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eeh power systemslaboratory

Hannes Vollenweider

Grid Integration of PV Systems and LocalStorage in Distribution Networks

Master ThesisPSL 1413

EEH – Power Systems LaboratorySwiss Federal Institute of Technology (ETH) Zurich

Expert: Prof. Dr. Goran AnderssonSupervisors: Dr. Christof Bucher, MSc Philipp Fortenbacher

Zurich, 30th September 2014

Abstract

In this Master thesis the integration of PV systems and Battery EnergyStorage Systems (BESS) in the distribution grid is examined. The simu-lation framework and the algorithms that were implemented in this thesisare applicable to general distribution grids. However, the main focus of thisthesis is to study the concrete example of a distribution grid in the city ofZurich. The electric utility of the city of Zurich (ewz) has initiated the pilotproject BESS Dora-Staudinger-Strasse. In this project a BESS is installedin a distribution grid that consists of loads and PV systems. In the currentconfiguration the grid is fed by three transformer stations. When the BESSis installed, a new grid configuration can be tested: two transformer stationswill be disconnected and the remaining transformer station will supply theentire load. Consequently, the loading of the lines and transformers willincrease. The main goal of the project is to determine how the safe oper-ation of the grid can be ensured with the BESS in the experimental gridconfiguration. If the project is successful, future grid expansions due to loadincreases or newly installed PV systems could be replaced by the installationof a BESS.

In the first part of this thesis the dimensioning of the BESS is discussed.Initially, a simplified grid independent model is studied. An algorithm ispresented which determines the minimum amount of storage capacity thatis needed to reduce the loading of the cables below a specified threshold.In the future load increases due to the introduction of electric vehicles areexpected in the distribution grid. The effects of future electromobility onthe dimensioning of the BESS are also presented in this thesis.

In the second part of the thesis the operation of the BESS is discussed. Itcould be verified that the storage capacity of the BESS is sufficient to ensurethe safe operation of the grid in the experimental configuration. Since fore-casts of the load and the PV generation must be taken into account for theoptimal dispatch of a BESS, a model predictive control (MPC) scheme wasused. In this thesis an algorithm which maximises self-consumption and apeak shaving algorithm were implemented. The advantages and disadvant-ages of the two algorithms are described in this thesis.

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A question that is not directly related to the BESS is also examined in thisthesis: How much PV generation can be integrated in the distribution gridwithout violating voltage or thermal constraints? The PV hosting capa-city is calculated for the current and the experimental grid configurations.Furthermore, different methods to increase the PV hosting capacity are dis-cussed.

Kurzfassung

Die vorliegende Masterarbeit untersucht die Integration von PV Anlagenund Batteriespeichersytemen im Verteilnetz. Die Simulationswerkzeuge undAlgorithmen, die in dieser Arbeit entwickelt wurden, sind fur die Analyse vonVerteilnetzen universell einsetzbar. Der Schwerpunkt dieser Arbeit wurde al-lerdings auf die Analyse eines ausgewahlten Verteilnetzes in der Stadt Zurichgelegt. Das Elektrizitatswerk der Stadt Zurich (ewz) hat das PilotprojektBatteriespeicher Dora-Staudinger-Strasse ins Leben gerufen. In diesem Pro-jekt wird ein Batteriespeichersystem in einem Verteilnetz installiert, dasneben Haushaltslasten auch PV Anlagen enthalt. In der heutigen Konfigur-ation des Netzes wird das Netzgebiet mit drei Transformatorenstationen ver-sorgt. Sobald der Batteriespeicher installiert ist, kann eine neue Netzkonfig-uration getestet werden: Zwei Transformatorenstationen werden vom Netzgetrennt und die verbliebene Transformatorenstation ubernimmt die Versor-gung der Lasten. Dies hat zur Folge, dass die Kabel und Transformatorenstarker belastet werden. Das Hauptziel des Pilotprojektes BatteriespeicherDora-Staudinger-Strasse ist herauszufinden, wie der sichere Betrieb des Net-zes in der experimentellen Netzkonfiguration mithilfe des Batteriespeich-ers gewahrleistet werden kann. Wenn das Projekt erfolgreich ist, konntenzukunftige Netzausbauten durch die Installation eines Batteriespeichersys-tems ersetzt werden.

Im ersten Teil der Arbeit wird die Dimensionierung des Batteriespeichersbehandelt. Zunachst wird ein vereinfachtes Netzmodell verwendet. Ein Al-gorithmus wird vorgestellt, der die minimale Batteriekapazitat bestimmt,um die Belastung der Kabel unter einer vorgegebenen Schwelle zu halten.In der Zukunft werden grossere Lastspitzen im Verteilnetz aufgrund derzunehmenden Verbreitung von Elektrofahrzeugen erwartet. Die Auswirkun-gen von zukunftiger Elektromobilitat auf die Dimensionierung des Batter-iespeichers wird im Rahmen dieser Arbeit ebenfalls besprochen.

Im zweiten Teil der Arbeit wird der optimale Betrieb des Batteriespeichersuntersucht. Es wurde festgestellt, dass die Kapazitat des Batteriespeich-ers ausreicht, um den sicheren Betrieb des Netzes in der experimentellenNetzkonfiguration zu gewahrleisten. Da fur den optimalen Einsatz des Bat-

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teriespeichers Prognosen fur die Last und die PV Produktion benotigt wer-den, wurde ein modellpradiktiver Regler eingesetzt. In dieser Arbeit wurdenzwei Regelalgorithmen implementiert. Der erste Algorithmus maximiert denEigenverbrauch von Solarstrom. Der zweite Algorithmus verringert die Last-spitzen im Netz. Die Vor- und Nachteile der beiden Algorithmen werden indieser Arbeit beschrieben.

Eine Frage, die nicht direkt mit dem Batteriespeicher zusammenhangt, wirdebenfalls in dieser Arbeit behandelt: Wieviel Solarstrom kann in das Ver-teilnetz integriert werden, ohne Spannungsgrenzen oder thermische Grenzenzu verletzen? Die Aufnahmekapazitat fur Solarstrom wird fur die heutigeund die experimentelle Netzkonfiguration berechnet. Zudem werden ver-schiedene Methoden zur Steigerung der Aufnahmekapzitat fur Solarstromdiskutiert.

List of Acronyms

APC Active Power Curtailment

BESS Battery Energy Storage System

DOD Depth of Discharge

DSM Demand Side Management

DSS Dora-Staudinger-Strasse

MIP Mixed Integer Programming

MPC Model Predictive Control

NLP Nonlinear Programming

OLTC On Load Tap Change

OPF Optimal Power Flow

RHC Receding Horizon Control

RPC Reactive Power Control

SOC State of Charge

WW Wolfswinkel

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Contents

1 Introduction 1

2 Problem Definition and Methodology 9

3 Theoretical Background 133.1 Voltage Stability in Distribution Grids . . . . . . . . . . . . . 133.2 PV Hosting Capacity . . . . . . . . . . . . . . . . . . . . . . . 153.3 Battery Storage Systems . . . . . . . . . . . . . . . . . . . . . 173.4 Model Predictive Control . . . . . . . . . . . . . . . . . . . . 22

4 Models and Computational Methods 254.1 PV Systems and Loads . . . . . . . . . . . . . . . . . . . . . . 254.2 Cables and Transformers . . . . . . . . . . . . . . . . . . . . . 264.3 Battery Storage System . . . . . . . . . . . . . . . . . . . . . 274.4 Power Flow Calculation and Optimisation . . . . . . . . . . . 28

5 Simulation Tools and Data Basis 315.1 NEPLAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315.2 MATPOWER . . . . . . . . . . . . . . . . . . . . . . . . . . . 325.3 Load Profile Generator . . . . . . . . . . . . . . . . . . . . . . 325.4 PV Profile Generator . . . . . . . . . . . . . . . . . . . . . . . 335.5 Data Basis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

6 Generic Battery Sizing Algorithm 356.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356.2 Description of Algorithm . . . . . . . . . . . . . . . . . . . . . 366.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396.4 Effects of Future Electromobility . . . . . . . . . . . . . . . . 42

7 Case Study Wolfswinkel 497.1 Project Description . . . . . . . . . . . . . . . . . . . . . . . . 497.2 PV Hosting Capacity . . . . . . . . . . . . . . . . . . . . . . . 507.3 Short Circuit Studies . . . . . . . . . . . . . . . . . . . . . . . 637.4 Improved Battery Sizing Algorithm . . . . . . . . . . . . . . . 65

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viii CONTENTS

8 Storage Dispatch Algorithms 698.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 698.2 Maximise Self-Consumption . . . . . . . . . . . . . . . . . . . 718.3 Peak Shaving . . . . . . . . . . . . . . . . . . . . . . . . . . . 758.4 Peak Shaving with Dynamic Limits . . . . . . . . . . . . . . . 788.5 Comparison of Algorithms . . . . . . . . . . . . . . . . . . . . 78

9 Conclusions 81

10 Outlook 85

Bibliography 89

Source Code 91

Acknowledgements 93

Chapter 1

Introduction

On 25 May 2011 the Swiss Federal council decided on the nuclear phase-out.The five existing nuclear power plants are allowed to continue operating untilthe end of their technical lifetime. However, the planning of new nuclearpower plants in Switzerland is abandoned [1]. The decision was triggeredby the Fukushima Daiichi nuclear disaster on 11 March 2011. Besides theconcerns about the safety of nuclear power plants there are two other mainfactors that must be taken into account in the discussion about the futureelectricity system.

• Limited supply of fossil and nuclear fuels

• Reduction of CO2 emissions in power sector to prevent climate change

Up to now Switzerland could meet its demand for nuclear fuels and produceelectricity at competitive prices. However, in [2] it is suggested that thissituation could change in the future. An advantage of the current Swisspower generation portfolio is that the CO2 emissions of the power plants arelow. Currently, the Swiss electricity is produced by the following sources:Hydro power (56%), nuclear power (39%), thermal and other plants (5%)[1]. With the revised CO2 law Switzerland has committed to reducing theemissions of greenhouse gases by 20% until 2020 with respect to the emissionlevels of 1990 [3].

Since the decision of the nuclear phase-out by the Swiss Federal Council nu-merous studies about the future electricity system of Switzerland were pub-lished by different stakeholders. The Swiss government compiled a roadmapfor the future of the Swiss electricity sector in the report Energieperspektiven2050. Furthermore, the electric power industry and environmental organ-isations presented their positons in [4][5] and [6]. In the following the mainfindings of a study conducted by the ETH Zurich will be discussed [7].

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2 CHAPTER 1. INTRODUCTION

In the report Energiezukunft Schweiz options for a transition to a sustainableenergy system in Switzerland until the year 2050 are presented. It is assumedthat electric energy will become even more important in the future energysystem. Heat pumps and electric vehicles will enable the use of electricenergy in the heating and the transport sector. By using electric energy,CO2 emissions can be reduced if the electricity is generated from renewablesources. In Fig. 1.1 the projection of the future electricity generation andconsumption in Switzerland until the year 2050 is given. In all scenarios theenergy consumption in Switzerland will increase from the current level of63 TWh/a (including grid losses of 7% but without losses of storage pumps).The increase in demand by a growing population and the use of electricenergy in heating and transport is expected to outweigh the savings fromthe more efficient use of electric energy. In the Medium Load scenario theelectricity consumption in 2050 is predicted to be 79 TWh. 43 TWh of theyearly energy demand will be used in winter and 36 TWh in summer. If alifetime of 50 years is assumed for the Swiss nuclear power plants, the datesof shutdown will be as follows: Beznau I 2019, Beznau II and Muhleberg2022, Gosgen 2029 and Leibstadt in the year 2034 [1]. In the scenario byETH Zurich the nuclear phase-out is predicted to happen in 2020, 2030 and2040 in steps of 8-9 TWh. In the year 2050 there will be a missing generationof 41 TWh in the medium load scenario. In winter there will be a shortageof 24 TWh and in summer an additional generation of 17 TWh is required.Today Switzerland’s yearly energy consumption and production is balanced,however, there is an import of 4 TWh in winter and an export of 4 TWh insummer. [7]

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2010 2020 2030 2040 2050

Load

Conventional Generation

50% AdditionalGeneration High Load

Medium Load

Low Load

Scenarios

Yea

rly G

ener

atio

n /

Loa

d (

TW

h)

Fig. 1.1: Scenarios for future electricity generation and consumption inSwitzerland [7].

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In Fig. 1.2 the evolution of the additional production from renewable en-ergy sources in Switzerland is depicted. The resulting gap in the year 2050of 41 TWh cannot be closed by the additional production of 31 TWh fromrenewables. There is still a missing production of 10 TWh that would needto be generated in gas power plants or imported from neighbouring coun-tries. In winter (missing production of 7 TWh) the situation is worse thanin summer (missing production of 3 TWh). The scenario for the expansionof renewables is ambitious. Today it is questionable if the assumed poten-tial of additional hydro, biomass and geothermal can be fully tapped. Thedisappointing results in the geothermal project in St. Gallen raises doubtswhether geothermal power production will ever contribute significantly tothe electricity production in Switzerland [8]. The most promising renewableenergy source in Switzerland is PV. In 2050 an additional production of 14TWh could be realised. In the case of PV the production in winter amountsto 4 TWh and in summer 10 TWh can be additionally generated. A draw-back of PV electricity generation is the low energy return on investment(EROI). In [9] an EROI of 6.8 is found for PV modules. In contrast, windenergy has a higher EROI. The EROI of wind turbines is 18 according to[9]. Unfortunately, the good locations for wind energy in Switzerland arerather limited. [7]

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2010 2015 2020 2025 2030 2035 2040 2045 2050

PV

Geothermal

Wind

Biomass(including waste)

Additional Hydro

Additio

nal P

roduct

ion fro

m R

enew

able

s (T

Wh)

Fig. 1.2: Additional production from renewables to close the gap betweengeneration and consumption in the medium load scenario [7].

In an electric power system the generation must be equal to the load at everyinstant of time to keep the frequency constant. With a higher production of


intermittent energy sources like wind or solar the task of keeping the systemstable will become more difficult in the future. Switzerland is in the favour-able position of having a large amount of dispatchable renewable energysources available for the control of the grid. Today hydro storage plantshave an installed capacity of 8.1 GW [10]. Pumped hydro storage plantsadd an additional flexible power of 1.4 GW [10]. In the future the powerof pumped hydro storage plants will be increased to 5 GW if all plannedprojects are realised. The total storage capacity of all reservoirs would be9 TWh in this scenario. About 200 GWh of the 9 TWh are pumped hydrocapacity. Fig. 1.2 shows the future power profile in Switzerland in a weekin summer for the medium load scenario. It can be seen that the peak PVin-feed is 14 GW. The generation from wind and run-of-river hydro is 4 GWresulting in a total electricity generation of 18 GW. The load in Switzerlandin summer is 10 GW. Adding the 5 GW of pumped hydro storage plants,that are used to store the PV generation in this scenario, yields a total elec-tricity consumption of 15 GW. Consequently, additional storage that is ableto consume 3 GW during a few hours is needed. [7]

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Wind

PV

Electricity Consumption

Run-of-river Hydro

PumpedStorage

CH

40 h

Mon Tue Wed Thu Fri Sat Sun MonSunSat

Pow

er [G

W]

˜

Fig. 1.3: Future power profile of Switzerland in a week in summer [7].

The technical options for storing electrical energy are presented in Fig. 1.4.It can be seen that compressed air energy storage and batteries would bepossible alternatives to pumped hydro. The energy conversion efficiency ofpumped hydro is 80 %. Batteries have an efficiency of up to 85 %, however,the amount of energy that can be stored in a battery is much smaller. Theefficiency of compressed air energy storage are quite low (40 %-50 %) if no

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heat storage is used. Furthermore, the locations for installing compressedair energy storage are limited in Switzerland. [7]

System power ratings, module size

High-power supercapacitors

Lead-acid battery

High-power ywheels

NiMH

Li-ion battery

NaS battery

Flow batteries: Zn-Cl, Zn-BrVanadium redox New chemistries

High-energysupercapacitors

NiCd

Dis

char

ge ti

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at ra

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pow

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Seco

nds

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WG 1WM 1Wk 1 10 MW 100 MW10 kW 100 kW

NaNiCl 2 battery

Pumpedhydro

UPSPower quality

T & D grid supportLoad shifting

Bulk powermanagement

Compressed airEnergy storage

Advanced lead-acid battery

Fig. 1.4: Different storage technologies for electrical energy [11].

An advantage of using batteries can be seen from Fig. 1.5.

GridkLevelk1

GridkLevelk3

GridkLevelk5

GridkLevelk7

GridkLevelk2

GridkLevelk4

GridkLevelk6

Import/Export

GridkControl

TransmissionkGrid

kkkkkkkkk220/380kkV

SubtransmissionkGrid

kkkkkkkkkkkkkkk36-220kkV

RegionalkDistributionkGrid

kkkkkkkkkkkkkkkkkkkkkkkkk1-36kkV

LocalkDistributionkGrid

kkkkkkkkkkkkkkkkUpktok1kkV

Transformers

Transformers

Transformers

Fig. 1.5: Grid Levels in the Swiss Power System [12].


Most of the PV generation will be connected to the local distribution grids(grid level 7). If the PV energy is stored in pumped hydro plants, which areconnected to grid level 1, the energy has to be transported via all grid levels.This may result in the need for grid expansions. If the PV energy is storednear the site of generation, grid losses can be reduced and investments forgrid expansions can be avoided. [7]

It is clear that the transition from a network with big, centralised powerplants to a grid with lots of small decentralised producers requires that thesituation in the local distribution grids is studied more carefully. In thepast the fit and forget approach was applied in the planning of distributiongrids. The cables were built with sufficient capacity reserves in order toaccommodate future load increases. Consequently, the maximal loading ofcables in the distribution grid is 40% on average and a lot of money hasbeen invested in components that are not used to their full potential [13].An advantage of this strategy is that the grid does not need to be monitored.Therefore the current state of the grid is not known to the distribution gridoperator in contrast to the situation in the transmission grid. However, withthe introduction of smart grid devices, monitoring technologies will also beavailable on the distribution level.

Figure 1.6 illustrates three important parameters of an exemplary distri-bution grid (PV production, load and battery capacity) and possible futuredevelopments.

PVMProduction

LoadBatteryMCapacity

25

50

75

100

PVMHostingMCapacity

EconomicallyMviableMbatteryMcapacity MaximumMexpectedMload

IncreaseMinMbatteryMcapacity

dueMtoMimprovedMtechnologyM

LoadMincreaseMdueMto:

-MElectromobilityM

-MCompactMbuildingMdesign

-MHeatMPumps

Fig. 1.6: Current state of grid with respect to PV penetration, maximalload and installed battery capacity and possible developments in the future.

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In the future it is expected that distribution grids will be operated closerto its technical limits. Enabling technologies are smart grid devices andbatteries.

Chapter 2

Problem Definition andMethodology

One main goal of distribution grid operators is to increase the utilisationof cables and transformers. For the dimensioning of these components thepeak power consumed by the households is pivotal. Normally the load peakoccurs on evenings in winter. In the future large load peaks could also beintroduced by the charging of electric vehicles. Since the peak loads of thehouseholds are often much higher than the average load a low utilisation ofcables and transformers results. A solution to this problem is the installationof battery energy storage systems (BESS) in the distribution grids. If thebattery is charged during low load hours and discharged during peak hoursthe peaks in the loading of the cables and the transformers can be reduced.Therefore costly grid expansions can be deferred with the installation of aBESS. An advantage of using batteries is that they can be quickly installedat arbitrary locations in the grid. Therefore safety margins in grid planningcan be reduced [14]. In this thesis the dimensioning and the operation of aBESS for peak shaving applications is examined.

Another important problem for grid operators is the integration of PV sys-tems in the network. If the feed-in of PV energy reaches high levels, unac-ceptable voltage rises may occur. The quantity that defines the maximumtolerable PV feed-in without violating voltage limits or the rating of cablesor transformers is known as the PV hosting capacity of a grid. As in thecase of high load peaks, batteries can be used to mitigate the effects of highPV generation. In this case the battery is charged during peak feed-in anddischarged in times of low PV generation. The most critical time of theyear is usually on weekends in summer. The optimal grid integration of PVsystems is the second main theme of this thesis.

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10 CHAPTER 2. PROBLEM DEFINITION AND METHODOLOGY

All simulation tools and algorithms in this thesis are applicable to a generaldistribution grid. However, the main focus of this report is to study the gridof the pilot project BESS DSS carried out by the Electric Utility of the Cityof Zurich (ewz) in the quarter Affoltern in Zurich. In the pilot project a500 kWh BESS will be installed in a block of flats with existing PV systems.

In Fig. 2.1 the principle of peak shaving with a BESS is depicted. Theload and maximum PV potential data is taken from the node DSS of thepilot project. Currently, only about 30% of the maximum PV potential isinstalled in DSS. The black plane represents the feed-in limit of the grid.The volume above the black plane is the energy stored in the battery duringpeak PV feed-in. When Pnet gets negative this energy will be consumedlocally. It can be seen that the energy stored in the battery is not very largeif the feed-in limit is only slightly smaller than the maximum PV feed-in.Therefore BESS are ideally suited for peak shaving because batteries withhigh storage capacities are still very expensive.

PNet=

PPV-P

Load/kW

Day Hour0 3 6 9 12 15 18 21 24

0

200

400−200

−100

0

100

200

300

Fig. 2.1: Illustration of PV peak shaving with a battery.

Structure

In the following the structure of the report is presented. Chapter 3 con-tains the most important theoretical concepts that were used in this thesis.In chapters 4 and 5 information about the models, simulation tools anddata basis is summarised. In chapter 6 the dimensioning of a BESS for

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peak shaving applications is discussed for the general grid independent case.An algorithm was implemented to determine the required minimum stor-age capacity for a given tolerable grid loading. In chapter 7 the calculationof the PV hosting capacity for the pilot project is discussed and differentmethods for the grid integration of PV systems are examined. Furthermore,the chapter contains information about the calculation of short circuit cur-rents and a more sophisticated version of the Battery Sizing Algorithm ispresented. Chapter 8 examines different operation strategies for the BESS.The maximise self-consumption and peak shaving operation strategies arepresented and benchmarks to assess the performance of the algorithms arecalculated. Finally, the results are summarised and ideas for future researchare presented.

Research objectives

In this section the most important research objectives are summarised forease of reference.

• Develop algorithm to determine the required storage capacity for re-ducing the grid loading in a generic grid independent situation.

• Include voltage constraints in the battery sizing algorithm. Determinerequired storage capacity for the grid in the pilot project.

• Verify results of the battery sizing algorithms by implementing differ-ent algorithms to operate the BESS.

• Determine PV hosting capacity of the grid in the pilot project forcurrent and experimental grid configuration.

• Compare different strategies for increasing the PV hosting capacity ofthe grid.

Chapter 3

Theoretical Background

In this chapter the most important theoretical concepts that were used in thisthesis are discussed. In the first part of the chapter the effects of distributedgeneration on the voltage in distribution grids is studied with a simple model.In many cases voltage limit violations are more critical than overloading ofcables or transformers. Subsequently, the PV hosting capacity of a grid isdefined and different measures to integrate more PV production withoutgrid expansion are discussed. In the next section the applications of batterystorage systems are presented and benchmarks for the assessment of PVbattery systems are introduced. A short discussion about the ageing ofbatteries is also included. The last section of this chapter deals with themodel predictive control concept.

3.1 Voltage Stability in Distribution Grids

Without distributed generation the power flow in the grid is unidirectionalfrom the feeding transformers to the household loads. In the future thesituation in distribution grids will become more complicated because loadflows will change direction in times of high PV generation. In this sectionthe effects of distributed generation on the voltage in distribution grids willbe studied with a simple model. The following derivation is mainly basedon [15].

In Fig. 3.1 a simple model for the calculation of the voltage in a node withdistributed generation is depicted. The connection to the grid is modelledas an infinite bus with a voltage of 1 pu and a phase angle of 0 . Thecharacteristics of the connecting cable is modeled with the grid impedanceZgrid. The goal of the following derivation is to calculate the voltage Upros

at the prosumer (electricity production and consumption) node.

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14 CHAPTER 3. THEORETICAL BACKGROUND

Zgrid = Rgrid + j Xgrid

Ipros~UgridUpros

Infinite BusDistributed Generation

Household Loads

Fig. 3.1: Simple model of distributed generation in a distribution grid.

The voltage Upros is given by Eq. 3.1.

Upros = Ugrid + Zgrid Ipros (3.1)

If the net feed-in power at the prosumer node is denoted by Spros the fol-lowing equations hold

Spros = Ppros + jQpros (3.2)

Spros = Upros I∗pros (3.3)

With the approximation Upros ≈ Ugrid equation Eq. 3.3 yields

Ipros =S∗pros

Ugrid∗(3.4)

By substituting equation Eq. 3.4 and Zgrid = Rgrid + jXgrid in Eq. 3.1 thevoltage at the prosumer node results in

Upros = (1 + Ppros Rgrid +Qpros Xgrid)︸︷︷︸Upros,re

+j (Ppros Xgrid −Qpros Rgrid)︸︷︷︸Upros,im

For most nodes in distribution grids the approximation U2pros,re U2

pros,im

is valid. If the imaginary part of Upros is neglected Eq. 3.5 is obtained.

|Upros| = 1 + Ppros Rgrid +Qpros Xgrid (3.5)

In this thesis a maximum tolerable voltage rise of 3% is assumed [16]. Withthis assumption the following inequality must hold

Ppros Rgrid +Qpros Xgrid < 0.03 (3.6)

From Eq. 3.6 it can be seen that the magnitudes of Rgrid and Xgrid areimportant for keeping the voltage at acceptable levels in distribution grids.The DC resistance RDC of a cable is given by Eq. 3.7.

RDC = ρl

A(3.7)

3.2. PV HOSTING CAPACITY 15

The parameter ρ denotes the electrical resistivity of the conductor material,l is the length of the cable and A designates the cross-sectional area of thecable. In order to calculate the AC resistance additional factors accountingfor the skin and proximity effect must be considered. However, the depend-ence on l and A remains the same. If long cables with small cross-sectionalareas are installed in a grid, as is the case in rural areas where PV systemsare built on remote farms, Rgrid and Xgrid will be large at the end of theline. Therefore the installation of PV systems is critical in these cases andonly little active power feed-in from PV plants is possible. The owners ofhouses at the end of long cables face more problems feeding in PV electricitythan people living near the local transformer station.

The active power Ppros that is fed in the grid is given by the installed PVcapacity. However, the reactive power Qpros can be varied to a certain ex-tent. Today, most of the loads in distribution grids are inductive (Qpros<0).Eq. 3.6 indicates that Ppros can be increased if the PV inverters consumereactive power (Qinv<0). This approach for the control of the voltage indistribution grids is known as Reactive Power Control (RPC). Nowadays,all inverter manufacturers support RPC in their products. New invertersallow the definition of droop curves for the consumption of reactive poweras a function of the voltage in the grid. The effectiveness of RPC dependsmainly on the X ′/R′ ratio of the grid. The reactance X ′ of cables in thelow voltage grid is approximately 0.07 Ω/km and does not depend much onthe diameter of the cable [16]. Eq. 3.7 shows that the resistance of a cableis inversely proportional to the cross-sectional area of the cable. Thereforethin cables have a small X ′/R′ ratio and are not well suited for RPC. Incontrast RPC is a useful measure for thick cables with a big X ′/R′ ratio. Adrawback of RPC is that the loading of the cables increases because of theadditional consumption of reactive power. Therefore RPC is not an optionin grids where the lines are already heavily loaded.

3.2 PV Hosting Capacity

One main goal of this thesis is to determine the maximum amount of PVgeneration that can be integrated in the Wolfswinkel grid. In this sectiona precise definition of the terms PV penetration and PV hosting capacityis given. Furthermore, the limiting factors for the integration of PV indistribution grids are identified and different measures to improve the PVhosting capacity of a grid are discussed.


3.2.1 Definition

The PV penetration of a distribution grid is defined as follows [16]

PV penetration =Yearly energy produced by PV

Yearly energy consumed by loads

From the above definition it can be seen that the PV penetration of a gridcontains no information about the amount of PV energy that can be usedlocally.

The PV hosting capacity is the maximum PV penetration at which no tech-nical or legal constraints are violated in the grid [16].

3.2.2 Limiting Factors

Normally one of the following two factors limits the integration of PV energyin a distribution grid.

• Voltage limits are violated

• Overloaded components (e.g. cables or transformers)

Which one of the above problems is most critical depends on the specificconfiguration of the grid. However, voltage limit violations are usually morecritical than the overloading of transformers or cables.

3.2.3 Methods for Enhancement

In recent years many papers have been published about measures to facilitatethe integration of PV systems in the local distribution grid. In [17] differentmethods to increase the PV hosting capacity of a grid are summarised. Themost important methods are given below.

• Demand Side Management (DSM)

• Active Power Curtailment (APC)

• Reactive Power Control (RPC)

• Different orientations of PV systems

• Storage

• On Load Tap Change Transformer (OLTC)

• Low voltage grid reinforcement

3.3. BATTERY STORAGE SYSTEMS 17

Recent studies have found that the potential for DSM is smaller than pre-viously estimated [18]. APC is always an option to mitigate problems inlocal distribution grids caused by PV. However, it is not desirable to loseproduction during the hours with highest irradiation. The potential of RPCis discussed in section 3.1 of this report. Different orientations of PV panelshelp to lower the peak production during noon. However, the yearly PV elec-tricity production is also reduced significantly. Therefore this method is notvery promising. The use of batteries to integrate addtional PV generationis studied in detail in this thesis. OLTC can help to reduce the voltage inthe grid during peak hours. In the Wolfswinkel grid no OLTC transformersare available. If all of the before mentioned methods are not sufficient tointegrate the desired amount of PV production, grid reinforcement is themethod of last resort.

3.3 Battery Storage Systems

In [14] four applications of a BESS are discussed:

• Peak shaving

• Schedule compliance in liberalised power systems

• Integration of distributed generation

• Primary frequency control

In this thesis only peak shaving and the integration of distributed generationis discussed. However, for the economic operation of a BESS it is import-ant to be able to provide as many services as possible. Recently, the firstBESS was allowed to participate in the market for primary control energyby Swissgrid. The provision of primary control could be combined with peakshaving for example.

3.3.1 Battery Technology

The currently available battery technologies are shown in Fig. 3.2 withthe corresponding values of specific power and specific energy. It can beseen that lithium-ion batteries have the highest energy and power densities.Therefore lithium-ion batteries are widely used in laptops, cell phones andelectric vehicles. In the pilot project Wolfswinkel a lithium iron phosphate(LiFePO4) battery is used.


Specic energy (W h kg -1 )

Spec

ic

pow

er (W

kg

-1)

0

010

020

030

0

50

Li ionLi(TM)O2- C

Li metal-polymer

Sodium-sulfur

Nickel-cadmium

Leadacid

V redox ow

Sodium-metal

chloride

Nickel-metal

hydride

Li ionLiFePO4- C

100 150 200

Fig. 3.2: Comparison of different battery technologies [11].

3.3.2 Battery Ageing

All batteries are subject to ageing even if they are not used at all. Thisprocess is known as calendar ageing and results in a smaller usable batterycapacity and an increased internal resistance. The lifetime of a battery isdefined as the period of time until the initial battery capacity is reducedto 80% [19]. During operation additional stress factors occur that lead toaccelerated battery ageing. In [14] the following stress factors are listed.

• High and low State of Charge (SOC)

• High Depth of Discharge (DOD)

• High current rate (C) or high power rate (CP)

The SOC is a measure for the amount of charge stored in a battery. In thisthesis the SOC is used synonymously with the stored energy in a battery.For lithium-ion batteries a state of charge of approximately 45% leads tominimal ageing. The definition of the depth of discharge (DOD) is illustratedin Fig. 3.3. A battery cycle is defined as the event of battery charging,followed by the discharge of the battery to the initial level. The DOD is ameasure for the change in SOC during a battery cycle [20].


Turning Point

SOC

SO

CPow

er/

pu

DOD = 0.3

DOD = 1DOD = 0.5

DOD = ?

1 2 3 4 5 6 7 8 9

1 2 3 4 5 6 7 8 9

0

0.5

1

−1

0

1

Fig. 3.3: Definition of Depth of Discharge (DOD) [20].

The number of full battery cycles during a year is given by Eq. 3.8.

n =

∫|Pbat|dt

2C(3.8)

C denotes the energy storage capacity of the battery and Pbat is the powerof the battery.

For a detailed study of battery ageing the distribution of the DOD of allbattery cycles is important. Fig. 3.4 shows that high DOD lead to an accel-erated ageing of the battery. In the operation of a BESS care must be takenthat the battery is not stressed too much so that the calendar lifetime canbe reached. An advantage of the LiFePO4 chemistry is that this technologyhas practically no calendar life limitations [21].

Num

ber

of C

ycl

es

DOD / %

0 10 20 30 40 50 60 70 80

103

104

Fig. 3.4: Guaranteed number of cycles for different depths of discharge ofa typcial lithium-ion battery [20].


3.3.3 Self-consumption and Self-sufficiency

In the design of PV systems the self-consumption rate and the degree of self-sufficiency are two important quantities. In the future, feed-in tariffs willdecline and electricity consumed from the grid is expected to become moreexpensive [22]. Thus, the local use of electricity will become advantageousin the long term. By using batteries in combination with PV systems theself-consumption rate and the degree of self-sufficiency can be increased. Inthe following the definitions of the self-consumption rate and the degree ofself-sufficiency for a PV battery system are given. The information in thissection is taken from [19].

The self-consumption rate is defined as follows

s =EDU + EBC

EPV(3.9)

where EDU is the PV energy that can be directly used, EBC denotes theenergy used for battery charging and EPV is the total PV energy. The self-consumption rate is a measure for the amount of PV energy that can beused locally without stressing the grid.

The definition of the degree of self-sufficiency is given in Eq. 3.10. Thedegree of self-sufficiency is also known as the degree of autarky.

a =EDU + EBD

EL(3.10)

EDU is the PV energy that can be used locally, EBD is the energy used forbattery discharging and EL is the energy consumed by the loads. The degreeof self-sufficiency is a measure for the share of the load that can be suppliedif the connection to the grid is not used at all.

In Fig. 3.5 and Fig. 3.6 the annual self-consumption rate and the annualdegree of self-sufficiency are shown for a single household as a function ofthe installed PV and battery capacities. The PV and battery capacitiesare normalised to the yearly energy consumption of the household in MWh.The figures show that the self-consumption rate decreases and the degreeof self-sufficiency increases if more PV is installed. Without battery storageand with a PV system size of 1 kWp/MWh a self-consumption rate and adegree of self-sufficiency of 30% are achievable. Without batteries the self-sufficiency rate remains below 40% even if a large amount of PV is installed.If a battery capacity of 1 kWh/MWh is available a self-consumption rate of59% and a degree of self-sufficiency of 56% are feasible. A detailed discussionof the two diagrams can be found in [19]. In this thesis the self-consumptionrate and the degree of self-sufficiency are calculated for a system consistingof a large BESS and the accumulated PV production and load of severalhouseholds in chapter 8.


0.0 0.5 1.0 1.5 2.0 2.5

0.0

0.5

1.0

1.5

2.0

2.5

90%

20%

30%

40%

50%

60%

70%

80%

PV system size in kWp/MWh

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%Self-consumption rate

Usa

ble

batt

ery c

apaci

ty in k

Wh/M

Wh

Fig. 3.5: Annual self-consumption rate of a typical household with a PVbattery system [19].

0.0 0.5 1.0 1.5 2.0 2.5

0.0

0.5

1.0

1.5

2.0

2.5

10%

20%

30%

40%

50%

60%

70%

80%

PV system size in kWp/MWh

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%Degree of self-sufficiency

Usa

ble

batt

ery c

apaci

ty in k

Wh/M

Wh

Fig. 3.6: Annual self-sufficiency rate of a typical household with a PVbattery system [19].


3.4 Model Predictive Control

For the optimal operation of storage devices it is important to have predic-tions of the future PV generation and load profiles. For example, in thecase of peak shaving for the integration of large amounts of PV, it must beensured that the battery has enough remaining capacity at noon. In thissection the model predictive control (MPC) concept is presented that canbe used to accomplish this task.

In MPC the following optimisation problem is solved to find the optimalinput Ut for a linear system that should be controlled

J∗t (x(t)) = minimizeUt

N∑k=0

q(xt+k, ut+k)

subject to xt+k+1 = Axt+k +B ut+k, k = 0, . . . , N

xt+k ∈ X , ut+k ∈ U , k = 0, . . . , N

xt = x(t)

where Ut = (ut, . . . , ut+N-1) is the control input sequence. The stage costsq(x, u) describe the costs that are associated with being in state x andapplying control input u. By choosing q appropriately, different controlobjectives can be achieved. The dynamics of the linear system that shouldbe controlled is given in the first equality constraint in discrete state spacerepresentation. One main advantage of MPC is that constraints on x andu can be directly included in the formulation of the optimisation problem(x ∈ X and u ∈ U) [23].

In the receding horizon control (RHC) formulation of MPC a finite predictionhorizon N is considered. The principle of how the prediction horizon is rolledforward is shown in Fig. 3.7. At time instant t the state x of the systemis measured and the optimisation problem stated above is solved for anoptimisation horizon of N time steps. In the next time step only the firstelement ut of the solution vector Ut is applied to the system as an input.The optimisation horizon is rolled forward and the optimisation problem issolved again with the updated values to determine the optimal input ut+1.

3.4. MODEL PREDICTIVE CONTROL 23

past future

reference

predicted outputs

manipulated inputs

manipulated inputs

predicted outputs

Fig. 3.7: Rolling forward of optimisation horizon in Receding Horizon Con-trol [23]

Chapter 4

Models and ComputationalMethods

The first step in the analysis of a distribution grid is the modelling of allcomponents connected to the grid. In the first part of this chapter themodels of the PV systems, the loads, the cables, the transformers and theBESS are presented. In distribution grids the application of the DC powerflow approximation that allows the quick calculation of transmission gridsis not possible. The last section of this chapter contains a discussion of thecomputational methods that were applied in this thesis.

4.1 PV Systems and Loads

Figure 4.1 depicts the connection of a PV system to the grid. The peakpower of the PV modules is known as the DC power of a PV system. TheAC power of a PV system is defined as the maximum rated apparent powerfeed-in by the inverter. In this thesis the AC power is chosen to be 10%smaller than the DC Power. Therefore a small share of the PV generationis curtailed during peak hours. For the study of the effect of PV systems onthe grid the net power of a household is important. In this thesis the netpower is defined as follows: Pnet = PPV-PLoad. The modelling of every singlehousehold as an individual node in the power flow computation is often notrequired in the analysis of distribution grids. A more appropriate approachis the combination of several households in a single node.

25

26 CHAPTER 4. MODELS AND COMPUTATIONAL METHODS

PV generator

PV inverter

Loads

=~

==

Circuit breaker

888.888kWh Energy meter

Grid

MP

PT

AC

/D

C

DC Power

AC Power

Fig. 4.1: System components of a residential PV installation [19].

4.2 Cables and Transformers

In the simulation tools that were used in this thesis the unified branch modelshown in Fig. 4.2 is used to model cables and transformers [24]. Zkm =Rkm + j Xkm is the line impedance and Y sh

km = Gshkm + j Bsh

km is the shuntadmittance. In the software NEPLAN (cf. section 5.1) all parameters ofthe unified branch model are included. However, in the simulation toolMATPOWER (cf. section 5.2) the shunt conductance Gsh

km is neglected. Forcables the results of the two simulation tools do not deviate much if Gsh

km isneglected. However, the omission of the shunt conductance is problematic inthe case of transformers. In order to model the transformer losses correctlythe following fix was implemented. MATPOWER allows the adding of shuntconductances to all nodes. The shunt conductance Gsh

km of the transformercould be added to the neighbouring nodes. With this fix the results of thetwo simulation tools are in accordance.

Zkm

Ykm Ykm VmVksh sh

Fig. 4.2: Model for cables and transformers.

4.3. BATTERY STORAGE SYSTEM 27

4.3 Battery Storage System

The block diagram of a BESS is shown in Fig. 4.3. The energy is storedin a large number of lithium-ion batteries that are connected in series andparallel to form a battery pack. An important component in a BESS isthe battery management system (BMS). In [21] the following functions of aBMS are listed.

• Maximise the battery’s capacity (balancing)

• Do not allow the battery to be used outside its safe operating area

• Estimate the battery’s state of charge (SOC)

• Report to users and external devices

During charging and discharging of the battery the electrical energy is con-verted between AC and DC by a four-quadrant inverter. Therefore an ar-bitrary combination of active and reactive power consumption or generationis possible. The connection to the grid is established with a transformer.

Control System

AC/DC Conversion

Filter

BESSController

Battery system

BMS BMS BMS

GridConnection = ~

AC Grid

Control and Protection

Fig. 4.3: System components of a Battery Energy Storage System (BESS)[25].

In Table 4.1 the data of the BESS in the pilot project Wolfswinkel is shown.With a battery capacity of 500 kWh the BESS in the quarter Wolfswinkelwill be one of the largest batteries in Switzerland.

28 CHAPTER 4. MODELS AND COMPUTATIONAL METHODS

Table 4.1: Data of BESS in pilot project Wolfswinkel.

Parameter Value

Battery capacity C 500 kWhBESS power Pbat,max 120 kW

In Fig. 4.4 the model of the BESS that is used in this thesis is depicted.For the sake of simplicity only a simple model of the BESS is used in thisthesis. The charging and discharging efficiencies of the BESS are assumedto be 90% (ηcharge = 0.9 and ηdischarge = 0.9). Additionally a rate of self-discharge ηself-discharge could be included in the model of the battery. How-ever, ηself-discharge was neglected in the simulations in this thesis.

Battery Energy Storage System

ηself-discharge

ηcharge ηdischarge

Fig. 4.4: Simple model of battery energy storage system.

In state space representation the BESS is modelled with Eq. 4.1

x(k + 1) = x(k) +

[Ts ηcharge

C,− Ts

C ηdischarge

]u(k) (4.1)

where x(k) is the SOC of the battery at time step k, u = [Pcharge, Pdischarge]denotes the input vector with the battery charging power Pcharge and thebattery discharging power Pdischarge, Ts is the simulation step size and Cdenotes the capacity of the BESS.

4.4 Power Flow Calculation and Optimisation

For the calculation of power flows a conventional Newton-Raphson algorithmwas used in this thesis. No special algorithms for the solution of power flowsin local distribution grids were applied [26]. The feeder representing theconnection to the regional distribution grid (11 kV) is chosen as the slackbus of the system (Uslack = 1 pu and θslack = 0 ).

The DC power flow approximation is a useful tool for the quick calculationof transmission grids. In transmission grids the ratio X/R is big and strongPθ and QU couplings exist. In distribution grids X and R are of the same

4.4. POWER FLOW CALCULATION AND OPTIMISATION 29

magnitude and the power flow equations cannot be linearised in the sameway as in transmission grids. This fact has consequences on the solutionof optimisation problems in distribution grids. The classical optimal powerflow (OPF) problem is defined as follows [24]

minimizex,u

f(x, u)

subject to g(x, u) = 0

h(x, u) ≤ 0

xmin ≤ x ≤ xmax

umin ≤ u ≤ umax

where x = [V, θ] is the state vector of the power system, u = [Pgen, Qgen]is the input vector containing the powers of the generators that can bedispatched, g(x) are the power flow equations, h(x) are the line loadingconstraints, xmin and xmax are the voltage limits and umin and umax arethe power limits of the generators. The goal is to dispatch the generatorssuch that the costs of generation defined by the objective function f(x,u) areminimised.

If the full power flow equations are used, the above problem is a nonlinearprogramming problem (NLP). However, if the DC approximation is appliedand linear or quadratic objective functions are used, the problem is a linearor a quadratic programming problem. Many efficient solvers exist for linearand quadratic programming problems.

For the solution of optimisation problems in distribution grids two strategiesexist. One option is to try to linearise the power flow equations (voltagesensitivity matrix) [16]. The other option is to use the full power flow equa-tions and solve nonlinear optimisation problems with nonlinear solvers (e.g.KNITRO [27]). If storages and MPC control is introduced in the prob-lem formulation the complexity of the problem increases. In [28] differentsolution strategies that can be used in this case are compared.

In this thesis, the power flow equations are not directly included in the for-mulation of the optimisation problems. If voltage limits are considered, thepower flow problems are solved separately and the results are used as con-straints in the formulation of the optimisation problems. With this simplifiedapproach fast simulation times could be ensured. The software YALMIP wasused for the formulation of the optimisation problems that were studied inthis thesis [29]. The advantage of using YALMIP is that different solvers forthe soluton of the optimisation problems can be specified. In this thesis thesolver CPLEX was used [30].

Chapter 5

Simulation Tools and DataBasis

For the computations in this thesis the simulation tools NEPLAN and MAT-POWER were used. The input data for the simulations was generated witha load profile generator and a PV profile generator developed at ETH Zurich.In this chapter the interaction of these tools is explained. The access to goodquality data for the simulation of electric power systems is often a problembecause of data privacy and confidentiality requirements. The data basis ofthis thesis is the second main theme of this chapter.

5.1 NEPLAN

The grid model of the pilot project BESS DSS was created in NEPLAN [31]according to the data provided by the electric utility of the city of Zurich(ewz). An advantage of using NEPLAN is that the graphical user interfacemakes it easy to get a quick overview of the situation in a grid. The drawbackof NEPLAN is that it is a closed source commercial program. Therefore itis difficult to add new components and control algorithms to the simulationsin NEPLAN. In this thesis NEPLAN was used to get a graphical overviewof the situation in the grid at critical instants of time and to validate theresults obtained with MATPOWER. Furthermore, the calculation of theshort circuit currents was carried out in NEPLAN.

31

32 CHAPTER 5. SIMULATION TOOLS AND DATA BASIS

5.2 MATPOWER

The software MATPOWER [24] allows the calculation of power flows inMatlab and was developed at the Cornell University [24]. The code is pub-lished under the GNU General Public License (GPL) and well documented.An advantage of using MATPOWER in the development of new softwarefor power system analysis is that the powerful computation and plottingfunctionalities of Matlab can be combined with the capabilities of MAT-POWER. Since the simulation model was originally created in NEPLANthe data had to be exported to MATPOWER. For this purpose a NEPLANextension and the Grid API developed by Fortenbacher [28] was used. Withthe NEPLAN extension a XML file containing the grid data was created.This file could then be imported in MATPOWER with the Grid API. Theprocess of exporting data from NEPLAN to MATPOWER is depicted inFig. 5.1.

NEPLAN

XML File Grid API

MATPOWER

MATLAB

Fig. 5.1: Exporting data from Neplan to Matpower.

5.3 Load Profile Generator

For the generation of the load profiles measurement data with a time res-olution of 1 minute of 53 households in the node Dora-Staudinger-Strasseof the pilot project was available. The load profiles of the households weremeasured between November 2012 and December 2013. The load profile gen-erator used in this thesis was developed at ETH Zurich [32] and allows thegeneration of realistic load profiles from high resolution measurement data.In Fig. 5.2 the working principle of the load profile generator is shown.

5.4. PV PROFILE GENERATOR 33

domestic load pattern1 min. resolution,

measurements or syntheticbottom-up data

reference load profile15 min. resolution

statistical analysisload distribution function

load duration functioncorrelation reactive power

adaptation to reference profilepost-processing of distribution

function

load profile generation

Fig. 5.2: Generation of load profiles from high resolution measurementdata with load generator developed at ETH Zurich [32].

Before the load profile generator can be used, the software must be calibratedwith the high resolution measurement data. This step is described on theleft side of Fig. 5.2. After the calibration of the load generator load profileswith variable yearly energy can be created as described in the lower part ofFig. 5.2. In the pilot project Wolfswinkel measurements of the yearly energyflows through the outgoing cables in the transformer stations were available.This data was used to define the energy contents of the load profiles.

5.4 PV Profile Generator

For the generation of realistic PV data a PV profile generator that was de-veloped at ETH Zurich was used [33]. The PV generator requires irradiationdata for the whole range of azimuth and inclination angles that can occurin PV systems. This data was created with the software Meteonorm [34].The irradiation profile generated by Meteonorm does not correspond to realmeasurement data of a certain year. In Meteonorm the irradiation data isartificially generated from measurement data of different weather stations.Consequently, a typical irradiation profile is generated. Prior to version 7of the software only irradiation data with a temporal resolution of 1 hourcould be generated. With the release of version 7 of the software models forthe generation of minute data were introduced.

34 CHAPTER 5. SIMULATION TOOLS AND DATA BASIS

5.5 Data Basis

An overview of the simulation data used in this thesis can be found in Ap-pendix A. The following three sets of input data were used for the simulationsin the subsequent chapters.

• Scaled measurements DSS

• Simulated input data DSS

• Simulated input data quarter Wolfswinkel

The first set of input data is created by scaling the cumulated power pro-files of the 53 households in Dora-Staudinger-Strasse (measurements) to theyearly energy demand of the entire node. In order to take into account thecoincidence factor of the loads the cumulated power profile of the measure-ments in DSS is shifted by a week before it is added to the original cumulatedpower profile. This procedure is repeated until the required amount of en-ergy in DSS is reached. Only the PV generation that is currently installed inDSS is considered in this dataset. The PV profile was generated with the PVprofile generator. The temporal resolution of this dataset is 1 minute. Thescaled measurements DSS dataset was used for the simulations in chapter 6.

The two other sets of input data are based on measurements in the trans-former stations of the yearly delivered electric energy. The measurementdata of the yearly delivered electric energy was provided by the electric util-ity of the city of Zurich (ewz). This data was used as an input for the loadprofile generator to create load profiles with a duration of one year. Bothdatasets have a temporal resolution of 15 minutes.

The dataset simulated input data DSS was created by using the PV profilegenerator and the load profile generator. Only the currently installed PVgeneration is considered in this dataset. This set of input data is used forthe Improved Battery Sizing Algorithm described in section 7.4 and for thesimulations in chapter 8.

The third dataset is used to study the grid of the pilot project if the max-imum rooftop solar potential is installed. In this scenario the quarter Wolf-swinkel is considered and not only the node DSS. In the appendix it can beseen that the PV generation is bigger than the loads in the months May,June and July. A PV penetration of 63% is achieved in this scenario. Inorder to compare different PV expansion scenarios, additional PV profileswere generated with the PV profile generator. These scenarios are describedin section.

Chapter 6

Generic Battery SizingAlgorithm

In this chapter a rule-based algorithm for the dimensioning of a BESS forpeak shaving applications is presented. The algorithm is applicable in casesof high PV penetration and high loads. In the following a generic gridindependent situation is studied. Therefore the voltage at the BESS gridconnection, which depends on the grid impedance (cf. chapter 3), is notconsidered. In the last section of this chapter the effects of future electro-mobility on the dimensioning of the BESS are described.

6.1 Model

In Fig. 6.1 the model that is used for the dimensioning of the BESS isshown. The BESS is installed in a prosumer (electricity production andconsumption) node with PV generation and the cumulated load of severalhouseholds. The goal of peak shaving is to keep the consumed or fed-inpower |Pnet| in the prosumer node below a threshold Plim. The thresholdPlim is determined by the thermal limits of the cables.

~Infinite BusPV Generation

Household Loads

BESS

Pnet

Fig. 6.1: Model for the dimensioning of a BESS for peak shaving applica-tions.

35

36 CHAPTER 6. GENERIC BATTERY SIZING ALGORITHM

Either high feed-in from PV systems in summer or high electricity consump-tion from households in winter can be the limiting factors for the dimension-ing of the BESS. The input data that is used in this chapter is described inthe appendix.

6.2 Description of Algorithm

In this section a rule-based algorithm for the dimensioning of the BESS forpeak shaving applications is presented. The residual power in the prosumernode is defined as follows.

Pres = PPV − Pload (6.1)

The general working principle of the Battery Sizing Algorithm is depictedin Fig. 6.2. In the following it is assumed that the grid loading limits forPres>0 and Pres<0 are not equal. With this assumption the required storagecapacities for reducing PV feed-in peaks and household load peaks can becompared. The algorithm requires as an input the grid loading reductionsPred,up and Pred,low.

The resulting grid loading limits are Plim,up = max(Pres) − Pred,up andPlim,low = min(Pres) + Pred,low. In the first step the upper grid loadinglimit is reduced and the required storage capacity is calculated. The uppergrid loading limit is the limiting factor in the dimensioning of a BESS incase of high PV generation. This case occurs on weekend days in summerat noon. In the second step the lower grid loading limit is increased and therequired storage capacity is calculated. The lower grid loading limit is thelimiting factor in the dimensioning of a BESS in case of high loads. Thiscase occurs on cold days in winter in the evening. In the third step theneeded battery capacity to satisfy the upper and lower limit is calculated bytaking the maximum of the required storage capacities of both cases.

Check lower limitCheck upper limitDetermine battery

capacity

Fig. 6.2: General principle of the Battery Sizing Algorithm.

The control of the BESS for checking the upper limit is shown in Fig. 6.3.If Pres > Plim,up the battery is charged with the surplus PV production.The energy stored in the battery is fed into the grid as soon as Pres <Plim,up. Therefore the battery is normally fully discharged in this operationmode. In this chapter it is assumed that no limit on the BESS power exists.However, it is clear that the power of the battery must be at least equal tothe maximum of Pred,up and Pred,low.

6.2. DESCRIPTION OF ALGORITHM 37

Battery idle

Battery chargingBattery discharging

Pres < Plim

Pres > Plim

Pres < Plim Pres > Plim

Pres = Plim

Battery fully discharged

or Pres = Plim

Fig. 6.3: Check upper grid power limit.

Figure 6.4 shows the resulting battery SOC for a day in spring if the controlstrategy in Fig. 6.3 is applied. The required battery capacity during thewhole year to satisfy the upper grid limit is equal to the maximum SOCof the battery. It is assumed that the battery is fully discharged at thebeginning of the year.

Upper limit

Battery SOC

Pres

Presin

kW

andBatterySOC

inkW

h

Hour

2 4 6 8 10 12 14 16 18 20 22 24−150

−100

−50

0

50

Fig. 6.4: Miniumum Storage Algorithm checking upper limit on a day inspring.

The control of the BESS for checking the lower limit is shown in Fig. 6.5.If Pres < Plim,low the load in the prosumer node is supplied with energystored in the battery. In this operation mode a virtual SOC is used that canassume negative values. This is not a problem as long as the virtual SOC ofthe battery returns to zero a the end of the year. In this case virtual SOCvalues can be transformed to real SOC values by shifting the virtual SOCcurve upwards by the magnitude of the minimum virtual SOC that occursduring the year. Then the battery would be fully charged at the beginningand the end of the year. Using a virtual SOC in this operation mode has the


advantage that the control scheme for checking the upper and lower limitsis fully symmetrical. The battery is charged as soon as Pres > Plim,low.Therefore the battery is normally fully charged in this operation mode.

Battery idle

Battery chargingBattery discharging

Pres = -Plim

Pres < -Plim

Pres < -Plim

Pres > -Plim

Pres > -Plim

Battery fully charged (SOC = 0)

or Pres = -Plim

Fig. 6.5: Check lower grid power limit.

Figure 6.6 shows the resulting battery SOC for a day in winter if the controlstrategy in Fig. 6.5 is applied. The battery is discharged in the eveningto supply the peak load and charged during the night. The time intervalwith low load during the night is sufficient to fully charge the battery. Therequired battery capacity to satisfy the lower grid limit during the whole yearis equal to the maximum absolute value of the virtual SOC of the battery.

Lower limit

Battery SOC

Pres

Presin

kW

andBatterySOC

inkW

h

Hour

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48−300

−250

−200

−150

−100

−50

0

Fig. 6.6: Battery Sizing Algorithm checking lower limit on two days inwinter.

The algorithm presented in this chapter determines the storage capacitiesfor satisfying the upper limit and the lower limit separately. If the feed-inand consumption limits are critical on the same day, this approach is notsufficient. In this case it can happen that the upper limit is violated at noonand the lower limit is violated during peak load in the evening. An ideal

6.3. RESULTS 39

control strategy for the BESS would then be a combination of the upperand lower limit control schemes. In chapter 7 a more sophisticated versionof the Battery Sizing Algorithm is presented that can handle this situationwith model predictive control (MPC).

6.3 Results

The battery SOC during the whole year that results if the BESS is designedto satisfy only the upper limit is shown in Fig. 6.7. It can be seen that abattery capacity of 16 kWh is sufficient for a grid loading reduction of 30 kWbecause the PV peaks do not contain much energy.

Upper limit

Battery SOC

Pres

Presin

kW

andBatterySOC

inkW

h

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec−300

−250

−200

−150

−100

−50

0

50

100

Fig. 6.7: Yearly residual power and battery SOC for 30 kW upper limitgrid loading reduction.

Figure 6.8 depicts the battery SOC during the whole year that results ifthe BESS is designed to satisfy only the lower limit. It can be seen thata battery capacity of 540 kWh is required for a grid loading reduction of125 kW. In this case the peak consumption in the evening is supplied bythe battery. The BESS cannot be fully recharged during the critical days inJanuary at this grid loading reduction level.

In Fig. 6.9 the required storage capacity versus the grid loading reduction isshown for the upper and lower limits. The amount of storage that is neededfor a grid loading reduction in the upper and lower limit case is of the samemagnitude. The shape of the curves in Fig. 6.9 depends strongly on theinput data that is used for the algorithm. If the grid loading reduction gets


bigger than 120 kW a strong increase in the required storage capacity isobserved. This knee in the curve arises because the battery cannot be fullycharged during the night before the next peak load occurs.

Lower limit

Battery SOC

Pres

Presin

kW

andBatterySOC

inkW

h

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec−600

−500

−400

−300

−200

−100

0

100

Fig. 6.8: Yearly residual power and battery SOC for 125 kW lower limitgrid loading reduction.

Lower limit

Upper limit

Storagecapacity

inkW

h

Grid loading reduction in kW

0 20 40 60 80 100 120 1400

100

200

300

400

500

600

Fig. 6.9: Required storage capacity as a function of grid loading reduction.

However, a closer look reveals (Fig. 6.10) that for small grid loading reduc-tions the amount of storage that is required for satisfying the upper limit

6.3. RESULTS 41

is smaller than for the lower limit. Obviously, the PV peaks contain lessenergy than the load peaks for small grid loading reductions.

Lower limit

Upper limit

Storagecapacity

inkW

h

Grid loading reduction in kW

0 5 10 15 20 25 30 35 400

2

4

6

8

10

12

14

16

18

Fig. 6.10: Magnified version of Fig. 6.9 for small grid loading reductions.

The most important relation for a distribution grid operator is the requiredstorage capacity as a function of the absolute grid loading (Fig. 6.11). Itcan be seen that in the case of the pilot project BESS DSS the lower limitdetermines the amount of storage capacity that is required.

Lower limit

Upper limit

Storagecapacity

inkW

h

Grid loading in kW

20 40 60 80 100 120 140 160 180 200 2200

100

200

300

400

500

600

Fig. 6.11: Required storage capacity as a function of grid loading.


If the voltage at the BESS grid connection point is neglected and only thethermal loading of the cables is considered the upper and the lower gridloading limits are symmetrical. Therefore a second version of the BatterySizing Algorithm was implemented which requires only the absolute gridloading reduction as an input. The output of the algorithm is only thecritical curve for the dimensioning of the BESS in this case. For example inFig. 6.11 only the green curve would be shown.

6.4 Effects of Future Electromobility

In the future high load peaks in the distribution grid are expected due tothe charging of electric vehicles. In the following a possible scenario forthe introduction of electric vehicles in the city of Zurich is presented. Thescenario is based on the study Bedarf Ladeinfrastruktur Zurich that wascarried out by the company Protoscar SA in 2012 [35]. In this study threescenarios for the introduction of electric vehicles in Zurich are compared(Fig. 6.12). The company Alpiq forecasts a fast introduction of electricvehicles in Zurich. In contrast, the Swiss Federal Office of Energy predictsa slower transition to electric vehicles in the passenger car market. Theelectric utility of the city of Zurich (ewz) has decided to consider a mediumscenario for the planning of the grid. In the year 2020 11,200 electricallypowered automobiles are expected on the streets of Zurich.

2012 2013 2014 2015 2016 2017 2018 2019 2020

25 000

0

5 000

10 000

15 000

20 000Scenario Alpiq

Scenario ewz

Scenario BFE

Fig. 6.12: Electric vehicles (Battery electric vehicles and plug-in hybridelectric vehicles cumulated) in Zurich according to scenarios from Alpiq,ewz, and Bundesamt fur Energie (BFE) [35].

6.4. EFFECTS OF FUTURE ELECTROMOBILITY 43

This would be a market share of 8.1% in the passenger car market. 10% ofthe 11,200 cars will be battery electric vehicles and 90% are expected to beplug-in hybrid electric vehicles according to the study.

In order to determine the effects of future electromobility on the planningof the distribution grid, detailed load profiles for the charging of electricvehicles are required. At ETH Zurich, a traffic simulation for the city ofZurich was developed [36]. In this simulation the individual behaviour of10% of all passenger cars in Zurich is modelled. From this simulation detailedload profiles for the charging of electric vehicles can be derived [37]. In thisthesis the data of the traffic simulation was used to create load profiles forthe node Dora-Staudinger-Strasse (DSS) of the pilot project. The estimatedpopulation in DSS is 340 persons. The rate of motorisation in Zurich is 368vehicles per 1000 people [38]. This results in an estimated number of 125vehicles in DSS. If a market share of 8.1% is asssumed in 2020, approximately10 electric vehicles are expected in DSS in 2020. For the charging of theelectric vehicles the following two strategies were considered.

• Uncontrolled charging: Vehicles are charged as soon as they arrive attheir destination.

• Time-of-use tariff with today’s tariff structure: high tariff (Mondayuntil Saturday from 6 a.m. to 10 p.m.), low tariff (rest of the time).

The following additional parameters were used in the simulations.

Table 6.1: Parameters for simulations including electric vehicles.

Parameter Value

Charging power (single phase) 3.5 kWCharging power (three phase) 11 kWBattery capacity 16 kWhSimulation duration 1 dayTemporal resolution 15 min

Today most electric cars parked at home are charged from standard ACsockets with a power of 3.5 kW. If it is assumed that the car owners re-turn home in the evening and only need the car the next morning (Sleep& Charge) a charging power of 3.5 kW is sufficient to fully charge the bat-tery until the next morning. The chosen battery capacity in this scenariorepresents the state of the art in battery technology for electric vehicles.For example the plug-in hybrid electric vehicle Chevrolet Volt has a batterycapacity of 16 kWh. The charging profiles of the electric vehicles were simu-lated during a day with a temporal resolution of 15 minutes. In order to geta load profile for the entire year, the simulated data is repeated after each


day. In Fig. 6.13 and Fig. 6.14 the resulting charging profiles are depicted.The high charging peaks after 10 p.m. in the time-of-use tariff scenario areexplained by the switch from the high tariff to the low tariff.

Time-of-Use Tariff

Uncontrolled Charging

Power

inkW

Hour

2 4 6 8 10 12 14 16 18 20 22 240

2

4

6

8

10

12

14

Fig. 6.13: Charging profiles of electric vehicles in DSS with 3.5 kW chargingpower for uncontrolled charging and time-of-use tariff control schemes.

Time-of-Use Tariff

Uncontrolled Charging

Power

inkW

Hour

2 4 6 8 10 12 14 16 18 20 22 240

5

10

15

20

25

30

35

40

Fig. 6.14: Charging profiles of electric vehicles in DSS with 11 kW chargingpower for uncontrolled charging and time-of-use tariff control schemes.


The influence of the electric vehicles on the dimensioning of the BESS isshown in Fig. 6.15 and Fig. 6.16.

Lower Limit without Electromobility

Lower Limit Electromobility

StorageCapacity

inkW

h

Grid Loading in kW

80 100 120 140 160 180 200 2200

100

200

300

400

500

600

Fig. 6.15: Comparison of BESS dimensioning with uncontrolled charging(power 3.5 kW) and BESS dimensioning without electromobility.

Lower Limit without Electromobility

Lower Limit Electromobility

StorageCapacity

inkW

h

Grid Loading in kW

80 100 120 140 160 180 200 2200

100

200

300

400

500

600

700

Fig. 6.16: Comparison of BESS dimensioning with uncontrolled charging(power 11 kW) and BESS dimensioning without electromobility.

It can be observed that the difference between fast charging (11 kW) andslow charging (3.5 kW) is more pronounced at high grid loading reductions.


In Fig. 6.17 and Fig. 6.18 the comparison of the uncontrolled charging andthe time-of-use tariff control schemes are shown.

Lower Limit Uncontrolled Charging

Lower Limit Time-of-Use

StorageCapacity

inkW

h

Grid Loading in kW

80 100 120 140 160 180 200 2200

100

200

300

400

500

600

Fig. 6.17: Comparison of BESS dimensioning with time-of-use tariff (power3.5 kW) and BESS dimensioning with uncontrolled charging (power 3.5 kW).

Lower Limit Uncontrolled Charging

Lower Limit Time-of-Use

StorageCapacity

inkW

h

Grid Loading in kW

80 100 120 140 160 180 200 2200

100

200

300

400

500

600

700

Fig. 6.18: Comparison of BESS dimensioning with time-of-use tariff (power11 kW) and BESS dimensioning with uncontrolled charging (power 11 kW).

It can be seen that the time-of-use tariff control requires slightly less storagecapacity than the uncontrolled charging. This observation can be explained


by the lower charging power required by the time-of-use tariff control duringpeak load (18 p.m. until 22 p.m.).

The results presented in this section give a first estimate for the influence offuture electromobility on the dimensioning of a BESS. However, the chargingprofiles in the pilot project BESS DSS might look a bit different. In a quarterwith residential houses it is expected that the electric vehicles are chargedmainly in the evening. In the charging profiles used in this section the peakloads occur at noon. This discrepancy is explained by the fact that theresolution of the traffic simulation is not sufficient to model a single quarterof the city of Zurich. The influence of the motorway services, which is locatedin node k-BBS-111 of the pilot project cannot be observed, either.

Chapter 7

Case Study Wolfswinkel

In chapter seven the distribution grid of the pilot project BESS DSS isstudied. In the first part of the chapter the PV hosting capacity of the gridis computed and different scenarios for the integration of PV are examined.In the following section the calculation of short circuit currents in the gridis presented. It is important to ensure that short circuits can be reliablydetected and cleared in all grid configurations. In the last part of this chaptera more sophisticated version of the Battery Sizing Algorithm is presented.By applying MPC, voltage constraints and limits on the available batterypower can be taken into account in the dimensioning of the BESS.

7.1 Project Description

The distribution grid of the pilot project BESS DSS is shown in plan A 01in appendix. The PV and load data of the whole quarter and the node DSSis summarised in the appendix. Currently, the loads in the quarter are sup-plied by the three transformer stations Wolfswinkel, Teufenwiesenstrasse andDora-Staudinger-Strasse (plan A 02). In the coming months a BESS with acapacity of 500 kWh will be installed in the node DSS. When the BESS isinstalled the configuration of the grid will be changed in the following way.Transformer stations Teufenwiesenstrasse and Dora-Staudinger-Strasse willbe disconnected and the loads will be supplied by transformer station Wolf-swinkel. Furthermore, the grid will be meshed by connecting the cables inthe distribution boxes. Like all distribution grids, the grid of the pilot pro-ject is currently operated radially because the power flows are clearly definedin this configuration. The topology of the experimental configuration withthe BESS is shown in plan A 03. In the experimental configuration thecables and transformers will be loaded more and the grid is operated closer

49

50 CHAPTER 7. CASE STUDY WOLFSWINKEL

to its technical limits. Thus, a load increase at transformer station Wolf-swinkel (WW) can be obtained without actually changing the loads in thegrid.

7.2 PV Hosting Capacity

In this section the following scenarios for the integration of PV are discussed.

• Current grid configuration with maximum rooftop solar potential

• Experimental grid configuration with maximum rooftop solar potential

• Experimental grid configuration: Business as usual scenario

• Experimental grid configuration: Grid optimal expansion

• Experimental grid configuration: Reactive power control

• Experimental grid configuration: Gradual increase of PV penetration

A detailed explanation of the scenarios is given at the beginning of each sub-section. The maximum tolerable voltage rise due to PV feed-in is assumedto be 1.03 pu in this thesis [16]. Furthermore, the loads are considered inthe calculation of the voltages in the grid.

7.2.1 Finding Critical Cases

For the integration of PV in distribution grids the instant of time withmaximum PV feed-in and minimum load is important. Normally, weekendsin summer are most critical. In order to find the critical cases, the instantsof time with maximum PV feed-in and minimum load are determined forall nodes in the grid. The critical case can then be found by determiningthe instant of time that is critical for the maximum number of nodes. Thefollowing simulations are based on the dataset described in the appendix.For this dataset the critical case occurs on 29 June (time step 17323).

7.2.2 Current Grid Configuration

In this section the current grid configuration is considered. The grid isoperated radially and all three transformer stations are connected.


Maximum PV Penetration

The first step in the assessment of a grid with PV systems is to determinethe maximum PV generation that can be realised on the roofs of the houses.For the quarter Wolfswinkel the PV potential of all houses was calculatedby taking into account the available roof area, the inclination and the ori-entation of the roofs. The city of Zurich has calculated a solar cadastrefor all houses in Zurich [39]. Compared to the solar cadastre of Zurich theassumed PV potential in this thesis is optimistic. All suitable roof areas areused exclusively for the installation of PV panels. With these assumptionsa maximum PV penetration of 63% can be reached for the whole quarter.The definition of the PV penetration can be found in section 3.2.

The loading of the lines in case of maximum PV penetration is shown inFig. 7.1. In order to distinguish the lines of the grid a unique ID is assignedto each cable section. The assignment of the IDs to the cables can befound in plan A 08 in the appendix. It can be seen than the line 368 inHorensteinstrasse 31 is overloaded. However, this cable is very short and agrid expansion would pose no problems if a large PV system was installed onthis roof. Alternatively, active power curtailment (APC) could be applied.

I/I

maxin

percent

Line ID

368 197 270 320 203 332 19 23 328 102 106 416 420 380

20

40

60

80

100

120

140

Fig. 7.1: Line loading in current grid configuration with 63% PV penetra-tion.

The loading of the transformers is below 50% in WW and DSS (Fig. 7.2).Therefore the outage of one of the two parallel transformers can be handledwithout overloading the remaining transformer.

In Fig. 7.3 the distribution of the voltages in the grid is shown. All voltages


are below 1.03 pu. Thus, the voltage constraints can be satisfied even incase of maximum PV penetration. Unless otherwise specified, it is assumedthat the PV inverters feed only active power into the grid (power factorcos(φ) = 1).

WW

TWS

DSSS/S

maxin

percent

Transformer ID

432 435 438 444 4470

10

20

30

40

50

60

Fig. 7.2: Loading of transformers in current grid configuration with 63%PV penetration.

Number

ofNodes

Voltage in pu

1 1.02 1.04 1.06 1.08 1.1 1.12 1.14 1.160

5

10

15

20

25

30

Fig. 7.3: Voltages in current grid configuration with 63% PV penetration.


The minimum and maximum voltage in the whole grid on the critical dayin the end of June is shown in Fig. 7.4. The maximum voltage occurs atnoon when the PV feed-in reaches its maximum.

Voltagein

pu

Hour

0 2 4 6 8 10 12 14 16 18 20 22 240.98

0.99

1

1.01

1.02

1.03

1.04

1.05

1.06

Fig. 7.4: Minimum and maximum voltage in current grid configurationduring critical day with 63% PV penetration.

In summary, it can be noted that high PV penetrations are not a problemin the current grid configuration.

7.2.3 Experimental Grid Configuration

In this section the experimental grid configuration is considered. The gridis meshed and all loads are supplied by the transformer station Wolfswinkel.

Maximum PV Penetration

In this section the results for the maximum PV penetration scenario in theexperimental grid configuration are presented. It can be seen in Fig. 7.5that several lines are overloaded up to 30%. Furthermore, Fig. 7.7 showsthat the voltage limit of 1.03 pu is severely violated. Voltages of up to 1.14pu occur.

The overloaded lines and the nodes with voltage violations are highlightedin plan B 01 in the appendix. The lines E30721 and E30825 connecting thetransformer station WW with the DSS are the most critical. The highest


voltages occur in DSS and HSS at the end of the cables. This result wasalready obtained in chapter 3.

I/I

maxin

percent

Line ID

368 380 388 42 38 400 396 126 142 138 114 110 412 4080

20

40

60

80

100

120

140

Fig. 7.5: Line loading in experimental grid configuration with 63% PVpenetration.

S/S

maxin

percent

Transformer ID

435 4320

10

20

30

40

50

60

70

80

90

Fig. 7.6: Loading of transformers in experimental grid configuration with63% PV penetration.

The transformers are operated close to their technical limits. In the ex-perimental grid configuration the loading of the transformers is bigger than


50%.

Number

ofNodes

Voltage in pu

1 1.02 1.04 1.06 1.08 1.1 1.12 1.14 1.160

5

10

15

20

Fig. 7.7: Voltages in experimental grid configuration with 63% PV penet-ration.

Figure 7.8 shows that overvoltages of up to 14% occur during peak PVfeed-in.

Voltagein

pu

Hour

0 2 4 6 8 10 12 14 16 18 20 22 240.9

0.95

1

1.05

1.1

1.15

Fig. 7.8: Minimum and maximum voltage in experimental grid configura-tion during critical day with 63% PV penetration.

In summary, it can be seen that high PV penetrations are critical in the


experimental grid configuration. Line loading and voltage limit violationsoccur.

Business As Usual Scenario

Today, the best locations for PV systems are often used first and grid con-straints are normally not considered in the choice of a new site for PV in-stallations. In the scenario presented in this section it is assumed that onlythe biggest PV systems (>10 kW) are built and smalller PV installations(<10 kW) are not realised.

I/I

maxin

percent

Line ID

380 368 388 400 396 412 408 42 38 142 138 197 126 1140

20

40

60

80

100

120



S/S

maxin

percent

Transformer ID

435 4320

10

20

30

40

50

60

70


The roofs that are used for PV installations are highlighted in red in plan B02. The houses highlighted in blue will have no PV installations. The plotsare similar to the results of the Maximum PV penetration scenario. ThePV penetration is reduced to 52% because the smaller PV systems are notbuilt. In this scenario severe voltage limit violations occur and the lines areoverloaded.

Number

ofNodes

Voltage in pu

1 1.02 1.04 1.06 1.08 1.1 1.12 1.14 1.160

5

10

15

20

25

30

Fig. 7.11: Voltages in experimental grid configuration with 52% PV pen-etration.


If new PV systems are installed in an uncoordinated way, problems withovervoltages and overloaded lines can occur. In the following different meth-ods to increase the PV hosting capacity are examined.

Grid Optimal Expansion

In the previous section it was observed that the voltage of nodes at the endof cables is most critical. Therefore one method to increase the PV hostingcapacity would be to build no PV systems on houses at the end of longcables. The houses that have PV systems in this scenario are highlighted inred in plan B 03. The houses coloured in blue do not have PV systems. Itcan be seen that many houses in HSS are not considered for PV expansion.Furthermore, the PV system in DSS is assumed to have a peak AC powerof 110 kW. This is the amount of PV that is currently installed in DSS.Therefore the buildings in DSS are coloured in cyan in plan B 03. A PVpenetration of 46% is reached in this scenario.

I/I

maxin

percent

Line ID

197 203 142 138 23 19 102 106 270 54 126 114 110 620

10

20

30

40

50

60

70

80



S/S

maxin

percent

Transformer ID

435 4320

10

20

30

40

50

60


Figure 7.14 shows that the maximum voltage in the grid can be reducedsignificantly in this scenario. The maximum voltage occurs in the node DSS.The transformers and the lines are not overloaded in this scenario.

Number

ofNodes

Voltage in pu

1 1.02 1.04 1.06 1.08 1.1 1.12 1.14 1.160

5

10

15

20

25

30



If the distribution grid is considered in the planning of new PV systems,the PV hosting capacity can be increased. However, it is difficult to imple-ment this method in daily business because distribution grid operators mustprovide non-discriminatory access to the grid for their customers.

Reactive Power Control

In this scenario the application of RPC is studied to increase the PV host-ing capacity. Nowadays, all inverter manufacturers support RPC in theirproducts. New inverters allow the definition of droop curves for the con-sumption of reactive power as a function of the voltage in the grid. In orderto determine the effects of RPC on the grid voltage a simpler approach isused in this thesis. It is assumed that all PV inverters feed-in power with aconstant power factor cos(φ) = 0.95 (inductive). If the active power feed-inof the inverter is denoted by Pinv the reactive power feed-in can be calculatedwith the following formula.

S2inv = P 2

inv +Q2inv and Pinv = Sinv cos(φ)⇒ Qinv = Pinv

√1

cos2φ− 1

I/I

maxin

percent

Line ID

380 368 388 400 396 42 38 412 408 114 110 142 138 1260

50

100

150

Fig. 7.15: Line loading in experimental grid configuration with 63% PVpenetration


S/S

maxin

percent

Transformer ID

435 4320

20

40

60

80

100


With RPC the maximum voltage in the grid can be reduced from 1.14 puto 1.08 pu (Fig. 7.17). However, the main drawback of RPC can also beseen in this scenario. The loading of the lines is increased to almost 150%(Fig. 7.15).

Number

ofNodes

Voltage in pu

1 1.02 1.04 1.06 1.08 1.1 1.12 1.14 1.160

5

10

15

20

25



RPC is an interesting option for the pilot project BESS DSS to reduce thevoltages in the grid. Overloading of cables is only a problem if a very highPV penetration is reached.

Gradual Increase of PV Penetration

Maxim

um

voltagein

pu

PV penetration in percent

0 10 20 30 40 50 60 701

1.05

1.1

1.15

Fig. 7.18: Maximal voltage in grid as a function of PV penetration.

In this section the maximum voltage and the maximum loading of the linesin the grid as a function of the PV penetration are considered (Fig. 7.18 andFig. 7.19). The maximum rooftop solar potential is scaled homogeneouslyto reach the desired PV penetration. Voltage limit violations are more crit-ical than line loading violations. The voltage limit of 1.03 pu is violated ata PV penetration of 23% while the PV penetration can be increased to 48%before the line loading limits are violated. Thus, the PV hosting capacityof the grid is 23% if no additional methods for the better integration of PVare applied. If RPC is used, a PV hosting capacity of 35% can be achieved.The maximum loading of the lines is approximately 80% in this scenario. Itis assumed that all PV inverters feed-in power with a constant power factorcos(φ) = 0.95 (inductive).

Figure 7.19 shows that the maximum line loading decreases for small PVpenetrations. The curve starts to rise again at a PV penetration of 13%.Thus, the line losses can be reduced by the integration of PV in a distributiongrid. In other reports it was found that the overall grid losses are minimisedat a PV penetration of approximately 25% [16].

7.3. SHORT CIRCUIT STUDIES 63

Maxim

um

lineloadingin

percent

PV penetration in percent

0 10 20 30 40 50 60 7020

40

60

80

100

120

140

Fig. 7.19: Maximal line loading in grid as a function of PV penetration.

7.3 Short Circuit Studies

The calculation of the short circuit currents for the grid in the pilot projectBESS DSS relies on the following assumptions.

• Single phase to ground fault is considered

• Short circuit power in MV feeder Wolfswinkel S′′sc = 146 MW

• Nominal voltage in medium voltage grid Vn = 11 kV

• Short circuit current in MV feeder Wolfswinkel

I ′′sc =S′′sc

Vn

√3

= 7.7 kA

• The grid is operated in the experimental configuration (meshed grid)

In Table 7.1 the resulting short circuit currents for short circuits in thenodes k-BBS-111, k-HSS-32 and k-DSS-1 are given. The short circuit loca-tions are shown in plan C 01.


Table 7.1: Results of short circuit calculation.

Short Circuit Location I ′′sc

k-BBS-111 1.19 kAk-HSS-32 1.71 kAk-DSS-1 1.70 kA

In distribution systems it is important that the short circuit currents do notfall below a minimal value. Therefore short circuit locations at the ends ofthe cables are considered because the short circuit currents are smallest inthis case (high grid impedance). The required minimal short circuit currentis defined by the fuse that is used to protect the cable. If the short circuitcurrent is high enough, the fuse blows fast enough. The requirements onthe minimal short circuit currents for different cable diameters are given inTable 7.2.

Table 7.2: Minimum short circuit current for different cables. [40]

Cable type Fusing I ′′sc,min

3x240/240 400 A 1350 A3x150/150 315 A 1150 A3x95/95 250 A 820 A3x50/50 160 A 490 A3x25/25 100 A 305 A3x10/10 63 A 190 A

If the above requirements are satisfied, it is ensured that the fuse at thetransformer station will blow within 120 seconds. Thicker cables requirehigher short circuit currents to detect a fault because the currents duringnormal operation are also higher. The numbers defining the cable typedenote the cross-sectional area of the phase and neutral conductors in mm2.In plan C 01 the types of all cables in the grid of the pilot project are given.Comparing Table 7.1 and Table 7.2 shows that the short circuit currentsare high enough to ensure a clearance of the faults within 120 seconds. Forexample in node k-BBS-111 a short circuit current of 820 A is required andthe actual short circuit current is 1190 A.

7.4. IMPROVED BATTERY SIZING ALGORITHM 65

7.4 Improved Battery Sizing Algorithm

In chapter 6 a generic grid independent situation is studied. In this sectiona more sophisticated version of the Battery Sizing Algorithm is presentedwhich includes grid constraints. The following features are added in com-parison to the algorithm described in the previous chapter.

• Algorithm works if feed-in and consumption limits are critical on thesame day

• Battery power limit is considered in the dimensioning of the BESS

• Voltage limits in the grid are considered

In this chapter an algorithm based on model predictive control (MPC) isused for the dimensioning of the BESS. The model that is used for the MPCcontrol is described in chapter 8. In the following only the changes that wererequired in addition to the basic MPC model are described. Since the batterycapacity is an unknown in the dimensioning of the BESS the decision vector uis augmented by the slack variable ε. The constraint SOC < Cbat is replacedwith the constraint SOC < 1+ε. The costs for the slack variable ε are chosenvery high in the objective function J . Therefore the algorithm tries to finda solution with the nominal battery capacity Cbat,nom first. If no solutioncan be found, the slack variable ε is increased. The maximum requiredstorage capacity during the year is found by computing (1 + ε) Cbat,nom.The nominal battery capacity is chosen to be 10 kWh in the simulations.With this choice the range of values of ε can be handled without problemsby computers for a BESS with a capacity of hundreds of kWh.

By using MPC the Improved Battery Sizing Algorithm can handle caseswhere feed-in and consumption limits are critical on the same day. In thesecases a rule-based control scheme is not sufficient for the optimal control ofthe battery. Constraints on the battery power are already included in thebasic MPC formulation described in chapter 8. In order to take into accountvoltage limits in the grid, the power flow calculation must be coupled withthe MPC optimisation framework. In this thesis MATPOWER is used todetermine the tolerable feed-in and consumption in the node DSS so thatthe voltages in the grid do not rise above 1.03 pu and do not fall below 0.91pu. For the calculation of the limits it is assumed that only active poweris consumed or fed into the grid by the BESS. The limits are calculatedby iterating over different values of the active power in the node DSS untilthe voltage constraints are violated. The calculated limits are then used asconstraints for the MPC controller. In Fig. 7.20 the interaction betweenMATPOWER and the MPC framework is shown in a block diagram.


MPC Controller BESSDynamic Power

Limiter

PV and Load Profiles

Voltage Constraints

Pgrid,genmax

Pgrid,loadmax

Fig. 7.20: Considering voltage constraints in Improved Battery Sizing Al-gorithm.

The resulting feed-in limit in DSS during a day in summer is shown inFig. 7.21. During night when the load is low in the grid only little powercan be fed into the grid in DSS before the voltage limit of 1.03 pu is violated.In the evening during peak load, a lot of power can be fed into the grid beforethe upper voltage limit is violated.

Feed-InLim

itin

kW

Time / h

0 2 4 6 8 10 12 14 16 18 20 22 24110

120

130

140

150

160

170

180

190

Fig. 7.21: Daily feed-in limit on 1 January in DSS.

In Fig. 7.22 the yearly feed-in limit in DSS is shown. It can be seen that thetolerable feed-in in summer is less than in winter because there is already alot of feed-in from PV systems in summer. The computation of the powerflows in MATPOWER at the different instants of time was computed inparallel using batch jobs. With parallel computing the simulation time couldbe reduced significantly.

7.4. IMPROVED BATTERY SIZING ALGORITHM 67

Feed-InLim

itin

kW

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec110

120

130

140

150

160

170

180

190

Fig. 7.22: Yearly feed-in limit in DSS.

Figure 7.23 shows the yearly consumption limit in DSS. The tolerable con-sumption in winter is less than in summer because the grid is already heavilyloaded in winter.

ConsumptionLim

itin

kW


120

130

140

150

160

170

180

Fig. 7.23: Yearly consumption limit in DSS.

In order to consider the voltage limits in the operation of the BESS theconstraints ugrid,load < Pgrid,load,max and ugrid,gen < Pgrid,gen,max are intro-


duced where Pgrid,load,max denotes the feed-in limit and Pgrid,gen,max is theconsumption limit. The Battery Sizing Algorithm described in chapter 6considers different lower grid loading limits Plim,low for the dimensioning ofthe BESS. The Improved Battery Sizing Algorithm considers the minimumof Plim,low and Pgrid,gen,max for the dimensioning of the BESS.

Figure 7.24 shows the results of the Improved Battery Sizing Algorithm to-gether with the results of the Battery Sizing Algorithm described in chapter6. It can be seen that a minimum battery capacity of approximately 160 kWhis needed to ensure that the power consumed from the grid in the node DSSdoes not get bigger than 117 kW. With a storage capacity of 160 kWh thevoltage in the grid does not fall below 0.91 pu during the whole year.

Lower Limit

Lower Limit MPC

Storage Capacity versus Grid Loading

StorageCapacity

inkW

h

Grid Loading in kW

90 100 110 120 130 140 150 160 170 180 1900

50

100

150

200

250

300

350

400

Fig. 7.24: Results of the Improved Battery Sizing Algorithm (MPC) andthe Battery Sizing Algorithm (rule-based).

Chapter 8

Storage Dispatch Algorithms

In this chapter the optimal control of the battery system in the pilot pro-ject BESS DSS is discussed. Two dispatch algorithms for the BESS arepresented: the Maximise Self-Consumption Algorithm and the Peak Shav-ing Algorithm. In the first part of the chapter the formulation of the MPCproblem is discussed. In the following sections the implementation of theMaximise Self-Consumption and the Peak Shaving algorithms is presented.In order to assess the performance of the algorithms different benchmarksare defined. In the last section of this chapter advantages and disadvantagesof the two control algorithms are discussed.

8.1 Model

In this section the basic model for the MPC control of the BESS is presented.The general problem formulation for MPC was given in chapter 3. In thissection the same notation is used. The decision vector u is defined as follows

u = [ubat,load, ubat,gen, ugrid,load, ugrid,gen, uprosumer,net] (8.1)

where ubat,load is the charging power of the battery, ubat,gen is the dischargingpower of the battery, ugrid,load is the power fed into the grid, ugrid,gen is thepower consumed from the grid and uprosumer,net = uPV − uload.

In Eq. 8.2 the general quadratic objective function for all algorithms in thischapter is given [14].

J = uTQuu+Ru+ (xsoc − xsoc,ref)2 QSOC (8.2)

69

70 CHAPTER 8. STORAGE DISPATCH ALGORITHMS

In the following xsoc,ref = 0.45 is assumed [20]. The values of Qu, R andQSOC depend on the specific algorithm that should be implemented.

The following constraints must be satisfied.

Cx(k + 1) = Cx(k) + Ts(ηchargeubat,load − η−1dischargeubat,gen) (8.3)

0.2 ≤ x ≤ 0.8 (8.4)

0 ≤ ubat,load ≤ Pbat,max (8.5)

0 ≤ ubat,gen ≤ Pbat,max (8.6)

0 ≤ ugrid,load ≤ Pgrid,load,max (8.7)

0 ≤ ugrid,gen ≤ Pgrid,gen,max (8.8)

ubat,load + ugrid,load = ubat,gen + ugrid,gen + upros,net (8.9)

upros,net = uPV,profile − uload,profile (8.10)

ubat,load ubat,gen = 0 (8.11)

Constraint (8.3) is the dynamics of the BESS. Constraint (8.4) specifiesthat the SOC of the battery must be between 20% and 80%. Thus, only60% of the battery capacity C can be used. Constraints (8.5) and (8.6)limit the charging and discharging power of the battery. Constraint (8.7)limits the power fed into the grid in the node DSS. Constraint (8.8) limitsthe power consumed from the grid in the node DSS. Constraint (8.9) isthe power balance in the node DSS. Constraint (8.10) is the definition ofupros,net. Constraint (8.11) prevents the concurrent charging and dischargingof the battery. This constraint is a bilinear constraint. Since CPLEX cannothandle bilinear constraints, equation (8.11) must be reformulated. In [41]the following solution is proposed.

pi ∈ 0, 1 ,ubat,gen

Pbat,max≤ pi,

ubat,load

Pbat,max≤ 1− pi (8.12)

The variable pi is a binary integer variable and defines whether the batteryis charging (pi = 0) or discharging (pi = 1). With this formulation theoptimisation problem that must be solved is a mixed integer programming(MIP) problem. CPLEX is able to solve MIP problems.

The parameters of the basic MPC simulation are given in Tab. 8.1

Table 8.1: MPC simulation parameters.

Parameter Value

Temporal resolution 15 minPrediction horizon N 96 timesteps (1 day)

8.2. MAXIMISE SELF-CONSUMPTION 71

It is important to note that the MPC controller needs information about fu-ture PV generation and electricity consumption. In the practical implement-ation of an MPC controller the length of the prediction horizon is limitedby the quality of the forecasts. Furthermore, the computational complex-ity of the optimisation problem increases if long optimisation horizons areconsidered. In the control of a BESS, normally, time steps of 15 minutesand a prediction horizon of 1 day is considered. In this thesis it is assumedthat perfect forecasts for the PV feed-in and the load are available. Withthis assumption the optimisation problem has not to be solved after eachtime step. An alternative approach is to control the system according to thesolution vector Ut and delay the update of the optimisation horizon. Withthis method simulation times can be reduced considerably.

8.2 Maximise Self-Consumption

Since 1 April 2014 all producers of electricity have the right to consume thegenerated power at the location of production [42]. With decreasing feed-intariffs and increasing electricity prices, self-consumption of PV power willbecome more attractive in the future. In this section it is examined how theBESS can be used to increase the rate of self-consumption and the degreeof self-sufficiency in DSS.

In order to maximise the self-consumption of PV electricity, the objectivefunction is chosen as follows.

Qu = diag([1, 1, 0, 0, 0])

QSOC = 2× 103

R = [0, 0, 1× 103, 1× 103, 0]

With this objective function the feed-in of electricity is not attractive becauseit is cheaper to store the electricity in the battery and use it later.

Fig. 8.1 shows the power balance in DSS and the SOC of the BESS duringfive days in summer. It can be seen that the BESS is charged as soon asthere is surplus PV production in DSS. When the PV generation decreasesin the evening the battery is discharged and the stored electricity can beused locally.

Without a BESS the rate of self-consumption is 95% in DSS. The rateof self-sufficiency is 17.3% without batteries. By using a BESS, the self-consumption rate can be increased to 100% and the self-sufficiency ratereaches 18.2%. Hence, the use of a BESS to maximise the self-consumptionin DSS is not worthwhile because the PV system is small compared to theload. The results for the node DSS are summarised in Table 8.2.


Table 8.2: Results of Maximise Self-Consumption Algorithm in grid op-timal PV expansion scenario for the node DSS.

Parameter Without BESS With BESS

Self-consumption rate 95% 100%Self-sufficiency rate 17.3% 18.2%

Another option would be to use the BESS to store the surplus PV productionof the whole quarter during peak PV feed-in. The results for this scenarioare shown in Fig. 8.2. It is assumed that the PV penetration is increasedin the whole quarter according to the Grid Optimal Expansion scenario. Inthis scenario a PV penetration of 46% is achieved. The available surplusPV generation in the whole quarter is shown in blue in the power balanceplot. The red curve is the amount of surplus PV energy that is used forcharging the battery. It can be seen that only a small amount of the totalavailable surplus PV energy can be stored in the BESS. Grid constraints arenot considered in the MPC formulation. The capacity and the power of theBESS are not changed in this scenario (C = 500 kWh,Pbat,max = 120 kW).

In Table 8.3 the resulting self-consumption and self-sufficiency rates for thewhole quarter are listed. Since the battery capacity is not sufficient to storethe surplus PV power, the self-consumption and the self-sufficiency ratescan only be increased slightly in the quarter. More storage capacity wouldhave to be installed in the quarter to increase the self-consumption and self-sufficiency rates significantly. However, with current battery technology thiswould be very expensive.

Table 8.3: Results of Maximise Self-Consumption Algorithm in grid op-timal PV expansion scenario for the whole quarter.

Parameter Without BESS With BESS

Self-consumption rate 52% 62.6%Self-sufficiency rate 24% 28.4%

8.2. MAXIMISE SELF-CONSUMPTION 73

SOC

Tim

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0.2

0.4

0.6

0.81

−15

0

−10

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Load

Charge

Battery

PV

Quarter

PV

DSS

Disch

argeB

atteryC

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P in kW

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0

0.2

0.4

0.6

0.8 1

−200 0

200

400

600

800

1000

1200

1400

Fig

.8.2

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mw

ithP

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balan

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and

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battery.

8.3. PEAK SHAVING 75

8.3 Peak Shaving

The Peak Shaving Algorithm was implemented to verify that the dimen-sioning of the BESS in chapter 6 is correct. The simulations show thatthe available storage capacity is sufficient to ensure that all constraints aresatisfied.

For the implementation of the Peak Shaving Algorithm, the objective func-tion is chosen as follows.

Qu = diag([1, 1, 0, 0, 0])

QSOC = 2× 103

R = [0, 0,−0.05, 0.13, 0]

Furthermore, the grid constraints Pgrid,load,max = 100 kW (maximum toler-able power feed-in) and Pgrid,gen,max = 110 kW (maximum tolerable powerconsumption) are included in the simulations. With these parameters theBESS operates in peak shaving mode. If the loading of the grid gets critical,the missing energy is taken from the battery. The power balance in DSSand the SOC of the BESS during five consecutive days in winter is shownin Fig. 8.5.

Yearlynumber

ofbatterycycles

Consumption limit in DSS in kW

100 105 110 115 120 125 1300

20

40

60

80

100

120

140

Fig. 8.3: Number of full battery cycles for different consumption limits inDSS.

In order to guarantee a long lifetime of the BESS, care must be taken in theoperation of the battery. Especially high DODs lead to accelerated ageingof the battery [14]. In figure Fig. 8.3 the number of battery cycles is shown


if the BESS is operated with the described Peak Shaving Algorithm. Equa-tion 3.8 is used for the calculation of the number of full cycles during thewhole year. For the storage capacity of the battery C only the usable bat-tery capacity is considered (C = 0.6 Cbat). Fig. 8.4 shows the distributionof the DODs of all cycles during a year. Cycles with a DOD between 30%and 35% occur most frequently. If information about the impact of differentDODs on the ageing of the battery is available, the effect of the overall DODdistribution on the lifetime of the battery could be quantified. The numberof battery cycles during a year and the distribution of DODs can be usedas benchmarks to assess the performance of the Peak Shaving Algorithm.These benchmarks can also be used to tune the parameters in the objectivefunction in order to lower the stress for the BESS and extend the batterylife.

Number

ofcycles

Depth of Discharge (DOD)

0 0.2 0.4 0.6 0.8 1 1.2 1.40

10

20

30

40

50

60

70

Fig. 8.4: Distribution of DODs of BESS in DSS.

8.3. PEAK SHAVING 77

SOC

Tim

ein

h

Loa

dC

har

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atte

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rid

Fee

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PV

Dis

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0.2

0.4

0.6

0.81

−20

0

−15

0

−10

0

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100

150

200

Fig

.8.5

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Alg

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8.4 Peak Shaving with Dynamic Limits

In the previous section constant grid constraints were assumed in the im-plementation of the Peak Shaving Algorithm. However, the feed-in andconsumption limits in DSS depend on the load and generation in the othernodes of the grid. In chapter 7 the feed-in and consumption limits in DSSduring a whole year were calculated. In this section an algorithm is presentedthat uses the feed-in and consumption limits as constraints in the optimisa-tion problem instead of the constant values Pgrid,load,max and Pgrid,gen,max.For the implementation of this algorithm in the BESS control, informationabout the feed-in and consumption in the whole grid would bed needed.Currently, this information is not available. However, it is planned to installGrid Boxes in the network of the pilot project BESS DSS [13]. With thesedevices the state of the whole grid would be known and a Peak Shaving Al-gorithm with dynamic limits could be implemented. If dynamic grid limitsare considered in the Peak Shaving Algorithm, 25 battery cycles during afull year would be needed to ensure that the voltage does not fall below 0.91pu in the grid.

8.5 Comparison of Algorithms

Peak shaving is used to reduce the peak loading of the grid. In the operationof residential PV battery systems the goal is normally to maximise the rateof self-consumption. Algorithms that maximise the self-consumption canhave adverse effects on the peak loading of the grid. This problem occursif the battery is fully charged until noon on a sunny day in summer. Thenthe full PV power must be fed into the grid [19]. The SOC of the BESSduring a whole year for the Maximise Self-Consumption Algorithm and thePeak Shaving Algorithm is shown in Fig. 8.6 and Fig. 8.7. It can be seenthat the BESS has enough capacity to store the PV generation of the build-ings in DSS. Therefore the conflict of objectives between the Peak ShavingAlgorithm and the Maximise-Self Consumption Algorithm does not occur.Moreover, the plots of the SOC during a full year suggest that a combin-ation of the two algorithms would be worthwhile to use the battery moreefficiently. Otherwise, the battery will be idle in summer (Peak Shaving Al-gorithm) or in winter (Maximise Self-Consumption Algorithm). The Max-imise Self-Consumption Algorithm requires 16 battery cycles and the PeakShaving Algorithm needs 74 battery cycles during the whole year. For thePeak Shaving Algorithm a fixed consumption limit of 110 kW is assumed.

8.5. COMPARISON OF ALGORITHMS 79

SOC


0.2

0.4

0.6

0.8

1

Fig. 8.6: SOC of BESS in DSS for Maximise-Self Consumption Algorithm.

SOC


0.2

0.4

0.6

0.8

1

Fig. 8.7: SOC of BESS in DSS for Peak Shaving Algorithm with fixedconsumption limit of 110 kW.

Chapter 9

Conclusions

In this Master thesis the dimensioning and the optimal control of the batteryenergy storage system (BESS) in the pilot project BESS DSS of the electricutility of the city of Zurich (ewz) is discussed. Furthermore, the PV hostingcapacity of the grid in the pilot project is calculated and different methodsto increase the PV hosting capacity are discussed. In this chapter the mostimportant results of the thesis are summarised.

• A rule-based Battery Sizing Algorithm was developed which determ-ines the minimum amount of storage capacity that is needed to reducethe loading of the cables below a specified threshold. In the first ver-sion of the algorithm a simplified grid independent model is used. Thealgorithm is applicable for the analysis of distribution grids with highPV feed-in and high loads. In the first version of the algorithm it isassumed that the feed-in and consumption limits are not critical onthe same day. With this assumption the cases of high PV feed-in andhigh loads can be treated separately.

• An improved version of the Battery Sizing Algorithm was implemen-ted. The Improved Battery Sizing Algorithm uses model predictivecontrol (MPC) to dispatch the BESS. Voltage constraints were con-sidered in the MPC formulation by calculating the maximum tolerableconsumption and feed-in at the grid connection point of the BESS.

• The results of the dimensioning algorithms showed that a storage ca-pacity of 160 kWh is required to ensure that the voltages in the griddo not fall below 0.91 pu. Voltage violations are more critical in theoperation of the grid than thermal constraints of the lines.

• The effects of future electromobility on the dimensioning of the BESSwere considered. Until 2020 only few electric vehicles are expectedin the area of the pilot project. Nonetheless, high load peaks can be

81

82 CHAPTER 9. CONCLUSIONS

introduced by electric vehicles if fast charging (11 kW) is used. Inthese cases it is important to ensure that the electric vehicles are notcharged during peak load (6 p.m. until 10 p.m.)

• Short circuit currents in the experimental grid configuration were cal-culated. It was found that the short circuit currents in the experi-mental grid configuration are high enough to ensure that the fuses willblow within 120 seconds at the transformer station.

• The PV hosting capacity was computed for the current and the exper-imental grid configurations. In the current configuration no problemsin the grid occur even if the maximum amount of PV generation isinstalled. If the maximum rooftop solar potential is realised, a PVpenetration of 63% can be achieved. In the experimental grid con-figuration voltage limit violations of up to 1.14 pu occur at maximumPV penetration. It was observed that voltage limit violations are morecritical than line loading constraints. The PV hosting capacity in theexperimental grid is 23% if a voltage increase of 1.03 pu is tolerated.One possibility to increase the PV hosting capacity is to consider thegrid impedance in the installation of new PV systems. This wouldmean that houses at the end of long cables would not be allowed toinstall PV systems. However, this approach is not realistic becausedistribution grid operators must provide non-discriminatory access tothe grid for their customers. Therefore, the effects of reactive powercontrol (RPC) were studied in this thesis. With RPC the maximumvoltage in the grid can be reduced to 1.08 pu in the maximum PV pen-etration scenario. A power factor of 0.95 (inductive) was assumed forthe PV inverters. RPC is an interesting option to reduce the voltagesin the experimental grid configuration. A drawback of RPC is theincreased loading of the cables. However, in case of a small PV pen-etration an increased loading of the cables is acceptable.

• A control algorithm for the BESS was developed that maximises theself-consumption in Dora-Staudinger-Strasse (DSS). Since the PV sys-tems in DSS are small compared to the load, only a small increasein the rate of self-consumption and the rate of self-sufficiency can beachieved. Furthermore, a scenario was discussed where the BESS isused to store the surplus production of PV systems that are installedin addition to the existing PV installations in the quarter. It wasfound that the rate of self-consumption of the whole quarter can beincreased from 52% to 62.6% with the BESS.

83

• A peak shaving algorithm was implemented that reduces the peakloading of the grid. With this algorithm it could be verified that thestorage capacity of 500 kWh in DSS is sufficient to reduce the gridloading below 110 kW.

• In order to operate the BESS economically, the battery should provideas many services as possible. In the pilot project it was found thatpeak shaving and the maximisation of the self-consumption rate inDSS can be achieved simultaneously with the BESS.

Chapter 10

Outlook

In this chapter topics for further research are presented.

• If experimental data from the pilot project BESS DSS is available, theresults of this thesis can be tested. All operation modes of the BESSthat are studied in this thesis will be experimentally tested on the realsystem.

• In order to consider voltage constraints in the MPC formulation, thepower flow equations must be included in the formulation of the op-timisation problem. Since the power flow equations are nonlinear indistribution grids, a nonlinear optimisation problem must be solved.It could be investigated if the resulting optimisation problems can besolved with reasonable computational effort by resorting to nonlinearoptimisation software.

• The main focus of this thesis was to study the grid of the pilot projectBESS DSS. The battery sizing algorithms developed in this thesis couldbe applied to other distribution grids. With the results of this analysisit would be possible to create generic plots for the dimensioning of aBESS.

• Economic aspects of the dimensioning and the operation of the BESSin the pilot project were not considered in this thesis. The requiredreduction in battery costs for an economical operation of the BESScould be determined.

• Two different approaches for the integration of battery systems in dis-tribution grids exist: residential batteries and centralised BESS. Thetechnical and economic potentials of distributed and centralised stor-age could be compared.

85

Bibliography

[1] Bundesverwaltung. (25 May 2011) Announcement nuclear phase-out.www.admin.ch/aktuell/00089/?lang=de&msg-id=39337.

[2] M. Dittmar. (2011) The End of Cheap Uranium. www.arxiv.org/abs/1106.3617.

[3] Bundesamt fur Umwelt. (7 January 2013) Revidiertes CO2-Gesetz.www.bafu.admin.ch/klima/12325/12329/index.html?lang=de.

[4] VSE, “Wege in die neue Stromzukunft,” 2012.

[5] ewz, “ewz-Stromzukunft 2012–2050,” November 2012.

[6] Umweltallianz, “Strommix 2035: 100% einheimisch, erneuerbar, effiz-ient,” 2012.

[7] G. Andersson, K. Boulouchos, and L. Bretschger, “EnergiezukunftSchweiz,” ETH Zurich, November 2011.

[8] sda. (14 May 2014) Aus fur Geothermie-Projekt. http://www.nzz.ch/aktuell/schweiz/stadt-st-gallen-stoppt-geothermie-projekt-1.18302266.

[9] D. J. Murphy and C. A. S. Hall, “Year in review — EROI or energyreturn on (energy) invested,” Annals of the New York Academy of Sci-ences, vol. 1185, p. 102–118, 2010.

[10] VSE, “Die Rolle der Pumpspeicher in der Elektrizitatsversorgung,”Basiswissen-Dokument, November 2012.

[11] B. Dunn, H. Kamath, and J.-M. Tarascon, “Electrical Energy Storagefor the Grid: A Battery of Choices,” Science, vol. 334, no. 6058, pp.928–935, 2011.

[12] VSE, “Netzauswirkungen von dezentraler und stochastischer Einspei-sung sowie von Import,” Basiswissen-Dokument, December 2013.

[13] S. Moser and R. Bacher, “GridBox Konzeptstudie,” Bundesamt fur En-ergie, 31 December 2011.

87

www.admin.ch/aktuell/00089/?lang=de&msg-id=39337

www.arxiv.org/abs/1106.3617

www.arxiv.org/abs/1106.3617

www.bafu.admin.ch/klima/12325/12329/index.html?lang=de

http://www.nzz.ch/aktuell/schweiz/stadt-st-gallen-stoppt-geothermie-projekt-1.18302266

http://www.nzz.ch/aktuell/schweiz/stadt-st-gallen-stoppt-geothermie-projekt-1.18302266

88 BIBLIOGRAPHY

[14] M. Koller, T. Borsche, A. Ulbig, and G. Andersson, “Defining a De-gradation Cost Function for Optimal Control of a Battery Energy Stor-age System,” PowerTech (POWERTECH), 2013 IEEE Grenoble, 16-20June 2013.

[15] S. Hashemi, J. Ostergaard, and G. Yang, “Effect of reactive power man-agement of pv inverters on need for energy storage,” in PhotovoltaicSpecialists Conference (PVSC), 2013 IEEE 39th, June 2013, pp. 2304–2308.

[16] C. Bucher, J. Betcke, and B. Bletterie, “DiGASP – Distribution GridAnalysis and Simulation with Photovoltaics,” Bundesamt fur EnergieBFE, 23 January 2014.

[17] C. Bucher, G. Andersson, and L. Kung, “Increasing the PV Hosting Ca-pacity of Distribution Power Grids – A Comparison of seven methods,”Paris, France, 2013.

[18] L. Kung. (15 November 2012) Demand Side Management: Erfahrun-gen und Einschatzungen der ewz. www.saee.ethz.ch/events/conference2012/Folien Lukas Kung.pdf.

[19] J. Weniger, T. Tjaden, and V. Quaschning, “Sizing and Grid Integra-tion of Residential PV Battery Systems,” HTW Berlin - University ofApplied Sciences, PVSEC 2013.

[20] M. Koller, “Optimierte Betriebsfuhrung eines 1 MW Batteriespeichersim Verteilnetz,” Master’s thesis, ETH Zurich, November 2012.

[21] D. Andrea, Battery Management Systems for Large Lithium-Ion Bat-tery Packs. Artech House, 2010.

[22] C. Williams, J. Binder, M. Danzer, F. Sehnke, and M. Felder, “Bat-tery Charge Control Schemes for Increased Grid Compatibility of De-centralized PV Systems,” Zentrum fur Sonnenenergie- und Wasserstoff-Forschung Baden-Wurttemberg, PVSEC 2013.

[23] M. Morari, “Introduction to MPC,” Lecture slides of course Model Pre-dictive Control at ETH Zurich, 2014.

[24] R. D. Zimmermann and C. E. Murillo-Sanchez, “MATPOWER 4.1,”User’s Manual, December 14 2011.

[25] R. Itschner. (March 21 2012) ABB – Ein Pionier im Bereich Batter-iespeichersysteme. www.ekz.ch.

[26] M. Ruh, “Power Flow Calculation in Distribution Systems,” Lectureslides of course Power System Analysis at ETH Zurich, 2013.

[27] KNITRO website. www.ziena.com/knitro.htm.

www.saee.ethz.ch/events/conference2012/Folien_Lukas_Kung.pdf

www.saee.ethz.ch/events/conference2012/Folien_Lukas_Kung.pdf

www.ekz.ch

www.ziena.com/knitro.htm

BIBLIOGRAPHY 89

[28] P. Fortenbacher, “Power Flow Modeling and Grid Constraint Handlingin Power Grids with High RES In-feed, Controllable Loads and StorageDevices,” Master’s thesis, ETH Zurich, November 7 2011.

[29] J. Lofberg, “Yalmip : a toolbox for modeling and optimization in mat-lab,” in Computer Aided Control Systems Design, 2004 IEEE Interna-tional Symposium on, Sept 2004, pp. 284–289.

[30] CPLEX website. http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer.

[31] Neplan website. www.neplan.ch/html/e/e home.htm.

[32] C. Bucher and G. Andersson, “Generation of Domestic Load Profiles –an Adaptive Top-Down Approach,” Istanbul, Turkey, 2012.

[33] C. Bucher, J. Betcke, G. Andersson, B. Bletterie, and L. Kung, “Simu-lation of Distribution Grids with Photovoltaics by Means of StochasticLoad Profiles and Irradiance Data,” Frankfurt, Germany, 2012.

[34] Meteonorm website. www.meteotest.ch/geschaeftsbereiche/sonnenenergie/meteonorm.

[35] M. Piffaretti. (September 26 2012) E-Infrastrukturen am BeispielZurich. www.mobilityacademy.ch.

[36] M. D. Galus, G. Georges, and R. A. Waraich, “ARTEMIS Ab-schlussbericht,” ETH Zurich, 2013.

[37] M. Gonzalez Vaya, M. Galus, R. Waraich, and G. Andersson, “On theinterdependence of intelligent charging approaches for plug-in electricvehicles in transmission and distribution networks,” in Innovative SmartGrid Technologies (ISGT Europe), 2012 3rd IEEE PES InternationalConference and Exhibition on, Oct 2012, pp. 1–8.

[38] Stadt Zurich, “Mobilitat in Zahlen: Ubersicht Kennzahlen, FokusStadtevergleich Mobilitat,” 2012.

[39] Stadt Zurich website. Solar cadastre. http://www.solarkataster.stadt-zuerich.ch/zueriplan/mapSolar.aspx.

[40] ewz, Minimum short circuit currents for different cables, 2014, Tech-nical documentation.

[41] P. A. Jonas, “Predictive Power Dispatch for 100% Renewable ElectricityScenarios using Power Nodes Modeling Framework,” Master’s thesis,ETH Zurich, July 2011.

[42] swissgrid, “Die 7 haufigsten Fragen zur Eigenverbrauchsregelung,”March 2014.

http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer

http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer

www.neplan.ch/html/e/e_home.htm

www.meteotest.ch/geschaeftsbereiche/sonnenenergie/meteonorm

www.meteotest.ch/geschaeftsbereiche/sonnenenergie/meteonorm

www.mobilityacademy.ch

http://www.solarkataster.stadt-zuerich.ch/zueriplan/mapSolar.aspx

http://www.solarkataster.stadt-zuerich.ch/zueriplan/mapSolar.aspx

Source Code

The organisation of the source code mirrors the chapters of this thesis. AllMATLAB files are located in the folder Matpower. The following folderscontain the main results of this thesis.

• pvHostingCapacity

• genericAnalysis

• caseStudyWolfswinkel

• storageAlgorithms

Furthermore, the NEPLAN simulation files for the PV and short circuitstudies are located in the folder Neplan.

Wherever possible open source tools and standards were used to documentthe results of this thesis. This should ensure that the files will be readablein the future.

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Acknowledgements

First of all I would like to thank Professor Goran Andersson for his supportin finding the topic of my Master thesis.

Furthermore, I would like to gratefully acknowledge Christof Bucher andPhilipp Fortenbacher for their guidance and support during this thesis. Itwas a pleasure to learn from their great expertise in PV grid integration andbattery systems during the past six months.

I would like to extend my thanks to the Electric Utility of the city of Zurich(ewz) for the access to information about the pilot project BESS Dora-Staudinger-Strasse.

Special thanks should be given to Marina Gonzalez Vaya for providing thedata about future electromobility in the city of Zurich.

Finally, I wish to thank my parents and my brother for their support andencouragement throughout my studies.

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eeh - eth zürich · eeh power system s laboratory ... increases or newly installed pv systems...

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