Integration of Variable Renewable Energy in National and International Energy Systems National and International Energy Systems

Department of Mechanical Engineering Aalto- DD Behnam Zakeri "As far as the laws of mathematics refer to reality, they are not certain, and as far as

178 Integration of variable they are certain, they do not refer to reality." / 2016 Albert Einstein renewable energy in

Integration of variable renewable energy in national and international energy systems national and international energy systems

Modelling and assessment of flexibility requirements

Behnam Zakeri

9HSTFMG*ahaaab+ ISBN 978-952-60-7000-1 (printed) BUSINESS + ISBN 978-952-60-6999-9 (pdf) ECONOMY ISSN-L 1799-4934 ISSN 1799-4934 (printed) ART + ISSN 1799-4942 (pdf) DESIGN +

ARCHITECTURE Aalto University Aalto University School of Engineering SCIENCE + Department of Mechanical Engineering TECHNOLOGY www.aalto.fi CROSSOVER DOCTORAL DOCTORAL DISSERTATIONS DISSERTATIONS

2016 Aalto University publication series DOCTORAL DISSERTATIONS 178/2016

Integration of variable renewable energy in national and international energy systems

Modelling and assessment of ﬂexibility requirements

Behnam Zakeri

A doctoral dissertation completed for the degree of Doctor of Science (Technology) to be defended, with the permission of the Aalto University School of Engineering, at a public examination held at the lecture hall E of the Main Building (Otakaari 1) on 30 September 2016 at 12 noon.

Aalto University School of Engineering Department of Mechanical Engineering Energy Efficiency and Systems Supervising professor Prof. Sanna Syri, Aalto University, Finland

Thesis advisor Prof. Sanna Syri, Aalto University, Finland

Preliminary examiners D.Sc. (Tech.) Satu Viljainen, Senior Expert, Fingrid Oy, Finland

Dr. Catalina Spataru, Lecturer, University College London, U.K.

Opponent Prof. Lennart Söder, KTH Royal Institute of Technology, Sweden

Aalto University publication series DOCTORAL DISSERTATIONS 178/2016

ISBN 978-952-60-7000-1 (printed) ISBN 978-952-60-6999-9 (pdf) ISSN-L 1799-4934 ISSN 1799-4934 (printed) ISSN 1799-4942 (pdf) http://urn.fi/URN:ISBN:978-952-60-6999-9

Unigrafia Oy Helsinki 2016

Finland

Abstract Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi

Author Behnam Zakeri Name of the doctoral dissertation Integration of variable renewable energy in national and international energy systems - Modelling and assessment of ﬂexibility requirements Publisher School of Engineering Unit Department of Mechanical Engineering Series Aalto University publication series DOCTORAL DISSERTATIONS 178/2016 Field of research Energy Technology Manuscript submitted 10 June 2016 Date of the defence 30 September 2016 Permission to publish granted (date) 15 August 2016 Language English Monograph Article dissertation Essay dissertation

Abstract National energy policies seek to increase the share of local renewable energy to meet the targets for climate change mitigation and to enhance the energy security. In this respect, large-scale integration of variable renewable energy (VRE) seems to be a predominant option. To cope with the intermittency of VRE, energy systems need to employ different "ﬂexibility" solutions to match energy supply and demand in different time scales.

This dissertation examines the flexibility of an energy system in integration of VRE by employing quantitative energy system modelling methods. First, the maximum flexibility of an energy system is quantified. Then, different flexibility solutions and their system-level benefits are compared. The dissertation examines the market value of flexibility to quantify the profitability of such options from an investor's viewpoint. Since energy systems are being further interconnected through international power markets, the role of such markets in integration of VRE is crucial. To examine this role, this contribution develops a market-based multi-region energy system model. The proposed model is an hourly simulation-optimization model capturing the main operational features of an international power market, as well as the local heat sector in each country.

The results indicate that the flexibility of the Finnish energy system is limited and needs to be improved to accommodate wind integration levels larger than 20% of the total electricity demand. For the examined energy system, large-scale heat pumps in connection with the district heating system and heat storage prove to be techno-economically the most promising flexibility option. The deployment of electrical energy storage is not profitable under today's market compensations, but the monetization of some externalities could make a business model for some storage technologies. The size of these system-level benefits of energy storage is quantified in this study. Last but not the least, the role of international power markets in providing flexibility is analysed. The integration of VRE in neighbouring countries entails overlapping impacts, which may diminish the flexibility of the whole system. The findings of this dissertation inform energy policy on the flexibility of the energy system to achieve the planned targets from high-level integration of VRE.

Keywords Energy Policy, Energy systems analysis, Flexibility, Power market coupling, Variable renewable energy. ISBN (printed) 978-952-60-7000-1 ISBN (pdf) 978-952-60-6999-9 ISSN-L 1799-4934 ISSN (printed) 1799-4934 ISSN (pdf) 1799-4942 Location of publisher Helsinki Location of printing Helsinki Year 2016 Pages 189 urn http://urn.ﬁ/URN:ISBN:978-952-60-6999-9

Tiivistelmä Aalto-yliopisto, PL 11000, 00076 Aalto www.aalto.fi

Tekijä Behnam Zakeri Väitöskirjan nimi Vaihtelevan uusiutuvan energian integroiminen kansallisiin ja kansainvälisiin energiajärjestelmiin – Joustavuusvaatimusten mallinnus ja arviointi Julkaisija Insinööritieteiden korkeakoulu Yksikkö Konetekniikan Laitos Sarja Aalto University publication series DOCTORAL DISSERTATIONS 178/2016 Tutkimusala Energiatekniikka Käsikirjoituksen pvm 10.06.2016 Väitöspäivä 30.09.2016 Julkaisuluvan myöntämispäivä 15.08.2016 Kieli Englanti Monografia Artikkeliväitöskirja Esseeväitöskirja

Tiivistelmä Kansalliset energiapolitiikkatoimet pyrkivät lisäämään paikallisen uusiutuvan energian osuutta ilmastotavoitteisiin pääsemiseksi ja kansallisen energiaturvallisuuden parantamiseksi. Suuren mittakaavan vaihteleva uusiutuva energia on tässä vallitseva vaihtoehto. Energiajärjestelmät käyttävät erilaisia joustavuutta lisääviä ratkaisuja sopeutuakseen vaihtelevaan uusiutuvaan tuotantoon, jotta energian tuotanto ja kysyntä vastaavat toisiaan eri aikajänteillä.

Tämä väitöskirja tutkii energiajärjestelmän joustavuutta kvantitatiivisilla energiajärjestelmien mallinnuksen metodeilla tilanteissa, joissa vaihtelevaa uusiutuvaa energiaa lisätään. Aluksi määritetään energiajärjestelmän maksimaalinen joustokyky. Sitten erilaisia joustoratkaisuja ja niiden systeemitason hyötyjä verrataan. Väitöskirja tutkii myös joustavuuden markkina- arvoa määrittääkseen joustovaihtoehtojen kannattavuuden investoijan näkökulmasta. Koska kansalliset energiajärjestelmät liittyvät koko ajan kiinteämmin toisiinsa kansainvälisen sähkömarkkinan kautta, sähkömarkkinan rooli vaihtelevan uusiutuvan energian integroinnissa on keskeinen. Tämän ilmiöön tutkimiseksi tässä väitöskirjassa on kehitetty markkinapohjainen monikansallinen energiajärjestelmämalli. Malli on tuntitason simuloinnin ja optimoinnin malli, joka pystyy mallintamaan sekä kansainvälisen sähkömarkkinan että paikallisten lämpöjärjestelmien pääpiirteet tarkastelumaissa.

Työn tulokset indikoivat, että Suomen energiajärjestelmän joustokyky on rajallinen ja että joustokykyä on parannettava, jotta siihen voidaan integroida yli 20% tuulivoimaa sähkön kokonaiskysynnästä. Tarkastellussa järjestelmässä suuret kaukolämpöjärjestelmiin kytketyt lämpöpumput ja lämpövarastot osoittautuivat teknis-taloudellisesti lupaavimmiksi vaihtoehdoiksi. Sähkön varastoinnin käyttö ei ole kannattavaa tämän hetken markkinoilta saatavilla korvauksilla, mutta ulkoisvaikutusten huomioiminen voisi luoda kannattavan bisnesmallin joillekin teknologioille. Näitä järjestelmätason hyötyjä määritetään työn case- tarkastelussa. Työssä on myös analysoitu kansainvälisten sähkömarkkinoiden roolia joustavuuden tarjoajana. Vaihtelevan uusiutuvan tuotannon integrointi aiheuttaa vaikutuksia myös naapurimaihin, ja yhteisvaikutukset voivat pienentää koko järjestelmän joustokykyä. Tämän väitöskirjan tulokset tuottavat tietoa politiikkatoimia varten, jotta suunnitellut vaihtelevan uusiutuvan energian tavoitteet voidaan saavuttaa.

Avainsanat energiapolitiikka, energiajärjestelmäanalyysi, joustavuus, sähkömarkkinoiden integroituminen, vaihteleva uusiutuva energia ISBN (painettu) 978-952-60-7000-1 ISBN (pdf) 978-952-60-6999-9 ISSN-L 1799-4934 ISSN (painettu) 1799-4934 ISSN (pdf) 1799-4942 Julkaisupaikka Helsinki Painopaikka Helsinki Vuosi 2016 Sivumäärä 189 urn http://urn.ﬁ/URN:ISBN:978-952-60-6999-9

Preface

First, I would like to express my gratitude to my supervisor Prof. Sanna Syri. I appreciate the flexibility and support she provided me with, which was a great help for me to design the scope of this dissertation. While leaving me flexible, her valuable guidance, her overall vision of the research gaps in the field, and her openness and availability for discussion and knowledge sharing greatly en- hanced the quality of this thesis. I also demonstrate my sincere appreciation to the examiners of this dissertation, Dr. Satu Viljainen and Dr. Catalina Spataru; and all anonymous reviewers of my publications who made valuable feedback and constructive comments on my work.

I would like to thank all my colleagues at the laboratory of Energy Efficiency and Systems, Aalto University. I appreciate the great company by Mikko Wahl- roos and Sam Cross; talking to you over the lunchtime was a good way for es- caping from tiring days. Exchanging ideas and thoughts with Sam was a valuable part of my time at Aalto, both from academic and life-related aspects. I also thank Samuli Rinne and Helin Kristo for cooperating with me on energy modelling topics. I should not forget to thank Dr. Aiying Rong, who was the only colleague I could find at the office for a short chat after 9 PM. I would like to restate my sincere appreciations for the funds by the Doctoral Program of the Aalto School of Engineering, and the STEEM project. The latter was a great opportunity to work and discuss with Vilma Virasjoki, Dr. Matti Koivisto and Dr. John Millar, which made me more familiar with the topics related to the power market. I also appreciate the great help by Jenni Lehtonen, Seija Erander- Luukkanen , Maija Pero, and Mika Pantsu, who made the administrative and IT processes smooth for me. I also thank my friends in the Helsinki region, to name a few, Javad and Aliakbar for the great time together.

I had two research visits during my PhD, which greatly broadened my horizons in my work. I’d like to thank the Sustainable Energy Planning research group at Aalborg University, in particular Professors David Connolly and Brian V. Mathiesen; for the warm welcome and their support during my stay in Co- penhagen. I highly appreciate the WholeSEM fellowship by University College London (UCL), which made my visit to the Energy Institute possible. Working with James Price and Marianne Zeyringer at UCL, in one of the most stressful times of my PhD, kept me energetic and motivated; I thank you both. I sincerely thank Liz Milner, Dr. Ilkka Keppo, and Prof. Neil Strachan for their support during this visit.

Especially, I would like to thank the persons with whom this journey became possible. Lina was one of the main reasons I applied for this PhD and moved to Helsinki. I most heartily appreciate the unlimited support given by her and by the Teir family. I am deeply grateful to Cathrine for her patience, understanding, and kind support during the last steps of this work. I wish I could have you next to me for celebrating the closure of this chapter too.

Most of all I would like to thank my parents. They gave me the opportunity and courage to freely decide on my educational path and on where to follow my dreams. This has let me try new experiences and explore new adventures. My dear mother! I hope I would be able someday to show my heartfelt gratitude to all the efforts you made for my education, and to make your wishes come true.

Espoo, 30 August 2016

Behnam Zakeri

Preface

Table of Contents

Preface ...... i Table of Contents ...... iii List of Abbreviations ...... v List of Publications ...... vi Author’s Contribution ...... viii 1. Introduction ...... 1 1.1 Background ...... 1 1.2 Scope and Structure ...... 3 1.3 Contribution of This Dissertation ...... 5 2. Integration of VRE in National Energy Systems ...... 7 2.1 Modelling of National Energy Systems ...... 7 2.2 Simulation of Hourly Wind and Solar Production ...... 8 2.3 Maximum Feasible Renewable Energy ...... 9 2.4 Flexibility Options in an Energy System...... 11 2.4.1 Flexibility of the Existing Energy Infrastructure ...... 13 2.5 Summary of Findings ...... 14 3. Market Value of Flexibility ...... 16 3.1 Cost Analysis of Electrical Energy Storage (EES) ...... 16 3.2 Value of Flexibility in Competitive Power Markets ...... 18 3.2.1 Case Study and Results ...... 18 4. Role of Flexibility Solutions in Integration of VRE ...... 21 4.1 Wind-Nuclear Dilemma ...... 21 4.2 Modelling of Flexibility Solutions in Energy Models ...... 23 4.3 System-Level Impacts of Flexibility Technologies ...... 25 4.4 External Power Market as a Flexibility Option ...... 28 5. Flexibility Requirements in International Energy Systems ...... 30 5.1 Modelling Paradigm behind Enerallt ...... 31 5.1.1 Challenges in Market-based, Multi-region Energy Modelling 32 5.2 Power Market Couplings and National Energy Systems ...... 34 5.3 Sweden’s Nuclear Phase-out and Wind Integration in Finland34 iii

5.4 Grid vs. Storage ...... 35 5.4.1 A Note on Economic Issues ...... 36 6. Discussion and Conclusions ...... 37 6.1 Main Findings of This Dissertation ...... 37 6.2 National vs. International: “That is the Question” ...... 39 6.3 The Final Note ...... 40 6.4 Quick Takeaways from This Dissertation ...... 40 6.5 Limitations and Directions for the Future Research...... 42 References ...... 43 Publications ...... 49

Preface

List of Abbreviations

ALCC annualized life cycle cost CAES compressed air energy storage CEEP critical excess electricity production CHP combined heat and power DH district heating EES electrical energy storage FDR flexible demand response HOB heat-only boilers LCC life cycle cost LCOE levelized cost of electricity generation Li-ion lithium-ion (battery) LHP large heat pumps (connected to district heating) MREI Maximum Renewable Energy Integration (an index) NaS sodium-sulphur (battery) NPE net power exchange NPP nuclear power plant O&M operation and maintenance PEC primary energy consumption PFC primary fuel consumption PHS pumped hydropower storage PTH power to heat (technologies) RES renewable energy source T&D transmission and distribution (of electricity) TSO transmission system operator TPP thermal power plant (excluding nuclear) VRFB vanadium-redox flow battery VRE variable renewable energy VRE-E electricity from a variable renewable energy source

List of Publications

This doctoral dissertation consists of a summary and of the following publications, which are referred to in the text as I-VI:

I. Zakeri B, Syri S, Rinne S. Higher renewable energy integration into the existing energy system of Finland – Is there any maximum limit? Energy 2015; 92(3): 244-59i.

II. Zakeri B, Syri S. Electrical energy storage systems: A comparative life cycle cost analysis. Renewable and Sustainable Energy Reviews 2015; 42: 569–96ii.

III. Zakeri B, Syri S. Economy of Electricity Storage in the Nordic Electricity Market: The Case for Finland. IEEE 11th International Conference on the Eu- ropean Energy Markets (EEM), Krakow, Poland, 28-30 May, 2014iii. DOI: 10.1109/EEM.2014.6861293

IV. Zakeri B, Rinne S, Syri S. Wind integration into energy systems with a high share of nuclear power – what are the compromises? Energies 2015; 8 (4): 2493-527.

V. Zakeri B, Virasjoki V, Syri S, Connolly D, Mathiesen BV, Welsch M. Impact of Germany's Energy Transition on the Nordic Power Market – A market- based multi-region energy system model. Energy 2016 (In press). DOI: 10.1016/j.energy.2016.07.083

VI. Zakeri B, Syri S. Intersection of national renewable energy policies in countries with a common power market. IEEE 13th International Conference on the European Energy Markets (EEM), Porto, Portugal, 6-9 June, 2016. DOI: 10.1109/EEM.2016.7521265

i Received the best paper award as a conference paper titled ”Maximum feasible renewable energy in Finland and techno-economic implications”, presented in the 9th Conference on Sustainable Development of En- ergy, Water and Environment Systems (SDEWES), Venice-Istanbul, 20-27 Sep 2014. ii Received the best publication award at the Aalto University School of Engineering, 2014. iii Received the third best paper award among the papers presented by postgraduate students at the EEM 2014 conference, Krakow, Poland. vi

Preface

Other contributions and co-authorships by the author, which served to inform this dissertation include:

1. Zakeri B, Syri S. Value of energy storage in the Nordic power market – Ben- efits from price arbitrage and ancillary services. IEEE 13th International Con- ference on the European Energy Markets (EEM), Porto, Portugal, 6-9 June, 2016. DOI: 10.1109/EEM.2016.7521275

2. Zakeri B, Syri S. Value of energy storage: The required market and policy supports. Book Chapter in: Delivering Energy Law and Policy in the EU and the US, Edited by Heffron RJ, Little GFM, Edinburgh University Press, 2016.

3. Syri S, Zakeri B. The Finnish energy policy: Fulfilling the EU energy & climate targets with nuclear and renewables. Book Chapter in: Delivering Energy Law and Policy in the EU and the US, Edited by Heffron RJ, Little GFM, Edin- burgh University Press, 2016.

4. Cross S, Zakeri B, Padfield D, Syri S. Is battery energy storage economic in islanded power systems? Focus on the island of Jersey. IEEE 13th International Conference on the European Energy Markets (EEM), Porto, Portugal, 6-9 June, 2016. DOI: 10.1109/EEM.2016.7521219

vii

Author’s Contribution

Behnam Zakeri has been the first author and the main contributor in the publications included in this dissertation. The details are the following:

Publication I: Behnam Zakeri developed the energy model for Finland in En- ergyPLAN, prepared required input data, conducted simulations, and wrote the manuscript with reviews and comments from prof. Sanna Syri and Samuli Rinne.

Publication II: Behnam Zakeri collected required input data and conducted model development and cost calculations. Behnam Zakeri wrote the manuscript, supported by prof. Sanna Syri’s review and comments.

Publication III: Behnam Zakeri developed the energy storage model and conducted case studies, under supervision of prof. Sanna Syri. Behnam Zakeri wrote the manuscript, backed with comments from prof. Sanna Syri.

Publication IV: Behnam Zakeri performed the data collection, wind integration modelling, modelling of energy system, simulations, and the preparation of manuscript, under supervision and guidance of prof. Sanna Syri. Samuli Rinne provided the initial data and model in EnergyPLAN. All the authors were engaged in reviewing the manuscript, discussion, and scenario development.

Publication V: Behnam Zakeri proposed the modelling paradigm, designed the research questions, and wrote the manuscript, under supervision of prof. Sanna Syri. Behnam Zakeri conducted data collection, model development, and simulations with comments from Vilma Virasjoki. All the co-authors, including David Connolly, Brian V. Mathiesen, and Manuel Welsch were engaged in com- menting on the modelling paradigm, introducing sources of data of their respective affiliated countries, discussing the results, and reviewing the manuscript.

Publication VI: Behnam Zakeri prepared the required input data, developed market-based multi-region energy model of the Nordic countries, and conducted case studies under supervision of prof. Sanna Syri. Behnam Zakeri wrote the manuscript, supported with comments by prof. Sanna Syri.

viii

Introduction

1. Introduction

1.1 Background

Energy systems are on the threshold of a new transition era. A conjuncture of different motivations such as climate change mitigation1 [1] and energy security has motivated regional and national energy policies towards further utilization of local renewable energy2. In this respect, the European Commission [2] has set a target to increase the share of renewables in gross final energy consumption up to 20% across the EU by 20203. Accordingly, a binding target is assigned to each Member State based on their available renewable energy sources (RESs) and potentials. For example, the renewable energy target for Finland, Sweden, and Denmark for 2020 is 38%, 49%, and 30%, respectively.

To achieve the EU energy targets, each Member State has outlined a National Renewable Energy Action Plan (NREAP) for advancing the share of different RESs. This includes an increased use of variable renewable energy (VRE), such as wind and solar energy. For instance, Finland is to augment the share of electricity from wind to 6000 GWh/a in 20204, which is an ambitious plan compared to 360 GWh/a in 2010 [3]. Denmark moves towards supplying 60% of electricity from wind in 2020, and furthermore, 85% of electricity from wind and solar PV in 2035 [4]. Different studies underline this crucial role of VRE as the cornerstone of future renewable energy systems. For example, it is estimated that 80% of energy demand will be met by VRE in a 100% renewable energy system for the EU [5].

The inherent variability and uncontrollability of VRE has motivated the deployment of flexibility5 solutions [6]. Flexibility solutions comprise a wide range of technologies and measures from generation across transmission to the demand side; for example, flexible power generation [7], energy storage [8], grid

1 According to The Intergovernmental Panel on Climate Change (IPCC), carbon emissions from the energy sector must be reduced by 50-70% by 2050 compared to 2010. This is required to limit the planet’s tempera- ture rise below 2 degree Celsius at the end of the 21st Century compared to the pre-industrial era.

2 According to the EU Directive 2009/28/EC [2], renewable energy means “energy from renewable non- fossil sources, namely wind, solar, aerothermal, geothermal, hydrothermal and ocean energy, hydropower, biomass, landfill gas, sewage treatment plant gas and biogases”.

3 The target has been further boosted to minimum 27% RESs in final energy consumption by 2030 [2].

4 In an amendment, this target was further extended to 9000 GWh/a by 2025 [3]. 5 In this study, the term flexibility refers to the ability of an energy system in balance of energy supply and demand over different time scales. 1

extensions [9,10], power market couplings [11], sectoral integration (e.g., the power and heat sectors) [12], and flexible demand response (FDR) [13]. These flexibility solutions reduce the amount of curtailed VRE, improve the levelized cost of electricity (LCOE) from VRE, and help the system to reach the planned targets of VRE integration. Furthermore, from the operational perspective, flexibility solutions maintain the smooth operation of different elements of an energy system by reducing, for example, distortions and stresses, utilization of fossil fuel back-up plants, and congestions in the grid.

The abovementioned system-level benefits may indicate the potential value of a flexibility solution, from a system designer or regulator viewpoint. However, that an energy system needs to be more flexible may not directly offer economic attractiveness for adopting flexible solutions. From the flexibility supplier’s perspective, providing such flexibility should be economically feasible. This calls for feasibility studies applicable to each individual case study. To conduct a proper cost-benefit analysis, the techno-economic and operational characteristics of each flexibility option should be examined separately. For example, only electrical energy storage (EES) comprises a wide range of technologies with different technical, functional, and economic features. Furthermore, EES technologies can be employed as potential flexibility solutions for various applications across the power supply chain with different revenue generation mechanisms [14]. Therefore, the promotion of flexibility solutions should be seen together with the market and regulatory drivers in the deployment of such technologies in the energy system in question [15].

Considering the above, it is commonly agreed that flexibility solutions are among the building blocks of a high-level VRE system. However, it is not straightforward to identify required revenue streams and supportive policies to justify the investment in such solutions. In liberalized electricity markets, the balancing needs for integration of VRE are commonly maintained in competitive market places, including day-ahead, intraday, and real time power markets. Hence, the suppliers of flexibility solutions are typically required to offer their respective flexibility service through such competitive environments. This calls for a market-based assessment and optimization of the profitability for a given flexibility solution. On the other hand, electricity markets in Europe are being increasingly coupled and integrated across national borders [16]. It is argued that this expansion of national power markets to wider geographical areas is itself a flexibility solution in smoothening of the intensity of electricity production from VRE [17]. Nevertheless, such power market couplings increase the complexity and uncertainty in estimation of profitability from investment in flexibility technologies in such interconnected regions [18,19].

Therefore, national energy systems encounter two predominant transitions in the near future: high-level integration of VRE and more market coupling with neighbouring energy systems. The assessment of flexibility requirements in such an emerging environment calls for integrated methods and new modelling

Introduction paradigms to capture both; (i) the dynamics of the domestic energy infrastructure, and (ii) the interplay between interconnected energy systems networked through competitive and volatile power markets. This dissertation aims at con- tributing in this regard.

1.2 Scope and Structure

This dissertation attempts to find acceptable answers to the following research questions: Q1. To what extent is a national-level energy system able to absorb VRE, without major investments in the flexibility of the system?

Q2. What is the market value of flexibility?

Q3. To what extent can different flexibility solutions contribute in balancing electricity from VRE in a national-level energy system? (i.e., system-level value of flexibility)

Q4. What are the implications of cross-border power market couplings for the integration of VRE and the related flexibility requirements?

This dissertation, in general, summarizes the methods, analyses, and results of the associated Publications. Figure 1 portrays the relationship between Pub- lications constituting this dissertation. The arrows show the use of methods (in red colour) and results (in blue colour) between Publications. For example, Pub- lication IV is informed by the methods applied in Publications I and III, whereas it makes the use of the results obtained in Publications I and II.

Figure 1. Relative relationship between Publications in this dissertation (The size of shapes and arrows has no specific meaning.) 3

In Publication I, a national energy system (Finland) is modelled by employing a publicly available modelling tool called EnergyPLAN [20]. The model is based on hourly and integrated analysis of the heating, power, transport, and industry sectors. By the aid of this model, we assess the flexibility of the energy system in different energy scenarios with high-level penetration of VRE. Chapter 2 discusses this topic in detail, and proposes a new indicator (Maximum RE Integra- tion, MREI) for quantifying the flexibility in energy systems.

To enhance the flexibility of an energy system, the deployment of flexibility solutions should be assessed. In this dissertation, EES is selected as a sample flexibility solution for more in-depth analysis. There are some reasons for this choice. EES systems comprise a wide range of stand-alone technologies that are applicable to numerous flexibility services in different levels in the power supply chain (from generation, transmission, distribution, to end-users). EES technologies are also applicable to different energy systems (cold or hot climate, central or decentralized, large or small, etc.). Publication II offers an extensive review on the life cycle costs (LCCs) of EES technologies, completed with a simple but robust cost analysis accommodating uncertainties. In Publication III, the market value of EES is examined in price arbitrage and some ancillary services for the Finnish power market. This way, the market value of flexibility from an investor’s viewpoint is evaluated. Chapter 3 summarizes the findings of Publica- tion II and III.

Next, after gaining an in-depth understanding on techno-economic functionality of the selected flexibility (i.e., EES) and its market value, this dissertation attempts to explore the role of different flexibility solutions in integration of VRE. To this end, Publication IV examines future high-level VRE scenarios for the chosen energy system (Finland), with considering possible flexibility solutions and planned inflexible generation capacity (nuclear). Therefore, the Finn- ish energy system modelled by EnergyPLAN in Publication I is further developed to include future energy transitions in Finland, and to examine the role of four flexibility solutions (see Chapter 4 for more details).

The evolution of this dissertation underlines the role of external power markets in assessing the flexibility of an energy system. In other words, in the study of energy scenarios for a national energy system, the applied modelling method relies on simplified assumptions in dealing with the impact of power flow from/to other neighbouring countries. However, the examined energy system in this dissertation has relatively large cross-border interconnection capacity, which makes the system interdependent to the outcome of the respective common power market. To address this emerging challenge, this dissertation develops a market-based multi-region energy systems model (called Enerallt). Publi- cation V introduces the modelling paradigm behind Enerallt and its relevance for the modelling of integration of VRE in international-level energy systems. Chapter 5 shortly introduces the functionality of Enerallt for some case studies related to the integration of VRE in the examined case study (Finland). This

Introduction

Chapter discusses the interplay of energy transitions in two neighbouring countries based on the main results of Publication VI.

Table 1 depicts the position of Publications and Chapters, with respect to the research questions in this dissertation.

Table 1. Relationship between research questions, publications, and chapters in this dissertation.

Research Main Theme Publication Chapter Question Q1 Flexibility of national-level energy systems I 2 Q2 Market value of flexibility (EES technologies) II,III 3 Q3 System-level value of different flexibility solutions IV 4 Q4 Flexibility in an international energy system V,VI 5

1.3 Contribution of This Dissertation

The results and findings of this dissertation serve the existing literature by offering additional insights and knowledge in different dimensions as follows.

Case study: The results directly inform the implications of future VRE scenarios in the examined case study, i.e., the Finnish energy system. This dissertation shows, inter alia, the indicative flexibility of Finland’s energy system, the implications of future wind-nuclear scenarios, the role of the Finnish heating sector in absorbing variability of VRE, and the market value of flexibility in the Finnish power market.

Universality of the results: While this dissertation has examined Finland as the case study, the results of energy systems studies are applicable to similar cases, especially in the cold climate. The findings of Publication II are universal and can be used in different studies worldwide. The methods used in this contribution are all applicable to other case studies, from national to international energy systems.

Modelling and analytical approaches: This dissertation has applied different approaches, of which some offer additions and novelty to the existing literature. This contribution has proposed an index for measuring the flexibility of national energy systems and an algorithm for maximizing the benefits of re- placeable EES systems, for example. More importantly, this dissertation fosters a new modelling paradigm for the study of intersection of national energy transitions in countries interconnected through a common power market.

Table 2 summarizes the contribution of each Publication, including approaches and models employed for the respective analysis. For more details, the reader may refer to the respective Chapter in this dissertation or the appended Publications.

Table 2. Modelling methods, employed/created tools, and main contributions in each Publication.

Publi- Modelling Model Main Approach Main Contribution cation Scope /Tool - Market-economic modelling - Flexibility index (MREI) I National energy EnergyPLAN system - Modelling of VRE integration - Max RE feasible II Cost analysis of - Probabilistic modelling of LCC Created in - LCC of EES EES technologies (Monte Carlo method) MATLAB - Market value of flexibility - Optimal size of EES, Created in III Cost-benefit of EES and optimal cycle num- - Optimization of size and cycles of MATLAB EES bers of batteries - Techno-economic optimization of - Wind-nuclear compro- IV National energy an energy system and its flexibility EnergyPLAN mise charts system - Wind integration simulations - Value of flexibility Enerallt - Combining a power International-level - Market-based multi-region energy V,VI (created in market model with an en- energy systems systems modelling MATLAB) ergy system model

The remainder of this dissertation is divided to five Chapters as follows. Chap- ter 2 discusses the modelling and simulation of high-level VRE in national energy systems. Chapter 3 deals with the market value of flexibility in a competitive power market. Chapter 4 draws on the results of Chapter 2 and 3 to study the role of flexibility solutions in the examined national energy system. The dissertation moves to the modelling and analysis of flexibility in international energy systems in Chapter 5. Discussion and main findings of this dissertation are summarized in Chapter 6.

Integration of VRE in National Energy Systems

2. Integration of VRE in National Energy Systems

2.1 Modelling of National Energy Systems

Modelling of different sectors and their interactions within a national-level energy system is a common method for informing national energy and environmental policies. The modelling practices are typically focused on the investiga- tion of energy balances, energy security, investment planning, and more recently, on the study of decarbonisation pathways.

With respect to the modelling method, energy models are in general catego- rized as optimization models, simulation models, power system and electricity market models, and qualitative and mixed-method models [21]. Quantitative energy system models are distinguished from each other based on the paradigm, spatial and temporal details, structure or topology, constraints and boundaries, and required parametric data [22]. For example, some models such as TIMES- MARKAL family [23] suggest energy planning based on a set of selective snap- shots in the system, such as conditions in the summer and winter peak time. Some other models, e.g., Balmorel [24], examine the system in more spatial and temporal details.

With respect to the integration of VRE in an energy system, a temporally detailed energy system analysis offers more capabilities in dealing with the variability of energy supply and demand, and the contribution of flexibility options [25]. For instance, an hourly analysis captures the hourly variability of the heat and power demand data, as well as power exchange with the neighbouring countries based on hourly electricity prices and the available exchange data. Deane et al. [26] argue that a time resolution of 5 min would improve the results of the hourly analysis merely by 1%, in terms of annual costs and benefits. To this end, and due to the availability of input data, this dissertation is structured based on the hourly energy system modelling. It means all the included publications report the results based on an hourly analysis.

Among different energy system modelling tools capable for the hourly analysis, EnergyPLAN6 is selected for Publications I and IV. EnergyPLAN employs a

6 More information about EnergyPLAN is provided at http://www.energyplan.eu/. The tool and its documentation are freely available for download. 7

deterministic approach for the hourly analysis of nation-wide energy systems. The tool offers various options for the study of the integration of different RESs. EnergyPLAN falls into simulation models, as opposed to optimization models with an optimum solution. The model provides the user with several options and strategies for the modelling and simulation of a given energy system based on the concept of Choice Awareness. Therefore, this is the user who decides between available regulation strategies for modelling their system in question, unlike models with one institutional framework. For example, the user can model the functionality of an energy system in integration of VRE, by changing the order between different balancing options. The model simulates an energy system based on either technical or market-economic schemes.

EnergyPLAN is widely used for the modelling of energy systems with a high share of combined heat and power (CHP) [27]. Moreover, the tool is capable to demonstrate the possibility of high-level VRE integration in different energy sectors (heating, power, and transport), as well as sectoral interplays. Therefore, EnergyPLAN is selected for the case study examined in this Chapter and Chap- ter 4, i.e., the Finnish energy system7. More details on the regulation strategies and the implemented modelling framework by the aid of EnergyPLAN to inform this dissertation is illustrated in Figure 1 in Publication IV.

2.2 Simulation of Hourly Wind and Solar Production

For the case of Finland, wind and solar photovoltaic (PV) are the two VRE technologies simulated in this study. For solar PV simulations, the “annual production” figures are obtained from [28] for three selective cities (Helsinki, Tam- pere, and Oulu). The cities are chosen based on population density and location. Then, the “hourly pattern” is simulated by using the hourly data of shortwave incoming solar irradiance, obtained from The NASA’s Modern Era Retrospec- tive-analysis for Research and Applications (MERRA) platform8 [29]. The data are obtained for a high-resolution spatial grid (0.5o x 0.6o) for the central and southern parts of Finland, which represents the main population centres. Next, the data is spatially averaged to create one representative hourly distribution (8760 h) for the whole country. Finally, for any given installed capacity of solar PV in the future scenarios, the hourly production is modelled by distributing the annual production over the year, with respect to the ratio of solar irradiance in each hour.

The modelling of high-level wind integrations starts with the actual wind production data as the basis. Since the installed capacity of wind in Finland is not very large (only 260 MW in the beginning of this study), the actual production data is adjusted before extrapolation for the future scenarios. This technique is based on comparing the duration curve of the actual data with the results of a

7 Besides the capabilities of EnergyPLAN, the choice of modelling tool was partly related to some institutional reasons, as well as the availability of the tool and its documentation.

8 Available at: http://disc.sci.gsfc.nasa.gov/mdisc/data-holdings/merra/merra_products_nonjs.shtml 8

Integration of VRE in National Energy Systems detailed wind integration study, performed by The Technical Research Centre of Finland (VTT) [30]. The method is described in detail in Publication IV, Sec- tion 2.3. By applying this method, the future multi-GW wind scenarios will ex- perience a more moderated production pattern (less peaks and troughs), compared to the case of the direct extrapolation of today’s wind production.

2.3 Maximum Feasible Renewable Energy

The penetration of renewable energy in large scales has attracted a wide range of studies. That an energy system can infinitely soar the share of RESs without facing resource scarcity, system integration challenges, and market-economic issues seems to be unrealistic [31]. Non-variable RESs, such as biomass and hydro, are resource- and site-constraint. On the other hand, abundant RESs are mainly variable in time and space, as such, called VRE. Therefore, it is crucial to quantify the maximum share of renewable energy that an energy system can integrate given the available resources, energy infrastructure, integration options, and other constraints in the system. Since the European countries under- take renewable targets, this question becomes more relevant: to what extent an energy system can increase the share of renewable energy.

For the case of Finland, the share of bioenergy9 in the future scenarios is estimated based on resource availability in terms of technical and economic potentials. We consider different conversion pathways (gasification, co-firing, bio- fuel10 production, etc.) to accommodate the use of the estimated bioenergy potential in different sectors of the energy system. The techno-economic implications are identified accordingly. The potential of hydropower seems to be fully harvested in Finland, leaving a small fraction for the future. Another potential in increasing the share of renewables examined in this study is the conversion of direct electric heating in residential houses into electric heat pumps. How- ever, a more extensive use of heat pumps, which involves the conversion of district heating (DH) customers, is not examined in this study. The use of geothermal energy and marine-based RESs are not considered in this study.

Therefore, VRE sources comprise the remaining potential for increasing the share of RESs. The maximum integration level of VRE into an energy system needs to be identified by a proper criterion to inform policy makers about the implications of high-level integration of VRE.

Different studies have proposed metrics for the assessment of the flexibility of power systems. Ma et al. [32] defines a flexibility index by evaluating the ramping rates of power production units in a system. Lannoye et al. [33] estimates

9 In this study, bioenergy refers to energy from biomass. Based on the EU RE directive (2009/28/EC) [2], biomass means the biodegradable fraction of products, waste and residues from biological origin from agri- culture, forestry and related industries including fisheries and aquaculture, as well as the biodegradable fraction of industrial and municipal waste”. 10 According to the EU RE directive (2009/28/EC) [2], biofuels represent “liquid or gaseous fuel for transport produced from biomass”. 9

the net load ramps by investigating ramping availability of individual plants in different time horizons. The mentioned methods require rather detailed information about different individual plants and their functional capabilities in different conditions. However, these power system studies typically neglect the flexibility of other energy sectors in integrating VRE, such as heat storage in the CHP-DH systems.

From an energy system perspective, Lund [34] suggests the term critical excess electricity production (CEEP), expressed in TWh/a. CEEP is analogous to the curtailment; it is a share of VRE that cannot be directly absorbed in an energy system, cannot be stored or converted to other energy carriers, and is beyond the cross-border power transmission capacity. Connolly et al [35] proposes a compromise coefficient, which relates the VRE integration with the con- servation in primary energy consumption (PEC). According to the latter, if marginal reductions in PEC between two consecutive wind integration levels would be higher than the yearly curtailed wind energy, that level of wind integration is still feasible.

In this Chapter, an indicator is proposed to identify the maximum level in the integration of VRE. The proposed method relates the issue of flexibility in an energy system with both primary fuel consumption11 (PFC) and power exchange with other countries. National-level energy systems are usually interconnected to neighbouring power systems, as such, the impact of VRE integration on the net power exchange (NPE) is important. In other words, the high-level integration of VRE will reduce power imports (or increases power exports), resulting in higher self-sufficiency of the energy system. Accordingly, this dissertation suggests the indicator of Maximum Renewable Energy Integration (MREI), explained in Eq. (1).

∆푁푃퐸 + ∆푃퐹퐶 푇푊ℎ/푎 푀푅퐸퐼 = [ ] (1) ∆푉푅퐸 푇푊ℎ/푎

The symbol Δ denotes the changes between two levels of VRE integration. In Eq. (1), ΔVRE stands for the incremental increase in VRE production, including the excess power that must be curtailed (i.e., both useful and excess VRE). ΔPFC denotes the reduction in PFC, so negative values are not desirable. With respect to the changes in the net power exchange (ΔNPE), the reduction in power imports (or increase in power exports) is considered a positive event and inserted by (+) sign. This way, a negative ΔNPE denotes higher electricity imports (or lower power exports) between any two levels of VRE integration, which is not desirable. Therefore, the higher the MREI, the more useful that level of VRE integration for the systems. This indicator shows that the addition of new installations of VRE can be accepted by the system, if the corresponding generated electricity (including excess power) would result in improving the power exchange and reducing PFC.

11 PFC includes the use of biomass as PEC. 10

Integration of VRE in National Energy Systems

The acceptable limit of MREI depends on the energy system in question. We describe the functionality of MREI through a comparison between three simple energy systems, as an example:

I. In an energy system heavily relying on condensing power production, each level of wind generation (e.g., in TWh) reduces PFC by a factor of 2-3, depending on the electrical efficiency of the examined thermal power plants (TPP). Hence, integrating more wind even with a part of it being curtailed is useful for the system as it significantly cuts PFC and carbon emissions. II. In an energy system with high-efficiency energy generation units, such as CHP with 80-90% overall efficiency, each TWh wind generation would result in less reduction of PFC compared to the case I. The system planner in this case would not benefit as much as the case I. III. In an energy system relying on power imports, each unit of wind generation would simply replace one unit of power imports. Thus, the system planner witnesses no difference in PEC nor in PFC. This is one of the reasons motivating the explicit inclusion of cross-border power exchanges in MREI, as opposed to the previous methods relying on PEC.

The above-mentioned example shows some simple general cases. An energy system may consider carbon emissions a more deciding parameter rather than PFC (although there is a relationship between the two). Publication I conserva- tively considers MREI=1 as a limit for the maximum VRE integration, meaning that an MREI smaller than one is not desirable. This would correspond to a wind curtailment of 2-3% per year in Finland, for example. However, in the future, with a lower cost for wind energy, one may see a much lower MREI still feasible.

2.4 Flexibility Options in an Energy System

By applying the energy systems modelling framework explained in Chap- ter 2.1, different sectors of an energy system and their possible interaction can be modelled. Then, high-level VRE scenarios are analysed by identifying the possible flexibility options in the system in different time scales. Table 3 depicts the already-existing flexibility options, emerging flexibility solutions, and non- flexible sectors considered in this dissertation for the case of Finland. Condens- ing TPPs are assumed flexible by changing their production from an hour to another. Ramping constraints are not considered as an input in the modelling, but a post-evaluation (repairing process) is applied to control the results. This repairing process compares the hourly results for electricity generation from TPPs with the maximum hourly ramp rates obtained from the national statistics [36].

Table 3. Today’s flexibility solutions, new flexibility solutions, and inflexible technologies considered in this study.

Flexibility Solution/Technology Chapter Group

- Flexible demand response (FDR) - Power transmission lines to neighbouring countries Available - Flexibility of district heating (DH) sector due to heat storage flexibility - Flexibility of CHP plants 2,4,5 optionsa - Flexibility of hydro storage - Power to heat (PTH) technologies, including large heat pumps (LHP) and electric boilers connected to DH - Thermal power plants (TPPs) Emerging flexibility - EES technologies 3,4,5 solutionsb Non- - Power to gas options examined N/A - Smart electric vehicles (EVs) solutions

- Nuclear power plants (NPPs) - Electricity generation from industrial CHP and waste generating Inflexible plants 2,4,5 sectors - VRE systems (wind, solar, and river-hydro in this dissertation) - Household electricity and heat production units (e.g., individual heat pumps, individual boilers, etc.) a The capacity or function of these options are, however, varied from today’s conditions in the study of future scenarios. b Flexible technologies examined in this study, which are not in operation today.

The baseload power generation at each hour is basically covered by NPPs, hydropower, and electricity from industrial CHP plants, which altogether remains relatively higher than 30-35% of the total demand throughout this study. The smart use of EVs as a flexibility option is not modelled separately in this study. One may consider the aggregate behaviour of smart EVs as a FDR solution or analogous to EES. However, the detailed impact of such vehicles on the load pattern and other system-level impacts are not studied in this dissertation.

By the aid of EnergyPLAN, the following procedure is simulated for each hour: 1. Electricity from non-flexible generation is deducted from load. 2. Electricity from VRE (VRE-E) is deducted from the remaining load after Step 1. 3a. If the residual load would be negative, the flexibility options contribute in balancing that excess VRE-E (see the order below). 3b. If the residual load would be positive, it will be met by the rest of the power production fleet based on the merit order. 4. In case of lower electricity prices in the external power market, electricity is imported. The price of electricity in the external power market is modelled as a function of power import (price elasticity). Therefore, power imports increase the price on the other side of the border. The procedure

Integration of VRE in National Energy Systems

is iteratively continued until the marginal cost of production inside the system would be equal to the external market, or the lines reach the bot- tleneck.

This sequential order in meeting energy demand in energy modelling, known as merit order-based generation, is seen as a simplified but acceptable way of dealing with national-level energy planning [37].

In case of excess VRE-E in the system (Step 3.1 above), the following options would balance the excess electricity [38]: I. Hydropower plants contribute in balancing excess VRE-E by storing excess power up to the storage limit. II. Electricity is exported to the external market, subject to the transmission capacity limits. III. FDR is activated. IV. LHPs enter the system to consume the excess power and produce heat. V. By the aid of LHP, the DH supply from CHP plants is reduced, and consequently, these plants can cut the respective power production. VI. Power production from CHP plants is further reduced, and the respective DH demand is met by the reserved thermal energy in heat storage plants. VII. Electric boilers contribute in reducing excess power and converting that to DH. VIII. If still needed, power production from CHP plants continues to be cut, and the respective DH load is met by back-up heat-only boilers (HOB).

2.4.1 Flexibility of the Existing Energy Infrastructure

In the assessment of the flexibility of an energy system in the future high-VRE scenarios, one may start with the modelling of the today’s energy system [39]. This way, the capabilities and potential of the existing energy infrastructure in absorbing VRE is quantified. Then, the policy makers are able to roadmap the future energy scenarios based on the knowledge of the strengths and limitations of the system in question, and by identifying the requirements for the future changes. Energy systems are to some extent dynamic and evolutionary, adopting to emerging needs and constraints. However, energy systems are path dependent with a long-term horizon for investment and planning related to the infrastructure [40]. Therefore, the study of the flexibility of an energy system at the present time will create a proper basis for an informed plan for moving towards the future VRE targets. Moreover, the modelling of the today’s energy system can be a basis for validating the model against the available statistics, which makes the planner find out the reliability of the modelling results.

2.5 Summary of Findings

By employing the modelling steps explained in this Chapter, the flexibility of an energy system in integration of VRE can be assessed and quantified. The results can be presented by using an indicator such as MREI, or simply by reporting the amount of excess VRE-E that needs to be curtailed. For example, Figure 2 illustrates the implications of different wind integration levels for the whole system in Finland. Let us assume a case A, in which installing 1 GW wind (with annual production of 2.15 TWh) in the beginning would cut PFC and NPE by 2.7 and 0.8 TWh, offering 3.5 TWh total benefits to the system. However, further wind installations do not benefit the system similar to the beginning: the higher wind integration, the lower potential for benefiting from the produced wind energy. For example, in another case called case B, 1 GW wind installations are added to the system when there would be already 7 GW installed wind capacity. This addition creates benefits of approximately 2.1 TWh/a, which is 40% lower compared to the case A. It means the dynamics of the energy system lacks a sufficient flexibility for absorbing VRE-E in the future, compared to the present day with low VRE integration levels.

Figure 2. Contribution of wind in reducing fuel consumption and power imports in different wind installation capacities in Finland

Moreover, from an economic perspective, wind integration reduces the total yearly costs12 (the discounted cost of capital plus operational costs) of the system until wind integrations up to 18-19%. This is due to the alleviation of burning

12 Considering a capital cost of 1.55-1.9 million€/MW, 20-30 yr lifetime, and 2.7-3% fixed O&M costs for wind installations. For the full list of cost assumptions, the reader may consult with Table A1, Appendix A, Publication I.

Integration of VRE in National Energy Systems fossil fuels and power imports. However, the higher wind integration would increase the annual costs of the system compared to today, as some of the possibility for harvesting wind energy shrinks. Another important observation is the lower flexibility of the energy system in intersection of the two VRE examined technologies. In case of wind integration without major solar PV, the Finnish energy system is capable to benefit from 18-19% wind integration13. However, installing solar PV in large scales would diminish the maximum potential for wind down to 16-17%. This calls for a more detailed analysis of priorities in promoting VRE technologies, as they may compete in employing the flexibility of the energy infrastructure in large-scale integrations. This contradicting impact may further deteriorate the expected benefits from each VRE technology indi- vidually.

13 Wind integration refers to the annual production of electricity from wind per total power demand (TWh/TWh). 15

3. Market Value of Flexibility

After identifying the flexibility limitation of an energy system, the next question would be: what flexibility options are needed to facilitate a large-scale integration of VRE. As depicted in Table 3, an array of flexibility solutions are on the agenda nowadays. The choice and promotion of each flexibility option depends on the availability, operational requirements, economic costs, and other socio- environmental impacts of that flexibility. Moreover, the choice of the flexibility depends on the existing energy infrastructure, as the integrator of the emerging solutions.

EES systems have recently attracted wide attention in research and policy fo- rums, as an important element of the future VRE-based energy systems. The European Commission has recognized EES as one of the “strategic energy technologies” for achieving the EU’s energy targets [41]. The US Department of En- ergy (DOE) has also underscored EES as a solution for the grid stability in coping with VRE [42]. EES systems comprise a wide range of technologies with different characteristics that make them sound a solution in different scales and market places. As EES systems are not widely employed in the examined case study, this dissertation opts EES for a more detailed analysis in this Chapter.

3.1 Cost Analysis of Electrical Energy Storage (EES)

Technical characteristics and operational features of different EES systems have been widely studied and reviewed in the available literature [43-45]. How- ever, the market-economic role of EES systems have remained obscure for energy experts, power suppliers, grid operators, and policy makers. To understand the market-economic value of EES, one needs to first identify the cost of such systems. The reported life cycle costs (LCCs) of gird-scale EES systems are limited in the literature. The costs are reported mainly based on scaling the size, modelling and simulations, and from different suppliers with different conditions (for example see [46-48]). Even the cost data of the commercial, grid-scale EES technologies, namely pumped hydropower storage (PHS) and comped air energy storage (CAES), are site-specific and not easy to generalize. This lack of adequate and transparent information regarding the economics of utility-scale EES systems is seen as one of the obstacles in making feasible business models, defining ownership structures, and proposing required regulatory supports [15,49,50]. To inform a proper cost-benefit analysis of the flexibility, this Chap-

Market Value of Flexibility ter deals with a comprehensive and comparative review of the LCCs of EES system in the available literature (reviewed in spring 2014). The LCC calculation method and the reviewed literature can be found in Publication II.

The LCCs of EES systems are directly interlinked with the application and related techno-economic specifications, e.g. charge/discharge cycles per day. Therefore, LCCs are examined for three well-known applications; including bulk energy storage, transmission and distribution (T&D) support services, and frequency regulation. Since the cost data of EES systems are rather dispersed and varying in the existing literature, this study applies a robust uncertainty analysis (Monte Carlo method) in reporting the LCCs of EES systems. Figure 3 compares the annualized LCCs (ALCCs) of seven EES systems. The LCCs typically comprise of the capital cost, cost of charging power, and O&M costs including the replacement cost for batteries. The ALCCs are calculated for an average electricity price of 50 €/MWh, discount rate of 6%, service lifetime of 10 yr, and 250 cycles per year. For more information, e.g., about discharge time and efficiency of each technology, the reader may consult with Publication II (Appen- dixes A and B).

Figure 3. Discounted LCCs of some EES systems applicable for energy arbitrage. VRFB: Vana- dium-Redox flow battery, Li-ion: lithium-ion battery, NaS: sodium-sulphur battery, CAES (above): aboveground CAES, CAES (under): underground CAES.

As Figure 3 suggests, the cost of electricity constitute a big share of the costs, which also depends on the efficiency of the technology. The cost data show a wider disparity range for batteries, except for NaS which has one major supplier in the market. The replacement and O&M costs are greater for batteries. Re- placement costs are directly related to the life cycle numbers, lifetime, and the charge-discharge regime.

3.2 Value of Flexibility in Competitive Power Markets

To promote investment in flexibility options in a given energy system, they need to prove to be economically viable. The economic feasibility of the flexibility depends on the ownership costs, possible services they can provide, the compensation for each service, and possible regulatory incentives. Therefore, the market value of flexibility solutions depends on the possible applications and market places that the flexibility provider can participate [14,51].

In liberalized electricity markets, electricity generation and consumption, and as such storage and flexibility, are mainly balanced through market mechanisms. Therefore, the profitability and value of the flexibility highly depends on the market conditions, which differs from a power market to another.

This dissertation applies an algorithm for maximizing the benefits of EES by price arbitrage (see Section B in Publication III). EES participates in the market when the difference between price of buying and selling electricity would be higher than the internal cost of storage for that cycle (referred to as the marginal cost of discharge analogous to the marginal cost of production). However, the cost-benefit analysis needs to consider the LCCs of EES. Hence, the LCCs are discounted on a yearly basis to compare with the respective yearly benefits. This dissertation proposes an algorithm for maximization of the benefits of replace- able EES systems, such as batteries (see Section C in Publication III). The higher charge/discharge of a battery generates greater revenues, but increases the risk of more replacement needs. The proposed method is based on an iterative procedure that helps the battery’s owner to account for possible replacements and their impact on the operating cycles offered to the market.

3.2.1 Case Study and Results

This study investigates the market value of five EES systems for the case of Finland. Since different technologies have different efficiencies and functional features, they offer different revenues. The value of EES is primarily examined for the day-ahead (Elspot) and balancing markets14. The motivation is the relatively higher price volatility in Finland compared to other Nordic countries, as well as the projections for the further needs for balancing in the Finnish power system. The possible compensation from reserve markets are also briefly compared with the energy-only applications.

Table 4 illustrates the profitability of different examined EES systems for the case of Finland simulated for the period of 2009-2013. The results show that even the cheapest EES (i.e., PHS) is not profit making in the market places examined. PHS would make a benefit-to-cost ratio of 25% in Elspot, and 75% in the balancing market, on average, in the examined period of 5 years. The benefits in the balancing market is much higher, since the EES owner gets paid in

14 The method for the estimation of benefits from balancing market is later improved in [52]. 18

Market Value of Flexibility the charging mode as well, by offering down-regulation. The examined economic benefits from the balancing market are theoretical, and the actual benefits encounter a big uncertainty.

Table 4. Yearly averaged benefits from the day-ahead (Elspot) and the balancing market for the period of 2009-2013 in Finland.

EES Day-ahead (Elspot) Balancing market

system Benefita Costs Ratiob Benefit Costs Ratio (€/kW- yr) - (€/kW- yr) - PHS 14.7 57.5 25% 43.2 57.6 75% CAES 8.1 100.1 8% 33.6 107.7 31% NaS 11.9 163.5 7% 16.5 170.1 10% Lead-acid 13.1 224.8 6% 21.3 225.0 9% VRFB 6.2 265.4 2% 15.0 266.1 6% a Benefits and costs are levelized for 1 kW of nominal capacity of EES, max 4 h discharge time, and 6% in- terest rate. b Benefit to cost ratio

The other interesting result from this analysis is the optimal size of EES for the Finnish power market. For example for PHS, the best size would be a system with 4 h charge/discharge. In other words, the bigger sizes of storage reservoir would not offer proportionally higher benefits in price arbitrage in Finland.

The aggregation of benefits of flexibility options is one of the key topics in the research agenda [51]. For aggregating the revenue streams, the flexibility provider needs to provide different services by participating in different market places. Sometimes, the regulatory framework limits the possibility of aggregation of benefits. For example, some of EES systems can provide T&D services to alleviate the peak and congestions, which results in the T&D investment deferral [53]. In some market designs, if the flexibility provider acts as an agent compet- ing with other producing units in competitive market places, that flexibility is prohibited to be managed or operated by the transmission system operator (TSO). This makes some of the potential benefits unreachable for the flexibility provider [15].

This dissertation briefly examines the possible benefits that EES could aggregate by offering multiple services. For this case, we assume that the market places defined for FDR are applicable for EES systems too, if the technical requirements are met. The data and specification is based on the guidelines provided by the Finnish TSO [54]. The results show that aggregating benefits from energy arbitrage in the day-ahead and balancing markets, normal reserve, and disturbance reserve would generate 195,000 €/a for each MW installed capacity of a battery energy storage (lead-acid). However, the costs of the battery are yet higher than the theoretical benefits. The actual benefits requires a more sophis-

ticated scheduling and allocation of storage capacity [55,56], which is not examined in this dissertation. On the other hand, other possible benefits such as black start and reactive power services are not examined either.

Overall, the results of this Chapter suggest that EES systems are not able to offer return on investment in the Finnish wholesale markets. The inclusion of different ancillary services and a theoretical aggregation of examined benefits yet to make batteries profitable. Therefore, the regulator needs to identify externalities applicable to EES, such as contribution in VRE integration and the related socio-environmental benefits, if the energy system is to deploy EES for providing the flexibility requirements.

Role of Flexibility Solutions in Integration of VRE

4. Role of Flexibility Solutions in Inte- gration of VRE

Chapter 2 showed that the flexibility of an energy system in integration of VRE is limited. Chapter 3 examined the market value of flexibility for EES systems, concluding that the market itself is not lucrative enough to pay off the costs of the EES. This Chapter attempts to examine the role of flexibility solutions from a system viewpoint. The goal is to monitor how flexibility options could serve the energy system in integration of VRE. EES is compared with three other flexibility options, including FDR, power to heat options (LHP and electric boilers), and interconnection to the neighbouring countries. The energy modelling is implemented by the aid of EnergyPLAN (see Chapter 2 for more information about the tool). Before dealing with the role of flexibility solutions, this Chapter looks into the energy policy of Finland, which revolves around the role of wind and nuclear energy in reaching the decarbonisation targets.

4.1 Wind-Nuclear Dilemma

The updated version of the Finnish Energy and Environmental Policy (2013) [57] underlines the coal use and power imports as two events to be substituted with new decarbonized energy production. The country is to add a new NPP (called Olkiluoto 3) with 1.6 GW installed capacity, and the construction of another 1.2 GW NPP has been started. If these two plants would be added to the existing nuclear capacity, Finland will see a nuclear fleet of 5.6 GW (in a country with 14.2 peak load and 9.4 GW average demand in 2014). It is expected that two old reactors would retire in 2020s.

Concurrently, Finland aims to augment the share of RESs in final energy consumption up to 38% by 2020, to fulfill the EU targets [58]. Figure 4 shows the share of different fuels in Finland’s PEC in 2013. While wind production was only 1 TWh in 2013, Finland has plans to boost the share of wind contribution up to 6 TWh/a by 2020 and 9 TWh/a by 202515 [59]. Therefore, the Finnish energy system is entering a new era: a high share of nuclear power and a relatively large wind capacity. This intersection of wind-nuclear is examined in detail in this study. As mentioned in Chapter 2, this study considers no flexibility from NPPs. However, the analysis accounts for the nuclear fuel replacement and

15 Considering an average capacity factor of 26%, each GW wind would generate around 2.2 TWh/a electricity in Finland. 21

maintenance downtime, which is usually done in low demand seasons (it means the capacity of NPPs is not fixed throughout the year in our modelling practices).

Figure 4. Share of different fuels and RESs in PEC in Finland 2013 (data from Statistics Finland [60])

First, the Finnish energy system is modelled by adding Olkiluoto 3 (OL3) to the existing NPP fleet. The results show that OL3 diminishes the flexibility of the Finnish energy system in wind integration. For example, the amount of excess wind generation from 6.5 GW installed wind after OL3 is equal to that of 7 GW wind before OL3 (see Figure 5 in Publication IV). This may seem obvious, as additional nuclear baseload constrains the room for the variations in wind generation. Another impact of OL3 is the change in the profitability of wind for the system. Wind integration under today’s conditions would reduce the total cost of the Finnish energy system (see the cost assumptions in Table B1, Appen- dix B, Publication IV). For example, installing 3 GW wind will benefit the nation by 82 million €/a. This implies that each MWh wind generation at this level would save 12.5 € for the system (due to fuel saving, emission reduction, lower power prices in the system and less imports, etc.). However, adding OL3 makes any wind integration level a cost burden for the energy system. OL3 already replaces a big share of expensive and emitting power production. Therefore, the reduction in the operational cost of the system due to wind integration is not big enough to pay off the investment costs related to wind. In calculating the benefits of wind integration, the decreasing impact of wind on power prices is directly taken into account in the market-economic scheme (see Chapter 4.2 and 4.4 for more details on market-economic versus technical optimization).

The analysis of the relationship between wind integration and nuclear capacity in Finland can be illustrated through a compromise chart, as shown in Figure 5. The state of the energy system in 2014 is highlighted with a red circle. One can monitor how the energy system would evolve under different wind-nuclear scenarios. The results indicate that maximum wind integration in the energy system is dependent on nuclear capacity by a concave, descending, polynomial

Role of Flexibility Solutions in Integration of VRE relationship. Without additional flexibility solutions, the energy system can integrate wind up to 20% today. This figure diminishes to less than 10%, if NPPs would meet more than 50% of the total electricity demand.

Figure 5. The wind-nuclear compromise chart illustrating the impact of different wind-nuclear capacities on power exchange and total costs of the energy system. (Colour indicator: maximum wind possible (red curve), share of net power imports per total electricity demand (blue dash lines), change in total costs of the energy system compared to 2014 per unit of annual power demand, M€/TWh/a (black line)).

4.2 Modelling of Flexibility Solutions in Energy Models

A number of studies have proposed alternative methods and approaches for quantifying the flexibility requirements of a given energy system in high-level VRE scenarios (see [61] for a review). In some studies [62], the required flexibility is estimated purely based on the tempo-spatial availability of VRE resources, without considering other integration possibilities or operational constraints in the examined system. A more precise and practical assessment of the flexibility requirements suggests the use of energy system models, to accommodate all the VRE integration options across energy generation, conversion, storage, and consumption phases [63]. To address this need, a number of tools and models have been developed to examine the role of different flexibility solutions in integration of VRE (see [64] for a review). Since the intermittency of VRE occurs in short time scales, such energy models should be able to evaluate the flexibility in an adequate temporal detail [65]. By employing such models, the implications of adopting different flexibility solutions in an energy system can be analysed. The results of such modelling efforts typically show the investment needs and total costs of the system, carbon emissions, plant utilization rates, and ultimately, the functionality of an energy system in high-VRE scenarios.

In Chapter 2.4, different flexibility options examined in this study were intro- duced. The flexibility options are modelled based on their technical parameters, 23

such as capacity, efficiency, charge/discharge time, duration, interaction with other elements of the energy system, and their availability in the events of excess VRE-E. The EnergyPLAN modelling tool provisions the choice of using a regulation strategy for simulating the system. Regulation strategies are in two main groups: technical optimization and market-economic optimization. In the technical optimization scheme, the goal is to run a system with the least excess VRE- E, as well as minimum thermal power generation and power imports. The market-economic scheme gives priority to the options with the least marginal production cost. Table 5 lists the flexibility options modelled in this study with respect to the operational strategy of each option in different modelling regulation schemes.

Table 5. Modelled flexibility solutions and their functionality in each regulation schemea

Regulation Scheme Flexibility Parameters Options Technical Market-economic Minimizing thermal - Duration FDR power generation and Minimizing the costs - Max capacity excess power

Exporting excess Power exchange based Inter- power or importing - Maximum capacity on power prices in the connector power when the sys- - Power prices market tem fails to supply

In conjunction with Makes changes in CHP and LHP to re- Heat CHP, LHP, and boilers, - Heat storage capacity duce excess power, storage to offer better business‐ - Duration condensing power, economic profits and heat-only boilers - Electric capacity Maximizing production Maximizing produc- - Efficiency and selling the production and minimizing - Storage capacity tion at the maximum Hydropower thermal power gener- - Annual water supply possible market prices ation and excess - Hourly distribution of to achieve the highest power water possible income - Variable O&M costs - Electric capacity Are given priority after Marginal operational - Thermal efficiency CHP solar thermal and in- cost compared to rele- - Electric efficiency units dustrial waste heat to vant options (among - Variable O&M costs cover heat demand CHP, boiler, LHP, and - Fuel specification heat storage) and the - Electric capacity Are given priority after least‐cost solution is - COP (Co-efficiency of LHPs CHP units to cover selected performance) the heat demand - Variable O&M costs

Role of Flexibility Solutions in Integration of VRE

Only used as part of Only used as part of Electric excess power regula- excess power regula- - Variable O&M costs boilers tion if specified in the tion if specified in the regulations strategy regulations strategy - Operates after all - Electric capacity Produce whenever the other electricity pro- - Electric efficiency electricity price is TPPs duction units if the de- - Variable O&M costs higher than the variable mand still remains (Or - Minimum capacity operational costs for grid stability) - Fuel specification - Charge in case of - Capacity (charge) excess electricity Optimise the profit of - Charge efficiency production the plant based on - Capacity (discharge) EES - Discharge in case of marginal costs and - Discharge efficiency systems condensing losses in the energy - Variable O&M power production conversion costs of both charge and discharge a This table is mainly based on the documentation of EnergyPLAN, available at http://www.energyplan.eu/

The model employs some technologies, such as heat storage and LHP, to support CHP systems in balancing their production following the VRE generation [66]. For example, heat storage is modelled with a look-ahead planning horizon to balance the DH demand so that the CHP plants connected to the DH network would control their production based on either power prices or excess VRE-E in the system. This way, the energy model maximizes the use of highly efficient units (here CHP) in different periods, including the periods of excess VRE. LHPs are another technology to serve the integration of VRE. In the modelling paradigm applied in this study, LHPs are assumed to be in conjunction with CHP and DH systems. This configuration is very often in the Nordic region, where a CHP “plant” comprises of a CHP unit, heat storage, back-up boilers, and in some cases LHP and electric boilers [67].

4.3 System-Level Impacts of Flexibility Technologies

Flexibility solutions are generally employed to augment the share of VRE in energy systems. The impact of such technologies can be seen in terms of reduction in VRE curtailment, costs, emissions, power imports, etc. In this Chapter, the impact of four flexibility options are evaluated, including FDR, LHP backed with heat storage, electric heating, and EES. Moreover, the role of cross-border interconnector is analysed by two cases: a) assuming the Finnish energy system as a closed system by applying “technical optimization” in the EnergyPLAN model, and b) the full provision of export capacity to other Nordic countries in the periods of excess VRE-E by employing “market-economic optimization” in the energy modelling tool.

To quantify the impact of each flexibility measure, a scenario for the future of the Finnish energy system is simulated, where the two new NPPs are operational

and the country’s wind target of 9 TWh/a would be achieved. Table 6 shows the projected changes in the Finnish energy system in 2020s compared to 2014. For more scenarios and sensitivity analyses, the reader may refer to Publication IV.

Table 6. The energy transitions in the Finnish energy system modelled for the 2020s.

Today Scenario Projected Change Unit (2014) (2020s) Nuclear capacity MW 2780 5580 Share of nuclear from total power production - 35% 52% Onshore wind MW 680 3600 Offshore wind MW 10 600 Total wind production TWh/a 1.1 9 Wind integration (from total demand) - 1.3% 10.5% Total power demand TWh/a 82.5 85 Electric vehicles (from total demand) TWh/a - 0.5 Max transmission capacity to the Nordicsa MW 2500 2500 a In EnergyPLAN, one interconnector can be dynamically modelled. Hence, the interconnection to the Nor- dic countries is modelled this way. A fixed power exchange is assumed together for Russia and Estonia.

The flexibility requirements can be analysed with respect to the magnitude and the impact of probable excess VRE-E on the load duration curve [68]. This dissertation examines the magnitude, frequency, and “time of the occurrence” of the excess VRE-E. The analysis demonstrate that if the Finnish power market would be able to export excess power in the periods of excess VRE-E, the system would see no major challenge in integrating high shares of wind and nuclear. On the other hand, assuming no market-based trade with other countries (technical optimization with EnergyPLAN), and modelling the Finnish energy system as a closed system, would result in an annual excess wind power of 2.5 TWh/a (equal to 27% wind curtailment). The results for the technical optimization scheme are illustrated in Figure 6.

Figure 6. The frequency and magnitude of excess power in different months of the year, for the case of the Finnish energy system modelled as a closed system

Role of Flexibility Solutions in Integration of VRE

Four flexibility solutions are modelled for reducing this scale of wind curtailment, with the following technical parameters:

 FDR with a flexibility to shift electricity across a time span of 24 h with no losses  LHP with COP= 3, backed with a heat storage with a time frame of 24 h for shifting thermal energy  Smart electric boilers with no storage  EES with 75% overall efficiency, 15 yr lifetime, and 8 h charge/discharge time, resembling a utility-scale NaS battery

First, a 300 MWe installed capacity of each solution is analysed. The results show that LHPs show the highest efficiency in reducing the hours with excess power, from 3500 h to less than 2500 h, which results in 700 GWh/a less curtailed wind. LHPs convert the excess VRE-E to heat and store it if the produced thermal energy is more than DH demand. FDR is the second efficient flexibility option. Since the time span of FDR and its efficiency is higher than EES, FDR offers more capability in relieving the excess power in this study.

Next, the total costs of the system after adopting each flexibility option are analysed. The result shows that LHPs would significantly reduce the total costs of the system by 427 million €/a. It means every MW installed LHP would absorb 2.3 GWh/a excess VRE-E, and would reduce the costs of the system by 1.42 million €/a. Benefiting from a COP of three, the produced heat would replace fuel-based heating systems and the system would benefit from savings gained in both DH and power sectors. On the contrary, the EES system would increase the total costs of the system, as the savings due to the flexibility would not balance out the capital investment needed. FDR is assumed with no investment cost, and as such, it reduces the system costs by harvesting a share of excess VRE-E. For more details on cost assumptions, the reader may visit Appendix B in Publication IV.

Finally, a sensitivity analysis is conducted to show the relationship between the installed capacity of different flexibility options and their capabilities in absorbing excess VRE-E (Figure 7). The results demonstrate that increasing the installed capacity of the LHPs is not resulting in a proportional reduction in the amount of excess VRE-E. FDR suggests a higher flexibility compared to the LHPs in installed capacities of over 600 MWe. However, both technologies offer relatively lower flexibility in higher installed capacities. The results reveal a sat- uration point for the LHPs around 500 MWe, where this technology is no longer reducing excess power in higher capacities. As the excess power seems to occur mostly in the warm seasons, when there is lower demand for heat, LHPs are not able to benefit the system by converting excess power to heat. The results indicate that each flexibility measure is limitedly able to reduce the excess power, and no technology alone can fully alleviate the excess power in the system.

Figure 7. Performance of flexibility solutions in different installed power capacity in decreasing the excess power

4.4 External Power Market as a Flexibility Option

The two cases of power exchange examined earlier – technical versus market- economic optimization –represent the following common philosophies in the modelling of energy systems with high VRE. The technical optimization supports the modelling of an energy system by merely relying on internal capabilities of the system in coping with VRE. This paradigm argues that in the future high-VRE scenarios, a country may not be able to rely on power exports in periods of excess VRE. The neighbouring countries may encounter excess VRE in the same period, due to the correlation between VRE resources in a close geographical area. Thus, the energy system should be designed so that it would cope with VRE by deploying on flexibility options inside the system. Most of the studies conducted by EnergyPLAN are based on this philosophy.

On the other hand, the market-based approach argues that VRE would typically receive a full priority for using the cross-border grid in periods of excess VRE (due to near zero marginal cost and/or supporting schemes). This approach is based on the assumption that the role of the existing international power markets cannot be neglected, especially in areas with a high interconnection capacity, such as the Nordic countries. The energy models considering one country at a time, like EnergyPLAN, are not capable to fully capture the future changes in the neighbouring countries. Hence, it will be a drawback of applying the market-economic method in very high VRE scenarios, as the energy transitions in the neighbouring countries are neglected. It means neighbouring energy systems act as a sink for the excess power in the system in question.

Therefore, in energy models like EnergyPLAN, the technical optimization approach tends to overestimate the flexibility requirements in an energy system, while market-economic scheme underestimates the needs for the flexibility. The

Role of Flexibility Solutions in Integration of VRE modeller may be able to apply some modelling techniques to make a compromise between these two extreme sides in such one-country modelling tools. However, the study of intersection of VRE scenarios in a group of interconnected countries calls for a market-based multi-region energy model. The next Chapter will elaborate more on this.

5. Flexibility Requirements in Interna- tional Energy Systems

Grid expansions and power market couplings are considered by the EU energy policy as solutions for increasing the share of VRE in Europe [19]. The Nordic power market together with six other European power exchanges are price coupled, which means that a common calculation algorithm will determine the day- ahead electricity prices in a wide area from Portugal to Finland [16]. Accord- ingly, all cross-border interconnectors will be optimally allocated to allow the transmission of electricity from cheaper areas to more expensive regions [69]. This will contribute to the optimal use of VRE-E in a wider geographical area, enabling further integration of VRE across Europe.

Different studies have examined the role of international power market couplings in providing flexibility for integration of VRE [70]. With respect to the Nordic power market, the role of the Norwegian hydro in absorbing VRE has been discussed in [71-73]. In these studies, the flexibility of market couplings and transmission expansions are investigated by using power market models, mainly focusing on the power sector (power production mix, hydropower reser- voirs, transmission networks, etc.). However, as shown earlier in this dissertation, the flexibility of energy systems in absorbing VRE is not limited to the power sector. Heat and power sectors are already interconnected inside the most of the Nordic countries, e.g., through CHP production. In future renewable-based energy scenarios, the transport sector will be electrified too [74]. Ac- cordingly, the future energy systems witness increasingly integrated heat, power, transport and other sectors [75], to integrate a high share of VRE [76].

Considering the above, different sectors of an energy system will be further linked to the power sector and power prices, and consequently, to the outcome of cross-border power exchange. In this respect, this dissertation proposes a modelling paradigm to analyse both national-level energy systems (with the possible integration of different energy sectors inside a country), and power exchange possibilities among interconnected countries. Therefore the proposed model is, inter alia, capable to compare the flexibility solutions inside a region (e.g., EES inside a country) with the possibility of cross-border power exchange for balancing VRE. This Chapter discusses the features of the proposed model (called Enerallt) and a case study analysed by the model. Figure 8 illustrates the different sectors of a sample multi-region energy system that can be modelled by Enerallt. While the heat sector is modelled locally, the power sector in each 30

Flexibility Requirements in International Energy Systems region is modelled as a part of a multi-region power market. The method for the modelling of each region resembles an energy system model, while the power market is modelled as a network of individual power systems.

Figure 8. Different elements of the modelling paradigm developed in this dissertation (Enerallt), and the methods applied in each section

5.1 Modelling Paradigm behind Enerallt

Multi-region energy models are capable for informing energy policy when dealing with a group of networked countries/regions. Among the existing multi- region energy models, MESSAGE [77], TIMES family models [23], and OSEMOSYS [65] offer capabilities for long-term investment planning by considering selective time slices per year. These models commonly reflect the viewpoint of a central planner who seeks to optimize the whole (multi-region) energy system over a long time horizon of several decades. These models are not usually designed to represent the operation of different power production units and flexibility solutions in liberalized power markets with short-term variability.

On the other hand, multi-region power system operation and optimal dispatch models are capable of representing short-term variability in load, VRE, and power plant operations. EMPS [78], WILMAR [79], and Balmorel [24] fall into this modelling group. EMPS has the capability of simulating hydropower production based on water value16 in a plant-level scheme. However, the detailed representation of the CHP production and heat sector seems yet to be developed in the model [80]. WILMAR is based on stochastic programming for short-term load and wind generation modelling. WILMAR simulates hydropower water values based on hydro inflow data in “long-term” mode. Balmorel offers more flexibility in the modelling of CHP plants connected to DH networks. Balmorel can apply some deterministic techniques for calculating hydropower water values [81]. Balmorel is also used for optimizing investment decisions in long-term planning as well.

16 Water value is the expected marginal value of the hydroelectricity stored in the reservoir with respect to alternative production modes in the power market in question. 31

Nevertheless, unlike the above-mentioned models, Enerallt is a simulation- based energy model. Enerallt is not designed to offer an optimal solution from the perspective of a central planner, who has a full knowledge and control over the whole multi-region energy system. The aim of developing Enerallt is to sim- ulate the interactions between different market participants in a real world electricity market. The decision makers do not benefit from a perfect knowledge about future power prices and the actions of other market participants. For example, power producers need to improve their future strategies based on the results from the past decisions made by themselves and other producers in the market. Koppelaar et al. [82] consider simulation-based models as useful tools for tracking agent information including learning from past behaviour and in- corporating network behaviour. Enerallt attempts to model this adaptive behaviour of different agents, as such, the model resembles a multi-agent simulation model [83].

However, Enerallt employs an optimization routine into the modelling too. The use of optimization in the model is related to the common power market part. The market will be optimized after simulating initial power supply curves from each region for each day. This practise resembles the role of a neutral power exchange in the real world (e.g., Nord Pool Spot). Therefore, the power exchange is responsible to optimize the day-ahead electricity market merely based on the received offers and bids, without interfering with the formation of such offers. This integration of optimization in the modular structure of a multi- agent based simulation model is one of the practiced electricity system modelling methods for informing energy policy [82]. Table 7 summarizes the main characteristics of Enerallt. For more details on the modelling paradigm and equations, the reader may consult with Publication V.

5.1.1 Challenges in Market-based, Multi-region Energy Modelling

The market-based modelling relies on the cost and tax assumptions. Different countries might introduce different energy taxes and fuel prices, which possibly results in different marginal costs for power and heat production. In this study, we assume equal marginal costs for power plants in all the examined regions. We also apply average DH prices at the country level, while DH prices vary significantly even between different cities in each country. In the version used in this dissertation, the power market model is based on short-term marginal costs of technologies, except for hydropower. However, some TPP producers may apply strategic (long-term) bidding in some hours. Hydro producers may apply different algorithms for optimizing their revenues, but we model the aggregate behaviour of all hydro producers in a given region without considering geographical limits.

The model is not able to reflect unexpected losses of power production capacity or interconnector capacity in particular hours in real life. We may have over- estimated the installed capacity of power plants in the input data, due to the lack of detailed knowledge about operating plants in each season in each country. It 32

Flexibility Requirements in International Energy Systems is worth to remind that the model proposed in this study is not a price forecast- ing model and, thus, is not suitable for bidding in the day-ahead market. The goal is to represent an indicative production pattern and price variations in the future scenarios to study the market evolution in the coming years.

Table 7. The main features of Enerallt

Feature Notes on Modelling

Time resolution Hourly, updating after each 24 h

Time horizon Maximum one year at a time (8760 h)

Modelling type Simulation of different agents

Modelling platform MATLAB

Spatial resolution Country level (price area level)

DH network Aggregated for each region

Technology details Aggregated based on fuel or technology type in each region

DH and electricity demand Modelled hourly, inelastic with respect to price

Power market Modelled based on Nord Pool (marginal cost based and market optimization) Power transmission net- Modelled as bidirectional lines with different capacities based work on transport modelling (no DC or AC power flow modelling) VRE-E Hourly production

Wind simulation Based on historical hourly production and modifications

Solar PV simulation Hourly production data and hourly resource simulation

Future prices of electricity Unknown to all agents

Hydropower Modelled based on water value, weekly hydro inflow

TPPs Modelled without unit commitment

CHP Three groups: industrial, small CHP, and large CHP

CHP strategies Three strategies: fixed hourly generation, based on hourly heat demand, and based on DH and power prices PTH ratio of CHP Fixed

DH price Fixed, average price for each country

Connected to DH and CHP, two strategies: increasing DH LHP and heat storage production from CHP, or increasing revenues from both DH and power market

5.2 Power Market Couplings and National Energy Systems

Power market couplings and grid expansions have different implications for interconnected countries. Market couplings ties power prices and, consequently, price volatility between interconnected countries [84]. Thus, the issue of the national power system planning, and as such, the national energy policy, may intersect with energy transitions in other interconnected countries. These

mutual impacts are more pronounced in areas with relatively large-scale cross- border transmission capacity.

In this respect, large-scale integration of VRE in one country can affect the interconnected regions, both by new opportunities and challenges. The interconnected countries may benefit from importing low-price electricity in hours with high VRE-E in other countries. Moreover, the connection of two areas with different load pattern, diverse power production mix, and different storage capabilities will contribute in balancing of VRE [85]. However, the growth in the installed capacity of VRE may pose challenges to the neighbouring power systems, e.g., by increasing fluctuations in power flow, which complicates the issue of transmission planning [86]. Moreover, the needs for flexibility in such international power markets will become more complicated [11]. For example, an analysis implemented by Enerallt shows that the energy transition in Germany has some implications for the flexibility of the Finnish energy system (see Pub- lication V for more details).

5.3 Sweden’s Nuclear Phase-out and Wind Integration in Finland

The Nordic Power Market is characterized with a large volume of hydro reser- voirs, mainly in Norway and Sweden. This large hydro storage is considered a potential solution for balancing VRE in the region. Moreover, the cross-border interconnection capacity is relatively high between the Nordic countries, which offers further flexibility in integration of VRE17[87]. On the other hand, the Nor- dic countries have ambitious VRE plans, mostly based on wind integration. For example, the recent debate in Sweden revolves around the early retirement of some NPPs and increasing the share of wind energy [88]. For example, Wagner and Rachlew [89] examine the total replacement of NPPs by wind energy. They conclude that the need for back-up generation to overcome the wind variability would significantly increase the carbon emissions and electricity prices in Swe- den.

There is a possibility of early closure of four reactors, i.e., Ringhals (R1 and R2) and Oskarshamn (O1 and O2), totalling 2800 MW nuclear power capacity. By the aid of Enerallt, we model this closure of NPPs and additional wind installations in Sweden, to analyse the possible impact of such energy transition on the flexibility of the examined case study, Finland. The input parameters and assumptions are shown in Table I in Publication VI.

The results show that this energy transition in Sweden will slightly reduce the operation of CHP plants in Finland, resulting in higher heat generation by HOBs. According to the results (Figure 4 in Publication VI), the flexibility of the Finnish energy system will diminish when Sweden installs higher wind power capacity. Finland could integrate up to 5 GW wind with less than 2% annual

17 The interconnector capacity per total installed power capacity is 30-45% in the Nordic countries, which is well above the EU’s 2020 target of 10%. 34

Flexibility Requirements in International Energy Systems wind curtailment, without considering the energy transition in Sweden. How- ever, increasing the wind capacity in Sweden by 8.5 GW would impose a 6-8% annual excess wind production in Finland (by assuming today’s flexibility of the Finnish energy system). The replacement of nuclear with wind in Sweden will require higher operation of condensing TPPs in Finland by up to 12% (in fixed power demand as today for both countries).

5.4 Grid vs. Storage

The study of mutual impacts of energy transitions in interconnected countries can better inform the debatable question of “grid versus storage” [19]. We analyse the impact of further interconnection between Sweden and Finland, on the flexibility of the Finnish energy system. We do this for the case study of nuclear phase-out in Sweden intersected with the wind-nuclear plans in Finland in 2020s (see Section C and D in Publication VI for the assumptions). In this case study, the Finnish energy system would face 290 GWh/a excess wind. Figure 9 illustrates the amount of curtailed wind in Finland after adding more interconnector capacity between Finland and Sweden, versus different EES capacity installed in Finland. The examined EES system is a generic battery with 8 h discharge time, 90% charging efficiency, 90% discharge efficiency, 1 %/day self- discharge, and suitable for bulk energy storage.

Figure 9. Comparing the role of additional cross-border interconnector with local electricity storage in absorbing excess power from wind in Finland

The results suggest that a higher interconnection capacity between the two countries is not able to significantly reduce the excess power from wind in Fin- land. This can be due to the correlation between windy periods in these neighbouring countries, and the correlation between other generation patterns (e.g., power from CHP). Comparatively, EES shows a higher capability in shifting excess electricity from peak windy hours, yet EES is not able to meet all the flexibility needs alone. There are several plans for grid expansions between the Nor- dic power market and the continental Europe. By employing Enerallt, we ana-

lyse the role of a new interconnection between Norway and Germany (Nord- Link) on the flexibility of the Finnish energy system. NordLink (with 1400 MW capacity) will enhance the flexibility in Finland by 15%, reducing excess wind from 290 to 248 GWh/a. This contribution in flexibility is equivalent to 2000 MW additional interconnector between Sweden and Finland.

5.4.1 A Note on Economic Issues

The average price of electricity in Finland was 36 €/MWh in 2014. With this in mind, the cost of excess wind generation would total around 10.4 million €/a for the case examined in this Chapter. By reducing wind curtailment from 290 to 75 GWh/a, an EES system with 2000 MW installed capacity would save 7.7 million €/a for the Finnish energy system. However, considering an average ALCC of 80 €/kW-yr for the cheapest EES (PHS), such storage capacity would cost approximately 160 million €/a. Therefore, EES would offer a benefit-to- cost ratio of 5% for absorbing the curtailed electricity, without considering other possible benefits of storage.

On the other hand, if the system would account for the role of such flexibility solution in integrating renewable energy, the EES system will be a candidate for benefiting from respective incentives (e.g., feed-in tariff) for wind generation. This resembles a case, where the wind farm operator would add a storage to the system to reduce the wind curtailment. Therefore, if the current feed-in-tariff premium of 105 €/MWh for wind generation would be taken into account, the system-level benefits of the examined EES would approximately reach 22.6 million €/a, equal to 12 € per installed kW capacity of EES per year. This can com- pensate approximately 15% of the total cost of the cheapest storage, i.e., PHS. This is the benefit of EES only in renewable integration. Considering other support services of EES for the grid, e.g., reducing the need for balancing and reserve power, the system regulator can quantify a supportive scheme to promote such flexibility.

Discussion and Conclusions

6. Discussion and Conclusions

6.1 Main Findings of This Dissertation

This dissertation employs quantitative, hourly, and whole-system energy modelling approaches to examine the issue of flexibility in integration of VRE in an energy system. The findings of this dissertation with respect to the initial research questions can be summarized as follows.

The integration of VRE offers benefits to an energy system by reducing fuel consumption, cutting carbon emissions, and reducing net power imports, among others. The results of this study show that harvesting these benefits depends on the flexibility of an energy system. The flexibility of an energy system is itself a function of different criteria. This includes power and heat demand pattern throughout the year and the correlation between them; heat and power production technology mix; storage possibilities in the energy sector; integration possibilities between electricity and other energy sectors; available VRE resources, their installed capacity mix, and their production pattern throughout the year; and the possibility of cross-border power exchange.

In early stages of VRE integration, i.e., VRE generation below 10% of total power demand, the system benefits are significant. This makes VRE technically and, in case of wind for the examined system, economically feasible. In this stage, the energy system replaces carbon emitting and expensive alternatives such as TPPs by VRE-E, which results in significant cuts in fuel use, emissions, and operational costs. However, the scale of the system benefits would not grow proportionally under higher levels of VRE integration. To accommodate higher levels of VRE, the system has to replace or reduce the production of more efficient plants too, such as CHP in this study. This makes the benefits of higher levels of VRE integration look less attractive, compared to the beginning. Fur- thermore, if the system would continue to install VRE capacity, a share of VRE- E will be beyond the system flexibility and has to be curtailed. Hence, without investment in flexibility, the system is not capable to fully benefit from VRE. This prevents the system from advancing the share of VRE in energy consumption, which makes the system fail in achieving the planned VRE targets. VRE curtailment further deteriorates the economic profitability of VRE for the system.

For example, under today’s conditions for the energy demand and system flexibility, the maximum feasible level of electricity from wind and solar PV remains below 20% and 8%, respectively, of the total power demand in Finland (technical optimum). From economic perspective, the most profitable wind integration level seems to be approximately 10% under cost assumptions of this study (economic optimum). This level wind energy corresponds to 4 GW installed capacity and 9 TWh/a production, which indicates that the national plans for wind integration by 2025 should be techno-economically feasible. However, Finland is to increase the share of NPPs, and nuclear technology is considered as an inflexible baseload generation in this study. Hence, the flexibility of the system for wind integration will shrink in 2020s after adding the new nuclear capacity. The path dependency of energy systems requires that any energy transition would account for the possible impacts on the flexibility of the system in the future. According to this analysis, the Finnish wind-nuclear policy would require a more flexible energy system for accommodating the planned benefits of these two carbon-free options in a technically and economically efficient way. The scale of the growth in energy demand is critical in this regard.

Market mechanisms and regulations are important in promoting investment in the flexibility of the system. According to the results, the market alone is not able to pay off the cost of EES systems in Finland. In other words, the market value of flexibility is not lucrative enough for the examined technologies. This situation may change in the future. The higher VRE in the system, the more need for balancing, can lead to more profits for flexible solutions offering that balancing. The inclusion of some ancillary services make low-cost batteries (such as NaS) to be near cost breakeven. It is worth to mention that the need for scheduling and allocation of EES capacity to different services would result in losing some of the revenues stacked in this study. The results of this study help the market regulator and policy makers to identify the gap between the costs and market-based benefits of EES. For instance, the system regulator can identify the system-level benefits of the flexibility, and compare those benefits with the market value of flexibility. Then, if the socio-environmental-economic benefits of flexibility for the system would fill the gap between the costs and market value of such technologies, the system planner may define efficient supportive mechanisms to accommodate such externalities relevant to the flexibility solution in question.

The dissertation examines these system-level benefits of four flexibility solutions, including FDR, LHPs (backed with heat storage), on-call electric boilers, and EES. The system benefits are evaluated in terms of the reduction in operational costs, i.e., fuel and carbon emission costs. The results indicate that LHPs (connected to DH systems) offer the highest capability in relieving excess VRE- E in the examined case. By converting excess power to heat and reducing heat from CHP and/or HOBs, LHPs show to be significantly cost-efficient for the system too. Each MW installed power capacity of LHPs would pay off the investment costs, and further save 1.4 million €/a for the system. However, adding

Discussion and Conclusions more and more LHPs capacity would not fully alleviate the excess power. It means the benefits of LHPs would not linearly grow in higher LHP capacities. The excess VRE-E in Finland seems to be more predominant in warmer seasons, when the demand for DH is relatively low. Therefore, LHPs with short-term heat storage are partly able in absorbing VRE-E, as the generated heat is not useable. FDR shows a different performance pattern in this respect: the absorbed VRE-E remains relatively proportional to the installed capacity of FDR. EES is the most expensive flexibility option today. The system-level benefits of EES are not even enough to cover the capital investment needed for these technologies.

However, these system benefits of EES are equivalent to 20% of the total costs of a low-cost battery technology. This can be an indication to define an incentive by the system regulator, if the goal is to maintain the required flexibility by promoting investors to invest in EES. In addition, EES would reduce the amount of the curtailed wind, which ultimately serves wind integrators to benefit from greater revenues from wind supporting schemes. Considering the feed-in-tariff for wind generation in Finland, this benefit would approximately stand for 4- 10% of the total costs of a battery. The abovementioned benefits, i.e., the reduction in operational costs of the energy system and economic benefits in wind integration would cover 24-30% of the cost of EES. It should be noted that the system level benefits might be lower than the direct stacking of the two figures, as the regulator has already accommodated some of the benefits in the respective incentives for wind integration. If these externalities would be realized, the flexibility providers may make feasible business models by investing on EES. Nevertheless, shifting a part of renewable integration subsidies from wind integrators to flexibility providers calls for a careful and well-balanced scheme to encourage the required investment for both technologies. A combined “wind- storage” support scheme may be a good compromise in this respect.

6.2 National vs. International: “That is the Question”

The evolution of this dissertation highlighted the role of international power markets in integration of VRE. According to the results, the Finnish energy system would harvest almost all the planned benefits of its wind-nuclear decarbonisation targets, if the external power market would fully serve Finland in absorbing excess electricity from VRE. This assumption is not too optimistic, since in a marginal cost-based electricity market, the excess VRE-E would probably result in very low or even negative prices in the Finnish price area. This makes power exports from Finland a feasible option to absorb the excess VRE-E. How- ever, the possibility of employing all the interconnector capacity as a flexibility solution in high VRE scenarios entails some risks and uncertainties. From a pure energy system viewpoint, the flexibility of the external power market in absorbing excess VRE-E depends on the power market conditions in the importing region as well.

To analyse these questions, this dissertation develops a market-based multi- region energy model of the Nordic countries. Then, the energy transition in Fin- land is further examined by considering the future probable transitions in neighbouring countries like Sweden. The results show that the flexibility of the Finnish energy system is very sensitive to the wind integration level in Sweden. Under today’s market conditions for Sweden and other Nordic countries, the Finnish energy system could install 4 GW wind capacity with less than 1% wind curtailment per year. However, if the Swedish energy system would add another 5 GW wind, the wind curtailment in Finland reaches near 4% (assuming a fixed power demand as today). A larger transmission capacity between the two countries is not able to reduce the intensity of such excess electricity.

The result show that a local flexibility solution is much more capable to encounter with excess power in Finland in the examined case, compared to a larger interconnector to Sweden. Comparatively, the interconnector between Norway and Germany (NordLink) would improve the flexibility of the Finnish energy system slightly higher compared to an equal transmission capacity added between Finland and Sweden. This indicates that the interconnection of countries with more distant geographical locations and more diverse flexibility options would offer better flexibility in integration of VRE in a multi-region system.

6.3 The Final Note

To achieve the planned benefits from VRE, the national energy policy needs to be informed by the study of energy policies in interconnected countries. Large-scale VRE integration and the associated flexibility requirements neces- sitate a new set of energy policies and market mechanisms. In highly interconnected regions, the early bird integrators of VRE in one region diminish the flexibility of the whole system, leaving the later market entrants in other regions with less profitability from VRE integration. These mutual impacts of national renewable energy policies in the coupled countries may compromise VRE targets in some countries, and consequently, in the whole system. This calls for a more efficient international coordination in energy planning, and consideration of regional energy transitions in drafting national VRE pathways to achieve the planned targets in integration of VRE. This would facilitate the optimal use of VRE resources and energy infrastructure in regions with common energy and environmental targets, such as the EU.

6.4 Quick Takeaways from This Dissertation

Table 8 summarizes the main findings of this dissertation with respect to the initial research questions. The numerical figures are general indications, reported based on the assumptions and subject to the limitations of this study. The reader is advised to read the relevant parts of the dissertation for more informed arguments and relevant information on each case.

Discussion and Conclusions

Table 8. A summary of main findings of this dissertation (subject to the assumptions and limitations in the modelling)

Research Topic Finding/interpretation Question

- Wind integration higher than 20% of the demand will entail an annual wind curtailment of about 3%

- 10% wind integration (4 GW installed capacity) offers the most economical level under today’s Flexibility of today’s Q1 cost assumptions energy system (2015) - Solar PV integration equal to 8% of total electricity demand will cause a yearly curtailment of 3%

- Solar PV and NPPs constrain the flexibility of the system for wind integration

- No profitability in energy-only markets (benefits less than 75% of the costs of the cheapest EES) Market value of - Inclusion of ancillary services significantly im- flexibility Q2 proves the profitability of EES (EES) - Batteries hardly able to be net profitable in the current market conditions

- LHPs reduce the system costs significantly (no cost for DH networks included, see Appendix B, Publication IV for the cost assumptions)

- LHPs the most efficient solution in reducing excess VRE-E, but not a panacea due to the lack of long-term heat storage - EES the most expensive examined option System-level value of Q3 flexibility - System-level economic benefits could recover 20% of the total costs of a low-cost battery - Subsidies payable to EES due to wind integration is equal to 4-10% of the total costs of batteries - Inclusion of system-level, socio-environmental- economic benefits of EES may suggest business models for investing in EES

- Assuming that the external power market would remain as 2015 and it could absorb the excess VRE-E from Finland; means no need for additional flexibility in wind capacities up to 4 GW in Finland

Role of external - Future wind integration in Sweden weakens the power market Q4 flexibility of the Finnish energy system (cross-border power - Internal flexibility (we examined EES) is more ef- exchange) ficient than further interconnection to a close region with similar power supply mix and production/consumption pattern - Expansion to wider areas with different load and production pattern improves the flexibility

6.5 Limitations and Directions for the Future Research

The limitations of Publications constituting this dissertation are discussed in the respective papers. In general, this dissertation is subject to limitations and shortcomings as the following. The methods used in this dissertation are deterministic, except of the LCC analysis of EES. Therefore, the results do not account for uncertainties in future energy scenarios, such as fuel and carbon prices, technology costs, energy demand, etc. In some limited cases, sensitivity analyses are conducted to improve the findings. For example, in Publication IV the uncertainty in wind generation is examined with 50 runs and the annual results showed maximum 5% difference from each other.

The cycling costs of TPPs are not calculated in this study, which makes the economic results less accurate. The detail representation of the power grid and the needs for grid expansion in the future VRE scenarios are not considered in this study. Like most of national and multiregional energy models, the energy systems examined are modelled as a node with interconnections to other regions. More spatial dimensions, such as a city-level DH system modelling, could improve the results. Solar PV modelling is spatially high-resolution, but based on the production rate, efficiency, and costs of today. The future cost reductions would change the economic results. The hourly pattern of the future energy demand is modelled based on the load pattern today. The future changes in the demand pattern due to, for example, FDR or electric vehicles are not taken into account.

The modelling tool employed for informing Chapter 2 and 4, EnergyPLAN, is a single country modeller. The results of these Chapters do not reflect the possible changes in the neighbouring countries in the future energy scenarios. The external power market in EnergyPLAN is modelled based on a linear relationship between power imports and power prices. This may entail simplifications, as power imports from the external power market are also a function of time of the year.

The modelling paradigm emerged through this dissertation (Enerallt) is not capable to model all the agents in an adequate detail. For example, hydro producers are modelled at country-level, based on their predominant pricing strategies (water value). Power demand is modelled inelastic in each hour. The power network is modelled based on network flow theory, which is listed as a general approach for regional/national energy planning [90]. However, a more informed analysis would apply DC or AC load flow models, where applicable. For more details on the limitations of Enerallt, the reader may consult with Chapter 5.1.1 in this dissertation, as well as Section 3.3 in Publication V.

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Department of Mechanical Engineering Aalto- DD Behnam Zakeri "As far as the laws of mathematics refer to reality, they are not certain, and as far as

178 Integration of variable they are certain, they do not refer to reality." / 2016 Albert Einstein renewable energy in

Integration of variable renewable energy in national and international energy systems national and international energy systems

Modelling and assessment of flexibility requirements

Behnam Zakeri

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ARCHITECTURE Aalto University Aalto University School of Engineering SCIENCE + Department of Mechanical Engineering TECHNOLOGY www.aalto.fi CROSSOVER DOCTORAL DOCTORAL DISSERTATIONS DISSERTATIONS

2016