................................................................. 9

INTRODUCTION ................................................................................................................... 10

ANCILLARY SERVICES SOLUTIONS FOR DSOS AND TSOS ....................................................... 11

2.1. VOLTAGE REGULATION ................................................................................................................................. 11 2.1.1. Reactive power control for voltage regulation................................................................................................. 11 2.1.2. On load tap changers combined with reactive power control for voltage regulation ..................................... 12 2.1.3. Active power curtailment for voltage regulation ............................................................................................. 12 2.1.4. Energy storage systems and voltage regulation .............................................................................................. 13

2.2. VOLTAGE UNBALANCE MITIGATION ................................................................................................................. 15 2.2.1. Utilization of DG inverters ................................................................................................................................ 15 2.2.2. Utilization of flexible loads – demand response ............................................................................................... 16 2.2.3. Utilization of ESSs ............................................................................................................................................. 16 2.2.4. Utilization of static transfer switches ............................................................................................................... 17

2.3. OVERLOAD ALLEVIATION ............................................................................................................................... 17

2.4. POWER SMOOTHING .................................................................................................................................... 20

2.5. VIRTUAL INERTIAL RESPONSE ......................................................................................................................... 23

2.6. ESS SIZING FOR INERTIAL RESPONSE ............................................................................................................... 25

2.7. COORDINATED PRIMARY FREQUENCY RESPONSE ............................................................................................... 26

NETWORK MONITORING TECHNOLOGIES AND TECHNIQUES................................................ 28

3.1. NETWORK MONITORING TECHNOLOGIES AND TECHNIQUES ................................................................................. 28

3.2. IDENTIFICATION TECHNIQUES FOR MODAL ANALYSIS OF POWER SYSTEMS .............................................................. 30 3.2.1. Single-signal identification techniques ............................................................................................................. 30 3.2.2. Multi-signal identification techniques .............................................................................................................. 32

3.3. REAL-TIME ESTIMATION OF INERTIA TIME CONSTANTS ....................................................................................... 33

3.4. EQUIVALENT MODELS FOR ADN ANALYSIS ....................................................................................................... 34 3.4.1. Static equivalent models for ADN analysis ....................................................................................................... 35 3.4.2. Dynamic equivalent models for ADN analysis .................................................................................................. 37

POWER CONVERTER IMPLEMENTATIONS ............................................................................ 39

4.1. THREE-PHASE INVERTER REVIEW .................................................................................................................... 39

4.2. CONVERTER TOPOLOGIES .............................................................................................................................. 39 4.2.1. Three phase two level inverter topology .......................................................................................................... 40 4.2.2. CHB topology .................................................................................................................................................... 41

4.3. MICROPROCESSORS ..................................................................................................................................... 41 4.3.1. Real-Time digital control .................................................................................................................................. 41 4.3.2. Project evaluation cycle ................................................................................................................................... 42

4.4. DIGITAL-CONTROL AND ANCILLARY SERVICES ................................................................................................... 42 4.4.1. RoCoF measurement ........................................................................................................................................ 42 4.4.2. Power Smoothing ............................................................................................................................................. 42 4.4.3. Voltage Unbalance Mitigation ......................................................................................................................... 43 4.4.4. Voltage regulation ........................................................................................................................................... 43

4.5. ESS INTEGRATION ....................................................................................................................................... 44 4.5.1. DC/DC power converter for ESS integration ..................................................................................................... 44

4.6. COMMUNICATION ....................................................................................................................................... 45

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REFERENCES ............................................................................................................................... 47

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Table of figures

FIGURE 1: FLOWCHART OF OLEC SCHEME. .................................................................................................... 18

FIGURE 2: FLOWCHART OF THE MULTI-LEVEL METHOD [72]............................................................................... 19

FIGURE 3: FLOWCHART OF THE STEP-RATE CONTROL STRATEGY [9]. .................................................................... 22

FIGURE 4: A) THREE PHASE TWO LEVEL INVERTER, B) THREE PHASE TWO LEVEL INVERTER WITH ZVS [298]. ................ 40

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Executive summary Scope of this deliverable (D1.1) is to summarize the state-of-the-art, challenges and possible technical

solutions regarding the three key objectives of ACTIVATE. Specifically, the deliverable will be

published as a public report in the project website and provide details and possible solutions in the

following main areas:

• operation control and stability issues in active distribution networks (ADNs); incorporation of

new emerging technologies and ancillary services for control and operation applications

• modelling and analysis techniques of the dynamic performance of power systems; novel

architecture technologies for network monitoring in ADNs

• state-of-the-art solutions for power converter operation and control in ADNs

The deliverable concludes the work carried out in work package 1 (WP1) “Requirements engineering

and state-of-the-art”.

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Introduction The structure of this deliverable is divided into three chapters to cover the state-of-the-art and

requirements related to the three project key objectives, respectively.

Chapter 2: Ancillary services solutions

Within this chapter, state-of-the-art solutions for ADNs, including operation control and stability

issues, are examined. Of main importance is the incorporation of the new emerging technologies in

control applications. Scope of this chapter is to address the resulting needs and possible solutions for

tools to better observe, understand and operate ADNs.

Chapter 3: Network monitoring technologies and techniques

All state-of-the-art techniques and technologies considering network monitoring, measuring,

modelling and analysis with special emphasis to ADNs are examined.

Chapter 4: Power converter technologies

State-of-the-art for power converter operation and control in ADNs. The technical specifications

regarding the power control, communication, recording capabilities and implementation

requirements are examined.

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Ancillary services solutions for DSOs and TSOs A key objective of ACTIVATE is to develop novel control strategies, incorporating energy storage

systems (ESSs), in order to address technical issues related to the steady state operation of ADNs

(overvoltages, voltage unbalances, and overloading). Main target is to optimize the steady-state

operation of ADNs by exploiting coordinated, generalized, and straightforward control strategies,

with low communication requirements. Moreover, novel dynamic control functionalities, such as

virtual inertia and power smoothing techniques, are planned to be developed to ensure the stable

and reliable network operation of ADNs.

Therefore, state-of-the-art solutions for ADNs, including operation control and stability issues, must

be examined. Of main importance is the incorporation of new emerging technologies in control

applications. Scope of this Chapter is to address the resulting needs and possible solutions for tools

to better observe, understand and operate ADNs. Specifically, voltage regulation and voltage

unbalance mitigation techniques in low- and medium- voltage networks, already proposed in the

literature, are reviewed and discussed in Sections 2.1 and 2.2, respectively. A review of methods

regarding ESS sizing, overload alleviation, power smoothing and virtual inertial response solutions is

presented in Sections 2.3, 2.4, 2.5 and 2.6, respectively.

2.1. Voltage regulation The conventional distribution grids were designed to operate under a unidirectional power flow,

i.e. from the HV/MV transformer towards end users connected either at the medium voltage (MV)

or the low voltage (LV) level. However, the penetration of distributed generation (DG) units in

distribution grids resulted in a bidirectional power flow, causing overvoltages along the network and

affecting conventional voltage regulation methods. Several solutions have been proposed in the

literature to tackle voltage limit violations in distribution grids. Grid reinforcement is an effective

solution; however, the associated investment cost may be restrictive [1]. Alternative methods utilize

various controllable components of the grid to tackle overvoltages, including the inverters of DG

units. These strategies include the utilization of active transformers with on load tap changers (OLTC),

active power curtailment (APC) of DG production, reactive power control (RPC) employing

photovoltaic (PV) inverters capabilities and distribution static compensators (D-STATCOMs), ESSs and

demand side management (DSM) [1] - [4]. The most important methods are described below, using

the abovementioned categories.

2.1.1. Reactive power control for voltage regulation

Network voltage can be controlled by injecting/absorbing reactive power into/from the grid. Several

methods have been proposed for the exploitation of RPC capabilities of DG inverters and

D-STATCOMs. A simple RPC method is introduced in [5] for DG inverters aiming to mitigate voltage

rise caused by active power injections. A coordinated active power dependent RPC method (Q(P)) is

developed in [6] based on the sensitivity matrix of the network and the local active power injection.

The Q(V) droop of distributed DG units is optimally adjusted for the voltage regulation of radial

feeders through the strategy developed in [7], aiming to line losses minimization, by utilizing voltage

sensitivities of radial feeders. In [8], a two-level control strategy for the voltage control is proposed

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exploiting the reactive power of DG units. At local level fast disturbances of voltage can be mitigated,

while the centralized level is utilized to allocate the reactive power contribution of each DG unit.

A nearly decentralized voltage control is introduced in [9] for the optimal allocation of reactive power

contribution among generation units. The proposed strategy targets to the minimization of MV

network losses, while it can be combined with the operation of OLTC transformers for further

reduction of losses. The authors in [10] propose a distributed RPC provided by DG inverters and

assisted by shunt reactors in case of DGs sharing a common point of connection with the grid. A

control for D-STATCOM is proposed in [11] to mitigate voltage fluctuations of both positive and

negative sequence voltage.

It has to be mentioned that although RPC methods are designed for both LV and MV distribution

grids, the effectiveness of reactive power in voltage regulation is lower in LV networks [12]. This

happens due to the highly resistive line characteristics of LV networks [1].

2.1.2. On load tap changers combined with reactive power control for voltage

regulation

The utilization of active distribution transformers equipped with OLTC is an effective tool for voltage

regulation, thus various methods have been proposed. Note that usually HV/MV transformers are

equipped with OLTC, while off load tap changer transformers are installed in the LV network [1].

Therefore, most of the methods refer to the MV grid, while they usually combine the utilization of

RPC capabilities of DG units along with OLTC control.

In [13], a two-stage methodology is introduced to regulate voltage in unbalanced radial distribution

grids, by optimally coordinating OLTC and static VAr compensator. A coordinated control for OLTC,

voltage regulator and schedulable DG units minimizing the number of regulating actions is developed

in [14], for the voltage regulation of distribution feeders. Furthermore, in [15], the use of

transformers with OLTC and wind turbine RPC capabilities is exploited; scope of this system is to

regulate voltages of distribution grids. A coordinated control of OLTC and reactive power of

distributed PV inverters is proposed in [16], suitable to handle different voltage conditions in multi-

feeder networks. Moreover, in [17], optimal voltage regulation is achieved by coordinating the RPC

of DG units combined with OLTC operations. Specifically, a methodology is proposed to solve the bi-

objective optimization problem of two conflicting objectives, i.e. minimization of power losses and

tap actions. Moreover, authors in [18] propose a scheme for voltage control in distribution networks,

through coordinated control schemes that prioritize the use of OLTC, the reactive and finally the

active power of inverters. In [19], a consensus-based distributed voltage regulation strategy is

introduced aiming to utilize effectively the active and reactive power of DG inverters and the OLTC.

2.1.3. Active power curtailment for voltage regulation

Even though RPC is an efficient tool for voltage regulation, network losses are inevitably increased

under the use of RPC methods, due to the increased current flow in distribution lines [20]. Moreover,

to exploit the reactive power capabilities of DG inverters, either a part of the active power has to be

lost, or the inverter has to be oversized [21]. For this reason, APC methods for distributed renewable

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energy sources (RES) units have been also proposed in the literature for voltage regulation of

distribution grids.

An active power capping method is developed in [22] to prevent voltage violations caused by

increased PV penetration, using local voltage and power measurements. The authors in [20]

proposed a local voltage regulation strategy through a droop-based APC for distributed PV inverters.

A similar APC droop-control is introduced in [23]. The methods exploit the voltage sensitivities to

uniformly allocate the curtailment of active power injections among the PV owners. In [24], two

coordinated PV control strategies are proposed aiming to enhance the fairness of APC schemes.

APC methods have been combined with RPC strategies to enhance the voltage regulation of

distribution grids. An optimal control of active and reactive power of PV inverters is presented in [25]

for the voltage regulation of LV distribution feeders. In [26], a distributed control scheme for voltage

regulation of LV feeders is introduced that prioritizes the use of reactive power before the use of

APC. The method requires only a limited communication among PV inverters. A local voltage

regulation method is developed in [27] through APC and RPC of PV inverters, based on short-term PV

power forecasts. Authors in [28] developed a local voltage regulation methodology for distributed

PV microinverters. The proposed method is based on the correction of voltage measurements at the

ac-side of the microinverters, aiming to ensure that unnecessary curtailment of active power and use

or reactive power is avoided.

It has to be noticed that although APC methods can efficiently regulate voltage in distribution

networks, especially in LV feeders, they result into a loss of renewable energy.

2.1.4. Energy storage systems and voltage regulation

Proposed RPC and APC techniques for voltage regulation are effective and can be applied on the

already existing DG units, however they result in increased network losses or green energy

curtailment. On the other hand, electrical ESSs can be utilized to efficiently control the voltage of

distribution grids, avoiding the above-mentioned problems. Based on [1] and [21], ESSs constitute

the most reliable and efficient solution for voltage regulation compared with APC and RPC

techniques, that are based on DG inverters and grid equipment (OLTC, D-STATCOM, etc). Several

methods for voltage regulation by the use of ESSs have been proposed in the literature. The methods

can be divided in two main categories based on the voltage level of the distribution network, i.e. MV

and LV oriented control methods. Note that, ESSs can be either operated by their owners (e.g.

prosumers), an aggregator, or the distribution system operator (DSO).

2.1.4.1. MV distribution network

2.1.4.1.1. Central (community level) ESS

The use of central ESS units for voltage regulation has been tested in various works in the literature.

An optimal scheduling and sizing method has been proposed in [29] for community based DSO-

operated battery ESS, providing among others voltage regulation services at the distribution feeder.

Authors in [30] proposed a coordinated control for PV inverters and battery ESSs for voltage

regulation, ensuring that deep discharge of battery is prevented.

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2.1.4.1.2. Distributed ESSs

Additionally, distributed ESS units may be connected in various points of a distribution grid to

efficiently aid voltage control. Voltage regulation is achieved by the utilization of reactive power of

distributed ESSs in [31], through a localized and a consensus-based control. Network loading

management is also achieved by a distributed control of the active power of the ESSs. Authors in [32]

proposed a coordinated control for voltage regulation (based on three fuzzy controllers managing

the OLTC of the transformer) as well as the reactive and active power of DG units and/or ESSs.

Moreover, in [33] a coordinated control of distributed battery ESSs for voltage regulation and

mitigation of frequency deviations is introduced. The control groups neighbor ESSs and prioritizes

the use of the largest ESS. In [34], a distributed control of heterogeneous ESSs is designed to provide

voltage/frequency support, while synchronizing the active/reactive power sharing and energy levels

of the participating ESSs.

2.1.4.2. LV distribution network

2.1.4.2.1. Central (community level) ESS

A methodology for the siting and sizing of central battery ESS is developed in [35]; the active and

reactive power contribution of the central ESS is utilized to maintain voltage into permissible limits

and thus increase the hosting capacity of the grid. In [36], the optimal sizing and placement of a

central battery is defined based on multiple objectives, such as the voltage regulation of the LV

network.

2.1.4.2.2. Distributed ESSs

The use of distributed ESSs for the voltage control of LV grids has been investigated in numerous

studies. The developed strategies of ESSs can be divided into centralized, distributed and localized

control.

Centralized control of distributed ESSs

A centralized control approach for distributed ESSs is introduced in [37] for the mitigation of

overvoltages in LV feeders, comprising also the reactive power capabilities of PV inverters. The

centralized coordination controller of [38] decides the charging/discharging profiles of distributed

ESSs and the tap changes of active distribution transformers for overvoltage mitigation. The main

target is to relieve the tap changer stress, by limiting the required voltage control actions. Authors in

[39] developed a centralized control for the cooperation of both utility- and prosumer-owned

batteries, that exploit the real and reactive power of the inverters to improve the voltage profile.

The coordination of PV inverters reactive power capability and droop-based controlled battery

storage systems is proposed in [40] and [41], to maintain the voltage into permissible limits. The

paper assesses the impact of line R/X characteristics on the performance of the proposed control and

the required size of distributed ESSs. Moreover, in [42] a coordination control scheme for distributed

utility-scale ESSs was proposed, aiming at voltage regulation. The control utilizes both reactive and

active power capabilities of ESS inverters, and efficiently selects the most appropriate ESSs to

regulate voltage, taking into consideration the lengthening of battery life span.

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Distributed control of distributed ESSs

Whereas centralized controls are capable of maintaining the voltage into the permissible limits, they

are based on the uninterruptable communication of the central and ESS controllers. In case that the

communication with the central controller is lost, the objective of the centralized strategy cannot be

guaranteed. To overcome this issue, distributed controls have been developed; such methods need

only a communication link between neighbor installations, while the control decision does not

require global information of the grid [43].

Authors in [44] designed a distributed control for battery ESSs based on the consensus algorithm to

regulate the voltage of LV feeders. A localized control is also utilized to maintain the battery state of

charge (SoC) into the operational limits. Authors in [45] developed two consensus-based controls for

voltage regulation, utilizing distributed battery ESSs connected with PVs. The controls ensure that

batteries contribute fairly to the voltage regulation taking into consideration the capacity and the

SoC of ESS. A similar control approach for voltage regulation is proposed by the same authors where

plug-in electric vehicles (PEV) are utilized for voltage regulation in [46]. In case the connected PEV

are not capable to mitigate overvoltages, APC of PV generation takes place.

Localized control of distributed ESSs

Both centralized and distributed methods for voltage regulation rely on the reliable and

uninterruptible communication between storage controllers. Therefore, they may not ensure

successful voltage regulation under communication errors. To avoid the dependence on

communication systems, several localized controls have been developed. The most common ESS

local control strategy, integrated in prosumer-owned storage systems, charges the battery as soon

as PV surplus is realized, while it discharges the storage when PV power in not sufficient to supply

the consumption [47]. Under the previous strategy, battery capacity is fully charged before the peak

PV generation. Hence, increased PV injections during noon are not avoided, resulting in overvoltage

incidents. To tackle this issue, a peak shaving control of ESS is introduced in [48] aiming to mitigate

overvoltages. The same work incorporates several local controls of ESS combining also the

capabilities of PV inverter for voltage regulation. Similarly, [49] proposed a battery management

strategy that activates the charging process, after PV production exceeds a predefined power

threshold, aiming to alleviate overvoltages. This threshold-based charging process was combined

with a discharging strategy by [50], to mitigate also undervoltages occurring during peak load periods.

Voltage limits violations are alleviated by efficiently controlling the ESS power and SoC level in the

localized control strategy of [51]. Furthermore, an adaptive battery charging and discharging

scheduling strategy is developed in [52] to alleviate voltage and thermal issues of distribution feeders

by efficiently utilizing the capacity of the ESSs.

2.2. Voltage unbalance mitigation

2.2.1. Utilization of DG inverters

The voltage unbalance can be efficiently mitigated in distribution grids by using the appropriate

equipment, such as series active power filters, shunt power filters, series-parallel compensators, and

static synchronous compensators [53]. Nevertheless, the installation of such equipment increases

the investment cost of the DSO. On the other side, the inverters of distributed DGs provide the

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capability to balance the power flow among the three phases of a LV distribution grid, and thus

numerous inverter-based controls have been developed. The authors in [54] proposed a scheme for

voltage unbalance mitigation through a sophisticated control of inverter injected currents. The

scheme is based on a damping control strategy that efficiently mitigates the voltage unbalance by

injecting higher currents in the phase with lower voltage and lower current in the phase with higher

voltage. This is achieved by varying the damping conductance of the inverter. A similar approach was

used by [55] and was tested both in single- and three-phase inverters.

The approach of the variable damping conductance of [54] and [55] was also combined with voltage

regulation methods. A two-layer control method is developed in [56] to mitigate overvoltages and

voltage unbalance in LV feeders. A local P(V) droop is utilized to mitigate voltage unbalance based on

the idea of the damping conductance, while a centralized control coordinates the OLTC and DG unit

actions for voltage magnitude regulation. The damping control strategy was combined with a voltage-

based droop in [57] to simultaneously mitigate overvoltage and voltage unbalance incidents.

Another approach to mitigate voltage unbalance is presented in [53], where the proposed method

aims to minimize the grid negative sequence voltage, by setting the negative sequence current of the

inverter to be inphase with the negative sequence current of the grid. Moreover, a two-layer reactive

power control is developed in [58] for voltage regulation, aiming to mitigate voltage imbalance

between phases. The control utilizes the single-phase PV inverters that are connected to the grid.

2.2.2. Utilization of flexible loads – demand response

Demand response has been also proposed as a measure to tackle voltage unbalance. Specifically,

thermostatically controlled loads (TCLs) are exploited in [59] to mitigate voltage unbalance in

microgrids through a control algorithm that is based on voltage sensitivity coefficients. The control is

designed to deploy the minimum required TCLs of the microgrid. In [60], the cooperation of PV

inverters and TCLs is proposed as a voltage unbalance mitigation method based on demand side

management for islanded microgrids. Authors in [61] proposed a combined voltage regulation

strategy using DR and OLTC management for low-voltage distribution grids. The integrated control

scheme manages residential appliances and transformer’s taps to mitigate voltage unbalance and

voltage magnitude violations.

2.2.3. Utilization of ESSs

Furthermore, ESSs may be utilized to improve voltage profile with respect to voltage unbalance in LV

distribution networks. A control of ESSs is proposed in [62] to reduce voltage unbalance of LV feeders.

The strategy aims to balance the net power of a PV prosumer exchanged with the grid using the

minimum power of ESS. The use of distributed single-phase batteries connected at the three phases

of a network is optimized in [39] to improve the voltage profile in terms of voltage magnitude and

voltage unbalance. Authors in [63] designed three voltage unbalance mitigation techniques for the

cooperation of distributed single-phase ESSs coupled with PVs, aiming to reduce voltage unbalance

in LV grids. A community energy storage (CES) connected in a common dc-link with all single-phase

PV dc/dc converters is used in [64] to alleviate voltage unbalances of radial LV feeders. The authors

proposed a charging/discharging control that mitigates the current flowing over the neutral

conductor, by offering an active power balance between phases. In [65] the capability of electric

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vehicles to mitigate voltage unbalance in LV feeders was investigated, under a droop-based charging

control.

2.2.4. Utilization of static transfer switches

Moreover, static transfer switches (STSs) can be utilized to mitigate the power unbalance of a feeder,

by rearranging the consumers between the phases [66]. A centralized control scheme for STSs is

developed in [67] for alleviating voltage unbalance in a distribution feeder with PV prosumers by the

management of the prosumers’ load between the three phases.

2.3. Overload alleviation

One of the most crucial incidents that power systems may experience is thermal limit violations,

typically owing to overloads. Thermal limit violations can damage the network equipment and limit

the number of DG units that can be connected to the grid. Hence, the absence of network overloads

is one of the most serious requirements of performance standards [68]-[79]. Due to the fact that

various methods can be adopted in order to alleviate overloads in network branches. In the relevant

literature several methods have been proposed such as [68], [70]:

• Generation rescheduling

• Load shedding

• Control using phase shifting transformers or FACTS devices

• Generation curtailment

• Control through HVDC links

• Line switching

In [68] an expert real-time system for overload alleviation is proposed, which combines the use of

phase shifting transformers with generation rescheduling. The power flow problem is solved in the

off-line analysis, where sensitivity factors and linear programming optimization are applied, in order

to create a knowledge base. If the proposed system detects an overload event, it will recommend an

appropriate change to the phase shifter angle. If the overload isn’t completely eliminated, the

generation will be rescheduled.

In [69] two different kinds of generation curtailment are examined as methods to alleviate overloads.

Specifically, these methods are utilized to permit the installation of more DG units in the grid without

impacting other consumers since the hosting capacity of a distribution network is limited by technical

criteria, e.g. overvoltage or overcurrent limitations. Firstly, the hard curtailment case is presented in

which all the DG units must be disconnected from the grid if an overload incident occurs. Then, the

soft curtailment case is described in which the generation is decreased enough to eliminate the

overload. It is worth mentioning that the annual energy yield might be decreased when the installed

capacity of RES increases, if the hard curtailment method is utilized.

In [70] an overload emergency state control (OLEC) scheme is presented, which uses generation

curtailment. According to OLEC there are three different types of overloads that should be alleviated;

severe, light and residual overloads. Severe overloads must be automatically alleviated by OLEC

before network elements are tripped. When the overload for emergency thermal rating equals to

zero and the one for normal thermal rating is greater than zero, it is considered as light overload.

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Light overloads can be alleviated by quick or slow generation curtailment. As residual overloads are

defined the remaining overloads after the alleviation of severe or light overloads. The proposed

scheme is depicted in the flowchart of Fig. 1.

In [71] an algorithm for optimum load shed is proposed for overload alleviation, employing teaching

learning-based optimization, which requires less computational efforts since it needs no algorithm-

specific parameters. The algorithm considers both the next interval predicted load and the present

loading condition, as well as line flows and voltage limits. In order to determine the optimum load

shedding of a bus the sensitivity of a severity index is utilized. The algorithm is also validated by using

another evolutionary technique.

In [72] a multi-level method is proposed, which utilizes the basic capability of ADNs to coordinate

Distributed Renewable Energy Sources (DRES) and flexible loads, in order to relief a transmission

network from overload conditions. According to the overload relief that is requested from the

transmission network, the method is divided in three layers; the active reconfiguration scheme,

which reduces the power losses, the load transferring scheme and the demand response scheme. All

of them are considered as load curtailment. The largest capacity of load curtailment is accomplished

when the demand response scheme is utilized. The proposed method is depicted in the flowchart

of Fig. 2.

Figure 1: Flowchart of OLEC scheme.

Data and measurements update

Network overload incident?

No Yes

Timer counting from the occurrence

Type of overload?

Severe

LightAlleviation

Overloadremains?

No Yes

Quick generation curtailment

Generation curtailment

Prediction of residual overload

Alleviation of light overloadElimination of residual overload

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Figure 2: Flowchart of the multi-level method [72].

In [73] a multi-objective method is proposed to manage network congestion via load shedding and

generation rescheduling. Specifically, a combination of different objective functions is utilized in

order to obtain the best solution. These functions are coupled with the main objective function, i.e.

the generation and load shedding cost minimization function, and they are presented below:

• Social welfare maximization function including demand response offers

• Load served error minimization function

• Load shedding minimization function

• Load served maximization function.

The proposed method can be utilized when the load is modelled as voltage dependent.

In [74] a control scheme based on model predictive control is presented to maintain the current rate

between preferable limits. In general, at each time step a new optimization problem is solved using

new measurement data. The control scheme utilizes a closed loop operation. Hence, the strategy is

generally stable to modelling errors and measurement noises, but at the same time becomes slower.

An algorithm is proposed in [75], utilizing bus sensitivities and cost in order to sort system buses. This

is a useful tool for the system operators since they can choose only the most “attractive” buses to

adjust their generation and consumption, and apply optimal overload alleviation strategies.

In [76] FACTs devices, unified power flow controllers (UPFC) and thyristor-controlled series capacitors

(TCSC) are utilized in order to alleviate overloads in a power system. Computational methods are

proposed to determine the most proper location and the most suitable settings for the installation

of these components. Also, two different algorithms are implemented, i.e. the real coded genetic

algorithm (RGA) and the particle swarm optimization algorithm (PSO). It is concluded that

overloading decreases with by increasing the use of FACTs. Overloading incidents remain almost

constant beyond a certain number of FACTs. When UPFC are utilized, the security margin is wider

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than in the case when TCSC are utilized. Also, RGA increases the security margin but demands more

computational time than PSO.

A congestion management approach is presented in [77], which adjusts the active power flow in the

transmission lines by using FACT and D-FACT devices. Specifically, the proposed control algorithm

determines the proper changes in the line reactance that should be applied in order to alleviate

overloads. From simulations, it is concluded that the automatic change of line impedance can relieve

the overloaded lines, hence, the power flow approaches the desirable limits.

In [78] a modified optimal power flow problem with relaxation of restrictions is solved in order to

determine the optimal amount of load that should be shed to alleviate overloads. The actions, which

are made, are:

• relaxation of the minimum voltage limits and the maximum power flow limits through

transformers.

• introduction of limits for the permitted load curtailment based on the importance of the load.

This way, if an overload incident with small duration occurs, only a small amount of load will be shed

in order to avoid unnecessary load cuts. On the contrary, if the duration of the incident is longer and

there are residual overloads after the first curtailment, the necessary load cuts are made to provide

congestion relief.

A flexible load shedding strategy is presented in [79], which takes into account the real-time dynamic

thermal line rating (RT-DTLR). RT-DTLR is an indirect method to relieve the network congestion since

the line capacity is increased when RT-DTLR is utilized. Nevertheless, if the line rating is not calculated

correctly, system instability risks are increased and other types of limits, e.g. voltage limits, can be

violated. The proposed strategy combines two different objective functions; one based on the system

risk increment and another based on load shedding variation, in order to deal with the benefits and

risks of RT-DTLR. Thus, an optimal compromise solution arises.

2.4. Power smoothing

The probabilistic nature of solar irradiance, the environmental temperature or the location, where a

PV system is installed, are some of the main reasons of output power fluctuations of PV systems that

affect the amount and the quality of the produced energy [80]-[90]. The variability of the output

power results in significant voltage fluctuations and the injection of high frequency components into

the distribution grid. In addition, if the duration of the power fluctuations is less than 10 min and the

grid is stable, they can be absorbed as frequency fluctuations without any risk by the grid [82]. On

the other hand, when their duration is higher, it is necessary to take measures in order to ensure the

proper operation of the utility grid via smoother power output of DG units. For this purpose, various

methods have been proposed in the literature [86], [90], such as:

• Generation curtailment via operation below the maximum power point (MPP) of the PV

system

• Utilization of dump loads (resistors that are used for dumping power when it is not needed)

• Utilization of ESS, such as:

o Electric double layer capacitors

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o Fuel cell system

o Flywheel energy system

o Superconducting magnetic energy system

o Battery energy storage system (BESS)

There are two main smoothing strategies of ESS, adopted in a series of papers; they are presented

below:

• Ramp-Rate Control: When a fluctuation occurs and its value exceeds a maximum allowable

ramp-rate value rmax, the control scheme is enabled, as described by (1)

( ) ( ) − + − − max maxΔ Δ Δ ΔG G GP t t t r P P t t t r (1)

where PG is the power that should be injected into the grid in order to smooth the fluctuation and t

is the time step [82], [83], [85].

• Moving Average (MA) Control: The basic idea of this control scheme is that the amount of

the power PG(t) that should be injected into the grid in order to smooth the power of the PV

system PPV(t), is calculated as the mean production value in a time window with duration T,

and it is described by (2) [82], [83], [85].

( ) ( )1

−

= T

G PV

t T

P t P t dtT

(2)

Based on the above concepts, a natural gas engine generator and a BESS are utilized in [80] in order

to smooth the PV output power with MA method. It is concluded that the generator is too slow to

mitigate higher ramp rates and the installation of BESS is essential. It is worth noticing that the

utilization of a gas engine increases the lifetime of the BESS because the burden of the BESS for power

smoothing is decreased.

In [81] an electric double layer capacitor is used to smooth PV output power fluctuations. MA method

is utilized for the calculation of the inverter output. The MA method is modified by adding a voltage

control unit in order to maintain the capacitor voltage constant. It seems that when the ramp rate is

reduced, the required capacitance decreases but the energy loss of the control scheme increases.

In [82] a method is proposed to calculate the minimum storage requirements and the maximum

power to mitigate the worst fluctuations at any PV plant size and maximum allowable ramp-rate.

Also, the “worst fluctuation model” is defined and three different battery recharging strategies are

examined. It is concluded that the storage system manages a small amount of energy; PV peak power

aggregation causes a decrease in capacity and power requirements of BESS. Hence, it seems wiser

the installation of a single storage system that manages the energy of multiple PV power plants.

A step-rate control strategy is proposed in [83] and it is compared with the ramp-rate control and

MA control scheme. It is based on strict compliance with the maximum ramp constraint rmax for a

defined time window Δt. The proposed strategy is depicted in the flowchart of Fig. 3. It is concluded

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that the main advantage of the MA method is its minimum storage capacity requirements. On the

contrary, this method presents higher losses (2-3 times) and the life span of BESS is degraded.

Figure 3: Flowchart of the step-rate control strategy [9].

In [84] a new ramp-rate control strategy for BESS is presented. In order to mitigate fluctuations and

improve the performance of the conventional ramp-rate method, an inverse characteristic of the

desired ramp-rate with the PV output ramp-rate is proposed. This control scheme in contrast to the

MA method, is independent of past PV data. The exported results of this method are comparable to

those of the MA method and the life degradation of BESS is improved since the BESS is not utilized

all the time. In addition, when severe fluctuations occur, the proposed method can provide tighter

control than the traditional ramp-rate scheme.

Two management strategies based on ramp-rate control are presented in [85]. The ESS sizing

requirements are lower by 50% than the conventional ramp-rate control. The first strategy utilizes

the PV inverters in order to limit ramping-up fluctuation incidents. The second strategy is a BESS SoC

control scheme that is based on the actual power of the PV system and the production limits. Due to

inverter limitation, the first strategy has higher losses than the second one.

A SOC based control scheme for output power fluctuation mitigation of PV and WP system is

proposed in [86]. The proposed scheme is divided in four stages:

• Determination of the initial target power of the BESS by a dynamic filtering controller or a

dynamic rate limiter.

• Determination of the target power of each power converter system.

• Determination of the modified target power of each power converter system.

• Determination of the target power for each unit.

The utilization of this control scheme delays the life degradation of BESS.

Fluctuation mitigation using MA method and a first-order low-pass-filter (LPF) is presented in [87],

where the SoC of BESS is maintained within a range by modifying the power output if BESS uses a

tuned gain parameter.

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In [88] a power control scheme for BESS is proposed, considering the SOC and charge-discharge

depth of BESS based on the LPF principle. This strategy can smooth the power output fluctuations.

As far as the BESS is concerned, the control scheme guarantees the proper operation and extends

the lifetime of the BESS since the storage system in not overcharged or overdischarged.

In [89] the utilization of three different methods to smooth the output power of PVs is presented.

The methods, which are examined and compared, are the utilization of BESS, dump load, and

generation curtailment by operating below MPP. The economic aspects of using these methods are

examined and 10-min radiation data are analyzed. It has been found that combining a BESS and

power curtailment is the most economical solution.

In [90] a new ramp-rate control strategy based on the exponential smoothing (ES) method is

proposed and it is compared with the MA method and the conventional ES method. The proposed

strategy limits the PV ramp rate within desirable limits (preventing over-smooth) and has some

advantages over the other two control schemes. Firstly, it utilizes all data points of the system. In

addition, the phenomenon of memory effect, which is found in the MA and the conventional ES

method is removed from the proposed control scheme, since the weights distributed in PV data

points are not equal and are based on PV ramp-rate. Also, the smoothing parameter “σ” is varied

according to PV ramp-rate. Finally, BESS does not operate all the time. Due to the fact that the size

of BESS is decreased and its lifespan is increased.

2.5. Virtual inertial response The majority of the DRESs are connected to the grid using grid-interfaced converters. As long as DRESs

constitute a small portion of the total installed generation capacity of the power system, stability

problems can be effectively addressed by conventional generators. Nevertheless, the ever-increasing

penetration of DRESs over the last years has led to the gradual decommission of conventional

generators, causing a series of technical problems related to safe and secure operation of the power

system. One of the most important problems is the reduction of the system inertia [91]. More

specifically, contrary to the conventional power plants which are connected to the grid via

synchronous generators, converter-interfaced DRESs lack of rotating inertia (rotor) and damping

mechanisms (mechanical friction and damper windings). As a result, power systems are more

vulnerable to power dynamics and system faults.

A promising solution to this problem is to modify the control and operation of the grid-interfaced

converters in order to imitate the behavior of synchronous generators. This idea is firstly proposed

in [92] and [93] by introducing the virtual synchronous machine (VISMA) concept. According to this

concept, the traditional synchronous machine model is employed to control the output currents of

the grid-interfaced converter. Therefore, by implementing the VISMA concept, each converter-

interfaced DRES can virtually provide inertia to the grid. It can be shown that, under certain

conditions, the dynamic performance of the VISMA model resembles the use of frequency droop

curves for converter-based microgrids [94]. A similar approach has been recently presented in [95],

where an algorithm is proposed to determine the rate of change of frequency (RoCoF) at the point

of interconnection with the grid based on the data received from a phase-locked loop (PLL). The

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variation of the output power (ΔP) in p.u. with respect to the RoCoF (in p.u.), is calculated by using

the well-established swing equation of synchronous machines, as follows:

=2P HRoCoF (3)

where H is the inertia constant, which usually varies from 0.1 up to 9 s. Note that in (3) the damping

windings that exist in a synchronous machine are neglected. Finally, this variation is forwarded to a

current control loop to build the desired output currents. However, a common drawback of the

above-mentioned methods lies on the use of the current control loop, making the DRESs to behave

as variable current sources, from a power systems perspective. Consequently, contrary to the

synchronous generators that operate in grid-forming mode, the grid-interfaced converters of the

DRESs operate in grid-following mode.

A similar idea to the VISMA model is proposed by Q.-C. Zhong and G. Weiss in [96], [97], and [98],

introducing the synchronverter concept. According to this concept, the detailed mathematical model

of the synchronous machine is incorporated into the grid-interfaced converter of the DRES. More

specifically, the converter operates as an ideal controllable voltage source is series with an

impedance. The angle and the magnitude of the voltage source are determined by the following four

factors: (a) the voltage at the point of interconnection with the grid, (b) the active power of the

primary source connected at the dc-link of the grid-interfaced converter, (c) the output reactive

power of the DRES, and (d) the dynamic behavior of the synchronverter as mathematically expressed

by the detailed model of the synchronous machine. Additionally, contrary to the VISMA model, the

synchronverter concept offers a grid-forming capability, since it operates as a controllable voltage

source. Improved versions of the synchronverter model are proposed in [99] and [100]. More

specifically, a new synchronization process of the synchronverter with the grid is presented in [99].

Its distinct feature is the lack of any dedicated synchronization unit. Additionally, the dynamic

performance of the synchronverter towards voltage and frequency deviations at the point of

interconnection with the grid is improved in [100] by developing a bounded dynamic controller.

Nevertheless, in all the above-mentioned implementations, the synchronverter cannot actively

control the output currents, since it operates as controllable voltage source. Thus, in case of a fault

close to the point of interconnection with the grid, the output currents of the synchronverter will be

uncontrollably increased, exceeding the nominal values. Contrary to the synchronous generators,

converters present a limited overloading capability. Therefore, these uncontrollable currents can

destroy the converter.

To overcome this problem, a restraining method of fast transient inrush fault currents is presented

in [101]. The main idea behind this method is that when a fault occurs the converter controller

activates an emergency condition. During this condition, the control of the grid-interfaced converter

switches from a voltage control loop, i.e., used in the synchronverter concept, to a current control

loop using a hysteresis algorithm. Nevertheless, according to the control theory [100], the switching

between different control schemes should be generally avoided to prevent oscillations and repeated

activation-deactivation cycles.

An alternative implementation for providing virtual inertia is presented in [102], [103], and [104].

This method builds on the existing control algorithms used for commercial PV plants. In particular,

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the converter of a PV plant usually operates as a current source with unit power factor. This means

that a change at the magnitude of the output current will affect the injected power to the grid. The

magnitude of the output current is determined by a controller aiming to keep the voltage at the dc-

link close to a reference value. A constant voltage at the dc-link indicates that no energy is stored at

the capacitor of the dc-link, i.e., the power generated by the primary source is also provided to the

grid. The authors in [102] propose a modified version of the above-mentioned scheme by dynamically

changing the voltage reference at the dc-link with respect to frequency variations. In this way, a

mismatch between the generated power of the primary source and the power provided to the grid

is created, forcing the capacitor at the dc-link to store or provide excess energy to grid, similarly to a

conventional synchronous generator. However, the capacitor used for commercial applications is

very small, leading to limited inertia response. Thus, additional short-term storage systems need to

be connected at the dc-link, e.g. ultracapacitors. Furthermore, these converters operate as a

controllable current source in grid-following mode.

All the above-mentioned drawbacks have been addressed in [105] by employing a sophisticated

control scheme. The proposed control scheme consists of two cascaded control loops: (a) current

and (b) voltage control loop. The former is the inner control loop and is employed to actively control

the output currents of the DRES. Thus, in case of a large disturbance, e.g. a fault, the output currents

can be actively controlled, avoiding this way the overloading of the converter. The later is the outer

control loop and is used to actively control the voltage of the DRES. Therefore, the DRES is seen from

the power system as a controllable voltage source providing grid-forming capabilities.

In [106], a new algorithm is proposed to provide virtual inertia under unbalanced operation

conditions. Finally, an improved damping strategy for virtual synchronous machines is proposed in

[107], operating as an enhanced power system stabilizer which is employed in synchronous

generators.

2.6. ESS sizing for inertial response The sizing of the energy storage systems to provide inertial response is a relatively new research topic

[108]. Currently, all the solutions that have been proposed in the research community are based on

deterministic and analytical approaches. A simple and straightforward approach for ESS in presented

in [109]. The required ESS size is calculated with respect to the worst-case scenario, i.e., a power

mismatch around 4% to 10%. The authors in [110] propose an exhaustive search to determine the

optimal size of the ESS under the worst-case scenario. The worst-case scenario corresponds to case

where the largest generation unit of the power system trips, since it is the event where the greatest

RoCoF and the deepest frequency nadir are likely to occur. According to this approach, a large DRES

is considered at the power system capable of providing virtual inertia. Afterward, an iterative process

is implemented by changing the inertia constant of the DRES to evaluate how the dynamic

performance of the power system is affected. The optimal value of the inertia constant, which

indirectly determines the ESS size, is searched among the feasible solutions that preserve the

frequency quality. The main advantage of this exhaustive search is the fact that it enables the

inclusion of nonlinear frequency dynamics to the optimization problem.

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In [111], an analysis is performed to estimate the inertia deficiency of a low-inertia microgrid. This

inertia deficiency directly determines the ESS sizing, since there is a strong coupling between inertia

constant and ESS size. Although this method focuses on low-inertia microgrids, it can be also applied

to large power systems as presented in [112]. More specifically, an analytical methodology is

developed for ESS sizing towards the provision of inertial and primary frequency response in [112].

The proposed method uses the preliminary knowledge of the power system without the ESS, i.e.,

system size, inertia constant, droop characteristics of the primary frequency response. By comparing

the actual system characteristics with the desired ones, the required ESS size is calculated. Finally, it

is demonstrated that the same ESS can be used to provide both inertial and primary frequency

response.

In [113], an analytical methodology based on the frequency characteristics of the power system is

proposed for sizing the ESS. Towards this objective, a simplified model of the power system is initially

derived to represent rotor and speed-governor dynamics. Afterwards, a parametric analysis is

performed where the size and the control parameters of the ESS are modified to evaluate their

impact on the dynamic performance of the simplified network. Based on the results of this analysis,

the most suitable ESS size is selected.

A frequency-based sizing methodology is developed in [114] to optimize a hybrid ESS consisting of

ESS with fast and slow response. By employing the Fourier analysis to the power imbalance between

generation and demand, the low and high frequency power fluctuations are supplied to slow and fast

ESS, respectively. It is worth mentioning that the analysis is performed in an isolated power system

with high penetration of wind generation. A similar approach is presented in [115], where a

methodology based on the equivalent inertia calculations is proposed for sizing a hybrid ESS to

provide both inertia and primary frequency response.

An optimization process is proposed in [116] to determine the optimal ESS sizing for inertial response.

Initially, a simplified dynamic model of the system is derived taking as important parameters, the

damping, and the inertia constant. Next, an optimization problem is solved to minimize the overall

installed capacity of the storage system, while satisfying technical constraints, e.g., permissible limits

of network frequency, etc. A similar approach is proposed in [117], where the ESS sizing problem is

formulated as a standard, cost-based optimization problem and solved using metaheuristic

optimization algorithm.

2.7. Coordinated primary frequency response Due to the advent of DRESs, the portion of the conventional power plants to the generation mix is

decreasing. As a result, DRESs should be actively involved in the frequency regulation of the power

system to ensure the stable and reliable grid operation.

The incorporation of the primary frequency regulation functionality to the DRESs constitutes a newly

emerging research topic. Currently, the main research effort has been devoted to the development

of methods for the provision of primary frequency regulation from large wind farms. More

specifically, in [118], a centralized, optimization-based control algorithm of the wind farm is proposed

to maintain a certain reserve of power to be utilized during primary frequency control. The algorithm

is designed to minimize the power losses within the wind farm by optimizing the share of evert

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individual wind turbine. An improved solution to the optimization process of [118] is presented in

[119]. The wind turbines of the wind farm are first grouped by means of clustering analysis according

to their wind profiles. In this way, the same control commands apply to each wind turbine belonging

to the same group. Consequently, each group can be modeled as a single wind turbine, reducing the

number of control variables and the computational complexity of the optimization problem. In [121],

an optimization method is applied to a wind farm to support three operation strategies: (a)

maximization of the wind farm power while maintaining a constant rotational kinetic energy, (b)

maximization of the wind farm kinetic energy while maintaining a constant output power, and (c) a

de-loaded strategy where the wind farm rotational kinetic energy is maximized for a fixed de-loading

margin. The three operation strategies are formulated as nonlinear optimization problems solved

separately by the central controller of the wind farm.

Contrary to the centralized methods presented above, a distributed approach is adopted in [120].

The authors developed a distributed Newton method to optimally distribute the power among the

wind turbines of a wind farm. Although the proposed method requires the wind turbines to exchange

limited information with their neighbors over a sparse communication network, it presents a super-

linear convergence rate.

The authors in [122] and [123] adopt the concept of the virtual power plant (VPP) to provide primary

frequency response to the grid. The VPP plant acts as an aggregator of resources of demand response

and wind farms, which are optimally controlled by employing an intraday and a close to real-time

scheduling algorithm.

In [124], a two-stage method is proposed for coordinating the droop controls between the wind

turbines and the associated energy storage systems for supporting the primary frequency control in

power systems. In the first stage, the available power reserve from the de-loaded wind turbines is

estimated in an efficient manner. In the second stage, the coordinated droop control between the

wind turbines and the energy storage systems is redesigned based on the available power reserve of

the wind turbines.

The provision of primary frequency response from distributed BESS is proposed in [125]. In particular,

the distributed BESS are optimally coordinated to maximize their profit by adopting a two-stage

approach. The first stage is related to the frequency regulation, where the regulation failure penalty

is minimized by optimally coordinating the operation of multiple storage systems in case of frequency

events. In the second stage, the state of charge of the participating storage systems is recovered to

a proper range to avoid regulation failure in the next frequency event.

Finally, the authors in [126] propose a framework for DRESs located at distribution networks to

provide primary frequency response. More specifically, a methodology is presented which

determines the parameters of the power/frequency droop curves that should be applied to each

DRES to guarantee a specific frequency regulation characteristic at the point of interconnection of

the distribution grid with the transmission system.

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Network monitoring technologies and techniques In the framework of ACTIVATE, a novel network monitoring architecture will be proposed to enhance

the observability and controllability of modern power systems. The proposed architecture combines

both distributed online network monitoring and analysis techniques [127] – [129] as well as

centralized monitoring techniques [130] – [132] to evaluate close to real-time the stability margins

of power systems [133]. Towards this objective, methods and tools are proposed to:

• Compute power system modes and mode shapes

• Estimate the inertia capability (i.e., inertia time constants) of individual active distribution

networks (ADNs) as well as the overall inertia of the power system

• Derive static and dynamic equivalent models for ADNs

Mode estimates are used to assess close to real-time the stability margins of the power system [133],

[134] and to provide alarm signals for potential instability events [135], [136]. Mode shapes are used

to identify coherent generators at a TSO level [132], [137] in order to develop simplified

representations of the transmission system [138] as well as to identify centers of inertia at the

transmission grid [139], [140]. Dynamic equivalent models are used to represent extended

distribution grids in stability studies [141] - [145]. Finally, network equivalents are used to represent

extended distribution grids or parts of them; static equivalents are used for the analysis of the normal

operation (mainly for power flow analysis [146], [147] and grid optimization [148]), while dynamic

equivalents for the analysis of the system dynamic performance (mainly stability).

Network monitoring techniques, that already proposed in the literature, are reviewed and discussed

in Section 3.1. Conventional single- and multi-signal identification techniques for the evaluation of

modal estimates are presented in Sections 3.2.1 and 3.2.2, respectively. A review of methods for the

online estimation of inertia time constants is provided in Section 3.3. Derivation of static and dynamic

equivalent models is discussed in Sections 3.4.1 and 3.4.2, respectively.

3.1. Network monitoring technologies and techniques During the last few decades, power systems undergo significant changes due to the integration of

several new technologies both on the transmission and the distribution side [149], e.g. electric

vehicles, DRESs, new types of power electronic interfaced loads, etc. Due to these new technologies

along with the ever-increasing power demand, power systems are now operating closer to their

operation limits [150]. In this context, new methodologies and techniques must be developed to

evaluate the power system health in close to real-time [149], [151]. Το enhance power system

security and reliability, synchrophasor measurements can be exploited to develop advanced wide-

area monitoring systems (WAMS); the vast deployment of synchrophasor technology offers a diverse

range of applications that are becoming increasingly useful for close to real-time grid operations,

such as disturbance detection, modal estimation of electromechanical oscillations, coherency

detection, voltage stability monitoring, etc. [152] – [154]. Recently, various techniques have been

developed based on measured data; a literature review regarding some of the network monitoring

technologies and techniques applied during the last years is presented below.

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In [137], a multichannel continuous wavelet transform based modal analysis was proposed; correct

estimation of the modal properties, i.e., frequency, damping, mode shapes and coherency is of major

importance for power grid operators. The adopted approach can successfully identify the dominant

modes using the information contained in multichannel measurements; the proposed approach is

also capable of estimating the mode shapes and coherent groups of generators. Similarly to [137], in

[132], a holistic framework is adopted to reveal the inherent electromechanical dynamics of the

system. Specifically, an eigensystem realization algorithm is developed to capture the dominant

modes, mode shapes, participation factors and coherent groups of generators using synchrophasor

measurements. Moreover, to identify several modes simultaneously and to estimate the

corresponding mode shapes, a stochastic subspace method using ambient data is proposed in [155].

Additionally, to identify in real-time coherent groups of generators following the appearance of a

disturbance, a novel methodology based on the correlation coefficients of rotor angle/speed

oscillations of generators is presented in [156]; in this work, to classify a number of generators into

coherent groups a clustering algorithm based on the correlation coefficients of generators

oscillations is employed. To estimate the frequencies, damping ratios, mode shapes and participation

factors, a new mode identification method was also introduced in [157], using ambient

synchrophasor data. The proposed method consists a hybrid measurement- and model-based

methodology to estimate the system state matrix in ambient conditions and provide accurate

estimation of modal knowledge for all modes.

Furthermore, in [158], a robust method is presented to locate disturbances using synchrophasor

measurements from the wide-area frequency monitoring network– FNET/GridEye [159], [160]; by

applying this method, the real-time distribution of electromechanical wave propagation speed can

also be calculated. In [161], a centralized Prony-based algorithm was extended to a distributed

estimation problem to compute inter-area oscillations of large power system networks using

synchrophasor data. The proposed architecture demonstrates how dispersed PMUs and phasor data

concentrators (PDCs) can communicate with each other cooperatively for wide-area oscillation

monitoring applications. Reference [129] extends the work of [161] by presenting two additional

distributed algorithms presented for estimating electromechanical oscillation modes, whereas [162]

shows that these methods can be effectively selected to eliminate asynchrony in wide-area

estimation problems. An unprecedented wide-area monitoring and control system for fast frequency

response, namely Enhanced Frequency Control Capability (EFCC), was proposed and validated in

[163]. The EFCC is able to detect and analyze the regional impact of disturbances, and deploy fast

and coordinated responses in consideration of the characteristics and capabilities of a range of

different resources.

In [164], a two-stage methodology was proposed to identify power system dynamic signature using

synchrophasor measurements and data mining. The first step is to predict the transient stability

status following the clearance of a transient disturbance in real-time, whereas at the second stage, a

new methodology based on data mining was proposed to predict detailed generator dynamic

behavior, provided that the system is determined to be unstable. Reference [165] enhances the

method introduced in [164]. In [165], the problem of online identification of generator grouping

patterns consists a one-stage multiclass classification problem compared to the two-stage process

described in [164]. Moreover, the order in which generators lose synchronism is identified as a

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supplementary procedure compared to the previous developed methodology. In [166], a

comprehensive data-driven methodology is proposed to identify and analyze power systems

oscillatory behavior in close to real-time. The proposed approach can identify critical groups of

generators that exhibit poorly or negatively damped oscillations in systems with renewable

generation, providing vital information to system operators. It is worth noticing that the results of

this work also revealed that the probability of the appearance of negatively damped oscillations for

certain generators in a power system might increase when RES are connected. A methodology for

the online identification of power systems dynamic behavior with an increased amount of power

electronics interface units is also presented in [149]. The study demonstrated that power electronics

interface units must be taken into account while developing online identification algorithms and also

while investigating the dynamic behavior of individual generators.

3.2. Identification techniques for modal analysis of power systems

3.2.1. Single-signal identification techniques

Vital information regarding grid oscillations and consequently the stability margins of the power

system can be provided by mode estimation [167]. Traditionally, eigenvalue analysis approaches are

applied on linearized dynamic power system models to obtain power system modes [168].

Nevertheless, the applicability of eigenanalysis is limited [169], e.g. for real-time applications or large

power system configurations. As an alternative, measurement-based system identification methods

are proposed to compute power system modes. Nowadays, measurement-based identification

techniques are favored due to the increasing deployment of synchronized measurement technology

at power systems, enabling the close to real-time estimation of oscillatory modes [170]. In this

context, novel control and monitoring applications can be performed.

Power system analysis is performed using measurements either from ambient, transient (ringdown)

or forced oscillations [171]. Ambient data is obtained when a system is working under an equilibrium

condition, and the major disturbance results from small-amplitude load variations [172], whereas

ringdown data are obtained from the system during transient operation following a major

disturbance or a fault [171], [172]. External mechanisms, e.g. cyclic loads or mechanical aspects of

generators, are typically associated with the introduction of forced oscillations into power systems

[173] – [175].

In the literature, several system identification techniques have been proposed to perform modal

analysis of power systems using ringdown responses. Linear measurement-based system

identification techniques can be classified into time-domain (TD) and frequency-domain (FD). To

perform modal analysis and study power system electromechanical oscillations the Prony method

was initially proposed by J.F. Hauer et al. [176]. To this date, Prony method is probably the most

well-established method [168], which has been extensively investigated in power system applications

[177] – [182]. Prony method has also been modified to include transfer function applications, e.g. to

obtain reduced-order transfer functions of large-scale systems [183], whereas a stepwise regression-

based Prony was further developed by Zhou et al. [167]. Other well-known identification techniques

are the eigenvalue realization algorithm (ERA) [184], the matrix pencil (MP) method [185], the

subspace state-space system identification (N4SID) [186], the prediction error method (PEM) [187]

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and the minimal realization approach [188]. Dominant modes can also be extracted in the FD. For

example, in [189], [190], the dominant modes are extracted using the fast Fourier transform (FFT),

combined also with the sliding window method for the estimation of mode damping. Furthermore,

in [191], a new hybrid FD/TD method was proposed to automatically identify the dominant modes

contained in ringdown responses.

To estimate power-system electromechanical modes, recursive methods have been also introduced.

In [172], a regularized robust recursive least squares method is presented based on PMU data,

whereas in [192], an online recursive algorithm that can be applied to both ringdown and ambient

data was proposed and evaluated. In addition, the application of an extended Kalman filter (EKF) was

discussed by Yazdanian et al. [193], whereas Peng and Nair based on the EKF, suggested an extended

complex KF (ECKF) and ECKF-based smoother (ECKFs) [194]. An efficient dynamic mode

decomposition technique for modal analysis of large data sets was introduced in [195]. Moreover,

identification of low-frequency electromechanical modes in power systems is performed using

Zoloratev polynomials and a digital Taylor-Fourier transform in [196] and [197], respectively. A

method for identifying interarea modes by using curve-fitting of the Laplace transform of the modal

transient response, obtained from a difference sequence between two sets of ringdown frequency

data was proposed in [198]. In [199], the vector fitting (VF) technique is proposed as an identification

method for the estimation of the dominant modes contained in ringdown responses of power

system. The application of VF was extended in [200], where a novel method, called ringdown time-

domain vector fitting, for the estimation of electromechanical modes in interconnected power

systems was introduced. Additionally, the applicability of the variational mode decomposition

technique to extract electromechanical oscillatory modes in power systems considering the time-

frequency analysis of nonlinear signals which arise after a large disturbance was demonstrated

in [201]. To estimate the modal parameters, an improved stochastic subspace identification method

using a combination of stationary wavelet transform and exact model order algorithm was proposed

in [202].

Although the above-mentioned methods present very good performance, they require the existence

of transient responses in the system, which makes mode estimation in continuous (near real-time)

difficult [168], [203]. For this purpose, ambient data are often employed, where information

regarding system transfer functions, and, consequently, system modes, are contained in the

spectrum of ambient responses [204]. Electromechanical mode identification from ambient data was

first considered in [205], where an autoregressive model of ambient data was used; this method was

later extended to include the autoregressive moving average model (ARMA) [206]. A stochastic

subspace identification technique was proposed by Ghasemi et al. [207]. Additionally, frequency-

domain decomposition [39] has been also applied for mode estimation in power systems. Motivated

by [208], the concept of extracting the time-domain exponential decay response of dominant modes

from ambient PMU data was extended to wavelet formulation in [209]. A distributed frequency

domain algorithm for real-time modal estimation of large power systems using ambient

synchrophasor data [210]. To enable ambient oscillation monitoring a two fast SVD (singular value

decomposition) computation was proposed by Wu et al. in [211]. Furthermore, to systematically

identify the dominant modes from both ringdown and ambient data, in [212], a novel dominant mode

estimation method for monitoring inter-area oscillations using PMU measurements was presented.

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3.2.2. Multi-signal identification techniques

Methods based on single inputs are related mainly with the section of the power system where the

corresponding signal was measured. To obtain better estimations of the system modes for the entire

system, multichannel techniques that include information from multiple measured signals can be

employed [213]. Therefore, it is imperative to generate one set of modal estimates based on multiple

data channels, e.g. from PMUs [214]. Several papers have been published in the literature regarding

multi-signal identification techniques.

Trudnowski et al. first proposed a simple extension to obtain a unique set of mode estimates by

simultaneously analyzing multiple signals using Prony analysis [215]. To estimate electromechanical

modes the multi-channel Prony method has been employed in several other works, e.g. [216]–[218].

Moreover, multi-channel Prony analysis was extended in [219], where a recursive solution was

proposed to make it more suitable for near to real-time applications. Furthermore, in [220], a

distributed multi-signal Prony analysis algorithm using consensus and subgradient updates was

proposed. Additionally, a multi-signal approach using the Forward and Backward Extended Prony

(FBEP) method and sliding window analysis on power system small-signal stability was investigated

in [221].

To obtain modal estimates in close to real-time based on multiple synchronized PMUs an algorithm

namely Frequency-Domain Optimization was developed in [222], whereas a distributed frequency

domain algorithm to monitor modes from multiple PMUs based on ambient data was presented in

[210]. Recently, in [170], the accuracy of the mode estimates was investigated using multi-signal

analysis, by extending the applicability of eight widely-known system identification techniques,

following the average energy approach originally proposed in [210], [223]. The same method was

adopted by [201], where the use of a variational mode decomposition technique for extracting modal

components was examined.

To estimate the damping ratio of power system modes directly from the Fourier Transform using

PMU data a multi-dimensional Fourier ringdown analyzer was proposed in [224], while reference

[225] extended this approach by applying SVD to the power spectrum before calculating the damping

estimates. As mentioned in Section 3.2.1, in [197], the modal estimates related to ringdown analysis

were computed through the digital Taylor–Fourier transform; the authors in [226] and consequently

in [227] extended the capability of the suggested method to process multiple signals. Additionally,

the Taylor-Kalman-Fourier and Alternating Kalman filters are proposed to extract modal information

and where also modified to enable multi-signal analysis; moreover, the performance of all methods

is compared to Prony multi-signal analysis.

The advances in PMU infrastructure have also enabled the application of other techniques based on

multi-channel signals. Recently, a spectral fitting approach that combines the numerical Laplace

transform with the VF method was presented to estimate electromechanical oscillatory modes and

mode shapes [228], whereas in [214] a multi-signal ringdown TD vector fitting method that can

analyze multiple ringdown signals simultaneously, was introduced. In [229], it was derived that the

linearized power system can be modelled using a multi-channel ARMAX structure; therefore, the

transfer function of the system and consequently the modes and mode shapes can be characterized

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using the parameters of the ARMAX model. It should be mentioned that this multi-signal

methodology provided also promising results when applied to ambient data. A new approach based

on multi-dimensional wavelets was presented in [230] and was used to identify the dominant modal

frequencies, damping ratios and to estimate the mode shapes of dominant oscillatory modes. In

addition, in [231], a dynamic mode decomposition (DMD) framework was successfully implemented

to analyze large datasets from multiple sources. More information regarding the abovementioned or

other relevant techniques is also discussed in [168].

3.3. Real-time estimation of inertia time constants In the literature, several approaches have been proposed for the estimation of inertia time constants.

A comprehensive literature review is provided below.

In [232], a method for the inertia estimation of synchronous generators is proposed. The method

requires measurements for frequency, mechanical and electrical power of every generator of the

examined power system. In practical applications, it is impossible to obtain all these measurements.

Therefore, this method is not suitable for real applications. To reduce the number of required

measurements, the same authors have proposed in [233], a method for the online estimation of

inertia time constants. The method requires as inputs real power and frequency measurements

recorded during system disturbances. The inertia time constant of each generator is determined

using the swing equation and the sliding window technique, which is used to determine the

frequency and the real power change due to a system disturbance. The overall system inertia is

computed based on the inertia estimates of the individual generators. The method requires accurate

identification of the time of disturbance, i.e. the exact time that the disturbance occurs. Therefore,

in [234], a method for the identification of the time of disturbance is proposed. The method is based

on the use of sequential window data and it is validated using simulated responses and laboratory

measurements. The presented results reveal that the performance of the method is considerably

affected by the definition of the sequential windows. Additionally, another type that may affect the

performance of the method is the type of the PMU [235].

In [236], a fifth order polynomial was fitted to measured frequency transients using a least squares

approximation. The purpose of the fitting procedure was to restrain the influence of oscillatory

components, that are superimposed on frequency signals due to intra- and inter-area oscillations.

The method is tested only on field measurements. Hence, its performance and accuracy cannot be

fully quantified. A similar approach is also presented in [237]. In this case, a linear model is used to

fit frequency signals and determine RoCoF. The method is further tested in [238], to estimate the

overall system inertia of the Great Britain power system. However, to perform satisfactorily, the

method requires the monitoring of the most critical power system nodes as well as the use of probing

signals.

In [239] a method which uses historical data to correlate generator capacity with RoCoF values and

inertia time constants is proposed. With this knowledge, the typical RoCoFs of a power system with

any amount of system capacity can be roughly estimated. However, the performance of the method

has been tested only on field measurements. Therefore, its accuracy cannot be fully quantified. A

Gaussian Markov Model is proposed in [240] to correlate frequency responses with inertia time

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constants. However, the model requires the fine-tuning of several parameters to provide satisfactory

results.

In [241] the relationship between power system eigenvalues and inertia time constants of

synchronous generators is derived. Additionally, a methodology based on the eigenvalue sensitivity

matrix is proposed to estimate in real-time inertia time constants of synchronous generators using

wide area measurements. In [138] and [242], the use of inter-area oscillations for the estimation of

inertia time constants is investigated. The implementation of these methods requires the

identification of pilot buses, i.e. buses that represent the center of inertia (COI). Therefore, in [139]

a method to determine COI and to estimate the overall power system inertial response is proposed.

The method initially identifies clusters of generators that form aggregate sources of inertial response.

Then, the overall dynamics of each cluster are synthesized using PMUs placed at selected buses that

represent the COI.

Finally, several methods have been proposed in the literature [243] – [247], to estimate the inertia

time constant as perceived by particular buses. These approaches are very useful to quantify the

virtual inertia of DRESs at the PCC with the transmission grid [244]. In [243]- [245] the use of ARMAX

models is discussed, in [246] the use of dynamic regressor is investigated, while in [247]the

micropetrubation method is proposed. The performance of the above-mentioned methods has not

been tested under both ringdown and ambient data. Additionally, it is worth noticing that only the

method proposed in [245] has been validated using field measurements. All other approaches have

been tested only in simulation environments.

3.4. Equivalent models for ADN analysis Traditionally, for power flow simulations and dynamic analysis, distribution grids were modeled as

aggregated loads, containing different types of individual components such as motors, lighting and

electronic devices [145] - [147]. However, the increased penetration of DRESs into distribution grids

will eventually alter the properties of these systems [142], [248], [249]. Therefore, during the last

years, power system operators and academia have initiated serious efforts to improve modeling

practices and to develop new aggregate equivalent models that can simulate more accurately the

complex behavior of future ADNs [141], [142], [248].

Towards this objective, several static and dynamic aggregated models have been proposed in the

literature. Static models express the real and reactive power at any time instant as algebraic

functions of the bus voltage magnitude and frequency at that instant [145], [146]. Static equivalent

models can be used for power flow analysis [146], [249] as well as to optimize grid operation [148].

However, static models do not take into account system dynamics and therefore are not suitable for

voltage and angular stability studies [145], [146]. For this reason, dynamic equivalent models are

adopted. Dynamic equivalents express the real and reactive power at any time as functions of the

voltage and frequency related to previous time instants, taking also into account the present instant

[145], [146]. Difference or differential equations are used to describe this type of models [146].

The main challenge concerning the development of equivalent models is to determine an aggregated

representation for the different types of loads and DRESs that are connected to the same distribution

grid [250]. Generally, the equivalencing procedure consists of two main stages [251]. In the first stage,

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a suitable model structure is specified, while in the second stage the model parameters are

derived [251]. Depending on the available information and insight of the true system, three model

structures can be used, namely white-box, black-box, and grey-box.

In the white-box approach [249], the topology of the distribution grid, the type and the control of the

DRESs as well as the exact composition of the load are assumed a priori known. The aim of white-box

modeling is to derive an exact mathematical model of the true system [249]. In many cases, the

development of white-box models is a very complex and hard-to-achieve procedure, since several

data are required [143]. In these situations, black- and grey-box equivalents are developed.

In the black-box approach [143], [249], as the other extreme case, the topology of the distribution

grid, the location and the control of the DRESs as well as the load composition are not known. Only

the input – output data of the true system are available [143]. The aim of black-box modeling is to

map the input data set to the output data set by adjusting free model parameters in order to force

the output of the equivalent model to become as similar as possible to the output of the true

system [249].

In grey-box approach [143], [144] basic information concerning the grid topology, the control systems

of the DRESs and the composition of the load are known. However, the exact components and their

rates are not available. Therefore, grey-box models are developed using the known structure of the

system with unknown parameters [143]. The parameters are then identified in a similar way to black-

box modeling [143], [144].

The estimation of model parameters can be performed using either the component- or the

measurement-based approach [251]. The component-based approach requires reliable data

concerning the load class mix, the load components, and a priori knowledge of typical characteristics

of individual devices. Therefore, the application of this method requires accurate data, which usually

cannot be determined in distribution networks due to their size and confidentiality issues [252]. On

the other hand, in the measurement-based approach, the model parameters are estimated from in-

situ measurements, using system identification techniques [252], [253]. This approach can be

especially favored in smart grid environments, where synchronophasor data can provide the required

measurements. This way, parameters are continuously updated, and accurate equivalent models are

derived close to real-time [253], [254].

3.4.1. Static equivalent models for ADN analysis

Traditionally, for power flow analysis, distribution grids are represented by constant power load

models (PQ model), exponential load models, or a combination of constant impedance, constant

current and constant power load models (ZIP model) [146]. The PQ model is the simplest equivalent

model and is adopted by 84 % of the system operators worldwide for steady state analysis [46]. The

exponential and the ZIP models consider the nonlinear characteristics of loads with respect to voltage

changes [146]. However, all above-mentioned models are oriented to the analysis of passive

distribution grids and fail to provide consistent results for the analysis of ADNs, since they completely

neglect control strategies and operational constraints of DRESs [249], [256].

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Therefore, new static equivalent models are required to simulate more accurately the steady state

behavior of modern ADNs and to determine with higher accuracy the flexibility margins of ADNs

[257], [258]. Towards this objective, during the last years, several white-, black-, and grey-box models

have been proposed in the literature. White-box models are mainly used to assess flexibility margins

of ADNs, while black- and grey-box models to facilitate steady state analysis, e.g. power flow analysis,

grid optimization. A comprehensive review of these models is provided in the next three paragraphs.

In [257], a white-box model is developed to determine the time-dependent flexibility of ADNs and to

control their TSO-DSO interconnection power flow. Specifically, a nonlinear set of algebraic equations

is developed to represent the steady state behavior of the ADN. Grid operational constraints are also

incorporated into the model. However, in this approach, real and reactive power flowing through the

interconnection point are estimated using sequential Monte Carlo simulations, resulting in increased

computational burden and increased execution times. A more sophisticated white-box model is

presented in [259]. Also, in this work, the grid is represented by nonlinear algebraic equations. The

model receives as input the real power of the ADN at the interconnection point and estimates the

capability chart of the reactive power by conducting optimal power flow analysis and by considering

grid operational constraints; the model proved to be accurate. However, it cannot be used to

evaluate the flexibility margin of real power at the interconnection point. To tackle this issue, in [258]

and [260] two white-box models, with similar concepts, are presented. In both approaches the grid

is represented by nonlinear sets of algebraic equations, while in both formulations grid operational

constraints are incorporated to the model. The real and reactive power at the interconnection point

are estimated by solving a sequence of optimal power flows. In both cases, simulation results are

used to demonstrate the accuracy of the methods. However, the derivation of both models requires

detailed data concerning grid topology, the location of DRESs and load composition. Therefore, these

models cannot be easily developed for extended ADNs.

To overcome the need of detailed data, a black-box equivalent model based on radial basis functions

and artificial neural networks (ANNs) has been developed in [261]. This model receives as inputs the

grid voltage at the interconnection point and estimates real and reactive power. The model has been

tested on a simulation environment for the analysis of passive grids and found to be accurate and

reliable. However, the performance of the model for the analysis of active grids is still an open issue.

In [262] a black-box static model, based on Ward equivalents, is proposed and further extended

in [263]. Model parameters are estimated via a least-square approach. The model is used to replace

extended parts of grids in power flow analysis. The model was tested on a simulation environment

for both passive and active grids containing synchronous generators. Validation results show that the

model is robust and accurate. However, the performance of the model has not been tested under

real field conditions and for different types of DRESs.

Static equivalent models for the analysis of ADNs can also be developed using the grey-box approach.

For instance, in [256] a grey-box equivalent model consisting of a ZIP load, an equivalent generator

and an equivalent branch is proposed. The model receives as input the grid voltage and estimates

real and reactive power at the interconnection point. Model parameters are estimated using a

least-square approach. The model has been tested using simulation results and proved to be more

accurate compared to conventional approaches, i.e. ZIP and exponential models. However, the

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model does not take into account the control strategies of DRESs. In [264], a grey-box modeling

approach based on enhanced reinforcement learning is proposed; the model considers spatial

uncertainties and reactive power control of DRESs. The model receives as inputs the wind speed, the

total active and reactive power of distributed wind turbines, the global irradiation, the ambient

temperature, the total active and reactive power of distributed PVs, and the voltage at the

interconnection point. Using these inputs, the model estimates the real and reactive power at the

interconnection point. However, the large amount of input data prevents the implementation of the

model to real-field applications. A simpler, but still accurate static grey-box model is proposed

in [249]. The model consists of a ZIP load and a DRES operated under several voltage control schemes.

Model parameters are estimated via nonlinear optimization. The model receives as input the voltage

at the interconnection point and estimates the real and reactive power. However, the performance

of the model has been tested only using simulation results. Additionally, another drawback of the

model is that completely neglects frequency control schemes that may apply to DRESs [265].

3.4.2. Dynamic equivalent models for ADN analysis

Traditionally, for dynamic studies, distribution grids are represented by exponential load models, the

ZIP model, and the composite model (ZIP model augmented with induction machine) [145].

Specifically, even nowadays, 72 % of power system operators use the exponential and the ZIP model

for stability studies [255], while 26 % use some form of the composite model [255]. Concerning the

modeling of distributed generation, 40 % of system operators neglect its influence on system stability

studies, 28 % simulate DRESs simply as negative loads, while 23 % use dynamic load models to analyze

its behavior [255]. Only 3 % of system operators have developed and use detailed dynamic models

for the analysis of DRESs [255].

Nowadays, in most of the modern distribution grids, the penetration of DRESs is reasonably low. This

may justify neglecting its influence on system stability studies [255]. However, anticipated increase

of DRESs, particularly those based on power electronic-interfaced units, will effectively change the

nature of power system dynamic responses [141], [142] and will inevitably affect the overall stability

margins [266], [267]. Therefore, new methods and modeling approaches are required [39]. Towards

this objective, during the last years, several dynamic equivalent models have been proposed in the

literature. A comprehensive review of the available methods and approaches is presented in the next

paragraphs.

In [268] and [269], white box equivalents for the dynamic analysis of ADNs are proposed. In these

approaches detailed information concerning the grid topology, the location of loads and DRESs as

well as detailed data concerning the control systems of the DRESs are required. Additionally, all

power system components are modelled in full detail. However, the use of detailed models for the

analysis of distribution grids can increase considerably the computational burden of dynamic

simulations [270]. Therefore, this type of models is generally avoided, and black- or grey-box

equivalents are mainly used for dynamic simulations [270].

In this context, in [271] - [273] black-box equivalent models based on the Hankel method are

proposed, while in [274] - [276] models based on the Prony method are derived. Moreover, in [277]

and [278] the Ν4SID method is used, whereas in [279] black-box equivalents based on the PEM are

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developed. In [280] and [281] black-box equivalents consisting of nonlinear equations and linear

transfer functions are developed. The parameters of these two models are estimated using the VF

technique. In [282] and [283] black-box equivalents based on ANNs are proposed. All the above-

mentioned models can simulate accurately several dynamic phenomena, e.g. small and large

disturbances. However, their main limitation is that the model parameters depend on a significant

degree on the operational and loading conditions of the grid [280] and [284]. Therefore,

methodologies to derive robust sets of parameters should be developed [284], [285].

In [143] and [144], a grey-box equivalent model is proposed. However, this model is not

observable [286]. Thus, its parameter cannot be fully estimated using only input / output data [286].

Therefore, a further insight concerning the grid topology is required, while accurate initial estimates

for the model parameters are also needed. To overcome this issue, in [78], a reduced representation

of the model presented in [143] and [144] is proposed and rules of thumb are derived to determine

the initial condition of the model parameters. A strong disadvantage of these models is that their

parameters are strongly affected from grid operational conditions [143]. Additionally, all the above-

mentioned grey-box models neglect the control strategies of DRESs. As a solution to this issue, several

grey-box equivalents which incorporate different control strategies for the DRESs have been

proposed in the literature. Among them, the most representative are the models of [288] - [290].

These models were tested in simulation environments and found to be accurate. However, their

performance has not been tested under real-field conditions. Additionally, it is worth noticing that

for the development of these equivalents, a large number of parameters must be identified. For

instance the models of [289] and [290] require the identification of 40 and 42 parameters,

respectively. This fact, in conjunction with the variability of model parameters under different

operational conditions, poses serious concerns regarding their implementation for real-time

applications.

To derive robust sets of parameters, i.e., parameters applicable for a wide range of operational

conditions, several approaches have been proposed. In [143] and [144] the use of statistical analysis

is proposed to derive mean and/or median values for the model parameters. In [291], multi-signal

parameter identification is proposed. The use of ANNs is investigated in [284] and [285] to generalize

model parameters. A comparative assessment of the above approaches is required to determine the

most accurate and reliable method.

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Power converter implementations During the last few years, several converter configurations have been proposed that incorporate new

dynamic functionalities, e.g. virtual inertia [126]. The main parts of a typical configuration of a three-

phase converter are the dc/ac part, the dc/dc converter, the ESS, and the output filter. The ESS can

be used for implementing either virtual inertia or power smoothing functionalities. Two pulse width

modulation (PWM) control signals are needed to control the dc/dc and the dc/ac converters. The

derivation of the PWM signals is carried out from a single processor unit, i.e., a microcontroller, based

on the current and voltage measurements acquired at the output of the dc/ac converter and the dc-

link. In the framework of ACTIVATE, a lab-scale three-phase converter prototype will be implemented

incorporating all functionalities to be developed within ACTIVATE. The prototype will correspond to

a DRES grid-interfaced converter with the ability to connect an ESS at the dc-link though a dc/dc

converter. Towards this objective, the prototype converter will support:

• concurrently implement all the control functionalities that will be developed within ACTIVATE

to provide ancillary services to the grid

• the grid-interfaced converter will neglect possible interactions with the electrical network

• monitoring capabilities in terms of applying power system identification methods

4.1. Three-phase inverter review While the grid-tied RESs are beneficial in improving voltage profile of power systems, the increased

penetration level of them is challenging regarding grid stability. Frequency instabilities and reverse

power flows along with high RoCoF value, arise critical issues for grid reliability. Depending on the

nature of RES, many power electronic converter topologies have been developed, in order to

facilitate the connection of RES in the grid and to jeopardize their adverse effects; total harmonic

distortion, DC current and uncertainty in power production. While in the field of wind power

production the referred problem has been solved [292], in PVs there are plenty of issues which need

to be addressed, e.g. DC harmonic injection due to DC-Link, dangerous inrush current, and adequate

power efficiency after two to three stages of power converter [293]. The advent of new

semiconductor technologies and microcontroller internet communication features pave the way for

new more advanced grids with higher penetration of RES and reduced hazard emissions.

4.2. Converter topologies In the field of power generation by PVs, the inversion process is constituted in multiple stages due to

the DC production of PV [294]. In this type of inversion, the last stage performs the DC to AC

conversion while the starting one (or the intermediate) stages achieve the voltage amplification

and/or the galvanic isolation. ESS such as batteries and ultracapacitors are connected in the same

way in the common DC-Link [295].

Recent technological achievements lead to focus on transform-less inverter topologies, which offer

a high degree of flexibility and plenty of inversion advantages; lower volume compared with the

conventional transformer-based topologies, cost-effective structures, reduced total harmonic

content etc. Nevertheless, for addressing the issue of DC current injection, they require extra circuits

to be installed. Furthermore, the lack of galvanic isolation induces hazardous charges between the

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surface of PV and the installed ground that may be dangerous for the personnel. However, several

transform-less inverter topologies have been proposed in the literature that can eliminate the

aforementioned problem [295] - [297].

Multilevel inverters result in staircase sinusoidal waveform that is closer to an actual pure sinusoidal

wave with low total harmonic distortion. Several DC voltage levels can be easily produced due to the

modular structure of PV arrays; therefore, multilevel topologies are fundamentally suitable for PV

systems. One of the most promising solution in the field of multilevel inverters is the cascade half

bridge inverter (CHB); the modular and scalable feature of the cascade inverter are the key

advantages of CHBs, as they may be extended to achieve even more number of levels [294].

4.2.1. Three phase two level inverter topology

The two level three phase inverter topology is the most reliable and well-established structure that

supports virtual inertia implementation either in virtual synchronous generators (VSG) or virtual

synchronous machines (VSM) [292]. Its well-known control logic and flexible design offers invaluable

solutions for test-bench development, which is intended for new virtual inertia algorithms testing.

While this topology is the simpler one among the synchronous transform-less inverters, advanced

circuity techniques can improve its performance dramatically; soft switching techniques, zero voltage

switch (ZVS) or zero current switch (ZCS) [298]. The classic one and the ZVS two level three phase

inverter topologies are illustrated in [298], respectively. In addition, the incorporation of new SiC

semiconductors leads inverter stage efficiency slightly over 99%, as referred in [299]. Thus, there is a

scheme with high efficiency that can support almost any new-coming virtual inertia algorithm with

robust control and with no high complexity.

Figure 4: a) Three phase two level inverter, b) Three phase two level inverter with ZVS [298].

Project Number: 229


4.2.2. CHB topology

Recently, in the field of virtual inertia development from PV panels, CHB inverter topologies are

usually referred in the literature [297], [301]. Their quite straightforward structure along with their

modular and flexible nature offer unique aspects in terms of power quality and near actual sinusoidal

waveforms. The separate cell per cell maximum power point tracking (MPPT) that implement,

exceeds in terms of power efficiency even the well-established multi-string inverters. While the need

for numerus switches and power converters is tent to reduce the overall system efficiency, the

advent of new sophisticated Silicon carbite (SiC) semiconductor technology seems to preserve their

enhanced performance.

4.3. Microprocessors Several microcontrollers, digital signal processors (DSPs) or computer including dSpace, Microchip,

Texas Instrument or STMicroelectronics are applicable to implement ancillary services with suitable

sensors, interface and configuration [298]. New generation of microcontrollers like STM32 provide

useful tools based on human machine interface (HMI) of things, which enable engineers to easily

develop graphical user interface (GUI), supporting real-time supervision and reconfiguration [302].

Their relatively small size allows compact design, which MCU and converter units are enclosed in the

same package, reducing thus the overall system complexity. The Internet of Things (IoT) enables grid

to leverage the word network and pave the way for the future grid forms [303], where multiple

converters are locally connected and cooperate, such as Microgrid or even the upcoming Web of

Cells (WoC) [304].

4.3.1. Real-Time digital control

The real-time (RT) digital control can be implemented using several technologies, such as:

• Microcontroller units (MCU)

• Digital Signal Processor-Based Controllers (DSC)

• Field Programmable Gate Arrays (FPGA)

• RT Rapid Prototyping systems (i.e. DSpace platform)

• Programmable Logic Controllers (PLC)

While PLCs are used mainly in industrial environment, MCUs, FPGAs and Prototyping platforms are

dedicated for testing energy system applications such as RES, electrical vehicles etc.

Nowadays, modern MCUs include high-performance dual-core processors with 32-bit architecture,

sufficient amount of FLASH memory and plenty of peripherals: many analog to digital converters

(ADC), several PWM modules and a large variety of communication protocols [305]. Their very high

computational capabilities, translated into execution of million instruction per second (MIPS) and in

million floating point operations (MFLOPS). The second one is very useful for enhancing

computational performance in power converter applications which operate on complex numbers

represented by trigonometric functions or using complicated algorithms (e.g. Kalman filter or

recursive least square algorithm), where floating points are essential in terms of high precision [306].

Modern devices mainly use Harvard architectures that support two distinct paths for instructions and

data flows. Saving, thus, time because the buses operate independently and simultaneously.

Project Number: 229


Therefore, the control system plays a vital role in any converter design. Today MCUs that support

DSP logics and FPGAs are the predominant hardware for power electronic converters. The MCU

executes all instructions in a sequential way using the CPU. In terms of multi-carrier processing needs,

FPGA based control schemes offer plenty of advantages because support parallel execution, unlike

DSPs [307].

4.3.2. Project evaluation cycle

Nowadays, power electronic converter control design is characterized by high complexity, thus high-

level design environments should be used. The sequence of power electronic converter development

starts with system-level simulation using programs like Matlab/Simulink, PSim etc; Next, hardware

development can be analyzed using circuit simulators like PSpice; they allow precise estimations,

optimizing power converter circuit performance [306]. Then, digital control can be developed and

tested using real-time prototyping methods, firstly in hardware-in-the-loop platforms such as DSpace

and next by MCU or FPGA, depend on system requirements.

GUIs of prototyping platforms offer valuable solutions for quick testing and development of

optimized control algorithms. Their main function is to generate codes in low level programming

languages automatically from structured block diagrams [307]. Thus, the engineers are allowed to

implement different control algorithms rapidly, using functionalities and peripherals which only MCU

and FPGA units can support.

4.4. Digital-Control and Ancillary Services

4.4.1. RoCoF measurement

One of the key elements that critically determines the performance of every virtual inertia support

scheme, is the ability of fast and accurate measurement of RoCoF. Since now this need was decent

covered by closed loop methods, where the most popular and mature technique is the PLL. While

the PLL is a well-established technique with robust design and trusted operation, it presents noise

sensitivity that leads to cumulative errors [308]. These kinds of divergence from the real measured

value may deteriorate the virtual inertia response and can be crucial for system reliability and

stability. Several studies attempt to address the later issue with the use of a double SOGI based

Frequency locked-loop (FLL) either in single phase [309], [310] or three phase applications [311].

Besides that, the implementation of FLL in the frame makes their analysis and tuning procedure more

complicated [312]. However, in the literature [310], [311] the FLL method is presented as the most

promising solution in terms of fast and accurate measurement of RoCoF.

4.4.2. Power Smoothing

While PV generation is clean and environmentally friendly, its uncertain power production is

inevitably its most important drawback. Natural conditions, such as light, temperature, climate

change and so on affect PV power fluctuations, resulting into significant challenges to the integration

of PVs in power grid systems. In recent years, the advent of ESS technologies provides new innovative

ways to overcome the inherent issues of PV power production. Integrating ESS or taking the

advantage from already installed storage units (i.e large UPS banks etc.), the active power output

Project Number: 229


characteristic of the new hybrid PV-ESS systems may improve large power grids credibility and

stability, by increasing also the penetration level of PVs.

Not only the advantage storage units’ capabilities, but also the sophisticated control algorithms that

are employed by power converters, would facilitate PVs integration. In the relevant literature there

are plenty of proposed algorithms that are deployed based on advanced predictive control schemes,

such as model predictive control MPC [313]. The multi-parameter nature of PVs power smoothing

lead us to employ holistic monitoring techniques, which are continuously recording all factors

(voltage, current, SoC etc.) of power production, in every stage of production (ESS condition, PV type,

light condition, season etc.) [314]. Thus, good approximate estimation of hybrid PV-ESS system total

response is achieved. Parameters such as SoC of ESS and the PVs momentary power generation

capability, are of high importance in terms of a proper grid-tied operation [315]. As illustrated in Fig.

4a, the voltage and current from the ESS and the PV are collected by MCU, whereas instantaneous

calculations are forming the control sequence that converters would employ, as depicted in Fig. 4b.

The benefits from the incorporation of IoT in low level real-time control schemes are obvious,

weather forecast along with GPS/GIS sensors for better location accuracy, would help to achieve

higher level of PV integration with less negative impact. While MCU is calculating SoC, special

attention is paying on certain storage limits, avoiding over-charge or over-discharge as far as possible.

Thus, depending on PV anticipated active power curve and SoC level, the control algorithm

determines the proper injected active power from the ESS in order to smooth hybrid system

output [316]. Fig. 4b illustrates the multi-parameter control scheme which the MCU is called to

support.

4.4.3. Voltage Unbalance Mitigation

Conventional electrical networks are mainly designed to accommodate unidirectional power flow

rather than bidirectional. The reverse power flow from the upcoming grids with high penetration

level of PVs, has the potential to cause several issues in terms of grid stability; overvoltage, cable

overheating, often voltage unbalances and reduce network overall efficiency. Specifically, voltage

unbalances are likely to become a significant barrier in terms of increased penetration level of PVs in

European electrical systems, due to restricted limit of 2% in EU [317]. There are several proposed

strategies that are employed to address voltage unbalances; double droop control method, active

power filters [318]. Most of them take the advantage of the forthcoming widely installed ESS,

compensating local voltage unbalances and keeping high grid system efficiency. The growth of

rooftop PV systems along with home installed ESS remodel the conventional LV networks, rising the

need for new decentralized voltage-control strategies [319].

4.4.4. Voltage regulation

The mass integration of DERs in power networks has arisen several problems related with network

stability. From the other hand, a cooperative operation of hybrid DERs under scalable control

architecture can enhance network reliability against their uncertain power production. In the

relevant literature [320]-[322] there are many proposed strategies which focus on the local voltage

control, employing PVs storage systems. The desirable result is achieved through voltage dependent

battery charging, followed by reactive power provision and PV power curtailment. Nevertheless, the

Project Number: 229


applicability of these methods under different SOC levels is not sufficiently covered. Moreover,

coordinated control strategies based on layered local communication system can suppress voltage

fluctuations in a critical node, employing droop-based or distributed control methods [323]. The

incorporation of voltage profile estimation in distributed control algorithms can enhance voltage

regulation locally.

It is regarded that behind a typical Microgrid PCC there is a DER that consist of a PV with an ESS and

a dc/ac inverter, and PCC can be regarded as a node. The primary control effort to regulate the

voltage in this specific node, this could be implemented with the proper cooperation of node

components. Its parts could be controlled by means of an FPGA that generates all the needed control

pulses and implements the related control algorithms for the dc/ac inverter and for PV and ESS

converters in parallel. In this case there are obviously not synchronization issues among node parts.

In this node, while the voltage exceeds the predetermined limits, it is necessary to change the power

injection that provided by ESS. The power control of the ESS adjusts the output power depend on

exceeded voltage limit; with active power curtailment mainly during peak PV generation or power

injection during load demand.

From the other hand, if the use of the same computational unit are not allowed, then the

employment of several communication protocols are inevitable. Ethernet, WiFi, ZigBee or CAN bus

protocols are used in terms of accurate and effective cooperation without latencies.

4.5. ESS integration The integration of the ESS and PV in a common DC-Link by DC/DC power converters, which is

connected to AC grid through aν DC/AC inverter would pave the way for the implementation of more

effective voltage-control strategies. Thus, ESS can provide enchantment to grid stability and ensure

the safe and normal LV grid operation, during undesired weather conditions. In addition, the

cooperative operation of multiple locally installed PV-ESS system under a proper decentralized

control, can alleviate overload and overvoltage in residential LV network.

4.5.1. DC/DC power converter for ESS integration

The most preferred type of DC/DC power converters that integrate ESS in electrical power systems,

is the step-up topology. Since the majority of DRESs require step-up converters for grid interfacing.

In the literature several step-up DC/DC converter topologies are proposed; two-inductor boost

converter, interleaved boost converter with two or three levels, synchronous rectification boost

converters [324].

One of the key features that render a DC/DC step-up converter topology is its bidirectional design.

While some DC/DC step-up converter topologies are presenting unique bidirectional capabilities,

their high control complexity render them inappropriate for multi-parameter control architectures.

Interleaved boost converter with two or more levels and synchronous rectification incorporation is

the most promising solution. In the literature the multi-level dc/dc converters are often refereed as

multi-phase [325]. Using this technique, the power stage of the dc/dc converter is divided into several

smaller power stages (two, three or more). However, the size and the current stress are greatly

reduced, leading to higher efficiency levels. In multi-stage power schemes, such as in our case, where

Project Number: 229


the ESS and the PV are connected to a common DC-Link through dc/dc converters and next to AC grid

through DC/AC inverter, maintaining the overall system efficiency as high as possible is of high

importance. This design has several advantages such as filter reduction, enhanced dynamic response,

improved thermal management and so on. The power components can be surface mounted devices

(SMDs) and the inductor can be integrated in the PCB, increasing thus its flexibility and applicability

(i.e. integrated in electric vehicles [326]). In terms of control implementation, there are many signals

to generate, depending on desired level design, all of them can be generated by an advanced MCU

or cooperative by an MCU and an FPGA in a Master-Slave control scheme.

4.6. Communication Future power distribution systems can be benefited from the systematic coordination of DERs. More

specifically, DERs can be cooperated to control the main quantities across a grid locally, such as power

line flows, frequency deviations and voltage magnitudes by controlling both their real and reactive

power injection in real time. In the literature, this is referred as optimal power flow (OPF), whereas

many distributed optimization algorithms are proposed to ensure network reliability [327], [328]. The

most of them are requiring a preexisting strong connected communication network [329], whereas

the local information can be transmitted across all DERs, despite that, the related communication

technologies are still under-deployed with limited available capabilities. The widely known methods

are summarized as follows:

• Fiber optic cables, through Ethernet protocols

• Power line carrier, through the industrial well-established PLC

• Multi-point microwave antennas, the classic WiFi protocols

• Combination of the latter techniques

Every communication protocol is characterized by its own inherent drawbacks, whereas in general

terms are strongly dependent with a cost/latency factor [330]. In home area network, where the

solar PV the ESS and the inverter are located, low-power wireless personal area network (LoWPAN),

narrow or board band PLC are used. The neighborhood area networks could be interconnected with

WiFi ad-hoc (mobile network), board-band WiFi (WiMax) or Ethernet cables, whereas the wide area

networks are employed high-speed Ethernet. Obviously, the multilayered communication

architectures suffer from unacceptable time delays, bandwidth restrictions and high installation cost.

For these reasons, the practical implementation of the distribution system management algorithms

has been challenged, even in decentralized low communication-based systems [331]. In recent

literature, there are a lot of works that attempt to address this communication barrier, incorporating

sophisticated algorithms based on advanced control theory or communication-less techniques; for

instance, hybrid voltage strategies that depend on neighborhood nodes support [332], [333]. Local

control techniques that require not sharing information among DERs based on local voltage

measurements [334] or game theory control [335] (regarded every DER as a player that try to

maintain its stability), decentralized stochastic control methods that are based on actuator type

systems [336]. However, it has been proven that these local schemes with a weak or no

communication bonds, are prone to make fail decisions and lead to loss of DSM performance.

Project Number: 229


The involvement of advanced estimation techniques strongly related with the geographical position

and historical data of the intermitted source, appears to be one promising solution in this way [337].

Since the well-established 4G communication network or the upcoming 5G are not incorporated in

real time power system control yet, new probabilistic methods have to be found in order to facilitate

the advent of DER dominated network. In addition, the implementation of communication networks

lacks of realistic scenarios such as radio frequency disturbances, noise spikes or communication loss

due to weather conditions.

Project Number: 229


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Principal Investigator Research Team Theofilos Papadopoulos 1) Nikolaos Papanikolaou 2) Dimosthenis Peftitsis

3) Eleftherios Kontis 4) Georgios Kryonidis

5) Angelos Nousdilis

6) Kalliopi Pippi

project: activate

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