workshop on pv applications in power networks

The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 810809

U

U

WORKSHOP ON PV APPLICATIONS

IN POWER NETWORKS

1st June 2021

Workshop

DISTRIBUTION BATTERY ENERGY STORAGE COORDINATION

FOR IMPROVED POWER NETWORK SUPPORT

Dr Alessandra Parisio

and

Dr Tianqiao Zhao, Dr Xiao Wang, Prof. Jovica V. Milanovic

http://crossbowproject.eu/

Outline

• Introduction and Motivation

• Dynamic fast frequency and voltage regulation

• Static fast frequency regulation

• Frequency regulation and congestion management

• Conclusions and future steps

Challenges in Renewable Integration

Mahmud, N. and Zahedi, A. (2016). Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation.

Renewable and Sustainable Energy Reviews, 64, 582–595.

• Distributed and renewable generation can result in significant benefits but

reduced total system inertia and reduced controllability

• The intermittency of renewable sources and its reversed power flows leads to new

balancing issues in both transmission and distribution networks

• The variability of renewable generation challenges the current practice of grid

regulation to maintain frequency and voltage stability

• At the distribution level, small photovoltaic systems generate reversed power flows

into the transmission level, resulting in significant voltage increases

Opportunities for Storage

Strategic Energy Technology Plan, European Commission and National Grid ESO, “Facilitating the transition to a flexible, low carbon energy system”, December 2019

Scully, J. (2020). Energy storage news. https://www.energy-storage.news/news/europesresidential-market-installed-745mwh-of-batterystorage-in-2019-solar/

• Storage is advocated as an excellent candidate to support safe and stable

operation of sustainable power grids and defer network investment

• System Operators (SOs) are exploring the emerging technical solutions that

storage technologies embedded within the distribution networks can afford

• Newly commissioned storage systems are mainly the small-scale ones, located at

the distribution level and close to the end-users, such as residential battery units

typically coupled with rooftop PVs, which showed a 57% annual increase in Europe

in 2020

Benefits of storage and PV integration

Strategic Energy Technology Plan, European Commission and National Grid ESO, “Facilitating the transition to a flexible, low carbon energy system”, December 2019

Scully, J. (2020). Energy storage news. https://www.energy-storage.news/news/europesresidential-market-installed-745mwh-of-batterystorage-in-2019-solar/

• Increased flexibility

• Reduction of the amount of clean electricity

curtailed at times of grid congestion or system

instability

• Reduction of imbalance charges and penalties

• Improved reactive power provision for dynamic

voltage control, even when solar is not available

• Storage participation into the balancing/energy and ancillary service markets

Motivation

• Explore the potential contribution from distributed storage technologies to efficient grid

operation and renewable integration

• In order for these flexible devices to provide an adequate service a very large number of

them must be efficiently aggregated and coordinated

Potential solution: Virtual Storage Plants

Aggregation of storage units with

same/different technologies at various

locations to provide grid support while

maximizing their performance and reducing

costs

Challenges and Contributions

• Additional uncertainties introduced by the integration of renewable generation

• New regulation services launched by system operators require dynamic and fast response capabilities

(within 1-2s)

• Huge control challenges to the coordination of a large number of geographically dispersed storage devices

and to the capability of dynamically responding to the time-varying network conditions

T. Zhao, A. Parisio, J.Milanovic, Distributed Control of Battery Energy Storage Systems for Improved Frequency Regulation, IEEE Transactions on Power Systems, 2020

T. Zhao, A. Parisio, J.Milanovic, Location-dependent Distributed Control of Battery Energy Storage Systems for Fast Frequency Response, International Journal of Electrical Power & Energy Systems, 2020

Contributions

• Scalable control frameworks to dynamically select and aggregate the most suitable combinations of

storage devices of any size that should be considered for effective service provision

• Optimal real-time coordination of thousands of storage devices accounting for both local and global

objectives

• Taking advantage of the emerging fast response capabilities (0.1-0.2s) of battery energy storage systems

Why Distributed Control

In order to be effective the control framework must

• have real-time capabilities and light computational

burden

• have adaptive and plug-and-play capabilities

• guarantee the constraint satisfaction

• integrate a feedback mechanism

• be scalable

DGDG

DG

Control center

DG

DGDG

DG

DG

LC LC

LC

LC

ESSESS

ESS

ESS

LC LC

LC

LC

Centralized Decentralized Distributed

DGDG

DG

Control center

DG

DGDG

DG

DG

LC LC

LC

LC

ESSESS

ESS

ESS

LC LC

LC

LC


DGDG

DG

Control center

DG

DGDG

DG

DG

LC LC

LC

LC

ESSESS

ESS

ESS

LC LC

LC

LC


Prone to failures and

communication

issues, less cost-

effective and robust,

lack of scalability

Suffer from

instability and sub-

optimality issues

Distributed vs. Centralized:

less communication, less

computation

Distributed vs. Decentralized:

more coordination, improved

solution

Distributed Optimisation-based Control

Dynamic fast frequency and voltage regulation• Pre-fault services and fast delivery

• Technique: distributed Online Convex Optimisation (OCO)

• Pros: computationally cheaper and asymptotic convergence to the optimum

Static fast frequency regulation• Post-fault service, fast delivery and short duration

• Technique: Alternating Direction Method of Multipliers (ADMM)

• Pros: fast practical convergence properties

Frequency regulation and congestion management• Simultaneous frequency regulation and congestion in multi-area networks

• Technique: Consensus and distributed primal-dual algorithm

• Pros: power network and VSPs dynamics embedded to drive the system to an equilibrium at

minimum cost

Outline






Online Convex Optimisation (OCO)

• At time t, the player chooses a strategy

without the knowledge of the current cost

• The player observes the revealed cost

function and incurs cost

• Regret: the difference between the cost

incurred and the best fixed point chosen

offline

• Goal: sub-linear regret function (on average

the algorithm performs as the best strategy

in hindsight)

Gordon GJ. Regret bounds for prediction problems. InCOLT 1999 http://www.cs.princeton.edu/ ehazan/tutorial/OCO-tutorial-part1.pdf

OCO-based control for ESS coordination

• Multi-agent system framework where each

ESS is an agent

• ESS setpoints calculated in less than 1s

• Plug-and-play functionality

• Suitable criteria optimised

Minimise ESS costs and maximise reward

Minimise tracking errors and deviations

• Global and local constraints satisfied

ESS Local Controller

OCO Problem Formulation

Fast dynamic frequency regulation Voltage regulation (reactive power)

subject to:

Min. Costs/Max. Reward

• Storage and frequency dynamics

• Power balance

• Operational and capacity constraints

• Service technical requirement

subject to:

Min. Tracking + Local voltage deviations

+ Costs

• Storage dynamics

• Network and inverters modelling

• Operational and capacity local

constraints

• Voltage-related local constraints

T. Zhao, A. Parisio, J.Milanovic, Distributed Control of Battery Energy Storage Systems for Improved Frequency Regulation, IEEE Transactions on Power Systems, 2020

T. Zhao, A. Parisio, J.Milanovic, Distributed Control of Battery Energy Storage Systems in Distribution Networks for Voltage Regulation at Transmission-Distribution Network

Interconnection Points, Control Engineering Practice, submitted

Distributed OCO Algorithm

1. Receive information from neighbouring agents

(e.g., voltage magnitudes, dual variables, local

estimation of the global information)

2. Consensus-based local estimation of global

information (e.g., supply-demand mismatch)

3. Local updates of the control inputs (e.g., active

and reactive power setpoints) and the

Lagrangian multipliers

Solution is based on the information at the last time

step (e.g., local voltage and SoC measurement)

Frequency Regulation – Plug and Play

IEEE 33-bus system. Ten BESS with maximum power ratings ranging from 4.8 to 5.5 MW. Unexpected

and sustained supply-demand mismatch at t=0, modelled as a random variable with a uniform

distribution U(22.5,27.5)MW. BESS7 fails at t = 4s and is recovered at t = 23s

Storage power outputs Frequency response

The system frequency is regulated to the nominal value within 30 seconds, as required, and the service

provision is sustained under unexpected plug-and-play operation

Frequency Regulation - Results

Total BESS power output

Computation times

Evolution of the regret function over time

Even for 2000 BESS, BESS

power setpoints are calculated

within 0.5s

Voltage Regulation - Results

IEEE 123-bus test feeder with 100 residential ESS (1.2 kW

rating each) and 3 commercial PV generators (60 kW rating

each) . Three TN-DN interconnection points (Nodes 31, 60 and

150)

Voltage profiles kept within the limits –TSO time-varying voltage

profiles satisfactorily tracked (maximum error of 0.8e−3)

Voltage profiles at all buses:

distributed approach

Voltage profiles at all buses:

decentralized approach

Outline






Problem Formulation

Fast static frequency regulation

subject to:

Min. Costs/Max. Reward

• Storage and frequency dynamics

• Power balance

• Operational and capacity constraints

• RoCoF and frequency nadir

requirements

T. Zhao, A. Parisio, J.Milanovic, Location-dependent Distributed Control of Battery Energy Storage Systems for Fast Frequency Response, International Journal of Electrical Power

& Energy Systems, 2020

Static Frequency Response - Results

IEEE 14-bus system. A total capacity of the installed RES in the system is 120 MW. Ten BESS with

maximum power ratings ranging from -4 to 4 MW.

Unexpected and sustained supply-demand mismatch of 12

MW occurs at Bus 11 at t=0.

Magnitudes of Z-bus matrix

With respect to PI-based control, RoCoF is reduced by 53%, the frequency nadir is improved from 49.18

Hz to 49.58 Hz and cost is reduced by 7%

Heat map of BESS power outputs

Static Frequency Response - Results

A modified IEEE 118-bus system with 100 BESS randomly distributed around the network

The computational time of the algorithm

is 0.18 s, which meets the fast-FR

service criterion of response time (1 s)

and is comparable with PI-based control

computational time (0.112s)

Convergence rates under different communication topologies

The topology has an impact on the

convergence rate, which is very fast

for all the topologies

Outline






Coordination of Multiple VSPs

X. Wang, T. Zhao, A. Parisio, Frequency regulation and congestion management by Virtual Storage Plants, SEGAN, to be submitted

power setpoint

Area 1

Tie line

ESS

ES

ESS

ES ES ES

ESESES

ES ES

DC DC

DC DC

DC

DC DC

DCDC

VA

Transmission level(top layer)

Virtual Storage Plant (VSP)

Area 2

Area 3

GG

G

1

2

3

4 5

6

7

8 9

G

GG

G

1

2

3

4 5

6

7

8 9

G

VSP

VSP

VSP

VSP

VA

VA

VA

VA

VSP Aggregator

VSP Aggregator

Tie line

Area 4 𝒩𝑝

G

Communication line Transmission line

Distribution level(lower layer)

L

RES

ሶ𝜃 = 2𝜋𝑓0𝜔

𝑀 ሶ𝜔 = 𝐶s𝑃𝑠 − 𝑃𝑑 − 𝐷𝜔 − 𝐶𝑃𝑒

𝑃𝑒 = 𝐵𝐶𝑇𝜃

ሶ𝑆𝑜𝐶𝑘𝑠 = −

1

𝐶𝐴𝑘𝑠 ∙ 𝜂𝑘

𝑠 𝑃𝑘𝑠, ∀𝑘 ∈ 𝒮

𝑇𝑘𝑠 ሶ𝑃𝑘

𝑠 = −𝑃𝑘𝑠 + 𝑢𝑘

𝑠 , ∀𝑘 ∈ 𝒮,

subject to:

𝐶s𝑃s − 𝑃d − 𝐷𝜔 = 𝐶𝑃𝑒

𝑃𝑒 ≤ 𝐵𝐶𝑇𝜃 ≤ 𝑃𝑒

𝑃𝑠 ≤ 𝑃𝑠 ≤ 𝑃𝑠

𝑆𝑜𝐶𝑠 ≤ SoC𝑠 ≤ 𝑆𝑜𝐶𝑠

min.𝑃s,𝜃,𝜔

𝑘∈𝒮

𝑐𝑘 𝑃𝑘𝑠

Distribution level:

Driving the storage devices within

a VSP to track the power setpoints

calculated by the VSP aggregators

and to achieve an agreed state of

charge (SoC)

Transmission level:

Coordinating VSPs to improve the

frequency profile and the

congestion management through

the tie lines of the multi-area

system

Outline






Conclusions and Future Works

- Distributed control framework for an optimal coordination of an arbitrary number of

storage devices for efficient grid support

- Storages coordinate each other without the need of any central entity and without sharing

any local private information with the TSO, the DSO or the storage aggregator

- Control framework to be adopted by an aggregator, such as a Virtual Storage Plant, or the

DSO

Future Works

- Extension to robustness against several other sources of uncertainty, including the

communication delays

- Analysis of the communication network design

- Extension to include demand technologies and the simultaneous provision of other

system services, e.g., new frequency services

Voltage Regulation - Results

PV and load power profiles

Coordination of Multiple VSPs - Results

Four-area networks with152 buses, 30 generators, 10% RES penetration, 17 cross-border tie lines. 5

VSP assets, each with 500 storage units (Power rating: 50 – 100 kW; Energy rating: 30 – 50 kWh). Flow

limit of 110MW. The penetration of renewables is around 10% at the rated load condition. 30% step-load

increase occurs in one area at 0.1 second.

Diagram of the testbed using DigSILENT and MATLAB

• VSP-DC: proposed control framework

• VSP-LFC: a dedicated LFC for VSPs

• Baseline: storage stays idle and the system

frequency is supported by the synchronous

generators

Coordination of Multiple VSPs - Results

Improved frequency regulation and

cross-border power transfer,

congestion issues avoided

0 50 100

Time (s)

-20

0

20

40

60

80

100

VS

P p

ow

er

(MW

)

0 50 100

Time (s)

-5

0

5

10

15

20

25

0 50 100

Time (s)

-5

0

5

10

15

20

25

(c) (d) (e)

Baseline VSP-LFCVSP-DC

no droopVSP-DC droop

workshop on pv applications in power networks

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