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Hybrid Wind, Solar and Storage Power Plant  in Electricity Market 

Emilio Barrachina Gascó

DTU Wind Energy Master-0010

July 2020

DTU Wind Energy Master - 0010August 2020

2nd version

Hybrid Wind, Solar and StoragePower Plant in Electricity MarketDK2 and SE2 Hybrid Power Plants

Supervisors:Professor Poul Ejnar SørensenResearcher Kaushik DasExternal supervisor:Professor Henrik StiesdalStudent:Emilio Barrachina Gascó

Fanzara, SpainAugust 6th 2020

b

Preface

This master thesis was prepared at DTU Wind Energy Department at the TechnicalUniversity of Denmark in fulfillment of the requirements for acquiring the DTU WindEnergy Master diploma.

The supervisors for this master thesis have been professor Poul Ejnar Sørensen andresearcher Kaushik Das, both from DTU Wind Energy Department.

The external supervisor for this master thesis has been professor Henrik Stiesdal fromStiesdal A/S company.

The complete period of work for this master thesis development has been five months,starting on February 2020 till the end of June 2020.

Fanzara, SpainJuly 1st, 2020

Emilio Barrachina GascóMEng and MSc

emilio.barrachinagasco@iicv.es

Abstract

During the last two decades we have seen how the renewable energy sources have beenexpanded all around the world evolving hand in hand because of the increase of envi-ronmental conscious market acceptance. These renewable energy sources have a greatchallenge which is the inherent variability as they have stochastic renewable powergeneration.

The unreliability of wind and solar photovoltaic to generate the energy demandedis the main reason for including energy storage technologies so that we gain dispatchabil-ity of WPPs and PVPPs. Energy storage systems have a really special advantage in thissense, as they can shift energy generation to later periods with higher electricity pricesand energy demand.

We are living really interesting times if we talk about hybrid wind and solar withenergy storage systems and I’m pretty sure they’re going to be crucial in the near future,and so this can happen, integration of several energy storaging within the hybrid windand solar pv plants is key to boost penetration of these technologies into the energymarket.

Index termsCorRES, time series, hybrid wind and solar pv power plant, battery energy storage sys-tem, wind power, solar photovoltaic power, correlation coefficient, spot market pricesand RES negative correlation, power and energy surplus, bidding model methodol-ogy, real time model methodology, forecast error minimization, reducing curtailment,reducing penalties.

Future perspectives

Last summer when I was attending to the Wind Energy Master Graduation week eventat DTU Risø Campus many seminars were offered within its fantastic and completeprogram prepared by Merete Badger and Nina Julh Madsen. Among them a HenrikStiesdal’s seminar aimed to last year finalist master students, Master of Wind Energy:What now?.

After listening to Henrik I decided that in my master thesis he had to be includeddue to his huge impact within the wind energy field of course, and for his impressiveenthusiasm communicating, vision of the future and extensive renewable knowledge in afield as exciting as wind energy, that will undoubtedly mark the future in a significantway for the next generations.

So we have had recently a phone call interview talking about many interesting andrelevant energy matters as follows. Needless to say that I am deeply grateful for the kindattention that Henrik Stiesdal has always had with me in the course of this master thesis.

Henrik Stiesdal is going to place some thermal storage technology in the first world’senergy artificial island which is going to be located at the Danish North Sea. It’s A10 [GW ] offshore wind and host electricity storage and power-to-X as well as housing, OMfacilities and HVDC converters for transmission and interconnectors. More informationcan be consulted at Copenhagen Infrastructure Partners’s website.

This project is going to be developed in 3 stages in line with increasing Danish electricitydemand so Henrik Stiesdal’s participation will take place once the project has progressedthrough the several phases planned but Henrik has already provided the project promotera pilot for phase one. Stiesdal Storage Technologies A/S has developed a thermal battery,a grid-scale energy storage concept that can provide a backup to RES for a much longerperiod than conventional batteries do.

iv Future perspectives

Talking to Henrik Stiesdal I asked him about HPP technologies and future perspectivesso he thinks that:

"HPP technology is going to be necessary in most places of the world. You can al-ways introduce more load we could say that we solve things by avoiding having overflowor wind and solar power production by having battery systems so then we can use theexcess power. But unless you accept very low efficiency of power rates then you’ll haveproblems in regenerating when you don’t have enough wind and solar. So the half of theproblem is the overflow but we can’t avoid the other half of the problem that’s the undersupply of energy and for that we need storage. Storage of electrical energy promises tomake wind and solar power more viable by offering a solution to the fluctuations in theenergy supply they produce".

"At the present time there is no a direct energy market for storage but it wasn’t awind market when we started there it was all about the subsidies in the early years, therewere subsidies in California, subsidies in Denmark and then the market came, and thesame will happen with the storage, it has to be some kind of support mechanism".

"But of course you can also have good part of your revenue from capacity, in otherwords you’re always ready to deliver power even though you might not need it now youactually get paid per kWmonth basis rather than per kWh basis. And the capacity marketis quite huge in many parts of the world, at least in the US, so in a well defined capacitymarket over there where you get a certain payment per kW and then we can do. So I dothink that can help in the distance case, so it’s not purely upping fast buying cheaper andselling expensive".

Finally talking to Henrik about seasonal storaging and the variable demand that powerproduction has to face at every single moment he says: "normally say that we need threetype of storages: the short terms that batteries can do, that’s like half an hour or an houror so and then we need the mid-term that’s what we do with thermal storage as that cando for a week, and then we need the seasonal storage which have to rely on chemistry soit’s all about hybrid them and that can last of course. There is of course a limit on theamount we can store but then we have ammonia instead, which is easy to keep so thatway for sure we’ll be able to solve it with these three types of storages"

Henrik StiesdalWind power pioneerCEO Stiesdal A/SGWECAmbassador

TaksigelserJeg vil gerne udtrykke min dybe og respektfulde tak til mine vejledere på hovedopgaven:Professor Poul Ejnar Sørensen og Researcher Kaushik Das fra DTU Vindenergi, for deresfantastiske vejledning, assistance, hjælp og støtte gennem hovedopgaven.Især i dissemåneder med COVID-19 med alt dette har forårsaget og konditioneret arbejdet meduniversitetsundervisning og -forskning. Jeg værdsætter højt deres markante bidrag i formaf tid og ideer, hvilket har gjort, at dette interessante arbejde har været en stimulerendeog berigende oplevelse gennem 5 måneder.

Jeg vil også gerne udtrykke min dybtfølte tak til min eksterne vejleder: Henrik Stiesdal,vindpioner fra Stiesdal A/S, for hans værdifulde samarbejde, venlig støtte og rådgivningunder dette arbejde.

Jeg vil også gerne udtrykke min tak til Researcher Matti Juhani Koivisto og Post-doc Juan Pablo Murcia León fra DTU Vindenergi for deres venlige bidrag og støtte iform af prognoser og målt energiproduktion til brug for denne hovedopgave.

Jeg vil gerne udtrykke min tak til Senior Scientist, Head of Master Studies, Merete Badgerhende altid fremragende effektiv opmærksomhed. Jeg vil gerne udtrykke min tak tilSekretær og Master Programme Coordinator Nina Juhl Madsen hende altid fremragendeeffektiv opmærksomhed.

Jeg vil gerne udtrykke min tak til alle undervisere involveret i DTU Master i Vin-denergi for deres entusiasme, dedikation og undervisning igennem de 9 kurser.

Jeg vil gerne udtrykke min tak til mine medstuderende: Didde Okholm Kvorning,Peter Fausbøll, Stephan Johannes Østergaard, José Alberto Navarro Martínez og LaínNieto Gómez. Det var virkelig en fornøjelse at være i masterprogrammet sammen med dig.

Jeg vil gerne udtrykke min tak til min ven, universitetskollega og forsker: Jaime ZabalzaOstos fra Centre for Signal and Image Processing - University of Strathclyde(Glasgow,UK),for hans gode råd i forbindelse med udarbejdelsen af denne hovedopgave.

Jeg vil gerne udtrykke min dybeste taknemmelighed til min moder, Juana Gascó Vivas,til min fader, Emilio Barrachina Martí og til min tante Encarna Gascó Vivas, for al deresindsats i form af at give mig min uddannelse.

Jeg vil gerne udtrykke min dybeste taknemmelighed til min onkel, Juan Carlos GascóVivas, for hans super støtte og gode eksempel på kamp, som for os alle er med hansdaglige indsats i de vanskelige øjeblikke, som livet har sat ham igennem.

vi Taksigelser

Jeg vil gerne udtrykke min dybeste tak til min elskede kæreste, Sonia Marmaneu Vidal,for al hendes forståelse og støtte under sådant et tidskrævende arbejde.

Sidst, men ikke mindst, tak til Audrey, min elskede hund, som har været sammenmed mig i 13 år, men som desværre gik bort i april måned.

Oversættelse foretaget af min ven og medlærer, Peter Fausbøll

AcknowledgementsI would like to express my most sincere and respectful gratitude to my master thesis supervisors:Professor Poul Ejnar Sørensen and Researcher Kaushik Das from DTUWind Energy department,for their fantastic guidance, assistance, help and support throughout this master final project.Especially during these months of COVID-19 with all that this has caused and conditioned thework of university teaching and research. I really appreciate their notable contributions of timeand ideas provided, which have made this interesting work a very stimulating and enrichingexperience during these 5 months period.

I would like also to express my sincere thanks to my external supervisor: Henrik Stiesdal, windindustry pioneer from : Stiesdal A/S company, for their valuable cooperation, kind supportand advise during this work.

I would like like to express my gratitude to researcher Matti Juhani Koivisto and postdocJuan Pablo Murcia León from DTU Wind department for their kind contribution and supportproviding forecast and measured power production data for this master thesis.

I would like to express my more sincere thanks to senior scientist and Head of Master StudiesMerete Badger her always excellent efficient attention.

I would like to express my more sincere thanks to secretary and Master Programme Co-ordinator Nina Juhl Madsen her always excellent efficient attention.

I would like to express my sincere thanks to all the professors involved in the DTU Mas-ter in Wind Energy for their enthusiasm, dedication and teaching all along the 9 courses.

I would like to express my gratitude to my master colleagues: Didde Okholm Kvorning,Peter Fausbøll, Stephan Johannes Østergaard, José Alberto Navarro Martínez y Laín NietoGómez, it has been a real pleasure sharing with them all this master time.

I would like to express my gratitude to my friend, university colleague and great researcher:Jaime Zabalza Ostos from Centre for Signal and Image Processing - University of Strathclyde(Glasgow,UK), for all his wise advise all along this master thesis work.

I would like to express my deepest gratitude to my mother, Juana Gascó Vivas, to myfather, Emilio Barrachina Martí and to my auntie Encarna Gascó Vivas, for all their effortshaving given me the education they gave me.

I want to express my deepest gratitude to my uncle, Juan Carlos Gascó Vivas, for his supersupport and great example of struggle that for all of us is with his daily effort in the difficult

viii Acknowledgements

moments that life has put him through.

I would like to express my deepest gratitude to my beloved girlfriend, Sonia MarmaneuVidal, for all her understanding and support in such a time-demanding job.

Last but not least to Audrey, my beloved dog who has been together with me during these last13 years that sadly passed away last April month.

AgradecimientosDeseo expresar mi más sincera y respetuosa gratitud a mis supervisores de tesina: CatedráticoPoul Ejnar Sørensen y al Doctor Kaushik Das del departamento DTU Wind Energy por sufantástica guía, asistencia, ayuda y apoyo durante esta tesina. Especialmente durante estosmeses de COVID-19 con todo lo que ello ha ocasionado y condicionado las labores de docenciauniversitaria e investigación. Realmente aprecio sus notables contribuciones e ideas aportadas,las cuales han hecho de este interesante trabajo una experiencia muy estimulante y enriquecedoradurante estos 5 meses.

Querría expresar mi más sincero agradecimiento a mi supervisor externo de tesina: Hen-rik Stiesdal, pionero de la industria eólica de la compañía: Stiesdal A/S, por su valuosacooperación, apoyo y consejo durante este trabajo.

Me gustaría expresar mi gratitud al científico investigador Matti Juhani Koivisto y al post-doctorando Juan Pablo Murcia León del departamento DTU Wind Energy por su amablecontribución y apoyo facilitando los datos de la previsión y medida de producción de potenciautilizados en esta tesina.

Deseo expresar mi más sincero agradecimiento a la investigadora científica senior y Direc-tora del Máster Merete Badger su siempre excelente atención.

Querría expresar mi más sincero agradecimiento a la secretaría y Coordinadora del MásterNina Juhl Madsen su siempre excelente atención.

Me gustaría trasladar mi más sincero agradecimiento a todos los profesores implicados enel máster de energía eólica de la DTU, por su entusiasmo, dedicación y enseñanzas a lo largode los nueve cursos que comprende este máster.

Quiero expresar mi gratitud a mis compañeros de clase: Didde Okholm Kvorning, PeterFausbøll, Stephan Johannes Østergaard, José Alberto Navarro Martínez y Laín Nieto Gómez,ha sido un verdadero placer compartir con ellos todo este tiempo en el máster.

Querría expresar mi gratitud a mi gran amigo, compañero de carrera and gran científicoinvestigador: Jaime Zabalza Ostos del Centre for Signal and Image Processing - Universityof Strathclyde (Glasgow,Reino Unido), por sus sabios consejos durante todo el trabajo de latesina.

Querría expresar mi más profunda gratitud y reconocimiento a mi madre, Juana GascóVivas, a mi padre, Emilio Barrachina Martí y a mi tía Encarna Gascó Vivas, por todos susesfuerzos para ofrecerme toda la educacion que ellos me han dado.

x Agradecimientos

Deseo expresar mi más profunda gratitud a mi tío Juan Carlos Gascó Vivas, por su apoyo ygran ejemplo de lucha que para todos nosotros és en su esfuerzo diario en los momentos difícilesque la vida le ha hecho pasar.

Querría expresar mi más profunda gratitud a mi querida novia, Sonia Marmaneu Vidal,por toda su comprensión y apoyo en un trabajo tan demandante de tiempo.

Por último pero no por ello menos importante a Audrey, mi querida perra quien ha estadoconmigo durante estos últimos trece años y que tristemente falleció el pasado mes de abril.

List of Figures1.1 Hybrid power plant + battery energy storage system . . . . . . . . . . . . . . . . . 21.2 Topology of connected HPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Types of HPPs: integration and operation of different generating modules . . . . . 31.4 World HPP and RES capacity share in 2024 . . . . . . . . . . . . . . . . . . . . . 3

2.1 Danish energy system - June 29th 2020 18:51 hrs . . . . . . . . . . . . . . . . . . . 5

3.1 Nordic area wind conditions - July 29th 2020 6.51 pm . . . . . . . . . . . . . . . . 83.2 DK2 HPP location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.3 DK2 HPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.4 SE2 HPP location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.5 SE2 HPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.6 CorRES block diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.7 Scatter plot: Power generated per hour vs El. Spot market prices . . . . . . . . . 13

5.1 Bidding model algorithm flowchart . . . . . . . . . . . . . . . . . . . . . . . . . . . 215.2 Bidding model algorithm flowchart - Charging detail . . . . . . . . . . . . . . . . . 215.3 Bidding model algorithm flowchart - Discharging detail . . . . . . . . . . . . . . . 225.4 Bidding model algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235.5 DK2 Bidding - Scenario 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255.6 DK2 Bidding - Scenario 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255.7 DK2 forecast - Scenario 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.8 DK2 forecast - Scenario 9 - P_LIMbatt = 50MW . . . . . . . . . . . . . . . . . . 265.9 DK2 forecast - Scenario 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275.10 DK2 forecast - Scenario 9 - P_LIMbatt = 50MW . . . . . . . . . . . . . . . . . . 275.11 DK2 forecast - Scenario 9 - P_LIMbatt = 100MW . . . . . . . . . . . . . . . . . . 285.12 Real time model algorithm flowchart . . . . . . . . . . . . . . . . . . . . . . . . . . 305.13 DK2 measured - Scenario 9 - P_LIMbatt = 5MW . . . . . . . . . . . . . . . . . . 325.14 DK2 measured - Scenario 9 - P_LIMbatt = 100MW . . . . . . . . . . . . . . . . . 325.15 DK2 measured - Scenario 9 - P_LIMbatt = 5MW and P_LIMbatt = 10MW . . . 335.16 DK2 measured - Scenario 9 - P_LIMbatt = 50MW . . . . . . . . . . . . . . . . . 335.17 Days with low Psurplus(t)− Pbatt = 5 and 10 [MW ] . . . . . . . . . . . . . . . . . . 345.18 Days with low Psurplus(t)− Pbatt = 50 and 100 [MW ] . . . . . . . . . . . . . . . . . 345.19 Full model algorithm flowchart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6.1 DK2 Forecast duration curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386.2 DK2 Measured duration curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396.3 DK2 P_HPP_F (t) - P_LIMbatt = 5[MW ] Ebatt = 100[MWh] . . . . . . . . . . 406.4 DK2 P_HPP_F (t) - P_LIMbatt = 25[MW ] Ebatt = 100[MWh] . . . . . . . . . 416.5 DK2 P_HPP_F (t) - P_LIMbatt = 50[MW ] Ebatt = 100[MWh] . . . . . . . . . 41

xii List of Figures

6.6 DK2 P_HPP_F (t) - P_LIMbatt = 75[MW ] Ebatt = 100[MWh] . . . . . . . . . 426.7 DK2 P_HPP (t) - P_LIMbatt = 5[MW ] Ebatt = 100[MWh] . . . . . . . . . . . . 436.8 DK2 P_HPP (t) - P_LIMbatt = 25[MW ] Ebatt = 100[MWh] . . . . . . . . . . . 436.9 DK2 P_HPP (t) - P_LIMbatt = 50[MW ] Ebatt = 100[MWh] . . . . . . . . . . . 446.10 DK2 P_HPP (t) - P_LIMbatt = 75[MW ] Ebatt = 100[MWh] . . . . . . . . . . . 44

List of Tables3.1 DK2 and SE2 RES scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2 DK2 HPP correlation coefficients - Power production per hour vs Spot market prices 133.3 SE2 HPP correlation coefficients - Power production per hour vs Spot market prices 143.4 Capacity factor and correlation of wind and solar power plant . . . . . . . . . . . 14

4.1 DK2 and SE2 RES E_RES_F - Scenario 9 - Forecast . . . . . . . . . . . . . . . . 164.2 DK2 and SE2 RES E_RES - Scenario 9 - Measured . . . . . . . . . . . . . . . . . 164.3 DK2 and SE2 RES CF and FWH - Scenario 9 - Forecast . . . . . . . . . . . . . . 164.4 DK2 and SE2 RES CF and FWH - Scenario 9 - Measured . . . . . . . . . . . . . . 164.5 DK2 and SE2 RES E_surplus_F - Scenario 9 - Forecast . . . . . . . . . . . . . . 174.6 DK2 and SE2 RES E_surplus - Scenario 9 - Measured . . . . . . . . . . . . . . . . 17

6.1 DK2 Ecurtailed_F (t) [GWh/year] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466.2 SE2 Ecurtailed_F (t) [GWh/year] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

7.1 DK2 and SE2 Scenario 9 P_RES_FE(t): MAE_RES_FE(t) and RMSE_HPP_FE(t) 497.2 DK2 Scenario 9 P_HPP_FE(t): MAE_HPP_FE(t) and RMSE_HPP_FE(t) 507.3 SE2 Scenario 9 P_HPP_FE(t): MAE_HPP_FE(t) and RMSE_HPP_FE(t) 507.4 DK2 and SE2 Scenario 9 P_HPP_FE(t) reduction[%] . . . . . . . . . . . . . . . 50

1 DK2 RES Scenarios comparison - Energy Produced and Surplused . . . . . . . . . 652 DK2 RES Scenarios comparison - Energy Produced Measured, Surplused and Curtailed 653 DK2 RES Scenarios comparison - Energy Produced Measured, Surplused and Curtailed 664 SE2 RES Scenarios comparison - Energy Produced and Surplused . . . . . . . . . 675 SE2 RES Scenarios comparison - Energy Produced and Surplused . . . . . . . . . 676 SE2 RES Scenarios comparison - Energy Produced and Surplused . . . . . . . . . 68

AEP Annual Energy Production [TWh/year]

BESS Battery Energy Storage System

CHP P Hybrid power plant cost [MW/Me]

CHP P_F Hybrid power plant forecast cost [MW/Me]

Crate Rate at which a battery is charged/discharged relative to its maxi-mum capacity [hour]

CF Capacity Factor measured [%]

CF_F Capacity Factor forecast [%]

CorRES Correlations in Renewable Energy Sources

DA Day Ahead

Ebatt Battery Energy Capacity [MWh]

Ecurt Annual Energy Curtailed measured [GWh/year]

Ecurt_F Annual Energy Curtailed forecast [GWh/year]

Ecurt_F Annual Energy Surplus forecast [GWh/year]

Esurplus Annual Energy Surplus measured [GWh/year]

FWH Full work hours [hours/year]

GWEC Global Wind Energy Council

HPP Hybrid Power Plant

HPS Hybrid Power System

MAE Mean Absolut Error [MW]

MAFE Mean Absolut Forecast Error [MW]

P_HPP (t) Hybrid Power Produced [MW]

P_HPP_F (t) Hybrid Power Produced Forecast [MW]

P_HPP_FE(t) Hybrid Power Produced Forecast Error [MW]

Nomenclature

xvi Nomenclature

P_LIM batt Battery charging/discharging capacity [MW]

P_SOC headroom(t) State Of Charge Headroom Power [MW]

P_SOC headroom_F (t) State Of Charge Headroom Power Forecast [MW]

P RES(t) Wind and Solar Power Production Generated [MW]

P RES_F (t) Wind and Solar Power Production Forecast [MW]

Pcharge(t) Power Charged [MW]

Pcurt(t) Power Curtailed Generated [MW]

Pcurt_F (t) Power Curtailed Forecast [MW]

Pdischarge(t) Power Discharged [MW]

Pdischarge max(t) Maximum Power Discharge [MW]

Pgrid Power Grid Connection Constrain [MW]

Pgrid headroom(t) Grid Headroom Power [MW]

Pmissing(t) Power Missing Generated [MW]

Pshortage(t) Power shortage Generated [MW]

Psurp(t) Power Surplus Generated [MW]

Psurp_F (t) Power Surplus Forecast [MW]

PP Power Plant

PV PP Solar Photovoltaic Power Plant

R Correlation coefficient

RES Renewable Energy Source

RMSE Root Mean Square Error [MW]

RMSFE Root Mean Square Forecast Error [MW]

SOC(t) State of Charge measured [p.u.]

SOC(t)_F State of Charge forecast [p.u.]

SOC0(t) Initial State of Charge measured [p.u.]

SOC0_F (t) Initial State of Charge forecast [p.u.]

TSO Transmission System Operator

V RE Variable Renewable Energy

WPP Wind Power Plant

ContentsPreface i

Abstract ii

Future perspectives iii

Taksigelser v

Acknowledgements vii

Agradecimientos ix

List of Figures xi

List of Tables xiii

Nomenclature xiv

Contents xvii

1 Introduction 11.1 Aim and motivation of the study . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Objective of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 HPP with BESS design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Advantages/values of hybrid power plants . . . . . . . . . . . . . . . . . . . . . 41.5 Scope of this master thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Background 52.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 What services can energy batteries provide? . . . . . . . . . . . . . . . . . . . 7

3 DK2 and SE2 HPP 83.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.2 DK2 power plant details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.3 SE2 power plant details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.4 Hybrid power plant scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.5 RES correlation study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4 HPP Case Scenario Study 164.1 Study case: Scenario 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

xviii Contents

4.2 CorRES time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5 Bidding, Real and Full model Algorithms 195.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195.2 Bidding model algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205.3 Real time model algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295.4 Full model algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6 Data Analysis 376.1 Duration curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376.2 Spot market bidding methodology . . . . . . . . . . . . . . . . . . . . . . . . . 406.3 Hybrid power production real time measured . . . . . . . . . . . . . . . . . . . 436.4 Energy surplus forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.5 Energy curtailed forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.6 Capacity factor forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

7 Forecast Error 487.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487.2 Forecast error metrics: MAFE and RMSFE . . . . . . . . . . . . . . . . . . . . 49

8 Conclusions and Future work 518.1 Main important aspects learnt . . . . . . . . . . . . . . . . . . . . . . . . . . . 518.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 518.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528.4 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Bibliography 54

Appendix A: Matlab Code 57

Appendix B: Measured Scenarios Comparison 65DK2 HPP - Measured . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65SE2 HPP - Measured . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

CHAPTER 1Introduction

1.1 Aim and motivation of the studyThe aim of this master thesis is to develop an algorithm methodology to minimize the hybridpower produced forecast error so it’s important to keep in mind that I’m not presenting anoptimization model in this work. I’ll show the dependency of the battery size used for thispurpose so using the forecast error we will reduce its variability. We will see what positiveeffects adding a battery within a hybrid wind and solar power plant has, its sensitivity effect,having as main advantages these:• reduction of the curtailment produced, bigger battery size less curtailment.

• repair of the forecast error

For that purpose I’m using a 1 Day Ahead, 1DA, power production forecast for wind powerplant, WPP, and for solar photovoltaic power plant, PVPP, too. So we will see how muchstorage system I’m including at the end.

This kind of forecasting is a medium term forecast type, being its key applications: scheduling,reserve requirement, market trading and congestion management,T. Tian and I. Chernyakhovskiy[16].

We have to take into account how every single scenario is affecting in the curtailment producedby each one. We must also take into account that the amount of curtailed energy dependson my algorithm design decision and the amount of energy generated, when more energy isgenerated then you also curtail more.

1.2 Objective of the studyTo develop a methodology model for the hybrid power plant, HPP, to bid in the spot marketand then minimize the forecast error in the real time model so we can reduce the appliedassociated penalties due to this error produced.

When playing with different battery sizes we will see how it’s affecting to power curtaileddrawing the important conclusion that the bigger the battery size is the better in terms ofcurtailment reduction.

It goes without saying that this increase in battery size will directly incur the consequentincrease in the price of energy storage system. It is important to keep in mind that the cost ofstorage continues to fall We only have to look at the storage price evolution.

2 1 Introduction

1.3 HPP with BESS designHere in Figure 1.1 we can see how the hybrid power plant, HPP, is designed including thebattery energy storage system, BESS:

Figure 1.1: Hybrid power plant + battery energy storage system

Here in Figure 1.2 we can see the AC and DC coupled HPP with BESS designs. Being themain advantage from AC connected HPP its ease of expansion as it enables more systems tobe added in parallel to either increase the storage power or the overall HPP capacity. Thosedesigns are out of this master thesis scope but if interested further relevant content can befound at Kaushik Das et al.[6].

(a) AC connected HPP (b) DC connected HPP

Figure 1.2: Topology of connected HPP

There are mainly two HPP configurations which are is the mostly developed right one, whilethe left one is an alternative version where solar converters can be removed in some situations.

1.3 HPP with BESS design 3

Figure 1.3: Types of HPPs: integration and operation of different generating modules

(a) Global map with HPP locations and types (b) Capacity share of wind, solar and storage tech-nologies in 2024

Figure 1.4: World HPP and RES capacity share in 2024

All this information presented in Figures 1.3 and 1.4 has been extracted from Wind Europereport, Renewable Hybrid Power Plants - Exploring the Benefits and Market Opportunities -July 2019 elaborated by Wind Europe.

4 1 Introduction

1.4 Advantages/values of hybrid power plantsThe main advantages are cost reduction and revenue increase.• Reduction in land cost

• Optical use of electric infrastructure and other infrastructure saves costs.

• Joint permitting process reduces risks and costs

• Shared resources reduce internal costs

• Joint site development reduces costs

• Less fluctuating production increases electrical infrastructure utilization

• Storage increases flexibility and number of accessible markets (Energy market, ancillaryservices market)

• Reduction of Forecast Error using storage

1.5 Scope of this master thesisThis report is divided in the following chapters:• Chapter 1 presents introduction

• Chapter 2 includes background

• Chapter 3 presents DK2 and SE2 HPP locations

• Chapter 4 presents scenario 9 study

• Chapter 5 includes Spot Market Bidding and Operational methodologies

• Chapter 6 includes data analysis scenario 9 study

• Chapter 7 presents forecast error P_HPP_FE(t)

• Chapter 8 are drawn the main master thesis conclusions

CHAPTER 2Background

2.1 Introduction

Figure 2.1: Danish energy system - June 29th 2020 18:51 hrsEnerginet.dk website

I’ve decided to start with this fantastic picture above, where the Danish Energy System can beperfectly understood. It was indeed one of the first figures commented at the very beginningof this master thesis work in our supervisory meeting. Show us the importance of the energyconnections between neighbouring countries and their energy tradings, with import and exportenergy.

Penetration rates of renewable energy sources, RES, expanded during the last years dueto increased environmental conscience and market acceptance. Another benefit of combiningwind and solar photovoltaic power from the TSO’s point of view is a better utilization of gridinfrastructure and reduced congestion: improving security of energy supply and system stabilityKaushik Das et. al.[8].

The main challenge with RES is their inherent variability due to the stochastic nature ofthe RES causing some difficulties as severe power ramps, up and down, and increased volatilityin the power generation in general. In future WPPs and PVPPs need to participate more inthe energy markets. Adding energy storage we will increase the dispatchability of RES. Wind

6 2 Background

and solar photovoltaic power don’t reliably produce power on demand, so including energystorage system is a mitigation strategy.

Negative impact associated to stochastic nature of RES can be reduced by coupling WPPsand PVPPs with BESS. Energy storage will be more relevant even where power prizes arecorrelated negatively with renewable power production.

Battery storage is one of several technology options that can enhance power system flexi-bility and enable high levels of renewable energy integration. Studies and real-world experiencehave demonstrated that interconnected power systems can safely and reliably integrate high lev-els of renewable energy from VRE sources without new energy storage resources Thomas Bowen;Ilya Chernyakhovskiy; Paul Denholm[6].

Energy storage allows RES to increase their revenue providing flexibility and time shift-ing of the power production. We have to take into account that increasing the integration ofRES also increases energy curtailment situations due to power overproduction compared togrid connection constrain.

If we store surplus power, Psurplus(t), at times of high output we can improve the RESproduction stabilization.

In order to participate in energy market, VRE sources need to reduce the uncertainty offorecast errors. Inclusion of storage can be a viable option not only to minimize the penaltiesdue to forecast uncertainties but also to maximize the revenue generation Kaushik Das et. al.[4].

Wind and solar photovoltaic power are used in convex optimisation algorithm for makingday ahead decisions on battery operation. One day ahead optimized results are used as inputfor the operating model.

Adding BESS in the WPP and PVPP we have several advantages as:• minimize the penalties due to forecast uncertainties.

• minimize the excess energy spilt.

• maximize the revenue generation.

Including BESS we will reduce the impact of the forecast error associated to wind and solarphotovoltaic power production forecasts so it’ll reduce balancing penalty too.

It’s important to say that this master thesis study is covering the HPP but not the hy-brid power system, HPS, as I’m not taking into account Pdemand(t) neither Edemand(t) in themethodology development scope of this master thesis work.

2.2 What services can energy batteries provide? 7

2.2 What services can energy batteries provide?To answer these questions there is an excellent NREL document called "Grid-Scale Battery Stor-age Frequently Asked Questions" by Thomas Bowen; Ilya Chernyakhovskiy; Paul Denholm[5].

Including this paragraph extracted from this cited document:

["...Arbitrage: Arbitrage involves charging the battery when energy prices are low and dis-charging during more expensive peak hours. For the BESS operator, this practice can provide asource of income by taking advantage of electricity prices that may vary throughout the day.One extension of the energy arbitrage service is reducing renewable energy curtailment. Systemoperators and project developers have an interest in using as much low-cost, emissions-freerenewable energy generation as possible; however, in systems with a growing share of VRE,limited flexibility of conventional generators and temporal mismatches between renewable energysupply and electricity demand (e.g., excess wind generation in the middle of the night) mayrequire renewable generators to curtail their output. By charging the battery with low-costenergy during periods of excess renewable generation and discharging during periods of highdemand, BESS can both reduce renewable energy curtailment and maximize the value of theenergy developers can sell to the market..."]

As we’re going to see all along this master thesis work all this is going to be fully stud-ied and explained completely in detail. We could summarize it saying that using a really bigbattery size we’re making sure that we’re reducing power curtailed but as we’ll find includedwithin the models methodology development presented, it’s not that simple, and many aspectsmust be taken into account to get a complete picture of it.

CHAPTER 3DK2 and SE2 HPP

3.1 IntroductionDK2 and SE2 HPP distance is: 877.06 [Km].

Figure 3.1: Nordic area wind conditions - July 29th 2020 6.51 pmSource:Windy.com

3.2 DK2 power plant details 9

3.2 DK2 power plant detailsDK2 HPP location:Ringsted, Ringsted Municipality, Region Zealand, 4100, Denmark. The geographic coordinatesfor DK2 hybrid power plant are:Latitude: 55o 27’ 8.256" North and Longitude: 11o 49’ 25.429" East

Figure 3.2: DK2 HPP locationSource:Global Wind Atlas

(a) Mean power density (b) Wind speed rose (c) Mean wind speed

Figure 3.3: DK2 HPPSource:Global Wind Atlas

10 3 DK2 and SE2 HPP

3.3 SE2 power plant detailsSE2 HPP location:Storsjön, Östersunds kommun, Jämtland County, Region Norrland, Sweden

The geographic coordinates for SE2 hybrid power plant are:Latitude: 63o 13’ 15.594" North Longitude: 14o 20’ 33.474" East

Figure 3.4: SE2 HPP locationSource:Global Wind Atlas

(a) Mean power density (b) Wind speed rose (c) Mean wind speed

Figure 3.5: SE2 HPPSource:Global Wind Atlas

3.4 Hybrid power plant scenarios 11

3.4 Hybrid power plant scenarios

WPP+PVPP Scenario DK2 and SE2 RES Pgrid [MW ]Scenario 1 0WPP+100PVPP 100Scenario 2 25WPP+75PVPP 100Scenario 3 50WPP+50PVPP 100Scenario 4 75WPP+25PVPP 100Scenario 5 100WPP+0PVPP 100Scenario 6: Overplanting case 80WPP+60PVPP 100Scenario 7: Overplanting case 100WPP+40PVPP 100Scenario 8: Overplanting case 105WPP+35PVPP 100Scenario 9: Overplanting case 120WPP+20PVPP 100

Table 3.1: DK2 and SE2 RES scenarios

The hybrid wind and solar pv power plant scenarios studied for DK2 and SE2 power plantlocations can be seen in Table 3.1 above. Where 4 overplanting scenarios have been also takeninto account for DK2 and SE2 power plant Nordic locations. We can see how in every singlescenario there is a power grid connection constraint, Pgrid = 100 [MW ] which is going to beone of the input parameters for the bidding model algorithm later presented in Chapter 5.

From all the nine scenarios studied and work all along this master thesis development I’mpresenting mainly scenario 9 in this document as I found it more interesting than the othereight. I have also seen the effect of increasing the wind ans solar pv installed capacity in everysingle one of them and as I’ve been working with two Nordic power plant locations, DK2 andSE2 the effect of the solar photovoltaic has less effect than it would in other locations like, forexample in Spain or France, where capacity factors for PVPP are higher than in Denmarkor Sweden. It goes without saying that the weight and generation behaviour of wind energyproduction is completely different from solar pv.

To understand the enhanced value of HPP different studies in terms of capacity factors, vari-ability, curtailment, value of storage etc. It should be noted that the value of HPP is enhancedwhen the evacuation capacity to the grid is limited. It can be interpreted that for limited gridconnection, maximum weather resources should be used to generate maximum power close tothe evacuation capacity to maximize revenue. Kaushik Das et. al.[8]

3.5 RES correlation studyI’m presenting here a correlation study between RES power production and spot market prices.Spot market prices have been obtained from Nordpool website: https://www.nordpoolgroup.com/historical-market-data/, It can be read a complete and detailed correlation study hereMatti Koivisto et. al.[10].

12 3 DK2 and SE2 HPP

This is the block diagram of the main parts of CorRES. Simulated available power gener-ation means the generation before, for example, curtailment:

Figure 3.6: CorRES block diagramMatti Koivisto et. al.[10]

CorRES have provided the following time series:• Wind power production forecast WPP_F (t) [p.u.]

• Solar pv power production forecast PV PP_F (t) [p.u.]

• Wind power production measured WPP (t) [p.u.]

• Solar pv power production measured PV PP (t) [p.u.]

These power production time series will be used as inputs for the bidding and real time modelslater in Chapter 5. It will be then when I take into account the wind and solar capacity to getP_RES_F (t) and P_RES(t).

3.5 RES correlation study 13

I’m presenting the study correlation from the time series obtained using CorRES softwarefor year 2016 for DK2 and SE2 wind and solar pv power plants. In this figure we can seeforecast and measured correlation studies between power production per hour vs spot marketprices. Storage is specially relevant for market where power prices are negatively correlated, aswe are going to see that happens with this two Nordic hybrid power plants.

Figure 3.7: Scatter plot: Power generated per hour vs El. Spot market prices

Here in Table 3.2 I’m including the correlation factor results for DK2 and SE2 for all the 9scenarios:

HPP correlation coefficient Forecast MeasuredScenario 1 R: 0.1155 0.1153Scenario 2 R: 0.0217 0.0156Scenario 3 R: -0.0945 -0.1149Scenario 4 R: -0.1588 -0.1878Scenario 5 R: -0.1844 -0.2142Scenario 6 R: -0.1188 -0.1429Scenario 7 R: -0.1528 -0.1813Scenario 8 R: -0.1588 -0.1878Scenario 9 R: -0.1727 -0.2025

Table 3.2: DK2 HPP correlation coefficients - Power production per hour vs Spot marketprices

14 3 DK2 and SE2 HPP

HPP correlation coefficient Forecast MeasuredScenario 1 R: 0.0746 0.0719Scenario 2 R: 0.0177 0.0207Scenario 3 R: -0.0594 -0.0579Scenario 4 R: -0.1087 -0.1163Scenario 5 R: -0.1288 -0.1392Scenario 6 R: -0.0775 -0.0788Scenario 7 R: -0.1039 -0.1106Scenario 8 R: -0.1087 -0.1163Scenario 9 R: -0.1196 -0.1292

Table 3.3: SE2 HPP correlation coefficients - Power production per hour vs Spot marketprices

Reviewing these Tables 3.2 and 3.3 firstly we can easily see the impact of WPPs installedcapacity when is gaining more participation in the different scenarios presented. Secondly thereis another important conclusion we can draw from these tables, and is that PVPPs participationis relevant too, that can be seen if we compare scenario 5 with 8 and 9, where correlationcoefficients, R, in absolute value are lower than correlation coefficient for scenario 5.

One could say, then why we just pick scenario 3 if is having a lower R value? The an-swer for that is the power generation, that is lower than in scenarios 8 and 9. And this isprecisely why I think is a good idea choosing for this study just Scenario 9.

So taking into account scenario 9 now, if we analyze scenario correlation coefficient resultfirstly we can observe that the correlation is negative, the electric prices are higher when therenewable generation is lower and upside down, when the power produced is reaching a highervalue then the prices are lower, the law of supply and demand. Secondly the higher the cor-relation factor R value is the better, in terms of R absolute value, the smaller the value the better.

Here is when the BESS becomes more relevant, in terms of improving its revenue beingable to sell the energy when prices are higher. As it allows us to move the power in timetransferring to the grid at later times when the price is higher Jose L. Crespo-Vazquez.[14].

Another correlation study between DK2 and SE2 wind and solar power plants and capacityfactors can be also found at: Table.1 Kaushik Das et. al.[8], where the following results arepresented in Table 3.4:

PP Location Wind Power CF [%] Solar PV Power CF [%] RDK2 PP: 42 12 -0.1574SE2 PP: 24 10 -0.1206

Table 3.4: Capacity factor and correlation of wind and solar power plant

3.5 RES correlation study 15

The stronger the negative correlation the better regarding e.g. utilization of the gridconnection and balanced energy output.

CHAPTER 4HPP Case Scenario Study

4.1 Study case: Scenario 9As commented previously in Chapter 3, I’m focusing my study on scenario 9, as I’ve foundit more interesting, so all the results and analysis that I’m presenting belong to the study ofthis scenario. For all calculations power grid connection constraint Pgrid = 100 [MW ] has beentaken into account. Here in the following Tables 4.1, 4.2, 4.3, 4.4, 4.5 and 4.6 I’m presentingsome relevant scenario 9 main figures:

Scenario 9 DK2 E_RES_F [TWh/year] SE2 E_RES_F [TWh/year]120WPP+20PVPP 0.471 0.303

Table 4.1: DK2 and SE2 RES E_RES_F - Scenario 9 - Forecast

Scenario 9 DK2 E_RES_F [TWh/year] SE2 E_RES_F [TWh/year]120WPP+20PVPP 0.468 0.281

Table 4.2: DK2 and SE2 RES E_RES - Scenario 9 - Measured

Scenario 9 DK2 CF [%] DK2 FWH [h] SE2 [%] SE2 FWH [h]120WPP+20PVPP 53.75 4708.5 34.63 3033.6

Table 4.3: DK2 and SE2 RES CF and FWH - Scenario 9 - Forecast

Scenario 9 DK2 CF [%] DK2 FWH [h] SE2 [%] SE2 FWH [h]120WPP+20PVPP 53.46 4683.1 32.09 2811.1

Table 4.4: DK2 and SE2 RES CF and FWH - Scenario 9 - Measured

4.2 CorRES time series 17

Scenario 9 DK2 Esurplus_F [GWh/year] SE2 Esurplus_F [GWh/year]120WPP+20PVPP 30.403 13.273

Table 4.5: DK2 and SE2 RES E_surplus_F - Scenario 9 - Forecast

Scenario 9 DK2 Esurplus [GWh/year] SE2 Esurplus [GWh/year]120WPP+20PVPP 25.509 8.997

Table 4.6: DK2 and SE2 RES E_surplus - Scenario 9 - Measured

4.2 CorRES time seriesCorRES, Correlations in Renewable Energy Sources, is a simulation tool developed by DTUWind Energy department. It’s capable of simulating both wind and solar power generation.

Correlations in Renewable Energy Sources (CorRES) is a simulation tool developed at TechnicalUniversity of Denmark, Department of Wind Energy capable of simulating both wind andsolar generation. It uses a unique combination of meteorological time series and stochasticsimulations to provide consistent VRE generation and forecast error time series with temporalresolution in the minute scale.

Such simulated VRE time series can be used in addressing the challenges posed by theincreasing share of VRE generation. These capabilities will be demonstrated through threecase studies: one about the use of large-scale VRE generation simulations in energy systemanalysis, and two about the use of the simulations in power system operation, planning, andanalysis Matti Koivisto et. al.[10].

Application of VRE generation simulation tools can be used:• in the estimation of adequacy of reserves in power systems

• in stability analysis

• long-term transmission system planning

• electricity market studiesThere are different approaches for VRE generation simulation:• meteorological reanalysis models for generating the underlying meteorological fields

• stochastic time series models for simulating the time series of interest

18 4 HPP Case Scenario Study

To assess the value of storage for revenue maximization of a wind power plant. The resultsshow that proposed algorithms can increase the revenue by more than 10[%] compared to theoperation of WPP without BESS.

CorRES outputs are:• Forecast RES power production

• Measured RES power productionIn this study case, RES are wind and solar photolvotaic.

Forecast and measured wind and solar power production time series obtained with DTUCorRES tool are the starting point of this master thesis work. These power production timeseries belong to years 2015 and 2016 for DK2 and SE2 wind and solar photovoltaic power plantspecified locations. So I’ve worked with hourly power production data for these two mentionedyears. As I’m not presenting a variability study I’m combining both years in order to workwith only one data for each scenario getting more weightage to the obtained results. I have tosay that in a very beginning stage of this work I used persistent PVPP forecast before usingCorRES forecast. Persistent forecast is a poor forecast which consist of assuming for the nextperiod that the power will be the same that it was in the last past one. The challenge of usinga bad forecast, as persistent is, battery will be used quite a lot, so a really big battery will beneeded.

We can read more about time series in this really interesting article: "Simulation of transconti-nental wind and solar PV generation time series" Edgar Nuño et. al.[12].

CHAPTER 5Bidding, Real and Full model

Algorithms5.1 IntroductionThe criteria for designing the HPP is to minimize the forecast error, P_HPP_FE(t), notto have stable power production over the day. So reducing the forecast error means that youwill be penalized less for it, then this is the main improvement of this methodology here presented.

The purpose of including within this hybrid power plant a BESS is precisely to reducethe forecast error P_HPP_FE(t) as well as to reduce curtailment including an appropiatebattery energy storage system size.

Another important factor is the amount of energy than can be charged and discharged to/fromthe battery in one hour time. This is because it is not possible to get more that a limited amountof energy supply from the battery as well as is not possible to charge the battery all the amountwe would like to charge it. This is where P_LIMbatt parameter becomes important as it isthe power battery limitation for charging and discharging operation. In this chapter I am pre-senting how it is taken into account as a parameter for the bidding and real time modes analyzed.

Power surplus, Psurplus(t), is used to charge the battery too, so it will reduce forecast er-ror too. Every single time we have Psurplus(t) then we take the opportunity to charge thebattery and when we don’t then we just bid what we have, P_HPP_F (t).

And finally when it is not possible to include all this Psurplus(t) produced within the bat-tery then we spill this power, commonly known as Pcurtailed(t). Curtailment is spilling theenergy, letting it go as we are not able to storage it within BESS. This situation is going tohappen every single time that battery is full charged and from RES we are producing morepower than Pgrid then battery won’t be able to accommodate that power produced, thenPcurtailed(t) will be spilt. Just like I am showing, in this master thesis, increasing the batterycapacity, Ebatt we’ll reduce Pcurtailed(t) value.

One of the main battery parameters is the rate at which a battery is charged/dischargedrelative to its maximum capacity, known as Crate.

From this study we can see and understand how important is to have a really good powerproduction forecast for the hybrid power plant. It will reduce the forecast error and of coursethe battery size needed too. In order to participate in energy market, VRE need to reduce theuncertainty of forecast errors. So this is why storage is a viable option: to minimize penal-

20 5 Bidding, Real and Full model Algorithms

ties due to forecast uncertainties and to maximize the revenue generation Kaushik Das et. al.[4].

The hybrid power production forecast P_HPP_F (t) is going to be our 1DA bid for realtime model, that is going to be one of the inputs for it. This is the main reason for thebidding model design, as we are trading with this hybrid power production forecast not withthe P_RES_F (t) or with SOC_F (t).

5.2 Bidding model algorithmIn this model I’m working with the actual power production values provided by CorRES, thatis to say:

P_WPP_F (t) + P_PV PP_F (t) = P_RES_F (t) [MW ] (5.1)

Bidding model inputs:• Wind and solar power forecast: P_RES_F (t) [MW ]

• Power grid connection constrain: Pgrid = 100 [MW ]

• Battery capacity: Ebatt [MWh]

• Initial state of charge: SOC0_F = 0.5 [p.u.]

Parameters:• Power battery limitation for charging and discharging P_LIMbatt [MW ]

• Rate measure at which a battery is charged/discharged relative to its maximum capacity:Crate [h]

Assumption:• Sampling time resolution: 1 [hour]

Bidding model outputs:• State of charge forecast: SOC_F (t) [p.u.]

• Hybrid power produced forecast: P_HPP_F (t) [MW ]

• Power curtailed forecast: Pcurtailed_F (t) [MW ]

5.2 Bidding model algorithm 21

5.2.1 Bidding model flowchartHere I’m presenting how bidding model works, where complete methodology can be seen inthe next three figures. First one, Figure 5.1 is a complete overview showing how it works,meanwhile Figure 5.2 and Figure 5.3 are showing in detail charging and discharging procedures.

Figure 5.1: Bidding model algorithm flowchart

Figure 5.2: Bidding model algorithm flowchart - Charging detail

22 5 Bidding, Real and Full model Algorithms

Figure 5.3: Bidding model algorithm flowchart - Discharging detail

The dispatch SOC_F(t) for this preliminary bidding model the following equations havebeen taken into account:

P_SOC headroom_F (t) = (1− SOC_F (t− 1)) · Ebatt [MW ] (5.2)

Pcharge_F (t) = minimum(Psurplus_F (t), P_SOC headroom_F (t), P_LIMbatt) [MW ](5.3)

Pdischarge max_F (t) = (SOC_F (t− 1)− 0.5) · Ebatt [MW ] (5.4)

SOC_F (t) = SOC_F (t− 1) + (Pcharge_F (t)− Pdischarge_F (t))/Ebatt [MW ] (5.5)

Pgrid headroom(t) = minimum(Pgrid − P_RES_F (t), 0) [MW ] (5.6)

Pdischarge_F (t) = minimum(Pdischarge max_F (t), Pgrid headroom_F (t), P_LIMbatt) [MW ](5.7)

It has helped myself to clearly better understand how the bidding model is working and how itis providing the P_HPP_F (t).

In order to provide more consistency between bidding and real time models this is the definitivebidding model implemented, it has already checked and is providing the same results as previousbidding model version too. And finally here in Figure 5.4 is the bidding model algorithm whichhas also been included within the Matlab code attached in AppendixA:

5.2 Bidding model algorithm 23

Figure 5.4: Bidding model algorithm

We can see here how the SOC_F(t) dispatch is working properly, I’ve included two dynamiclimiters that rules the battery operation completely, letting the power produced getting in whenit’s possible to meet the model design conditions for battery charging and discharging.

To get that battery capacity will always be above the 50 [%] of its capacity is crucial toset the SOC_F (t) limits have been set correctly, that is to say, between: [0.5 : 1] [p.u.]

Another important thing done is to check in every single moment that we are working with thesame units and not mixing them, which of course, needless to say would affect badly to themodel methodology designed and completely to the results obtained. As it’s commonly saidbeing sure that we’re not mixing apples with bananas is extremely important here.

5.2.2 Bidding Model Study CasesI’ve distinguished 2 different cases to be studied and included within the bidding algorithm andMatlab code presented in AppendixA, which are:

1st operational condition:P_RES_F (t) > Pgrid [MW ] (5.8)

then we have:Psurplus_F (t) [MW ] (5.9)

2nd operational condition:P_RES_F (t) <= Pgrid [MW ] (5.10)

then we have:Psurplus_F (t) = 0 [MW ] (5.11)

In Table 4.5 have been already presented E_RES_F and Esurplus_F values for scenario 9 inDK2 and SE2 hybrid power plants.

24 5 Bidding, Real and Full model Algorithms

5.2.3 Bidding model algorithm behaviourIt’s really important to be aware that battery charging and discharging can not happen at thesame time, so if you charge then you don’t charge and vice versa. Basically we will chargewhen we will have available Psurplus_F (t) due to RES overproduction and will discharge whenwe won’t have it, of course we’ll be able to do it having energy stored previously taking intoaccount SOCF (t) limitation that must always be [0.5 [p.u.] : 1 [p.u.]].

When do I charge the battery?

Every single time I have Psurplus_F (t) and it’s room in the battery for it, otherwise powerproduced will be curtailed partially or entirely depending on the available room for energystoring. It is important to take into account that P_LIMbatt will limit the charge and dischargeoperation. Taking a look to Figure 5.4 we can see that we’ll charge when we’re not discharging,in other words, when Pbatt is negative then is meeting the limiter with down limit zero, so it’snot discharging.

When do I discharge the battery?

Pdischarge_F (t) is calculated as we want to discharge what it takes to get. As we havejust seen it’s important to take into account +/− Pbatt_max_F limit values that will limit thecharge and discharge operation. Eventually I’m taking the minimum value just to really knowwhat power is discharged and supplied to grid. Ultimately in the next adder Pdischarge_F (t)is compared to Pdischarge_F_req(t), fruit of both is P_missing_F (t) that is aggregated toPgrid, then we have obtained our bid for real time model, P_HPP_F (t).

What is Pcurtailment_F_total(t)?

Power curtailment is the power spilt because there is no room for it within the battery,this power produced is not going into the grid. It could be used for selling it later. We have totake into account a really important aspect, curtailment power is can come from two placeswithin the Figure 5.4, which are both limiters, when signal is overlimits. So to calculatePcurtailment_F_total(t) we have to apply the following formula:

Pcurtailment_F_total(t) = Pcurtailment_F (t) + P ′curtailment_F (t) [MW ] (5.12)

5.2 Bidding model algorithm 25

5.2.4 Bidding model resultsBidding model results are widely presented in Chapter 6, but here we can see a quick plottingcomparison for all the forecast time series taking into account 2015 and 2016 years, for severalP_LIMbatt parameter values: 5, 10, 50 and 100 [MW].

(a) P_LIMbatt = 5MW (b) P_LIMbatt = 10MW

Figure 5.5: DK2 Bidding - Scenario 9

(a) P_LIMbatt = 50MW (b) P_LIMbatt = 100MW

Figure 5.6: DK2 Bidding - Scenario 9

To facilitate its better understanding what I’m going to present now is only two daysdetailed study so we’ll be able to fully understand the forecast results provided. Firstly we willsee a clear case of charging operation as we have plenty of Psurplus_F (t).

I have chosen two consecutive days, June 17th and 18th from the power production timeseries provided by CorRES, where is a clear example of Psurplus(t) existence.

I’m presenting a complete study including different P_LIMbatt values: 5, 10, and 50 [MW]and Crate = 1 [hour].

26 5 Bidding, Real and Full model Algorithms

(a) P_LIMbatt = 5MW (b) P_LIMbatt = 10MW

Figure 5.7: DK2 forecast - Scenario 9

Figure 5.8: DK2 forecast - Scenario 9 - P_LIMbatt = 50MW

Reviewing Figures 5.7 and 5.8 we can check how Pcurtailed_F (t), SOC_F (t) and Pdischarge_F (t)is varying according to the P_LIMbatt parameter value taken in every single case. I’m present-ing here in this work specifically in Chapter 6 a complete data analysis using the duration curves.

Secondly now we will see a clear case of discharging operation providing its energy to the gridas we don’t have much of Psurplus_F (t) produced this time. I have chosen for showing this twoconsecutive days in June 2015, June 8th and 9th for DK2 HPP.

5.2 Bidding model algorithm 27

(a) P_LIMbatt = 5MW (b) P_LIMbatt = 10MW

Figure 5.9: DK2 forecast - Scenario 9

Figure 5.10: DK2 forecast - Scenario 9 - P_LIMbatt = 50MW

28 5 Bidding, Real and Full model Algorithms

Figure 5.11: DK2 forecast - Scenario 9 - P_LIMbatt = 100MW

In these Figures 5.9, 5.10 and 5.11 we can check how Pcurtailed_F (t), SOC_F (t) andPdischarge_F (t) is varying according to the P_LIMbatt value taken in every single case. In thissecond case as advanced can be clearly seen how low is Psurplus_F (t) compare to the first caseabove presented. It can be also seen clearly the power generation intermittency of RES.

5.3 Real time model algorithm 29

5.3 Real time model algorithmIn this model I’m working with the actual power production values provided by CorRES, thatis to say:

P_WPP (t) + P_PV PP (t) = P_RES(t) [MW ] (5.13)

taking into account the WPP and PVPP capacity installed, as CorRES is providing time seriesin per units, so for scenario 9. Another input for this real time model will be hybrid powerproduction forecast, P_HPP_F (t), output from the previous model algorithm, bidding model.

Real time model output is the P_HPP (t), so after obtaining this value we are able to calcu-late the P_HPP_FE(t) and quantify how good our hybrid power production forecast has been.

As we have also seen within the bidding model that the battery charging and dischargingoperation can not happen both at the same time. So if you charge then you don’t charge andvice versa. So basically we will charge when we’ll have Psurplus(t) and will discharge when wewon’t and, of course, having stored energy previously within the BESS to be provided to the grid.

It’s really important to take into account that our bid is: P_HPP_F (t), 1 DA, so it meansthat we are saying that our forecast obtained the previous day, is what is going to happen inthe actual day, that could be fully understood checking the real time model flowchart presentedin this fifth chapter, point 5.3.2.

Once we have run both models, bidding and real time model, then hybrid power producedforecast error, P_HPP_FE(t), can be calculated as:

P_HPP_FE(t) = P_HPP (t)− P_HPP_F (t) [MW ] (5.14)

5.3.1 Inputs, outputs, parameters and assumptions

Real time model inputs:• Wind and solar power production measured: P RES(t) [MW ]

• Power grid connection constrain: Pgrid = 100 [MW ]

• Hybrid power production forecast: PHP P_F [MW ]

• Battery capacity: Ebatt [MWh]

• Initial state of charge: SOC0 [p.u.]

Parameters:• P_LIMbatt [MW ]

• Crate [h]

30 5 Bidding, Real and Full model Algorithms

Assumption:• Sampling time resolution: 1 [hour]

Real time model outputs:• State of charge: SOC(t) [p.u]

• Hybrid power produced measured: P_HPP (t) [MW ]

• Power curtailed: Pcurtailed_total(t) [MW ]

5.3.2 Real time model flowchart

Figure 5.12: Real time model algorithm flowchart

5.3.3 Real Time Model Study casesI’ve distinguished 3 different cases to be studied and included within the real time algorithmand Matlab code, which are:1st operational condition:

P_RES(t) > P_HPP_F (t) [MW ] (5.15)

then we have:Pbatt(t)_req < 0 [MW ] (5.16)

2nd operational condition:

P_RES(t) < P_HPP_F (t) [MW ] (5.17)

then we have:Pbatt(t)_req > 0 [MW ] (5.18)

5.3 Real time model algorithm 31

3rd operational condition:

P_RES(t) = P_HPP_F (t) [MW ] (5.19)

then we have:Pbatt(t)_req = 0 [MW ] (5.20)

So we are calculating Pbatt(t)_req as:

Pbatt(t)_req = P_HPP_F (t)− P_RES(t) [MW ] (5.21)

and taking into account that the corresponding operational condition Pbatt(t)_req will bepositive, negative or zero.

Our priority is to minimize the hybrid power plant forecast error, P_HPP_FE(t). Sothis time we are not keeping the 50 [%] of the battery capacity all the time as I have presentedin bidding model algorithm. As soon as we get power we will try to provide it to the grid if it’spossible. I’m remarking again that it is not possible to charge and discharge at the same time,this has been also taken into account in the real time model algorithm.

5.3.4 Real time model algorithm behaviourIt is really important to be aware that battery charging and discharging can not happen at thesame time, as happened with previous model presented, bidding model. So if you charges thenyou can’t charge and vice versa.

When do I charge the battery?Every single time I have Psurplus(t) and it’s room in the battery for it, otherwise it will becurtailed. It is important to take into account P_LIMbatt that will limit the charge anddischarge operation. Taking a look to Figure 5.12 we can see that we’ll charge when we are notdischarging, in other words, when Pbatt is negative then is meeting the limiter with down limitwith zero value, so it’s not discharging.

When do I discharge the battery?Pdischarge(t) is calculated as we want to discharge what it takes to get. As we have just seen it’simportant to take into account +/− Pbatt_max limits that will limit the charge and dischargeoperation. I’m taking the minimum value just to really know what power is discharged andsupplied to grid. Finally in the next adder Pdischarge(t) is compared to Pdischarge_req(t), fruitof both is P_missing(t) that is aggregated to P_HPP_F (t), our bid from bidding model,then we have obtained our bid for real time model, P_HPP (t).

What is Pcurtailment_total(t)?It’s the power spilt because there is no room for it within the battery, this power produced isnot going into the grid. It could be used for selling it later. We have to take into account areally important aspect, curtailment power is can come from two places within the Figure 5.12,which are both limiters, when signal is overlimits. So to calculate Pcurtailment_total(t) we haveto apply the following formula:

Pcurtailment_total(t) = Pcurtailment(t) + P ′curtailment(t) [MW ] (5.22)

32 5 Bidding, Real and Full model Algorithms

5.3.5 Real time model resultsMeasured results are widely presented in Chapter 6, but here we can see a quick comparisonplotting for all the forecast time series taking into account 2015 and 2016 years, for severalP_LIMbatt values.

Figure 5.13: DK2 measured - Scenario 9 - P_LIMbatt = 5MW

Figure 5.14: DK2 measured - Scenario 9 - P_LIMbatt = 100MW

5.3 Real time model algorithm 33

To facilitate its understanding what we are going to do now is only a two days detailedstudy so we’ll be able to fully understand the forecast results provided.

I have chosen two consecutive days, June 17th and 18th 2016 from the time series provided,where is a clear example of Psurplus(t) existence.

I’m presenting a complete study including different P_LIMbatt values: 5, 10, 50 and 100[MW] and Crate = 1 [hour].

(a) P_LIMbatt = 5MW (b) P_LIMbatt = 10MW

Figure 5.15: DK2 measured - Scenario 9 - P_LIMbatt = 5MW and P_LIMbatt = 10MW

Figure 5.16: DK2 measured - Scenario 9 - P_LIMbatt = 50MW

In these figures above we can check how Pcurtailed(t), SOC(t) and Pdischarge(t) are varyingaccording to the P_LIMbatt value taken in every single case. I’m presenting here in this work

34 5 Bidding, Real and Full model Algorithms

specifically in Chapter 6 a complete data analysis using the duration curves.

Secondly we will see a clear case of discharging operation providing its energy to the grid as wedon’t have much of Psurplus(t) power produced this time. I have chosen for showing this twoconsecutive days in June 2015, June 8th and 9th for DK2 HPP.

(a) P_LIMbatt = 5MW (b) P_LIMbatt = 10MW

Figure 5.17: Days with low Psurplus(t)− Pbatt = 5 and 10 [MW ]

(a) P_LIMbatt = 50MW (b) P_LIMbatt = 100MW

Figure 5.18: Days with low Psurplus(t)− Pbatt = 50 and 100 [MW ]

Finally in these Figures 5.17 and 5.18 we can see how Pcurtailed(t), SOC(t) and Pdischarge(t)are varying according to the P_LIMbatt value taken in every single case. In this second case asadvanced can be clearly seen how low is Psurplus(t) compare to the first case above presented.It can be seen also clearly the intermittency of RES power generation again, as it has alreadyhappen with the forecast too.

5.4 Full model algorithm 35

5.4 Full model algorithmHere full model algorithm is presented, where forecast and real time models are integratedtogether, taking into account the time execution sequence for both.

Full model inputs:• Wind and solar time series: WPP_F (t), PV PP_F (t), WPP (t) and PV PP (t) [p.u.]

• Wind and solar capacities P_WPP_F , P_PV PP_F , P_WPP and P_PV PP [MW ]Parameters:• Wind power capacity P_WPP [MW ]

• Solar pv power capacity P_PV PP [MW ]

Assumption:• Time resolution: 1 [hour]

Full model outputs:• HPP power produced: P_HPP (t) [MW ]

• Hybrid power plant forecast error: P_HPP_FE(t) [MW ]

5.4.1 Full model flowchart

Figure 5.19: Full model algorithm flowchart

36 5 Bidding, Real and Full model Algorithms

5.4.2 Full model algorithm behaviourCorRES software is providing us the wind and solar production time series, for bidding and forreal time models, in [p.u.]. As we’re having different wind and solar capacities for every singleone of the scenarios, e.g. for scenario 9, having as results the following:

P_RES_F (t) = (WPP_F (t)) · P_WPP + (PV PP_F (t)) · P_PV PP [MW ] (5.23)

P_RES(t) = (WPP (t)) · P_WPP + (PV PP (t)) · P_PV PP [MW ] (5.24)

Then, firstly P_RES_F (t) is taken into account within the bidding algorithm meanwhileP_RES(t) will be entering within the real time model.

Secondly once we have already obtained P_HPP_F (t), as bidding model output, that will beone of the inputs for the real time model together with the previously mentioned P_RES(t).

Finally we’re obtaining the full model outputs, which are: P_HPP (t) supplied to the grid andP_HPP_FE(t). As we have seen during this master thesis hybrid power plant forecast errorwill tell us how good the forecast, which was our 1 day ahead bid. All the hybrid power plantforecast error metrics calculated are provided in detail in Chapter 7

CHAPTER 6Data Analysis

6.1 Duration curves

Duration curves is another way of plotting the ramp rates, which haven’t been used here,but this time the results are plotted in a sorted manner, e.g. based on its value and notchronologically. Then we are able to check for a certain number of hours how much the powervariation is. The maximum and minimum values are exactly the same as in the time seriesplotting as I haven’t changed anything about how I’m calculating from time series plottingmethod to this one. What the duration curve is telling us is for how long we can expect whatkind of changes, so we can see the power variability.

Cumulative distribution function: A cumulative frequency distribution is the sum of theclass and all classes below it in a frequency distribution which means that is the sum of theevents when the signal has a certain value. So in cumulative distribution function we arecounting how many times the signal has every single value. It can be also done for differentbins or changes in the power production.

What I’m including here in the first 2 figures, Figures 6.1a and 6.1b, following below isthe main relevant aspects for the forecast as:

• P_RES_F (t)

• Psurplus_F (t)

• Pcurtailed_F (t)

• Pcharge_F (t)

• Pdischarge_F (t)

• SOC_F (t) · Ebatt

• P_HPP_F (t)

I’ve changed the values of P_LIMbatt parameter taking: 5, and 50 [MW ] values maintainingE_batt size to 100 [MWh], these figures have been plotted using the bidding model presentedin Chapter 5, which are:

38 6 Data Analysis

(a) P_LIMbatt = 5MW (b) P_LIMbatt = 50MW

Figure 6.1: DK2 Forecast duration curves

Some of the observation that I can get from these figures reviewing is how is reducedPcurtailed_F (t) when P_LIMbatt is increased from 5 [MW ] to 50 [MW ]. As it was expectedand have been explained in this master thesis work one of the two main advantages of usingenergy storage is minimizing the curtailment, the bigger the better in terms of reducing powercurtailed.

Secondly, another important aspect of plotting these duration curves is to check SOC_F (t),then we can see that takes a little more time to get the 100 [%] of its capacity when P_LIMbatt

value is 50 [MW ]. It is understandable as battery size will be bigger too. Also it can be seen howthe SOC_F (t) is behaving as expected in this bidding model due to the design methodology,maintaining 50 [%] as minimum value of its capacity all the time.

Finally we can see what happens to P_HPP_F (t) in both cases, where for a higher P_LIMbatt

value and a bigger battery size we can provide during more time the power grid connectionconstrain value, Pgrid = 100 [MW ]. This would be the main conclusion, the bigger the better,in terms of battery size, as we can provide and comply with Pgrid during more time and thisexactly one of the actions we want to obtain from this model. It is important to remark thatbidding model presented is not the optimized model. I’m sure that can be presented othermodel versions which better optimize their results, but what it’s for sure is that the biddingmodel presented is working with consistency and reliability providing good results as we cansee.

Now I’m doing the same for the real time model, the next two figures are plotted using itand the main relevant aspects are:• P_RES(t)

• Psurplus(t)

• Pcurtailed(t)

• Pcharge(t)

• Pdischarge(t)

6.1 Duration curves 39

• SOC(t) · Ebatt

• P_HPP (t)

(a) P_LIMbatt = 5MW (b) P_LIMbatt = 50MW

Figure 6.2: DK2 Measured duration curves

Some of the observations that I can get from these figures reviewing is how is reducedPcurtailed_F (t) when P_LIMbatt is increased from 5 [MW ] to 10 [MW ]. Here we can alsosee another great difference compared to bidding model, which is that for lower P_LIMbatt

values curtailment is really high, increasing dramatically 20 [%] of the time. It’ seen also thatcurtailment is happening approximately 50 [%] of the time.

If we increase P_LIMbatt for a same Ebatt value we can easily see how the direct conse-quences of doing it are. There are mainly two: we’ll reduce dramatically curtailment till justhaving less than 5 [%] of the time and these curtailment values will be really high. Here wecan see another big difference compared to bidding model, and this is due to our main modelstrategy which is minimizing the forecast error, P_HPP_FE(t).

Again as expected one of the two main advantages of using energy storage is minimizingthe curtailment, the bigger the better but for real time model we can see that this also affectsto the high value of it when we increase P_LIMbatt values.

Here, in the real time model, it’s important to say that the design methodology for theSOC_F (t) is different to the previous model, it can be easily seen when checking the limitsof the dynamic limiters included within the real time model in the SOC dispatch. Secondly,another important aspect of plotting these duration curves is to check SOC_F (t), then we cansee that takes more time to get the 100 [%] of its storing capacity when P_LIMbatt = 50 [MW ],more than it does with bidding model too. Thirdly, battery is remaining completely empty morethan 35 [%] of the time for lower P_LIMbatt values, 25[%] of the time for higher P_LIMbatt

values as it happens with P_LIMbatt = 50 [MW ].

Again we can see what happens to P_HPP (t) in both cases, for a higher P_LIMbatt value,we can provide during more time the power grid connection constrain value, Pgrid = 100 [MW ].This would be the main conclusion, the bigger the better as we can provide and comply withthe Pgrid during more time and this exactly one of the actions we want to obtain from this

40 6 Data Analysis

model. It is important to remark that real time model presented is not the optimized modelneither. I’m sure that can be presented other model versions which better optimize the results,but what it’s for sure is that this real time model presented is working with consistency andreliability providing good results, as we can see.

Finally if we take into account bidding and real time model figures we can conclude thatwe are reducing the forecast error of the hybrid power plant, P_HPP_FE(t). This is goingto be studied in detail in the next chapter, Chapter 7 where I’m presenting the forecast errormetric calculated and their results.

6.2 Spot market bidding methodologyI have taken into account several different battery limits, P_LIMbatt [MW ] in both HPP loca-tions for this scenario 9 detailed study. I have also taken into account that Ebatt = 100 [MWh]and Crate = 1 [hour].

Changing P_LIMbatt because this parameter will affect to the forecast Pcharge_F (t), Pdischarge_F (t),Pcurtailed_F (t), and of course P_HPP_F (t) too.

6.2.1 Hybrid power production forecast

Figure 6.3: DK2 P_HPP_F (t) - P_LIMbatt = 5[MW ] Ebatt = 100[MWh]

6.2 Spot market bidding methodology 41

Figure 6.4: DK2 P_HPP_F (t) - P_LIMbatt = 25[MW ] Ebatt = 100[MWh]

Figure 6.5: DK2 P_HPP_F (t) - P_LIMbatt = 50[MW ] Ebatt = 100[MWh]

42 6 Data Analysis

Figure 6.6: DK2 P_HPP_F (t) - P_LIMbatt = 75[MW ] Ebatt = 100[MWh]

In these four Figures above, 6.3, 6.4, 6.5 and 6.6, we can clearly observe two main things: FirstlySOC_F (t) ·Ebatt which is reaching full charge just 15 [%] of the time for P_LIMbatt = 5 [MW ]which is only 12.5 [%] approximately, here we can see that in terms of battery full capacitycharged we need to increase P_LIMbatt values otherwise most of the time we’ll be below fullcharge situation.

Secondly, how Pcurtailed(t) is behaving when I’m increasing P_LIMbatt from 5 [MW ] till75 [MW ], also it can be seen that there is no change detected from 50 to 75 [MW ], having thesame Pcurtailed(t) value. In this case I would say that there is no justified reason for increasingP_LIMbatt parameter value, but we have to take into account that the other main reason ofincluding BESS is minimizing forecast error. So we’ll need to check first about it and after tomake the right decision looking at the whole picture.

Finally we can see how is P_HPP_F (t) just reaching Pgrid between the: [25% : 27.5%]of the time depending on the P_LIMbatt value taken. Here you can see another difference withthe real time model where P_HPP (t) can’t manage to reach Pgrid during as much time asbidding model does.

6.3 Hybrid power production real time measured 43

6.3 Hybrid power production real time measured

Figure 6.7: DK2 P_HPP (t) - P_LIMbatt = 5[MW ] Ebatt = 100[MWh]

Figure 6.8: DK2 P_HPP (t) - P_LIMbatt = 25[MW ] Ebatt = 100[MWh]

44 6 Data Analysis

Figure 6.9: DK2 P_HPP (t) - P_LIMbatt = 50[MW ] Ebatt = 100[MWh]

Figure 6.10: DK2 P_HPP (t) - P_LIMbatt = 75[MW ] Ebatt = 100[MWh]

As it has happened in the last point these four Figures above, 6.7, 6.8, 6.9 and 6.10, are showingmainly two aspects: Firstly SOC(t) ·Ebatt which is reaching full charge just [57.5%] of the time

less for P_LIMbatt = 5 [MW ] which is only [67.5%] approximately, here we can see that in

6.4 Energy surplus forecast 45

terms of battery full capacity charged we need to increase P_LIMbatt values otherwise most ofthe time we’ll be below full charge situation. If we compare to forecast figures from last pointthere is another aspect, the behaviour of SOC_F (t) and SOC(t) is different, and this becausebidding and real time model have different limits within the second limiter placed in SOCdispatch. Bidding model is keeping all the time this established range between: [0.5 : 1] [p.u.]meanwhile real time model isn’t.

Secondly, how Pcurtailed(t) is behaving when increasing P_LIMbatt from 5 [MW ] till 75 [MW ].But here it is a great difference with last point as I have detected a different behaviourPcurtailed(t). This is due to the real time methodology algorithm design which is slightlydifferent as it can be seen in Chapter 5.

Finally we can see how is P_HPP (t) just reaching Pgrid between the: [17.5% : 21%] ofthe time depending on the P_LIMbatt value taken. Here you can see another difference withthe bidding model where P_HPP_F (t) manage to reach Pgrid during a little more time.

6.4 Energy surplus forecastPgrid connection constrain is constant, its power value is: 100[MW ].

Psurplus(t)_F is defined as:P_RES_F − Pgrid [MW ] (6.1)

when:P_RES_F > Pgrid [MW ] (6.2)

So you can obtain Esurplus(t)_F just summing up its Psurplus(t)_F for all the time series.

DK2 Esurplus_F (t)Esurplus_F (t) = 30.404 [GWh/year] (6.3)

SE2 Esurplus_F (t)Esurplus_F (t) = 13.273 [GWh/year] (6.4)

6.5 Energy curtailed forecastPcurtailed(t), measured and forecasted, is defined as the power spilt that you can storage withinthe battery because it’s fully loaded. As I have said before one of the main effects of including aBESS is reducing Pcurtailed(t), storaging all the energy we can within the battery and supplyingit later to the grid.

So you can obtain Ecurtailed(t)_F just summing up its Pcurtailed(t)_F for all the time se-ries. This is one of the outputs of the spot market bidding model algorithm.

46 6 Data Analysis

6.5.1 DK2 Ecurtailed_F (t)Ecurtailed_F (t) will vary from one battery size chosen to other, also as we can see here in thetable below, will be Crate dependent. Concluding the much bigger battery size we includewithin HPP the better as we will reduce Pcurtailed(t) and Ecurtailed(t) so hybrid power plantforecast error, P_HPP_FE(t).

We are going to see here some results obtained with the bidding model presented in Chapter 7using Matlab code included in the AppendixA. It has been taken into account Ebatt = 100 [MW ]and Crate = 1 [hour].

P_LIMbatt [MW] Ecurtailed(t) [GWh/year]10 46.79720 6.68530 0.23640 0.23650 0.236

Table 6.1: DK2 Ecurtailed_F (t) [GWh/year]

So we can see how increasing 5 times P_LIMbatt we can highly reduce Ecurtailed_F (t).

6.6 Capacity factor forecast 47

6.5.2 SE2 Ecurtailed_F (t)

P_LIMbatt [MWh] Ecurtailed(t) [GWh/year]10 20.10220 5.83430 0.09540 0.09550 0.095

Table 6.2: SE2 Ecurtailed_F (t) [GWh/year]

So we can see how increasing 5 times P_LIMbatt we can highly reduce Ecurtailed_F (t).

6.6 Capacity factor forecastDK2CF_F (t) Capacity factor forecast for DK2 HPP is:DK2 CF_F = 53.75 [%]

and full work hours forecast for DK2 HPP is:DK2 FWH_F = 4708.50 [hours]

SE2CF_F (t)Capacity factor forecast for SE2 HPP is:SE2 CF_F = 34.62 [%]

and full work hours forecast for SE2 HPP is:SE2 FWH_F = 3032.12 [hours]

CHAPTER 7Forecast Error

7.1 IntroductionWe have to say that when we’re talking about minimizing the forecast error, as it’s our maincriteria and strategy followed in the presented methodology included in Chapter 5, we have totake into account the two dimensions of it, the power and the energy.

If we’re talking about power dimension, using the duration curves then what we can seeis having one determined battery size how much power I’ll be able to minimize in terms of thetime, in [MW] units.

On the other side if we take into account the developed algorithm then we can talk about theenergy side, the state of charge, SOC(t) in [MWh] units.

It’s already known that a storage unit consists in two parts:• electrical infrastructure mainly with the converter, [MW ]

• battery bank, which is placed behind, [MWh]The battery bank, energy side, is the most critical, which means how much battery and howmany battery hours we have. Both, power and energy side are duable, but second is morecostly. In this master thesis I’m not going through a cost study of the battery as it is out ofthe scope but it is important to know that it has changed dramatically during the last tenyears, nowadays it’s about Pricebattery bank = 230 [$/MWh]. What we have seen during the lastdecade is that this battery energy price for battery bank has been decreasing dramatically andit’s expected that this will happen even more. It should not be forgotten in cost calculationsthe battery converters price either.

So it will be a compromise between reducing the hybrid power plant forecast error, P_HPP_FE(t)and the overcost added buying more battery.

We don’t have to forget, as commented previously in this master thesis, we’re using thisbattery to utilize the curtailed power, that would also mean we have a little value of reductionof cost of energy, CHP P [e/MWh].

In this master thesis a complete study taking into account different energy capacities ispresented.

7.2 Forecast error metrics: MAFE and RMSFE 49

7.2 Forecast error metrics: MAFE and RMSFEWith the forecast error metrics we can see easily how relevant the effects of including an BESSwithin the wind and solar photovoltaic power plant are.

Forecast error metrics formulas:Mean Absolute Forecast Error:

MAFE = 1n·

n∑n=1|(yj − yj)| (7.1)

Root Mean Square Forecast Error:

RMSFE =

√√√√ 1n·

n∑n=1

(yj − yj)2 (7.2)

and this equation must always be fulfilled:

RMSFE ≥MAFE (7.3)

where:• yi is measured hybrid power production P_HPP (t) [MW ]

• yj is forecast hybrid power production P_HPP_F (t) [MW ]

being the hybrid power plant forecast error, P_HPP_FE(t), which can be calculated:

P_HPP_FE(t) = P_HPP (t)− P_HPP_F (t) (7.4)

being: Pgrid = 100 [MW ], Ebatt = 100 [MWh] and Crate = 1 [hour]. Previously using the timeseries provided by CorRES and taking into account the wind and solar capacity installed forscenario 9, I’ve calculated MAE and RMSE for P_RES_FE(t), where this is:

P_RES_FE(t) = P_RES(t)− P_RES_F (t) (7.5)

having the following results for DK2 and SE2 RES locations:

RES MAE_HPP_RES(t)[MW ] RMSE_RES_FE(t)[MW ]DK2 18.1 26.5SE2 14.9 22.9

Table 7.1: DK2 and SE2 Scenario 9 P_RES_FE(t): MAE_RES_FE(t) andRMSE_HPP_FE(t)

Knowing this two forecast error metrics then now I’m presenting how the inclusion of aBESS affects to the reduction of the P_HPP_FE(t) for DK2 HPP:

50 7 Forecast Error

P_LIMbatt [MW ] MAE_HPP_FE(t)[MW ] RMSE_HPP_FE(t)[MW ]5 7.0 16.325 6.7 16.650 6.7 16.875 6.7 16.8

Table 7.2: DK2 Scenario 9 P_HPP_FE(t): MAE_HPP_FE(t) andRMSE_HPP_FE(t)

And for SE2 HPP:

P_LIMbatt [MW ] MAE_HPP_FE(t)[MW ] RMSE_HPP_FE(t)[MW ]5 7.0 16.025 6.7 16.350 6.7 16.575 6.7 16.5

Table 7.3: SE2 Scenario 9 P_HPP_FE(t): MAE_HPP_FE(t) andRMSE_HPP_FE(t)

We can easily observe that including BESS has a real effect on the reduction of the forecasterror, between: [2.5 : 2.7] times for DK2 HPP and [2.1 : 2.21] times for SE2 HPP. Anotherinteresting conclusion that we can draw from these results is that we will have more successwhen we have better power production. With the figures I’ve calculated it can be seen that wecan reach 0.5 points of difference if we look at the reduction range.

Another important conclusion can be drawn from the results and is that as much biggervalue of P_LIMbatt the better for reducing forecast error. Please remember that I have usedfor this calculation an Ebatt = 100 [MW ].

Finally answering to the question How much can we reduce forecast error adding BESSin Scen. 9? here it’s the answer and results:

P_LIMbatt [MW ] DK2 P_HPP_FE(t) reduction[%] SE2 P_HPP_FE(t) reduction[%]5 61.28 53.1150 63.12 54.82

Table 7.4: DK2 and SE2 Scenario 9 P_HPP_FE(t) reduction[%]

CHAPTER 8Conclusions and Future work

8.1 Main important aspects learntDuring the master thesis work all along this five months period I’ve learnt the following aspects:• Relevance of battery energy storage system inclusion in wind and solar power plants.

• Study of the proper strategy to be applied to get a forecast error reduction.

• Developing of model methodology: bidding and real time algorithms.

• The importance of the optimization of both algorithms if we want to have success reducingpenalties.

• To work with forecast and real time data provided by CorRES.

• The effect of choosing a good forecast to be taken into account 1 Day Ahead as our bid.

• Relevance and impact of battery energy storage size in the forecast error reduction.

8.2 ContributionsReviewing the most relevant results obtained during this master thesis work I’ve have clearlypresented the following aspects:• Bidding model algorithm development and methodology

• Real time model algorithm development and methodology

• Forecast error metric calculations where can be checked the effectiveness of the models

• A complete data analysis of the SOC(t), Pcurtailed(t) and P_HPP (t)

52 8 Conclusions and Future work

8.3 ConclusionAs RES are totally weather dependent so are intermittent and stochastic resources and thereis an essential need of incorporate battery energy storage systems. It is known that if wewant to complete and allow them to be fully integrated within the energy market then BESSparticipation is absolutely necessary. RES are very valuable and adding energy storage systemsto the hybrid wind and solar plants if integrated in large scale. Including BESS we’re boostingand allowing them to provided system services and congestion reduction for power systems.

During this master thesis work reviewing all the data analysis provided I can conclude the highimpact that battery energy storage has on two main aspects, which are:• minimizing the forecast error, P_HPP_FE(t)

• minimizing the power curtailment, Pcurtailed(t)Another important aspect to be taken into account is the relevance of P_LIMbatt parametervalue, it has been fully reviewed the effect of its increase and how it affects to curtailment andforecast error reduction too. To have success with both aspects battery size is decisive, being

a rule of thumb, the bigger the better in terms of forecast error and curtailment reduction.Of course, needless to say that the model methodology algorithm design is crucial to achieveexcellent results. For that purpose it is necessary to establish correctly which is going to beyour strategy and priority. In this work it has been to minimize the forecast error.

Then BESS becomes more relevant, in terms of improving its revenue being able to sellthe energy when prices are higher. As it allows us to move the power in time transferring tothe grid at later times when the price is higher.

8.4 Future work 53

8.4 Future workFurther research aim to cover the following aspects:• To Develop model methodology algorithm to reach the optimal forecast error reduction.

• RES power generation forecasting using ensemble approach based on deep learning andstatistical methods.

• To include within the model methodology development the spot market prices as one ofthe inputs.

• To include new energy storage system combinations: electric + thermal and chemicalstorage to be added to the hybrid wind and solar pv plant.

• Integration of Power-to-X technologies storage in energy islands.

Bibliography

[1] IEA – International Energy Agency "Renewable Energy Market Update Outlook for 2020and 2021", May 2020.

[2] Katherine Dykes, Jennifer King, Nicholas Di Orio, Ryan King, Vahan Gevorgian, DaveCorbus, Nate Blair, Kate Anderson, Greg Stark, Craig Turchi, and Patrick Moriarty, "Oppor-tunities for Research and Development of Hybrid Power Plants", National Renewable EnergyLaboratory, May 2020.

[3] Wind Europe report "Wind energy in Europe in 2019 - Trends and Statistics", February 2020.

[4] Kaushik Das, Anatole Grapperon, Poul Ejnar Sørensen and Anca Daniela Hansen, "Optimaloperation of battery for wind-storage hybrid power plant", HYBRIDize project, December 2019.

[5] Thomas Bowen, Ilya Chernyakhovskiy, Paul Denholm, NREL "Grid-Scale Battery Storage:Frequently Asked Questions", September 2019.

[6] Anca Daniela Hansen, Poul Ejnar Sørensen, Kaushik Das, Edgar Nuño, JayachandraNaidu Sakamuri and Mufit Altin, "Dynamic modelling of Wind-Solar-Storage Based HybridPower Plant", Project: HYBRIDize, 18th International Wind Integration Workshop, Dublin,Ireland, October 2019.

[7] Wind Europe report, "Renewable Hybrid Power Plants - Exploring the Benefits and MarketOpportunities", July 2019.

[8] Kaushik Das et al., “Enhanced features of wind based hybrid power plants”, in Pro-ceedings of the 4th International Hybrid Power Systems Workshop, May 2019.

[9] Edgar Nuño,Matti Koivisto, Nicolaos A. Cutululis and Poul Ejnar Sørensen, "On theSimulation of Aggregated Solar PV Forecast Errors" October 2018.

[10] Matti Juhani Koivisto, Kaushik Das, Feng Guo, Poul Sørensen, Edgar Nuño, Nicolaos

Bibliography 55

Cutululis and Petr Maule, "Using time series simulation tool for assessing the effects of variablerenewable energy generation on power and energy systems", Wiley Interdisciplinary Reviews:Energy and Environment, Volume 8, Issue 3, pp. e329, 2018.

[11] Paulina Asbeck (Vattenfall), “Next-Gen Generation System: The symbiotic relation-ship of solar, wind storage hybrid power plants", 17th Wind Integration Workshop, October2018.

[12] Matti Koivisto et al.,"Using time series simulation tools for assessing the effects of variablerenewable energy generation on power and energy systems", August 2018.

[13] Lennart Petersen, Antonio Martínez, R.M Borsotti Andruszkiewicz, G.C. Tarnowski,N. Steggel, D. Osmond (Vestas), "Vestas Power Plant Solutions - Integrating Wind, Solar PVand Energy Storage", 3rd International Hybrid Power Systems Workshop, May 2018.

[14 ] Jose L. Crespo-Vázquez, C. Carrillo, E. Díaz-Dorado, Jose A. Martínez-Lorenzo, MdNoor-E-Alam , "Evaluation of a data driven stochastic approach to optimize the participationof a wind and storage power plant in day-ahead and reserve markets", May 2018.

[15] Edgar Nuño, Petr Maule, Andrea Hahmann, Nicolaos Cutululis, Poul Sørensen ,IoannaKaragali, "Simulation of transcontinental wind and solar PV generation time series", / Renew-able Energy 118 425-436 (2018), November 2017.

[16] Wind Europe report, "Wind energy and on-site energy storage", November 2017.

[17] Ekström, J., Koivisto, M., Mellin, I., Millar, R. J., Lehtonen, M., "A statistical model forhourly large-scale wind and photovoltaic generation in new locations.", IEEE Transactions onSustainable Energy, 8(4), 1383–1393, 2017.

[18] Tian Tian and Ilya Chernyakhovskiy, NREL "Forecasting Wind and Solar Generation:Improving System Operations", January 2016.

[19] Quan Minh Duong, Marco Mussetta, F. Grimaccia, Emanuele Ogliari and S. Leva, "HybridModel for Hourly Forecast of Photovoltaic and Wind Power", IEEE International Conferenceon Fuzzy Systems, July 2013.

[20] Yann Riffonneau, Seddik Bacha, Franck Barruel and Stephane Ploix, "Optimal PowerFlow Management for Grid Connected PV Systems With Batteries", IEEE Transactions onSustainable Energy, Vol. 2, No. 3, July 2011.

[21] Sercan Teleke, Mesut E. Baran, Subhashish Bhattacharya, Alex Q. Huang, "OptimalControl of Battery Energy Storage for Wind Farm Dispatching", IEEE Transactions on EnergyConversion, Vol. 25, No. 3, September 2010.

[22] Josef Kallrath, Panos M. Pardalos, Steffen Rebennack, Max Scheidt, "Optimization in theEnergy Industry", Ed. Springer, 2009.

56 Bibliography

[23] Luís M. Costa, Franck Bourry, Jérémie Juban and George Kariniotakis, "Management ofEnergy Storage Coordinated with Wind Power under Electricity Market Conditions", Proba-bilistic Methods Applied to Power Systems Conference, May 2008.

[24] Jorge Marquez Angarita and Julio Garcia Usaola, "Combining Hydro-Generation and WindEnergy Biddings and Operation on Electricity Spot Markets", Electric Power Systems Research77 393–400, June 2006.

[25] G.N. Bathurst and G. Strbac "Value of combining energy storage and wind in short-term energy and balancing markets", Electric Power Systems Research 67, 1-8, 2003.

[26] Magnus Korpaas, Arne T. Holen and Ragne Hildrum, "Operation and sizing of energystorage for wind power plantsin a market system", Electrical Power and Energy Systems 25,599–606, 2003.

[27] Jorge Marquez Angarita and Julio Garcia Usaola, "Combining Hydro-Generation and WindEnergy Biddings and Operation on Electricity Spot Markets", Electric Power Systems Research77 393–400, 2007.

[28] Danish Energy Agency - Energistyrelsen, "Denmark’s Energy and Climate Outlook(DECO19)", https://ens.dk/en/our-services/projections-and-models/denmarks-energy-and-climate-outlook.

[29] Swedish Energy Agency - Energimyndigheten,"Energy in Sweden 2019: An overview",https://energimyndigheten.a-w2m.se/Home.mvc?ResourceId=5794.

[30] Nord Pool Marke data - Day Ahead Prices, https://www.nordpoolgroup.com/Market-data1/#/nordic/table.

[31] IEA – International Energy Agency "Renewable Energy Market Update Outlook for2020 and 2021", May 2020.

Appendix A: Matlab CodeThis Matlab code presented has not been optimized but implemented following a clarity criteria,although I have already verified that the code is correct and the contribution of this masterthesis is valid, this code must be used for reference purposes only, without any warranty orliability for my part.

12 % ===================================================================== %3 % Emilio Barrachina Gasco %4 % s182804@student .dtu.dk %5 % Master thesis %6 % Wind Energy Master %7 % Technical University of Denmark %8 % July 1st 2020 %9 % ===================================================================== %1011 clear all; close all; clc;1213 %% CorRES TIME SERIES INPUTS1415 %% INPUTS1617 P_RES_F = xlsread (’DK2 RES_F ’,’Scenario 9’,’B2: B17545 ’);18 frequency = xlsread (’DK2 RES_F ’,’Scenario 9’,’C2: C17545 ’);19 P_RES = xlsread (’DK2 RES ’,’Scenario 9’,’A2: A17545 ’);2021 % Zoom study for only two days22 %{23 P_RES_F = xlsread (’DK2 RES_F ’,’Scenario 9’,’B2:B48 ’);24 frequency = xlsread (’DK2 RES_F ’,’Scenario 9’,’D2:D49 ’);25 P_RES = xlsread (’DK2 RES ’,’Scenario 9’,’C2:C49 ’);26 %}27 t_range = 1:17544;28 time = 1: length ( t_range );293031 SOC_pre_F = 0.5; % [p.u.]32 SOC_pre = 0.5; % [p.u.]33 P_grid = 100; % [MW], grid connection power constrain3435 C_rate = 1; % [hours], we can change it to 2, 4, 6, 8 hours36 P_LIM_batt = 50; % [MW], we can choose a higher value , 25, 50, 75 ,...37 % P_batt_max_F = P_LIM_batt / C_rate ;

58 Appendix A: Matlab Code

38 % P_batt_max = P_LIM_batt / C_rate ;39 P_batt_max_F = P_LIM_batt ;40 P_batt_max = P_LIM_batt ;4142 E_batt =100; % [MWh]4344 %% YEARLY RES ENERGY GENERATED AND CAPACITY FACTOR : FORECAST AND MEASURED4546 for t=time47 P_RES_F (t)= P_RES_F (t);48 E_RES_F =sum( P_RES_F );49 E_RES_F_year = E_RES_F /2;5051 P_RES(t)=P_RES(t);52 E_RES=sum(P_RES);53 E_RES_year =E_RES /2;54 end5556 E_RES_F_year = E_RES_F_year /1000000 % [TWh/year]57 E_RES_year = E_RES_year /1000000 % [TWh/year]5859 CF_F = (( E_RES_F_year ) /(8760) ) *1000000 % [%]60 CF = (( E_RES_year ) /(8760) ) *1000000 % [%]6162 % FULL WORK HOURS: FORECAST AND MEASURED63 FWH_F = (CF_F *8760) /100 % [hours/year]64 FWH = (CF *8760) /100 % [hours/year]656667 %% BIDDING MODEL68 for t=time6970 P_discharge_req_F (t)=- P_RES_F (t)+ P_grid ;7172 if P_RES_F (t) > P_grid73 P_surp_F (t) = P_RES_F (t) - P_grid ;7475 elseif P_RES_F (t) <= P_grid76 P_surp_F (t) = 0;77 end7879 if P_discharge_req_F (t) <080 P_discharge_req_F (t)=0;81 end82 P_batt_req_F (t)=- P_RES_F (t)+ P_grid ;83 if P_batt_req_F (t)>P_batt_max_F84 Delta_SOC_req_F (t)= P_batt_max_F *( -1/ E_batt );85 P_curtailment_F (t)=0;86 elseif P_batt_req_F (t)<-P_batt_max_F87 Delta_SOC_req_F (t)=- P_batt_max_F *( -1/ E_batt );88 P_curtailment_F (t)= P_batt_max_F - P_batt_req_F (t);89 else

Appendix A: Matlab Code 59

90 Delta_SOC_req_F (t)= P_batt_req_F (t)*( -1/ E_batt );91 P_curtailment_F (t)=0;92 end93 if Delta_SOC_req_F (t)+SOC_pre_F >194 SOC_F(t)=1;95 P_curtailment_F_ (t)= Delta_SOC_req_F (t)+SOC_pre_F -1;96 elseif Delta_SOC_req_F (t)+SOC_pre_F <0.597 SOC_F(t)=0.5;98 P_curtailment_F_ (t)=0;99 else100 SOC_F(t)= Delta_SOC_req_F (t)+ SOC_pre_F ;101 P_curtailment_F_ (t)=0;102 end103 Delta_SOC_F (t)=SOC_F(t)-SOC_pre_F ;104 SOC_pre_F =SOC_F(t);105 P_batt_F (t)= Delta_SOC_F (t)*(- E_batt /1);106 if P_batt_F (t) >=0107 P_discharge_F (t)= P_batt_F (t);108 P_charge_F (t)=0;109 else110 P_discharge_F (t)=0;111 P_charge_F (t)=- P_batt_F (t);112 end113 P_missing_F (t)=- P_discharge_req_F (t)+ P_discharge_F (t);114 P_HPP_F (t)= P_grid + P_missing_F (t);115 P_curtailment_total_F (t)= P_curtailment_F (t)+ P_curtailment_F_ (t);116 end117118 %% REAL TIME MODEL119 for t=time120121 P_discharge_req (t)=-P_RES(t)+ P_HPP_F (t);122 if P_RES(t) > P_grid123 P_surp (t) = P_RES(t) - P_grid ;124 elseif P_RES(t) <= P_grid125 P_surp (t) = 0;126 end127128 if P_discharge_req (t) <0129 P_discharge_req (t)=0;130 end131 P_batt_req (t)=-P_RES(t)+ P_HPP_F (t);132 if P_batt_req (t)>P_batt_max133 Delta_SOC_req (t)= P_batt_max *( -1/ E_batt );134 P_curtailment (t)=0;135 elseif P_batt_req (t)<-P_batt_max136 Delta_SOC_req (t)=- P_batt_max *( -1/ E_batt );137 P_curtailment (t)=P_batt_max - P_batt_req (t);138 else139 Delta_SOC_req (t)= P_batt_req (t)*( -1/ E_batt );140 P_curtailment (t)=0;141 end

60 Appendix A: Matlab Code

142 if Delta_SOC_req (t)+SOC_pre >1143 SOC(t)=1;144 P_curtailment_ (t)= Delta_SOC_req (t)+SOC_pre -1;145 elseif Delta_SOC_req (t)+SOC_pre <0146 SOC(t)=0;147 P_curtailment_ (t)=0;148 else149 SOC(t)= Delta_SOC_req (t)+ SOC_pre ;150 P_curtailment_ (t)=0;151 end152 Delta_SOC (t)=SOC(t)-SOC_pre ;153 SOC_pre =SOC(t);154 P_batt (t)= Delta_SOC (t)*(- E_batt /1);155 if P_batt (t) >=0156 P_discharge (t)= P_batt (t);157 P_charge (t)=0;158 else159 P_discharge (t)=0;160 P_charge (t)=- P_batt (t);161 end162 P_missing (t)=- P_discharge_req (t)+ P_discharge (t);163 P_HPP(t)= P_HPP_F (t)+ P_missing (t);164 P_curtailment_total (t)= P_curtailment (t)+ P_curtailment_ (t);165 end166167 %% FULL MODEL168169 for t=time170 P_HPP_FE (t)=P_HPP(t)-P_HPP_F (t);171 GRID(t)=P_HPP(t);172 P_curtailed (t)= P_curtailment_total (t);173 P_RES(t)=P_RES(t)-P_RES_F (t);174 end175176 %% ERROR METRICS : P_HPP_FE (t) MAFE and RSMFE177178 fprintf (’P_HPP_FE ERROR METRICS %s\n’);179180 MAE_P_HPP_FE = mean(abs( P_HPP_FE ))181 RMSE_P_HPP_FE = sqrt(mean (( P_HPP_FE ).^2))182183 %% ERROR METRICS : P_RES_FE (t) MAE and RSME184 fprintf (’P_RES_FE ERROR METRICS %s\n’);185186 MAE_P_RES_FE = mean(abs(P_RES))187 RMSE_P_RES_FE = sqrt(mean (( P_RES).^2))188189 %% PLOTTINGS190191 figure192 plot(time , P_RES_F )193 title (’DK2 HPP Scenario 9 - Time series - P\_RES\_F(t)’)

Appendix A: Matlab Code 61

194 xlim ([1 17544])195 xlabel (’Time [hours]’)196 ylabel (’P\_RES\_F [MW]’)197 grid on198199 figure200 plot(time ,P_RES)201 title (’DK2 HPP Scenario 9 - Time series - P\_RES(t)’)202 xlim ([1 17544])203 xlabel (’Time [hours]’)204 ylabel (’P\_RES [MW]’)205 grid on206207208 figure209 plot(time , P_surp_F )210 title (’DK2 HPP Scenario 9 - Time series - P_{surp }\_F(t)’)211 xlim ([1 17544])212 xlabel (’Time [hours]’)213 ylabel (’P_{ surplus }\_F [MW]’)214 grid on215216 figure217 plot(time , P_surp )218 title (’DK2 HPP Scenario 9 - Time series - P_{surp }(t)’)219 xlim ([1 17544])220 xlabel (’Time [hours]’)221 ylabel (’P_{ surplus } [MW]’)222 grid on223224 figure225 plot(time , P_curtailment_total_F )226 title (’DK2 HPP Scenario 9 - Time series - P_{ curtailed }\_F(t)’)227 xlim ([1 17544])228 xlabel (’Time [hours]’)229 ylabel (’P_{ curtailed }\_F [MW]’)230 grid on231232 figure233 plot(time , P_curtailment_total )234 title (’DK2 HPP Scenario 9 - Time series - P_{ curtailed }(t) - June 8th and

9th 2015 ’)235 xlim ([1 17544])236 xlabel (’Time [hours]’)237 ylabel (’P_{ curtailed } [MW]’)238 grid on239240 figure241 plot(time ,( SOC_F* P_LIM_batt ))242 title (’DK2 HPP Scenario 9 - Time series - SOC\_F(t) - June 8th and 9th

2015 ’)243 xlim ([1 17544])

62 Appendix A: Matlab Code

244 xlabel (’Time [hours]’)245 ylabel (’SOC\_F * P_LIM_batt [MW]’)246 grid on247248 figure249 plot(time ,( SOC* P_LIM_batt ))250 title (’DK2 HPP Scenario 9 - Time series - SOC(t) - June 8th and 9th 2015 ’

)251 xlim ([1 48])252 xlabel (’Time [hours]’)253 ylabel (’SOC* P_LIM_batt [MW]’)254 grid on255256257 figure258 plot(time , P_charge_F )259 title (’DK2 HPP Scenario 9 - Time series - P_{ charged }\_F(t) - June 8th

and 9th 2015 ’)260 xlim ([1 48])261 xlabel (’Time [hours]’)262 ylabel (’Power Charged [MW]’)263 grid on264265 figure266 plot(time , P_charge )267 title (’DK2 HPP Scenario 9 - Time series - P_{ charged }(t) - June 8th and 9

th 2015 ’)268 xlim ([1 48])269 xlabel (’Time [hours]’)270 ylabel (’Power Charged [MW]’)271 grid on272273 figure274 plot(time , P_discharge_F )275 title (’DK2 HPP Scenario 9 - Time series - P_{ discharged }\_F(t) - June 8th

and 9th 2015 ’)276 xlim ([1 48])277 xlabel (’Time [hours]’)278 ylabel (’Power discharged [MW]’)279 grid on280281 figure282 plot(time , P_discharge )283 title (’DK2 HPP Scenario 9 - Time series - P_{ discharged }(t) - June 8th

and 9th 2015 ’)284 xlim ([1 48])285 xlabel (’Time [hours]’)286 ylabel (’Power discharged [MW]’)287 grid on288289 figure290 plot(frequency ,sort( P_surp_F ))

Appendix A: Matlab Code 63

291 title (’DK2 HPP Scenario 9 - Duration Curve - P_{surp }\_F(t) - June 17thand 18th 2016 ’)

292 xlim ([0 1])293 xlabel (’Cumulated frequency ’)294 ylabel (’P_{ surplus }\_F [MW]’)295 grid on296297 figure298 plot(frequency ,sort( P_surp ))299 title (’DK2 HPP Scenario 9 - Duration Curve - P_{surp }(t) - June 17th and

18th 2016 ’)300 xlim ([0 1])301 xlabel (’Cumulated frequency ’)302 ylabel (’P_{ surplus } [MW]’)303 grid on304305 figure306 plot(frequency ,sort( P_curtailment_total_F ))307 title (’DK2 HPP Scenario 9 - Duration Curve - P_{ curtailed }\ _total \_F(t) -

June 17th and 18th 2016 ’)308 xlim ([0 1])309 xlabel (’Cumulated frequency ’)310 ylabel (’P_{ curtailed }\_F [MW]’)311 grid on312313 figure314 plot(frequency ,sort( P_curtailment_total ))315 title (’DK2 HPP Scenario 9 - Duration Curve - P_{ curtailed }\ _total (t) -

June 17th and 18th 2016 ’)316 xlim ([0 1])317 xlabel (’Cumulated frequency ’)318 ylabel (’P_{ curtailed } [MW]’)319 grid on320321 figure322 plot(frequency ,sort(SOC_F))323 title (’DK2 HPP Scenario 9 - Duration Curve - SOC\_F(t) - June 17th and 18

th 2016 ’)324 xlim ([0 1])325 ylim ([0 1.2])326 xlabel (’Cumulated frequency ’)327 ylabel (’SOC\_F [p.u.]’)328 grid on329330 figure331 plot(frequency ,sort(SOC))332 title (’DK2 HPP Scenario 9 - Duration Curve - SOC(t) - June 17th and 18th

2016 ’)333 xlim ([0 1])334 ylim ([0 1.2])335 xlabel (’Cumulated frequency ’)336 ylabel (’SOC [p.u.]’)

64 Appendix A: Matlab Code

337 grid on338339 figure340 plot(frequency ,sort( P_discharge_F ))341 title (’DK2 HPP Scenario 9 - Duration Curve - P_{ discharged }\_F(t) - June

17th and 18th 2016 ’)342 xlim ([0 1])343 ylim ([0 3])344 xlabel (’Cumulated frequency ’)345 ylabel (’Power discharged [MW]’)346 grid on347348 figure349 plot(frequency ,sort( P_discharge ))350 title (’DK2 HPP Scenario 9 - Duration Curve - _{ discharged }(t) - June 17th

and 18th 2016 ’)351 xlim ([0 1])352 xlabel (’Cumulated frequency ’)353 ylabel (’Power discharged [MW]’)354 grid on

Appendix B: MeasuredScenarios Comparison

DK2 HPP - MeasuredDK2 Energy surplused measured for hybrid power plantscenarios comparison

RES Scenarios ERES(t)[TWh/y] Esurplused(t)[GWh/y] Ecurtailed(t)[GWh/y]Sc.1: 0WPP+100PVPP 0.129 0.000 0.000Sc.2: 25WPP+75PVPP 0.189 0.000 0.000Sc.3: 50WPP+50PVPP 0.249 0.000 0.000Sc.4: 75WPP+25PVPP 0.309 0.000 0.000Sc.5: 100WPP+0PVPP 0.369 0.000 0.000Sc.6: 80WPP+60PVPP 0.372 2.525 2.525Sc.7: 100WPP+40PVPP 0.420 4.984 4.984Sc.8: 105WPP+35PVPP 0.432 6.347 6.347Sc.9: 120WPP+20PVPP 0.468 25.505 25.505

Table 1: DK2 RES Scenarios comparison - Energy Produced and Surplused

RES Scenarios ERES(t)[TWh/y] Ecurtailed(t)[GWh/y] Ecurtailed(t)[%]Sc.1: 0WPP+100PVPP 0.129 0.000 0.000Sc.2: 25WPP+75PVPP 0.189 0.000 0.000Sc.3: 50WPP+50PVPP 0.249 0.000 0.000Sc.4: 75WPP+25PVPP 0.309 0.000 0.000Sc.5: 100WPP+0PVPP 0.369 0.000 0.000Sc.6: 80WPP+60PVPP 0.372 2.525 0.68Sc.7: 100WPP+40PVPP 0.420 4.984 1.19Sc.8: 105WPP+35PVPP 0.432 6.347 1.47Sc.9: 120WPP+20PVPP 0.468 25.505 5.45

Table 2: DK2 RES Scenarios comparison - Energy Produced Measured, Surplused andCurtailed

66 Appendix B: Measured Scenarios Comparison

RES Scenarios ERES(t)[TWh/y] Ecurtailed(t)[GWh/y] Ecurtailed(t)[%] CF [%]Sc.1: 0WPP+100PVPP 0.129 0.000 0.00 14.70Sc.2: 25WPP+75PVPP 0.189 0.000 0.00 21.55Sc.3: 50WPP+50PVPP 0.249 0.000 0.00 28.40Sc.4: 75WPP+25PVPP 0.309 0.000 0.00 35.25Sc.5: 100WPP+0PVPP 0.369 0.000 0.00 42.10Sc.6: 80WPP+60PVPP 0.372 2.525 0.68 42.50Sc.7: 100WPP+40PVPP 0.42 4.984 1.19 47.98Sc.8: 105WPP+35PVPP 0.432 6.347 1.47 49.35Sc.9: 120WPP+20PVPP 0.468 25.505 5.45 53.46

Table 3: DK2 RES Scenarios comparison - Energy Produced Measured, Surplused andCurtailed

SE2 HPP - Measured 67

SE2 HPP - MeasuredSE2 Energy Surplused for hybrid power plant scenarioscomparison

RES Scenarios ERES_F (t)[TWh/y] Esurplused_F (t)[GWh/y] Ecurtailed_F (t)[GWh/y]Sc.1: 0WPP+100PVPP 0.118 0.000 0.000Sc.2: 25WPP+75PVPP 0.142 0.000 0.000Sc.3: 50WPP+50PVPP 0.166 0.000 0.000Sc.4: 75WPP+25PVPP 0.190 0.000 0.000Sc.5: 100WPP+0PVPP 0.215 0.000 0.000Sc.6: 80WPP+60PVPP 0.242 1.228 1.228Sc.7: 100WPP+40PVPP 0.262 2.029 2.029Sc.8: 105WPP+35PVPP 0.267 2.445 2.445Sc.9: 120WPP+20PVPP 0.281 8.997 8.997

Table 4: SE2 RES Scenarios comparison - Energy Produced and Surplused

RES Scenarios ERES_F (t)[TWh/y] Ecurtailed_F (t)[GWh/y] Ecurtailed_F (t)[%]Sc.1: 0WPP+100PVPP 0.118 0.000 0.000Sc.2: 25WPP+75PVPP 0.142 0.000 0.000Sc.3: 50WPP+50PVPP 0.166 0.000 0.000Sc.4: 75WPP+25PVPP 0.190 0.000 0.000Sc.5: 100WPP+0PVPP 0.215 0.000 0.000Sc.6: 80WPP+60PVPP 0.242 1.228 0.51Sc.7: 100WPP+40PVPP 0.262 2.029 0.77Sc.8: 105WPP+35PVPP 0.267 2.445 0.92Sc.9: 120WPP+20PVPP 0.281 8.997 3.20

Table 5: SE2 RES Scenarios comparison - Energy Produced and Surplused

68 Appendix B: Measured Scenarios Comparison

RES Scenarios ERES_F (t)[TWh/y] Ecurtailed_F (t)[GWh/y] Ecurtailed_F (t)[%] CF [%]Sc.1: 0WPP+100PVPP 0.118 0.000 0.00 13.46Sc.2: 25WPP+75PVPP 0.142 0.000 0.00 16.22Sc.3: 50WPP+50PVPP 0.166 0.000 0.00 18.98Sc.4: 75WPP+25PVPP 0.190 0.000 0.00 21.74Sc.5: 100WPP+0PVPP 0.215 0.000 0.00 24.50Sc.6: 80WPP+60PVPP 0.242 1.228 0.51 27.68Sc.7: 100WPP+40PVPP 0.262 2.029 0.77 29.89Sc.8: 105WPP+35PVPP 0.267 2.445 0.92 30.44Sc.9: 120WPP+20PVPP 0.281 8.997 3.20 32.09

Table 6: SE2 RES Scenarios comparison - Energy Produced and Surplused