SHORT TERM REGULATION in HYDROPOWER PLANTS USING BATTERIES a Case Study of Hydropower Plants in Lower Oreälven River

DEGREE PROJECT IN INDUSTRIAL MANAGEMENT, SECOND CYCLE, 60 CREDITS STOCKHOLM, SWEDEN 2020

SHORT TERM REGULATION IN HYDROPOWER PLANTS USING BATTERIES A case study of hydropower plants in lower Oreälven river

ARAAVIND SRIDHAR

ASHISH GUHAN BASKAR

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT

Department of Energy Technology, KTH Royal Institute of Technology Examiner: Prof. Viktoria Martin KTH Supervisor: Jagruti R. Thakur, Ph.D.

Industrial Supervisor: Kent Pettersson and Hans Bjerhag, Fortum Generation

Master of Science Thesis TRITA-ITM-EX 2020:568

Short Term Regulation in Hydropower Plants using Batteries: A case study of hydropower pants in lower Oreälven river

Araavind Sridhar Ashish Guhan Baskar Approved Examiner Supervisor Prof. Viktoria Martin Jagruti R. Thakur, PhD, KTH Kent Pettersson and Hans Bjerhag, Fortum Generation Commissioner Contact Person Jagruti R. Thakur

ABSTRACT

Hydropower is one of the oldest renewable energy (RE) sources and constitutes a major share in the Swedish electricity mix. Though hydropower is renewable, there exist some issues pertaining to the local aquatic conditions. With more environmental laws being implemented, regulating the use and management of water is jeopardizing the flexibility of hydropower plants. The decided national plan for new environmental conditions in Sweden is expected to start being implemented in 2025 and more restrictions are expected. Analysing a battery energy storage system's capabilities plants may improve flexibility in hydropower plant operation. This thesis is focused on the short-term regulation in lower Oreälven river where the hydropower plants Skattungbyn, Unnån and Hansjö are located. The combined hydropower plant and battery system is simulated being employed in the day-ahead market and a techno- economic optimization of the combined system is performed. The combined system's operation is modelled using Mixed Integer Linear Programming. The future electricity market analysis is modelled using Machine Learning techniques. Three different electricity market scenarios were developed based on different Swedish nuclear energy targets for 2040 to capture the future. The first scenario developed complies with the Swedish energy target of 100 % renewable production in 2040. The second scenario has still two nuclear power plants in operation by 2040 and the third scenario has the same nuclear capacity as of 2020. It is observed from the results that with the current battery costs (~3,6 Million SEK/MWh), the implementation of a battery system for the short term regulation of the combined battery/hydropower system is not profitable and the cost of battery needs to be less than 0,5 Million SEK/MWh to make it profitable. The thesis also discusses the possibility of utilizing batteries’ second life and the techno-economic analysis of their performance. Keywords: Short Term Regulation, Hydropower Plant, Battery Energy Storage System, Mixed Integer Linear Programming, Machine Learning, Second Life of The Battery.

iii

SAMMANFATTNING

Vattenkraft är en av de allra äldsta förnybara energikällorna och utgör idag en väsentlig del av Sveriges energimix. Trots att vattenkraft är förnybar, har den lett till vissa utmaningar i den lokala vattenmiljön. Som en följd av att fler miljölagar har implementerats för att reglera nyttjandet av vattendrag och sjöar, minskar flexibiliteten i vattenkraftproduktionen. Den av den svenska regeringen i juni 2020 beslutade nationella planen för miljöanpassning av vattenkraften i Sverige, förväntas börja genomföras med start 2025 och tros då resultera i fler flexibilitetsbegränsningar. Genom att analysera driften av batteriers energilagringssystem kombinerade med vattenkraftverk, bedöms flexibiliteten i sådana kombinerade system kunna ökas. Denna studie fokuserar på den kortsiktiga regleringen av nedre Oreälven med vattenkraftverken Skattungbyn, Unnån och Hansjö. En kombination av vattenkraftverken med batterisystem simuleras mot spot-marknaden och en teknisk-ekonomisk optimering av det kombinerade systemet utförs. Driften av det kombinerade systemet modelleras med linjärprogrammering och den framtida analysen av elmarknaden modelleras med maskininlärningstekniker. Tre olika scenarier för elmarknaden utvecklades baserade på målen för den svenska kärnkraften år 2040. Det första scenariot som utvecklades är i linje med det svenska energimålet om 100 % förnybar produktion till 2040. Det andra scenariot utvecklades med två kärnkraftverk fortfarande i drift 2040 och det tredje scenariot med samma kärnkraftskapacitet som 2020. Från resultaten kan särskilt noteras att med nuvarande batterikostnader (~3,6 miljoner SEK/MWh) kommer införandet av batterier för att kortsiktigt reglera vattenkraftverken inte att vara lönsamt om inte batterikostnaden reduceras till som högst 0,5 miljoner SEK/MWh. Denna studie diskuterar även möjligheterna att använda andrahandsbatterier samt en teknisk-ekonomisk analys för dess prestanda. Nyckelord: Kortsiktig reglering, vattenkraftverk, batterilagringssystem, linjär programmering, maskininlärning, återanvändning av batterier.

ACKNOWLEDGEMENTS

First and foremost, we would like to express our profound gratitude to our supervisor Kent Pettersson and our thesis manager Hans Bjerhag from Fortum. Their continuous support and guidance made our work possible. Furthermore, we are grateful to Jagruti Thakur, our supervisor from KTH Royal Institute of Technology, for her guidance and valuable insight into our thesis. Special thanks to Alessandro Ferraris from Fortum for his guidance and knowledge about grid agreements and the second life of the battery. Special thanks to Marco Blixt from Fortum for his insight about environmental laws and regulations in Oreälven. Special thanks to our friend Krishna Kumar Rathinam from KTH for his support in running some of the simulations for us. Above all, we would like to thank our parents, family and friends for their relentless support during our endeavours.

- Araavind Sridhar and Ashish Guhan Baskar.

TABLE OF CONTENTS

ABSTRACT ...... iii SAMMANFATTNING...... iv ACKNOWLEDGEMENTS ...... v LIST OF FIGURES ...... viii LIST OF TABLES ...... x 1 INTRODUCTION ...... 1 1.1 LITERATURE REVIEW ...... 2 1.2 OBJECTIVE AND RESEARCH QUESTION ...... 4 1.3 SCOPE AND LIMITATIONS ...... 5 2 BACKGROUND ...... 6 2.1 HYDROPOWER ...... 6 2.2 CASE STUDY OF THE HYDROPOWER PLANTS ...... 7 2.3 IMPACT ON AQUATIC LIFE WITH HYDROPOWER ...... 8 2.4 SWEDISH ELECTRICITY MARKET ...... 10 2.4.1 Elspot ...... 13 2.4.2 Elbas ...... 13 2.4.3 Regulating Power Market ...... 14 2.5 THE SWEDISH ELECTRICITY MIX ...... 16 2.6 SHORT TERM REGULATION...... 17 2.7 ENERGY STORAGE ...... 18 3 METHODOLOGY ...... 21 3.1 FUTURE ELECTRICITY PRICE FORECASTING MODEL (FEPFM) ...... 22 3.1.1 Original Least Square Regression (OLSR) ...... 23 3.1.2 Lasso Regression (LR) ...... 24 3.1.3 Ridge Regression (RR) ...... 24 3.1.4 Support Vector Regression (SVR) ...... 25 3.1.5 Artificial Neural Network (ANN) ...... 26 3.2 STEPS TO IMPLEMENT MACHINE LEARNING IN FEPFM ...... 27 3.2.1 Data Selection ...... 27 3.2.2 Data Pre-processing ...... 36

3.2.3 Data Split ...... 38 3.2.4 Training the machine learning model ...... 38 3.2.5 Testing the machine learning model ...... 38 3.2.6 Predicting future values ...... 39 3.3 BATTERY PROFITABILITY MODEL (BPM) ...... 50 3.3.1 System Interactions ...... 50 3.3.2 Components of BPM...... 54 3.3.3 Case Study Data ...... 61 4 RESULTS AND DISCUSSION ...... 65 4.1 RESULTS FROM FEPFM ...... 65 4.1.1 Scenario 1...... 68 4.1.2 Scenario 2...... 70 4.1.3 Scenario 3...... 72 4.2 RESULTS FROM BPM ...... 74 4.3 RESULTS FROM ECONOMIC ANALYSIS ...... 75 4.3.1 NPV Analysis of BESS and Hydropower plant ...... 75 4.3.2 Second Life of EV Batteries ...... 76 4.4 DISCUSSION ...... 78 4.5 SUSTAINABILITY ASPECT ...... 79 4.6 FUTURE WORK ...... 80 5 CONCLUSION ...... 82 6 BIBLIOGRAPHY ...... 84

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LIST OF FIGURES

Figure 1: Hydropower plants in focus [47], [48], [Author’s analysis] ...... 7 Figure 2: Movement of fishes in lower Oreälven [31] ...... 9 Figure 3: Electricity transmission network in the Nordics [61] ...... 11 Figure 4: Nordic electricity market bidding areas [48] ...... 16 Figure 5: Electricity production share in Sweden 1970-2017 [53, Author’s analysis] ...... 16 Figure 6: Electricity production in Sweden on January 21, 2019 – a cold winter day [50, Author’s analysis] ...... 17 Figure 7: Electricity price in Sweden (SE3 region) on January 21, 2019 – a cold winter day [13, Author’s analysis] ...... 18 Figure 8: Schematic explanation of the process employed in this thesis ...... 21 Figure 9: A simple neural network [81] ...... 26 Figure 10: Electricity price in different seasons during workdays ...... 28 Figure 11: Electricity price in different seasons during weekends ...... 29 Figure 12: Electricity price and load throughout a week in 2013 ...... 31 Figure 13: Electricity price vs electricity load during a typical workday ...... 32 Figure 14: Electricity load in different seasons during workdays ...... 32 Figure 15: Electricity load in different seasons during weekends ...... 33 Figure 16: Hydro reservoir amount from 2013 to 2019 ...... 34 Figure 17: Wind energy production from 2013 to 2019 ...... 34 Figure 18: CHP production from 2013 to 2019 ...... 35 Figure 19: Nuclear production over the years 2013-2020 ...... 36 Figure 20: Day-ahead electricity prices in SE3 from 2013 to 2019 depicting the extremities in price...... 37 Figure 21: Average load per day from 2013 to 2019 ...... 43 Figure 22: Ratio of the load to the average load per day in different seasons ...... 44 Figure 23: Average Hydro Reservoir amount in the future [63] ...... 45 Figure 24: Wind energy production for future - Base profile ...... 46 Figure 25: Average CHP production per day from 2013 to 2019...... 47 Figure 26: Ratio of CHP production to average CHP production per day in different seasons...... 48 Figure 27: Nuclear energy production per installed capacity - nuclear base profile ...... 49 Figure 28: Block diagram for the functioning of a run-off river hydropower plants with BESS ...... 50 Figure 29: Piece-wise linear function for the Kaplan turbine in Skattungbyn powerplant ..... 52 Figure 30: Point selection in the piece-wise linear function ...... 52 Figure 31: Block diagram for working of a Linear Optimization Model ...... 55 Figure 32: Block diagram for working of BPM...... 60 Figure 33: Hydropower plants in lower Oreälven ...... 61 Figure 34: Skattungbyn flow ...... 62 Figure 35: Unnån and Hansjö flow ...... 63 Figure 36: Sensitivity analysis of inputs ...... 67 Figure 37: Scenario 1 electricity price predictions ...... 69 Figure 38: Scenario 2 electricity price predictions ...... 71 Figure 39: Scenario 3 electricity price predictions ...... 73 Figure 40: Production planning of the combined system ...... 74

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Figure 41: Results from BPM for Skattungbyn ...... 75 Figure 42: Results from BPM for Unnån and Hansjö...... 76 Figure 43: Second life of the battery in Skattungbyn ...... 77 Figure 44: Second life of the battery in Unnån and Hansjö ...... 77 Figure 45: Daily revenue from BESS at Unnån and Hansjö ...... 80 Figure 46: Comparison of revenue from BESS and up-regulation market price ...... 81

LIST OF TABLES

Table 1: Living conditions for trout fish ...... 8 Table 2: Mortality rate for fishes with the size of 15 cm in hydropower plants in the lower Oreälven ...... 8 Table 3: Living conditions for grayling fish ...... 9 Table 4: Frequency response measures implemented in Sweden [50] ...... 15 Table 5: Summary of possible inputs for the FEPFM ...... 27 Table 6: Hour variable example ...... 30 Table 7: Season variable example...... 30 Table 8: Week variable example ...... 31 Table 9: Nuclear powerplants in Sweden ...... 35 Table 10: Scenario summary ...... 39 Table 11: Scenario 1 - 2040 Production ...... 40 Table 12: Decommissioning of nuclear powerplants in scenario 1 ...... 40 Table 13: Scenario 2 - 2040 Production ...... 41 Table 14: Decommissioning of nuclear powerplants in scenario 2 ...... 41 Table 15: 2040 Production - Scenario 3 ...... 41 Table 16: Electricity production and load in Sweden from 2013 to 2019 ...... 42 Table 17: Maximum flow in the powerplants in Oreälven ...... 51 Table 18: Specifications of the hydropower plants...... 61 Table 19: Electricity agreement values in the hydropower plants ...... 62 Table 20: Grid agreement costs [76] ...... 62 Table 21: Battery sizes considered ...... 63 Table 22: Time lagged variables for different input datasets ...... 65 Table 23: Results for different inputs using a different machine learning algorithm ...... 66 Table 24: Best case using the second life of the battery ...... 78

1 INTRODUCTION

The industrial era’s rapid increase in pollution and CO2 content in the atmosphere has led to the increased greenhouse effect and global warming in recent decades. In addition to it, the depleting fossil fuel resources and increase in natural fuels prices like oil and gas has led to exploring other sources of energy especially the non-conventional sources. Renewable energy is one such form of energy which uses natural sources like sun, water, wind, waves and geothermal whose availability is almost infinite. Along with that renewable energy has no greenhouse gas emissions. It is also advantageous to diversify energy supply through different sources and reducing the dependencies on imported fuels. In addition to this electricity produced from solar and onshore wind power has the least cost for electricity [1]. With all such posed advantages, renewable energy has been proven to be the ideal way to go forward. With the current growth rate in greenhouse gas emissions, climate change is expected to be drastic, making diverse living conditions for all habitats including humans and all forms of flora and fauna. Following this, the Paris Agreement was signed by nations worldwide. The agreement mainly deals with decreasing global warming. One main instance in that agreement was the targets of renewable energy production set by every country to mitigate greenhouse gas emissions. Sweden has a target to achieve 50 % renewable energy production by the year 2020 [2] and the whole of European Union has a target to achieve 20 % renewable energy production by the year 2020 [2]. Sweden reached its 2020 targets in the year 2012 and currently has 54 % renewable energy share whereas the European Union has a 17 % of renewable energy share in the electricity mix [3]. The 2040 energy targets for Sweden are 100 % renewable energy share in the electricity mix [4]. With such ambitious targets, renewable energy should be adapted everywhere and be used to its maximum potential, but renewable energy does come with some disadvantages. Renewable energy being dependent on natural resources are intermittent based on the availability of said natural resources. Another important drawback of renewable energy is the geographic limitations. As renewable energy is dependent on natural resources, it is only viable to obtain it where such natural resources exist, making it more geographically constrained [5]. One potential solution to the above posed drawbacks is integrating different sources of energy. Such systems where more than one source of energy production is employed is known as Hybrid Energy systems. Hybrid systems may increase the reliability of energy production than the standalone systems. There can be many variations in hybrid energy systems that are completely dependent on the availability of resources and capital to invest. One of the most common forms of hybrid energy systems is integrating an energy storage system along with an energy-producing system. By doing so, the excess energy which is produced in non-demand hours can be stored in the energy storage system and it can be used during a time of need when the energy production does not meet the energy demand. The possibility of integrating a battery energy storage system (BESS) with hydropower plants enables it to have flexible electricity generation. The integration of such BESS would also circumvent the environmental restrictions and the combined system can be regulated based on the electricity market. The combined BESS and hydropower system would yield better returns than a conventional hydropower plant and investing in this technology is further explored in this thesis based on a case study in lower Oreälven river in Sweden.

The thesis was executed in collaboration with Fortum Generation in Stockholm, Sweden. The lower Oreälven river was considered and the hydropower plants considered for this research are Skattungbyn, Unnån and Hansjö. The thesis report gives an overview of the research execution. Section 1.2 gives the thesis objectives and the research questions which would be answered. Section 1.3 explains the scope and limitations. Section 3 describes the various modelling methods used to evaluate the performance. The results are explained and discussed in section 4.

1.1 LITERATURE REVIEW Renewable energy transition is one of the most effective solutions to fight against climate change. Fossil fuelled powerplants contribute to a major share in global CO2 equivalent emission from fossil fuel utilization [6]. The decarbonization of the energy sector on a global scale is an urgent need to mitigate the effects on climate change. Around 90 % of the energy sector's carbon emissions can potentially be reduced by renewable transition [7]. Energy security has been a concern for humans, which led to policies of harvesting energy from locally available renewable sources and reducing the dependency on different regions of the world for fossil fuel supply. The rise in renewable energy share has led to a demand for energy stability and energy balancing in the energy network. This in turn pushes the transmission system operators (TSO) to limit the renewable energy from sources like solar and wind power as they are volatile and do not provide inertia to the system [8]. Unlike solar and wind power, hydropower is a plannable source of energy and the reservoir allows to schedule the production in the long term and short term. Hydropower plays a major role in energy balancing as it is flexible to operate. It has a spinning reserve, and the ramping capability allows it to quickly respond to any change of other generation in the electrical network. Hydropower possesses the capability to blank start as it does not require any form of support from the electrical grid [9]. The run-of-river hydropower (RoR) contributes to a large share of the total installed hydropower capacity in Sweden. It has less flexibility to operate as they have little to no storage. It depends mainly on the water flow rate in the river and is directly influenced by seasons. As it is a synchronous source and has a spinning reserve, it possesses inertia which contributes to grid stability. But the inflexibility to ramp the production limits its production planning based on market energy demand [10]. Scheduling of hydropower plants plays an important role in optimizing the production based on demand, water availability and best returns. Many mathematical techniques can be used to optimise a hydropower plant's operation. The scheduling of hydropower plants can be done in different time frames and it is classified as:  Long-term scheduling.  Mid-term scheduling.  Short-term scheduling.

As the name suggests, long-term scheduling is used for scheduling the hydropower plants production from one to five years ahead. Stochastic modelling is typically employed for long- term scheduling. Such models should use the aggregate reservoir models and are usually less accurate [11]. Mid-term scheduling can be used for scheduling the hydropower plants production from three to eighteen months. Mid-term models act as connecting links between long-term and short-term scheduling models and mid-term models have higher accuracy than long-term models [11]. Short-term scheduling can be used for scheduling the hydropower plants production from days to a couple of weeks ahead. Short-term models are solved as a deterministic problem, has relatively smaller time increments and are the most accurate of all the scheduling models [11]. Ge et al (2014) considered using a mixed-integer linear programming method to schedule 44 units of hydropower plants in China [12]. Ge et al (2014) included the water time delay and the results from this publication shows that the operational ability of the model is increased but makes it more computationally demanding [12]. Catalao et al (2010) used mixed-integer non- linear programming for hydropower plants present in Portugal. A non-linear relationship between the water flow and net head was considered in this publication [13]. Catalao et al (2010) further employed a mixed-integer quadratic programming model for two hydropower plants in Portugal. The model considers the head dependency and water release limitations. Results from this publication show that the proposed model has good performance [14]. Catalao et al (2010) considers usage of a non-linear approach to schedule the hydropower plants in Portugal considering head dependency [15]. Rotting et al (1992), used a Norwegian hydropower system with 35 plants that were scheduled using dynamic programming [16]. A large scale linear programming model was developed for hydropower plant scheduling in Tasmania, Australia by Piekutowski et al (1993) [17]. Using the hydropower plants in Taiwan, a model based on different dynamic programming and artificial neural network was developed for scheduling 24 hours ahead by Liang et al (1995) [18]. A coordinated algorithm was presented by Tufegdzic et al (1996) [19], using mixed- integer linear programming. A new approach based on the combination of grey system and linear programming was used by Lian (1997) [20] which shows more accurate results than the regular linear model. The results in [20] were checked by using a Tasmanian hydropower system which consists of 10 hydropower plants. Sjelvgren et al (1997) depict two models of mixed-integer programming using linear and non-linear relations in a Swedish hydropower system composing 32 hydropower plants [21]. Shawwash et al (2000) used a linear programming model which was considered for hydropower plants located in British Columbia, Canada, which consists of 7 hydropower plants [22]. Liang (2000) [23] used an artificial neural network with gaussian reduction noise algorithm for 11 hydropower plants located in Taiwan. Yuan et al (2002), used a chaotic genetic algorithm to schedule the hydropower plants production for the next 24 hours [24]. A mixed-integer linear programming was considered by Conejo et al (2002) considering head dependency and start up/down periods for scheduling 24 hours ahead [25]. Yuan et al (2008) used an enhanced particle swarm optimization to schedule for 24 hours of four cascaded hydropower plants [26]. A non-linear programming model was considered including head dependency to schedule hydropower plants production 168 hours ahead, considering three hydropower plants in Portugal by Mariano et al (2008) [27]. Similar to Mariano et al (2008) [27], Catalao et al (2010) also used non-linear programming including head dependencies to schedule 168 hours ahead for seven hydropower plants in Portugal [28].

A dynamic programming using Lagrangian multipliers was modelled by Wang (2009) for 27 hydropower plants located in China [29]. There has not been that much focus on RoR scheduling techniques; Ibrahim et al (2019) use a Genetic algorithm to schedule RoR hydropower plants in the River Nile basin in Ethiopia [30]. Hulsemann et al (1996) used combined linear programming and genetic optimization technique to schedule the RoR hydropower production [31]. The scheduling of hydropower plants production is dependent on the electricity market prices and it is an important parameter for any production optimization algorithm. The electricity prices depend on lot of different factors and there has been significant research on forecasting electricity price in the future. The electricity prices are mainly forecasted using time series models in which the time horizons vary from hour to even weeks [32]. This type of model uses the previous data and is a type of forecasting using a dependent variable based on previous data [32]. The most important forecasting methods can be further split into Linear and Non-Linear models. The linear model can be based on Autoregressive moving average models [33] [34], Transfer function [35], Linear regression models [36] [37] and dynamic regression models [38] [39]. The non-linear models can be based on General autoregressive conditional heteroscedastic (GARCH) [40] [34], Artificial neural network [41] [42] [43] and SVM models [44]. All of the forecasting models have their advantages and disadvantages, but it also depends on other important factors like the electricity market on which the model is employed, historical load, imports/exports, renewable energy forecast values, historical prices, demand elasticity, bidding strategies and many more [32]. There has been significant research on short- and medium-term electricity price forecasting but not on long-term electricity price forecasting. From the literature review, a significant research gap is identified in the usage of an energy storage system for RoR hydropower plants. Hydropower is a major source of the renewable energy system in which the reservoir type power plant can regulate the production based on the fluctuations in the demand. The licenses to operate hydropower plants in Sweden have some restrictions in terms of controlling the discharge right now but the license revision which is expected to happen in the near future according to the EU Water Framework Directive would specify more constraints in the utilization of the rivers that are utilized to produce hydroelectricity [45]. As Sweden has many RoR hydropower plants with and without reservoirs, the opportunity here is to implement short term regulation in RoR hydropower plants by adding batteries and not regulating the water flow in the river, to meet the environmental concerns.

1.2 OBJECTIVE AND RESEARCH QUESTION The objective of this thesis is to study the techno-economic feasibility of implementation of a battery energy storage system at the hydropower plants located in lower Oreälven, for short term regulation in the day-ahead electricity market. The main research questions which this thesis would address are: 1. What machine-learning algorithm is suitable for long term electricity price forecasting in the day-ahead electricity market? 2. What is the accuracy of future predicted day-ahead electricity prices?

3. What is the optimal production schedule of the combined run of river hydropower plant and battery energy storage for the future day-ahead electricity market? 4. What is the economic feasibility of combining BESS and hydropower plants?

1.3 SCOPE AND LIMITATIONS The scope of this thesis is limited to study the performance of stationary battery energy storage systems when combined with these hydropower plants. Skattungbyn powerplant even though being a reservoir type powerplant, in this case, was considered as a RoR hydropower plant due to its inflexibility in controlling the flow due to environmental constraints. The day-ahead electricity market was the only electricity market considered in this thesis as the size of hydropower plants is relatively small, thus the day-ahead market seems to be the ideal market to use. For BESS, lithium-ion battery technology was considered as the battery storage technology in this thesis, due to its high energy density and that it is more environmentally friendly than conventional lead-acid batteries. Mixed-integer linear programming model was used as it is computationally less demanding and the relationships in the combined system can be linearized. One of the limitations of the thesis is the exclusion of batteries in the future electricity grid. There is a lot of research going on batteries and how it brings more stability in the grid. Therefore, more batteries can be expected in the electricity grid in the near future, but currently, there is no historical data for how the battery would operate in the system in Nordpool, and hence it cannot be modelled with the machine learning algorithm to forecast the electricity prices and hence it was excluded in this thesis. Another limitation faced by this thesis is the limited research on long-term price forecasting. There is no benchmark which can be used to compare the forecasted electricity prices from this thesis. The inclusion of import/export electricity in Sweden on an hourly basis was not considered as it was complex to predict the future import/export values from 2020 until 2040. Also, the usage of mixed-integer linear programming uses linear relationships among variables which makes it simplified and makes the model less accurate.

2 BACKGROUND

This chapter explains the background of hydropower and the electricity market and provides a general explanation required to understand the thesis. 2.1 HYDROPOWER Hydropower is defined as the energy that is harnessed from the potential energy of falling water or kinetic energy of running water. It has been used for centuries for different purposes. Hydropower can be broadly classified into two forms of energy, based on the utilization of the converted energy:  Mechanical power  Hydroelectricity In mechanical power, the potential or kinetic energy of water is converted to mechanical power and is used to directly do mechanical work. It is the technology used e.g. in watermills and compressed air hydro. Watermills have been used for centuries in e.g. gristmills, sawmills and textile mills. In hydroelectricity, the potential or kinetic energy of water is converted to rotational mechanical power and then converted to electrical power by a generator. This is one of the earliest forms of renewable electrical energy that has been present since the late 19th century. In recent times, the use of hydropower for hydroelectricity is preferred due to the flexibility in the usage of electrical energy. Hydropower can also be classified based on the type of water resource available:  Run of river hydropower without reservoir: The flowing water is channelled to the turbine through the intake. The water flow in the river cannot be controlled or stored and the hydropower plant utilizes the flow of water within the natural range of the river. When there are high inflow and low load conditions, the water is wasted resulting in the low utility of the plant and vice versa. These plants are often used as baseload plants for the system as they cannot store the water. The net head of the plant can fluctuate based on the flow conditions.  Run-of-river with reservoir: Run-of-river reservoir plants can work as a baseload and short-term peak load plants. The net head of the plant can be controlled better due to the presence of the storage.  Reservoir hydropower: It is a typically large system that uses a dam to store water in a reservoir. Water storages can be used for storing water long term; from weeks to years depending on the actual conditions. There is higher flexibility by using the storage and dam system, which makes it able to serve both as baseload and peak load plant. A reservoir is an artificially created lake, based on the landscape and nature of the site. Many of the reservoirs around the world are river valleys which are converted to artificial lakes [46].  Pumped storage hydropower: This type of hydropower plant composes of two reservoirs at different heights. It has a similar dam structure as the storage hydropower and has an additional link between the reservoirs. It provides a peak-load supply, in which the water is pumped from a lower to an upper reservoir, using surplus energy at

low demand periods. When the electricity demand is high, water flows from the upper reservoir to the lower one through a turbine, producing electricity.

2.2 CASE STUDY OF THE HYDROPOWER PLANTS This thesis is focused on three hydropower plants in the lower Oreälven river as shown in Figure 1: a) Skattungbyn. b) Unnån. c) Hansjö.

Figure 1: Hydropower plants in focus [47], [48],[Author’s analysis] The power plants are located in the Dalarna county, in between the lakes Skattungen and Orsasjön. The first powerplant, which is located at the right extreme at the mouth of lake Skattungen, is Skattungbyn. It utilises the lake as the reservoir Skattungen. The hydropower plant in the middle is Unnån and the last powerplant to the left is Hansjö. Unlike Skattungbyn, the other two hydropower plants have no big reservoir attached to them and they are run-of- river hydropower plants operated by water level regulation (WLR). All these power plants are fully or partly owned and operated by Fortum and there is a restriction on the water discharge in these plants:  There is a minimum discharge in the plants based on the season.  The discharge from Skattungbyn can be changed only in a weekly timeframe.  The discharge can be decreased only if the discharge has remained constant in the previous week or the discharge has been decreasing in the previous weeks.  The discharge can be increased only if the discharge has remained constant in the previous week or the discharge has been increasing in the previous weeks. Hydropower being a highly treasured renewable energy resource, it still is considered to pose some drawbacks to the local environment [49].

2.3 IMPACT ON AQUATIC LIFE WITH HYDROPOWER Hydropower has been utilised as a source of energy for centuries and this is possible mainly by disrupting the natural flow. The main environmental impact of a hydropower plant is the construction of the dam and some of the consequences are for e.g., that some areas might be dry whereas some might be flooded. The change in the flow rate of water affects the fish and, in some cases, the waterways may disappear. There have been a lot of disappearances in flora and fauna in Sweden and biodiversity has decreased over the years due to hydropower [50]. It also poses a threat to the migration of fish and the most affected fish in Sweden for migration are salmon, eel, trout, lampreys, and asp. Also, the change in flow rate for short term regulation in hydropower affects the aquatic life [51]. There are mainly two fishes which are present in Oreälven and they are trout and grayling. Both need special environmental conditions to survive. The trout fish is one of the famous fishes in Sweden. The maximum age is around 18 years, but its actual lifetime is around 7-10 years. The main food for young trouts are insects and other small fauna that is present in the water. When they are old, they prey on other small fishes. The spawning of trout occurs in autumn and the fertilized fish eggs hatch during spring. It usually takes one to five years for the newly hatched fishes to migrate to large seas or lakes where it stays for a half to three years, mainly due to the need for food. Then the fish comes back to its birth area to spawn [52] [53]. Three factors mainly influence the spawning of fish: water velocity, depth of water and substrate content [53].

Table 1: Living conditions for trout fish

Type of fish Parameter Preferences Water Velocity 0,2 - 1,1 m/s Depth of Water 10 - 80 cm Spawning Trout Gravel and small pebbles (2- Substrate Content 64 mm in diameter) Water Velocity < 0,2 m/s Juvenile Trout Depth of Water 15 - 80 cm

In the Oreälven case, the trout spawn in the tributaries Unnån and Ämån. The juvenile trouts are the ones that hatch and are small in size. They stay in these stretches till they are ready to migrate downstream to the large lake Siljan, through Orsasjön, to feed. The connection between Orsasjön and Siljan can be seen in Figure 1. In Siljan they spend around a half to three years and then they come back to spawn and this process continues. During its downstream migration, the trout fishes have to either pass the turbines of Unnån and Hansjö where there is a risk, they might get harmed or via open spillways. The mortality rate of a typical fish with a length of 15 to 25 cm are shown in Table 2 [54].

Table 2: Mortality rate for fishes with the size of 15 cm in hydropower plants in the lower Oreälven

Hydropower Plant Mortality Rate (%) Skattungbyn 18 – 30 Unnån 14 – 22 Hansjö 13 – 19

One possible solution to avoid this is by creating fishways bypassing the turbines [53]. Such fishways would typically have a flow of 0,4 to 1,5 m3/s for upstream and 0,2 to 1 m3/s for downstream passage [53]. This overall route travelled by trout fishes is shown in the orange arrow below:

Trout

Grayling

Hydropower

plants

Figure 2: Movement of fishes in lower Oreälven [31] Grayling is another common fish in Sweden. In Oreälven it is mainly found in the stretch between Unnån and Skattungbyn. The grayling lives its entire life mostly in this stretch and is shown by the yellow arrow in Figure 2. The average maximum age of grayling fish is around 15 years. The main type of food is aquatic insects and fly larvae. As the grayling grows, it starts to prey on small fish, fish eggs, insects, and small mammals. They use their eye to hunt and the newly hatched grayling fish can start eating prey four days after hatching. The grayling spawns in spring. The spawning period varies from 2 to 24 days. The ideal water temperature for spawning is 2-10°C [52] [53]. Similar to that of trout, the grayling also has some preferences for spawning as discussed in table 3.

Table 3: Living conditions for grayling fish

Type of fish Parameter Preferences Water Velocity 0,4 - 0,7 m/s Spawning Grayling Depth of Water 10 - 110 cm Substrate Content 16 - 32 mm in diameter Water Velocity 0,1 – 0,5 m/s Juvenile Grayling Depth of Water 80 – 110 cm

The juvenile grayling gets pushed into larger depths of water and slower flow areas by the larger grayling fishes [55]. At the time of construction of hydropower plants in Sweden, permits were provided to the hydropower owners to discharge according to the laws of each time. The earliest mills and plants were according to the laws of 1742 and 1884. In 1918, the Water law (Vattenlagen) entered into force and it focused on the condition of the operation of hydropower plants without time limit. Recently, in January 2019, a new law was decided with a limit of 40 years for the conditions of the permits, but the permits as such are not withdrawn. The upcoming review of these conditions, according to the National Plan (NAP), decided by the Swedish Government in June 2020, is scheduled for Oreälven to be in 2026 or 2028 which would be expected to propose fishways and reserved flow for the aquatic life [56] [45]. Also, the frequent changes of discharge do affect the components and structures of the hydropower plant [57]. With all these constraints in play, it is practically impossible to control the flow of water in the lower Oreälven stretch as per the production needs. 2.4 SWEDISH ELECTRICITY MARKET Sweden de-regularized its electrical power market in the ‘1996 The Louisiana declaration’ signed in 1995 and gave the framework to form the Nordic Power Market, starting with Sweden-Norway on the 1st January 1996. The Nordic Power market is a de-regularized market in which the producers in the Nordics and Baltics have the right to sell their production on a power market exchange and the state monopolies who had been limiting their contribution in the past were abolished [58]. Such a joint market is advantageous due to the following features:  Utilize complementary production resources and consumption patterns.  Reducing risk by building on long term corporations.  Attractive to investors due to the presence of a huge market.  Better competition among the producers.  Better price formulation.  Increased efficiency with access to a larger production base. The Nordic Power System is an incorporation of the national electric grids of Sweden, Finland, Norway and East Denmark into one synchronous area. All the transmission grids are interconnected with direct AC connections and thus resulting in one grid AC frequency (nominal frequency = 50 Hz) as all the synchronous power generation in the areas are uniformly spinning [59]. The transmission of AC power through long distances and under-sea cables is quite complex to operate and to keep such huge synchronous systems stable is hard, as they are susceptible to pendulations which results in high frequency variations. Thus, through DC links the following countries are connected in the Nordic Power System: from Norway to The Netherlands, from Sweden to Germany, from East Denmark to West Denmark, from East Denmark to Germany, from Finland to Estonia, from Finland to Russia, from Sweden to Finland, Lithuania and Poland, Norway to West Denmark and Sweden to West Denmark [60] as shown in Figure 3.

Figure 3: Electricity transmission network in the Nordics [61] The Transmission Owners in the Nordics are the owners of the synchronous AC connection platform. They are Svenska kraftnät (Sweden), Fingrid Oy (Finland), Energinet (Denmark) and Statnett SF (Norway) along with the Baltic transmission operators Litgrid (Lithuania), Elering (Estonia) and Augstsprieguma Tīkls (Latvia). Nordpool is owned by Euronext (66 %) and the Nordic Transmission System Operators and Litgrid, retaining 34 % of the ownership through a joint holding company. The electricity market is composed of the following players and every player has a specific role and function which helps in the smooth operation of the electricity market:

 Producers/Power Plants: The producers are the owners of the power plants that can produce electricity. They can be from a large organization owning various power plants to a small scale powerplant owned by an individual. The producers can sell the electricity which is being produced to the power system retailers at the power exchange or local consumers.  Consumer: The consumers are the end-users of the electricity and they can get their supply of electricity from anybody including a local power producer or via the electricity retailers.  Power Exchange: The power exchange is the place where the market players can buy and sell electricity. Sweden is one of the countries along with Norway, Finland and the Baltics having a common power exchange platform known as Nordpool. The electricity in the involved countries is traded through Nordpool.  Retailers and Traders: Retailers and traders are the players who are involved in the power exchange by buying electricity from the producers and selling it to consumers. They can even buy and sell from a retailer/trader. They can provide price assurance to consumers and increase the competition in the power exchange. They might be subjected to large economical risks.  System Operators: The system operators play an important role in the electricity market. They are responsible for the safe operation of the power systems by being responsible for the balancing of the generation and consumption of electricity. In Sweden and Finland, the system operators are also responsible for the transmission grids. The system operator in Sweden is known as Svenska kraftnät, responsible for Sweden alone and they are also known as the Swedish Transmission System Operator (TSO).  Balance Responsible Players: Balance responsible players are financially responsible for the imbalance between generation and consumption. All the electric suppliers in Sweden are obliged to produce as much electricity as its electricity consumers use according to the electricity act and this is known as the balance responsibility. The balance responsible players help to maintain balance in production and consumption by supplying more in times of deficit electricity and consuming more in time of excess production. The producers can do their balancing by themselves or can buy it from a balance responsible player. There are cases when producers can buy the service of balancing directly from other producers.  Imbalance Settlement Administrator: There are cases when the balance responsible party fails to meet the generation and consumption of the producers and consumers involved in its agreement and during such cases, the imbalance settlement administrator intervenes and handles the situation. In Sweden, the imbalance settlement administrator is Svenska kraftnät. This is done by buying and selling electricity when there is an imbalance in the grid and the organization which is responsible for causing this imbalance has to pay Svenska kraftnät the cost for restoring the balance in the grid.

 Grid Owners: Grid owners are the people/organizations who own and maintain grids. They are also responsible for power quality and metering. The grid owners get funds through grid tariffs. These tariffs are usually regulated and can be based on:  Cost-based regulation: determined by how much it costs to build, operate, and maintain the grid.  Performance-based regulation: determined by the service provided. In Sweden, the electrical grids are divided into local, regional and the national grid. The national grid ranges throughout the whole country and it is known as the transmission grid. The national grid is maintained at a high voltage level, 220-400 kV, thereby reducing the transmission losses. The national grid is operated and maintained by the TSO Svenska kraftnät. The regional and local grids range through different counties and districts. The regional grids maintain the voltages between 20 and 130 kV and interconnect the national grid to the local grids, powerplants and electricity-intensive industries. The regional grids in Sweden are owned by grid companies, e.g., E.On Elnät Sverige, Ellevio and Vattenfall Eldistribution. The local grids maintain the voltages between 0,4 and 20 kV. They are used to transfer electricity from the regional grids to homes and commercial buildings and are also connected to small-scale electricity generation systems. In Sweden, there are 170 grid companies that operate and own the local and regional grids. Of those 129 are owned by municipalities [62]. The electricity market in the Nordics, Nordpool, is a platform where the physical trading of electricity happens in three-time perspectives as follows: 1. Day-ahead trading on Elspot. 2. Intraday trading on Elbas. 3. Regulating power market, within the operating hour. Elspot and Elbas are managed by Nordpool and the regulating power market is managed by Svenska kraftnät in Sweden, Energinet in Denmark, Fingrid in Finland, and Statnett in Norway 2.4.1 Elspot Elspot is a type of financial market in which the market participants can purchase and sell electricity in a closed physical auction for the next 24 hours. The trades are in megawatt-hours per hour. The process starts at 10:00 CET where the available capacities on interconnectors and the grids are published. The participants include both the consumers and producers of electricity. The participants have until 12:00 CET to submit their bids to Nordpool for the auction for the delivery hours the following day. The Euphemia algorithm is used to calculate the single price for each hour and zone. The hourly prices are then announced at 12:42 CET or later and the producers and consumers are obliged to produce and consume respectively as per the submitted bids [63]. 2.4.2 Elbas If there are any sudden changes to occur after the closing of the day-ahead market, as a change of weather conditions affecting the production of electricity, then Elbas would handle such variations [63]. This poses an opportunity for all the participants in the market for another chance to anticipate for more accurate weather forecasts or to make more profits by maintaining the power balance. The state of having a balance with the supply and demand in the network

towards the delivery time is beneficial as it reduces the need for reserves and their associated costs. Additionally, it also gives the producer to have the freedom of taking unexpected changes in consumption and other outages into account. Nordpool provides 15-minute, 30-minute, hourly and block products in the Intra-day market. The producers have their operating capacities specified to the TSO and at the time of power mismatch, the TSO specifies a certain provider to increase or decrease the production capacity to meet the demand. The intra-day capacities are updated automatically based on the volume and direction of the trades [63]. 2.4.3 Regulating Power Market Regulating Power Market is employed when there is a mismatch of demand and supply electricity, as the frequency of the electricity will be affected. There is a specific working range of frequency in the Nordics and to keep the system frequency within the desired operating range, the following frequency controls are employed in Sweden: Inertia, Primary Frequency Control, Secondary Frequency Control and Tertiary Frequency Control. 2.4.3.1 Inertia

The inertia in the system refers to the moving mechanical parts in the power system, also known as swing mass, which are directly connected to the system rather than through power electronics. Inertia is defined as the resistance to change the frequency within the system. It is always present and gets changed according to the mismatch in the system. Around 60-90 MWh of swing mass energy is stored in the electrical system as inertia [64]. 2.4.3.2 Primary Frequency Control

When the system experiences frequency deviations, the primary frequency control is employed. This type of frequency control gets automatically activated once the frequency is beyond the limits. The plants that act as the primary control adjust their production, making the frequency stay within the limits. It is classified as Frequency Containment Reserve for Normal Operation (FCR-N) and Frequency Containment Reserve for Disturbance Operation (FCR-D). This type of frequency control gets activated within seconds [59]. The FCR-N is active all the time but also under a very small threshold of limits ±0,1 Hz. The FCR-D gets activated when the changes in frequency are more than 0,1 Hz. The specifications of primary Frequency Reserves are shown in Table 4, in accordance with Svenska kraftnät. 2.4.3.3 Secondary Frequency Control

This type of frequency control is employed after the primary control. This type of control is activated in minutes compared to the primary one that is activated in seconds. It is also known as aFRR which refers to the Automatic Frequency Replacement Reserve. The volume requirement of aFRR is around 150 MW for Sweden. The primary control is used to restore the balance to the grid by consuming some reserves. However, still, the nominal frequency is unbalanced. On the other hand, secondary frequency control is used to make sure the primary frequency control can restore itself [65]. 2.4.3.4 Tertiary Frequency Control

The final frequency control is known as the tertiary frequency control or Manual Frequency Restoration Reserve (mFRR). As the name suggests it is operated manually and its main

purpose is to replace the other automatic frequency reserves so that it can be used again within 15 minutes. The capacity requirements are pretty high and usually might incorporate to start up some standby plants [59].

Table 4: Frequency response measures implemented in Sweden [50]

FCR aFRR mFRR FCR Normal Disturbance (Automatic) (Manual) Minimum bid 0,1 0,1 5 5 size (MW) Automatically Automatically through a Automatically Manually at the at a frequency central control at a frequency request of Activation deviation within signal if the deviation below Svenska the range 49,9 – frequency 49,9 Hz kraftnät 50,1 Hz deviates from 50,0 Hz 63 % within 60 50 % within 5 seconds and 100 seconds and up 100 % within Activation time 15 minutes % within 3 to 100 % within 120 seconds minutes 30 seconds Approximately Approximately Approximately Volume 200 MW for 400 MW for 150 MW for requirement Sweden Sweden Sweden

With the above frequency control measures, the companies with a balancing responsibility place bids for upwards or downwards adjustment of the electricity production and Svenska kraftnät (TSO) makes the call as required. The regulating resources are primarily made of hydropower [66]. The prices of electricity are not constant throughout the system but rather have a price based on its location. The entire Nordic power market is split into different regions based on a decision by the TSO of the country. Norway is divided into five areas, Sweden into four areas, Denmark into two areas whereas Finland, Estonia, Lithuania, and Latvia are considered one area each as shown in Figure 4. The main reason for this is to indicate the constraints in the transmission system and to ensure the regional market conditions are reflected in the price. The areas would receive different prices due to bottlenecks in the transmission system.

Figure 4: Nordic electricity market bidding areas [48] 2.5 THE SWEDISH ELECTRICITY MIX Sweden is one of few countries which have almost only fossil-free electricity production. The main contributors to electricity production are Hydropower, Nuclear, Wind Power and Biomass CHP plants [67]. In CHP power is produced as a by-product of producing heat. In Sweden, CHP is fed with biomass and it is operated based on heating demands and provides electricity as a by-product. In Sweden, the contribution of energy sources is as shown below, in Figure 5.

Figure 5: Electricity production share in Sweden 1970-2017 [53, Author’s analysis] From Figure 5, it can see that hydro and nuclear power are the main sources of electricity in the country. The contribution of solar, wind and CHP have been rising over the past years but still, it does not have a significant share in the electricity mix. Sweden has a target to reach 100 % renewable electricity production by the year 2040 [68]. Sweden’s target towards the renewable electricity goal can be visualized by the constant increase in the number of wind turbine installations [69] and as of 2019, a capacity of 8 983 MW wind turbines have been installed in Sweden [70] and a production of 20 TWh from them in the year 2019 [71]. Similar to wind energy, solar power has also been increasing over the years. In Sweden, there has been

a 67 % increase in installation of solar panels in households from 2017 to 2018 and it totals to 411 MW by the year 2018 [72]. The increase is mainly due to the provision of subsidies for the installation of photovoltaics [73]. The increase in renewable generation has one major drawback, an increase in volatility of the electricity prices due to the need of having the large conventional power plants as backups that have higher operation and ramping costs [74]. With the Swedish target of attaining 100 % renewable production by 2040, there will be a huge void due to phasing out of nuclear power plants and this needs to be filled by other renewable energy sources. Hydropower is one potential source that can help to fill up that void. Hydropower is used for regulation and balancing in different time resolutions: frequency regulation (sec - min), short term regulation (hours – days & weeks) and long term, (weeks – years) [75]. Hydropower poses inertia which assists in damping the frequency changes and can also contribute to the reactive power which maintains the voltage level in the grid. These properties of hydropower plants improve grid stability [75]. The current Constitutional law in Sweden states that no new “virgin” hydropower resources may be exploited and the only possible way to increase the hydropower production share is by combining it with other energy sources or storages or to upgrade already existing plants. 2.6 SHORT TERM REGULATION The Swedish electrical energy sources are predominantly consisting of nuclear, hydro, wind and CHP [67]. The nuclear power plants always act as a baseload and the CHPs produce electricity when heating is needed. Renewable energy apart from hydropower is intermittent, i.e., the solar and wind power plants produce electricity only during the presence of the sun and wind, respectively. Hydropower is used to balance the demand and varies throughout the day. Hence hydropower is used for short term regulation, i.e. during the day as the demand varies.

Figure 6: Electricity production in Sweden on January 21, 2019 – a cold winter day [50, Author’s analysis] From Figure 6, the production of energy from different sources can be seen for a regular winter day in Sweden. The production of hydropower is varying throughout the day based on the demand.

Figure 7: Electricity price in Sweden (SE3 region) on January 21, 2019 – a cold winter day [13, Author’s analysis] From Figure 7, it can be seen that the prices of electricity are varying throughout the day. At around 09:00 the electricity price is at maximum given that the load is at maximum and around 03:00 the electricity prices are lowest due to the minimal load in the night. It can be seen that the day has two peaks: one during the morning around 09:00 and the other one in the evening at around 18:00. Another important thing to note is that hydropower production is continuously varied to meet the load. One opportunity by looking at the price variations is to produce more energy during the times of high prices and produce less in the times where the prices are low. This is known as short term regulation. In a typical hydropower plant, this can be achieved by using a dam to store the water until it is needed for production. In lower Oreälven, the presence of the aquatic life has imposed strict rules in terms of discharging the water, making it hard to regulate the flow for flexibility and implement short term regulation. With the help of energy storage, a system can provide a varied power output and help in energy arbitrage [76]. Hence, one possibility to short term regulate would be by coupling a battery energy storage system to the hydropower plants. 2.7 ENERGY STORAGE Pumped hydro technology is the most common grid energy storage technology [77] and is used in 96,2 % of all the cases. Battery technology is making its way with an increase in renewable energy systems and with a decrease in the cost of batteries [78]. Battery storage is a well- established technology for electricity storage, the power and capacity of the battery depend on the electrode surface area [79]. Lead-acid batteries have existed from the 19th century and is a mature technology. It has been used for various applications as it possesses the following properties: low cost, high performance, high efficiency of 80-90 %, easily recyclable compared to other technologies and replaceable. High power discharge from lead-acid battery tends to decrease the capacity of the battery. This limits the depth of discharge of the lead-acid battery. A typical lead-acid battery has a 1 500 cycle life at 80 % depth of discharge. The wide application of lead-acid batteries has led to constant advancement in technology and improving the service life by more than 15

years. The disadvantage of lead-acid battery is that lead is hazardous and restricted by usage in various jurisdictions and the energy density is rather low [80]. Nickel-cadmium (NiCd) and nickel-metal hydride (NiMH) batteries compared to lead-acid batteries have a higher power density, higher energy density and a higher number of cycles. NiCd batteries have a good low-temperature operation, ranging from -20 ℃ to 40 ℃. As cadmium is a toxic material, the use of NiCd batteries has been confined to only stationary applications and prohibited for consumers use since 2006. NiMH batteries were developed to have improved properties than those of NiCd [80]. Lithium-ion batteries have gained importance in the storage technology and it is present in a wide range of applications from portable and mobile applications to cars and utility-scale applications. These batteries have efficiencies in the range of 95-98 %. A typical lithium-ion battery lasts for around 5 000 full cycles. Further development in technology can lead to higher numbers of cycles. The lithium-ion battery has a high safety risk as most of the metal oxide electrodes are thermally unstable. The risk is minimised by equipping a monitoring system to avoid overcharging and over-discharging by monitoring the state of charge [80]. Energy storage has been used in both grid-connected and off-grid cases. The usage of energy storage in off-grid applications is just to use it as a reserve and employ it when needed. On the other hand, the role of energy storage in on-grid cases is vast and dependent on the user. The usage of grid connected energy storage in the viewpoint of a utility is:  Load-shifting: Utilities can reduce their generation cost by storing energy at off-peak hours and discharging it at peak times. By doing so, the generation gap throughout the day would be flatter which leads to improved operating efficiency and cost reduction with fuel. It can also be ideal for renewable energy production utilities whose production depends on the weather conditions. When there is a surplus of power from renewables, the excess power can be stored in the energy storage instead of curtailing the production.  Power quality: Power utilities must keep the supplied voltage and frequency within the bounds to ensure power quality by adjusting the production based on supply. With the help of energy storage, if there is excess power production, then this excess power can be stored in the energy storage and if there exists a power deficit, then excess power can be drawn from the energy storage to maintain the power quality.  Making the transmission more effective: In a power network, congestion might occur when the transmission lines cannot be reinforced in time to meet the demand. During this time, energy storage which can be installed at an appropriate sub-station may mitigate the congestion caused.  Emergency power supply for protection: Energy storage can be used as an emergency power supply in case of an outage and protects the equipment. The usage of grid-connected energy storage in the viewpoint of consumers:  Load-shifting: When power-utilities may set up time-varying electricity prices, the consumers can employ energy storage to save electricity during low prices periods and use it from the energy storage when the prices are higher thus by achieving savings.  Emergency power supply: Consumers can employ energy storage to provide emergency power if there is a power outage.

 EV: Consumers can use EV’s to act as energy storage and help the electricity grid to be stable through Vehicle to Grid (V2G) and Grid to Vehicle (G2V). Stationary energy storage with hydropower will aid in optimizing the balancing strategy in the market. There has been only one hydropower plant in Sweden which is connected to an electrochemical battery. It is located in Forshuvudforsen, Sweden, and it is used in the Nordic frequency regulation market FCR-N [81]. Some hydropower plants are connected to an electrochemical battery in Canada and the USA but the main use of it is for preventing outages and reducing loads during peak demand periods [82] [83]. There is a new scheme in the market to utilize used batteries from EV unfit for vehicle application in utility storage. EV manufacturers typically replace their battery after it has lost 20 % of its initial capacity. These discarded batteries when coupled together with a battery management system can be ideal to work as a utility-scale battery [84] and utilization of such discarded batteries is known as the second life of the battery. Moreover, recycling of current lithium-ion batteries is expensive and the owners of EV need to spend money to recycle such batteries [85]. This can be a potential solution for investments where the costs of batteries are significantly reduced and making more investors interested in energy storage.

3 METHODOLOGY

This chapter explains about the methodological approach to forecast future electricity prices using machine learning Furthermore, the methodology of techno-economic analysis of BESS for RoR hydropower plant is explained.

Figure 8: Schematic explanation of the process employed in this thesis The steps which were undergone to perform this thesis are as follows: 1. Understanding the environmental issues and possible future environmental restrictions for the hydropower plants in focus. This was mainly done by gathering information through conducting interviews with personnel at the environmental department at Fortum Generation, who has worked with the lower Oreälven river. 2. Identification of technical limitations with respect to the agreement with the local grid operator and other electrical system limitations.

3. Learning about the current battery technology available in the market and understanding the implementation of BESS from the completed Li-ion battery project at Forshuvudforsen hydropower plant. 4. Interviewing dispatchers and forecasting specialists at Fortum to understand the functioning of the day-ahead electricity market and for analysing the future electricity market. 5. Literature review on methods of forecasting day-ahead electricity price and production optimisation of hydropower plants. 6. Developing machine learning algorithm to forecast the electricity prices in the day- ahead market. 7. Building the optimization models to imitate the real-time operation of the combined hydropower plant and battery system and evaluating the performance of it. 8. Economically analysing the combined hydropower plant and battery system for Net Present Value (NPV). As shown in Figure 8, there have been two models that are developed separately and then finally interlinked with one another. The models are:  Future Electricity Price Forecasting Model (FEPFM).  Battery Profitability Model (BPM). The FEPFM deals with the forecasting of electricity prices for different scenarios to evaluate the performance and profits yielded by the battery in the future scenarios using the BPM. The BPM models the battery storage system operating together with the hydropower plant and determines the profits yielded by the same. Both models are implemented in Python as it is an open-source programming software widely used around the world due to it being a high-level, general-purpose programming language. The obtained future electricity price results from FEPFM are taken as inputs for the BPM. 3.1 FUTURE ELECTRICITY PRICE FORECASTING MODEL (FEPFM) The future electricity prices are used as an input for the BPM to optimize the operation of the combined hydropower and battery system and analyse the performance of the combined system in the future. The prediction of electricity prices is typically done by energy companies and consultants to track the effects of different aspects in the electricity market and change their business model accordingly. The prediction of electricity prices can be based on numerous mathematical models and various machine learning algorithms. In FEPFM, machine learning is used to forecast the electricity prices. Machine learning is a type of data analysis that identifies mathematical relationships and patterns within enormous sets of data. Machine learning is one of the most common and sophisticated techniques which can be used to forecast electricity prices. Machine learning is split into three sub-divisions:  Supervised Learning: In this type, the input and outputs of a dataset are known and a relation between the inputs and the outputs is identified.  Unsupervised Learning: In this type, the output is not known but a dataset with data or values is fed in and the relationship between every possible data is identified.

 Reinforced Learning: In this type, the program navigates a dynamic environment and receives feedback and corrections based on its actions and alters its path accordingly. In FEPFM the output is known, i.e., the electricity price, so supervised machine learning is implemented in this model. Supervised machine learning is further classified into two types: o Classification: When the output received is a category like “true”, “false”, “red” or “blue” and so on. o Regression: When the output received is a real value like numbers. With electricity prices being real numbers, FEPFM will focus on regression methods. There are several types of regression methods available but in FEPFM the following machine learning methods are considered:  Original Least Square Regression (OLSR).  Lasso Regression (LR).  Ridge Regression (RR).  Support Vector Regression (SVR).  Artificial Neural Network (ANN). All these machine learning algorithms are further explained briefly in the upcoming sections from 3.1.1 to 3.1.5. The algorithms are performed in FEPFM to find the best-fit algorithm and use it for predicting the future electricity prices. FEPFM is used to predict electricity prices from 2020 to the year 2040. 3.1.1 Original Least Square Regression (OLSR) Original Least Square Regression is a linear combination of inputs to provide the output. It is the most common and simplest form of regression. It is further classified based on the number of inputs fed into the regression model. If there is only one input then it is called the simple linear regression, whereas when the number of inputs is more than one then the model is known as multiple linear regression [86]. A simple original least square regression has the following equation:

푦 = 푏0 + 푏1 ∗ 푥 4-1

Where y is the output

x is the input variable

b0 is a constant

b1 is the weight assigned to the input x The weights are calculated in OLS regression by minimising the objective function:

푚 1 4-2 푀퐼푁 [ ∑(푦 − 푥 ∗ 푏 )2] 2 ∗ 푚 푖 푖=1 Where m is the total number of data points.

In this formula, y is the actual output value and x*bi is the predicted value. When these are subtracted the error is calculated and by minimising this function the error is minimised and the value of w for which the lowest value of this function occurs is selected as the best fit. Multiple original least square regression is similar to that of simple linear regression, but it has more than one input. The equation for multiple input linear regression is:

푦 = 푏0 + 푏1 ∗ 푥1 + 푏2 ∗ 푥2 + ⋯ + 푏푛 ∗ 푥푛 4-3 Where y is the output

x1, x2, … xn are the input variables

b0 is a constant

b1, b2, …. bn are the weights assigned to the inputs x1, x2, … xn respectively The weights are calculated in OLS multiple regression by minimising the objective function:

푚 1 푀퐼푁 [ ∑(푦 − 푥 ∗ 푏 − 푥 ∗ 푏 − ⋯ − 푥 ∗ 푏 )2] 2 ∗ 푚 1 1 2 2 푛 푛 푖=1 4-4

Where m is the total number of data points. In OLSR, the model is not penalized for the choice of the weights, i.e., when training, if a certain input is important, then a large weight may be assigned to that input which sometimes might lead to overfitting of data and making the model less accurate. 3.1.2 Lasso Regression (LR) Lasso regression has the same equation as the linear regression but the weights on the equation are calculated by minimising a different equation [87]. The weights in lasso regression are calculated based on the objective function:

푚 푝 1 푀퐼푁 [ ∑(푦 − 푥 ∗ 푏 − 푥 ∗ 푏 − ⋯ − 푥 ∗ 푏 )2 + 훼 ∑ |푏 |] 2 ∗ 푚 1 1 2 2 푛 푛 푗 4-5 푖=1 푗=1

Where y is the output

x1, x2, … xn are the input variables

b1, b2, …. bn are the weights assigned to the inputs x1, x2, … xn respectively 훼 is the coefficient In this method, the model is penalized for its choice of absolute weights. By doing this the overfitting of the data can be minimised. From the above equation, the new parameter α is calculated, which is the coefficient to penalize the weights [87]. 3.1.3 Ridge Regression (RR)

It also has the same equation as that of simple linear regression and similar to lasso regression [86]. It also penalizes the model selection for weights, but the objective function for ridge regression is different and is:

푚 푝 1 푀퐼푁 [ ∑(푦 − 푥 ∗ 푏 − 푥 ∗ 푏 − ⋯ − 푥 ∗ 푏 )2 + 훼 ∑ 푏 2] 2 ∗ 푚 1 1 2 2 푛 푛 푗 4-6 푖=1 푗=1

Where y is the output

x1, x2, … xn are the input variables

b0 is a constant

b1, b2, …. bn are the weights assigned to the inputs x1, x2, … xn respectively and 훼 is the coefficient. Ridge regression makes the penalization more severe by including the squared values of the weights. Hence, the weights would both have a small absolute value and evenly distributed values. The values would also be closer to zero [87] . 3.1.4 Support Vector Regression (SVR) Support Vector Regression is the type of regression that provides the flexibility to define the acceptable error and then find hyperplane (for inputs more than 2) to fit the data which was used to train. Compared to the above-mentioned forms of regression, SVR’s objective function is to minimize the coefficients. The error term which is defined by the user is handled in the constraint’s equation [88]. The equation of SVR is similar to that of linear regression but the objective function is not the same. The objective function for SVR is:

푛 1 푀퐼푁 ∑ |푏 |2 2 푗 푗=1 4-7 The constraint equation in SVR is:

|푦 − 푏1 ∗ 푥1 − 푏2 ∗ 푥2 − ⋯ − 푏푛 ∗ 푥푛| ≤ ɛ 4-8

Where y is the output

x1, x2, … xn are the input variables

b1,b2, …. bn are the weights assigned to the inputs x1, x2, … xn respectively and ɛ is the maximum error (epsilon). Kernels1 are the functions that are used to map lower-dimensional data into higher dimensional data, i.e., converting the non-linear data into a higher dimensional space which is then used to identify the weights. Kernels play an important role in SVR. Various types of kernels that can

1 SVR algorithm uses a set of mathematical functions that are defined as kernels. Their function is to take data as input and transform it into the required form

be used in SVR like ‘linear’, ‘poly’, ‘sigmoid’, ‘rbf’, and ‘precomputed’ but ‘rbf’2 is the most common and preferred one when the dataset has a huge number of observations. 3.1.5 Artificial Neural Network (ANN) ANN is inspired by the biological neural networks in animal brains. ANN is more complex and more sophisticated than the above-mentioned regression methods. Any such neural network has the following layers: an input layer, one or more hidden layers and, an output layer, as shown in the Figure 9.

Figure 9: A simple neural network [81]

The working of the neural network can be explained based on the example in Figure 9. There are two input variables, four hidden layer variables and one output variable. There is a total of twelve weights which are to be calculated. There are eight weights between the input layer variables and hidden layer variables: W1, W2, W3, W4, W5, W6, W7 and W8. There are four weights with the hidden layer variables and the output variable: W9, W10, W11 and W12. The first step is to find out the weights between input and hidden layers. Here the model acts like an automated feature engineering in which the weights automatically figure out how to combine the features and selects the best features which would provide high predictive accuracy. Then the weights for the hidden and output variables are calculated just like a normal regression model by using the self-engineered features from the other side (input and hidden layers) [89]. The main advantage of a neural network is that it can incorporate more complex and non-linear relations in the model, which makes it more adaptive to the dataset which is provided. ANN also poses some disadvantages: like more computationally demanding and might be prone to overfit data.

2 RBF is the most common kernel in SVR because it has localized and finite response along the entire x-axis 26

3.2 STEPS TO IMPLEMENT MACHINE LEARNING IN FEPFM Procedure for implementing machine learning in FEPFM: 1. Data selection. 2. Data pre-processing. 3. Data splitting. 4. Training the machine learning model. 5. Testing the machine learning model. 6. Predicting future values using the most accurate model. 3.2.1 Data Selection The machine learning model uses historical data to form relationships between the input and output variables. The historical data needs to contain both inputs and outputs and these data were accessed from Svenska kraftnät [61] and Nordpool [63] for the period 2013 to 2019. The timeframe was set between 2013 and 2019 due to data availability. The output for the FEPFM is the electricity price and to identify the inputs for FEPFM, different variables were analysed, and the selected inputs were:  Hour of the day.  Workday/Weekend.  Season.  Consumption of electrical energy (load) in Sweden (MWh/h).  Hydro-reservoir amount (GWh).  Wind energy production (MWh/h).  CHP energy production (MWh/h).  Nuclear energy production (MWh/h).  Lagged electricity price (SEK).  Lagged average electricity price of previous days (SEK). A summary of all the inputs considered in FEPFM are shown below:

Table 5: Summary of possible inputs for the FEPFM

Input Parameter Number of Variables Data type of Considered Considered Variables Workday/Weekend 2 Binary Seasons 4 Binary Hour of the day 24 Binary Load 1 Real Number Hydro Reservoir Amount 1 Real Number Wind Production 1 Real Number CHP Production 1 Real Number Nuclear Production 1 Real Number Lagged Electricity Price 0-2 Real Number Lagged Average Electricity 0-2 Real Number Price of Previous days

3.2.1.1 Time Variables (hour, workday/weekend, season)

The prices of electricity in the region SE3 alone was considered as the hydropower plants considered in this thesis are in region SE3. The prices were obtained from Nordpool [63]. An example of electricity prices in different seasons during workdays can be seen in Figure 10.

Figure 10: Electricity price in different seasons during workdays

An example of electricity prices in different seasons at weekends is shown in Figure 11.

Figure 11: Electricity price in different seasons during weekends From Figure 10, it can be observed that there is typically one peak during the morning around 09:00 to 10:00 and another peak in the evening around 18:00-20:00. This pattern is repeated throughout the workdays in all seasons. Figure 11, the profile of price on weekends can be observed and there is not much of change throughout the day in all seasons. The influence of the time of the day in FEPFM is captured by having 24 different variables for each hour with binary values (h1-h24). If the time is the first hour of the day then, h1 is 1, and h2 to h24 will be zero. Similarly, if e.g., the fourth hour is considered, h4 is 1, and the rest of all variables related to the hour is 0. The example for the hour variable is shown in Table 6.

Table 6: Hour variable example

Time of the day Hour Variable 08:00 20:00 h1 0 0 h2 0 0 h3 0 0 h4 0 0 h5 0 0 h6 0 0 h7 0 0 h8 1 0 h9 0 0 h10 0 0 h11 0 0 h12 0 0 h13 0 0 h14 0 0 h15 0 0 h16 0 0 h17 0 0 h18 0 0 h19 0 0 h20 0 1 h21 0 0 h22 0 0 h23 0 0 h24 0 0

The season influences the electricity prices as well, which is observed from Figure 10 and Figure 11. Hence, four separate variables are assigned for every season similar to the day preference variables with binary values: s1-s4, in the order winter, spring, summer and autumn. If a summer day is considered, then s1, s2, s4 are 0 and s3 is 1. Similarly, if a winter day is considered, then s2, s3, s4 are 0 and s1 is 1.

Table 7: Season variable example

Season Season considered Variable Winter Spring Summer Autumn s1 1 0 0 0 s2 0 1 0 0 s3 0 0 1 0 s4 0 0 0 1

The example of season variable can be observed in Table 7. The effect of workday and weekend on the electricity price can be observed in Figure 12.

Workday Weekend

Figure 12: Electricity price and load throughout a week in 2013 The variation of the weekly electricity price is shown in Figure 12. The week shown starts with Monday and ends with Sunday. It can be observed that during the working days from Monday to Friday, there are significantly two peaks in a day and the price of electricity and load is relatively higher than for the Weekend (Saturday and Sunday). The pattern of electricity prices during the Weekend days from Saturday to Sunday seems rather constant with not much variation. Hence the day preference plays a role in determining the prices and two variables are assigned, one for workdays (Monday to Friday) and one for weekends (Saturday to Sunday), with both assigned as binary values (w1 and w2). By doing so, if a Monday is considered, then the w1 is 1 as Monday is a workday and w2 is 0 since Monday is not on the weekend. Similarly, if a Saturday is considered, w1 is 0 and w2 is 1. The example of the week variable can be observed in Table 8.

Table 8: Week variable example

Week Weekday Variable Monday Tuesday Wednesday Thursday Friday Saturday Sunday w1 1 1 1 1 1 0 0 w2 0 0 0 0 0 1 1

Overall, 24 binary variables are used to identify hour, 2 binary variables are used to identify the day and 4 binary variables are used to identify the season used in the model. 3.2.1.2 Electrical Load

Electricity consumption in Sweden (the electricity load) also plays an important role in correlation with the electricity price. The electricity load is obtained from Svenska kraftnät [61].

Figure 13: Electricity price vs electricity load during a typical workday From Figure 13, it can be observed that one of the factors affecting the price of electricity is the electricity load in Sweden and it is almost directly proportional to the electricity price.

Figure 14: Electricity load in different seasons during workdays

Figure 14 shows the variation of load during workdays (Monday-Friday) in different seasons. Similar to the electricity prices, the load has a similar pattern during the weekdays and has a separate pattern in summer.

Figure 15: Electricity load in different seasons during weekends Figure 15 shows the load variation on weekends in different seasons. It is similar to that of electricity price, with two peaks in a day except in summer. Hence, it is observed that the electricity load plays an important role in determining the electricity prices and it was considered as one of the inputs in the model. 3.2.1.3 Hydro Reservoir Amount

The next variable which plays a role in determining electricity price is the hydro reservoir stored energy amount (GWh). It is the amount of electrical energy which can be extracted from the water reserves in Sweden. This value is provided for the entire Swedish region as a whole in Nordpool [61]. These data are available in a weekly manner as shown in Figure 16 and hence for all the hours in one week, the value of the hydro reservoir stored energy is set to be a constant value. From the figure, it can be seen that the lowest point occurs during March-April and then the spring flood comes and raises the amount of the hydro reservoirs until autumn and then the reservoir stored energy decreases until the spring flood comes again. This pattern is repeated every year and it depends on the precipitation, snowfall and the energy used by hydropower and can be seen in Figure 16. Hydro reservoir amount is considered as one of the inputs in the model

Figure 16: Hydro reservoir amount from 2013 to 2019 3.2.1.4 Wind Energy Production

Wind energy is expected to increase drastically in Sweden, with more and more wind farms being installed in recent times and this trend is expected to continue for many years still to come. With the increasing amount of wind power installations in Sweden, the overall prices of electricity would go down and thus it plays a significant role in determining the electricity price. Hence the wind production for the whole of Sweden is considered and the production data are obtained from Nordpool [61]. The wind energy production is considered as one of the inputs in the model.

Figure 17: Wind energy production from 2013 to 2019

3.2.1.5 Biomass CHP Energy Production

The production from biomass CHP plants is seasonal and varies throughout the year as observed from Figure 18. The production of CHP is also set to increase in the future to facilitate the phasing out of nuclear energy. The energy production from CHP powerplants is obtained from Svenska kraftnät [61] and is considered as one of the inputs in the model.

Figure 18: CHP production from 2013 to 2019 3.2.1.6 Nuclear Energy Production

The production from Nuclear power constitutes a major share in the Swedish grid and the nuclear powerplants that are currently employed in Sweden are shown in Table 9 [90].

Table 9: Nuclear powerplants in Sweden

Reactor Name Model Net Capacity First Grid (MWe) Connection Ringhals 4 W (3-loops) 1 130 1982-06 Forsmark 3 ABB-III, 1 172 1985-03 BWR-3000 Ringhals 3 W (3-loops) 1 062 1980-09 Forsmark 2 ABB-III, 1 118 1981-01 BWR-2500 Forsmark 1 ABB-III, 990 1980-06 BWR-2500 Ringhals 1 ABB-I 881 1974-10 Oskarshamn 3 ABB-III, 1 400 1985-03 BWR-3000

Sweden has a target of completely phasing out of Nuclear power by the year 2040 and it acts as a baseload throughout the day with very small fluctuations. Hence a separate variable is assigned in FEPFM for the production from nuclear power and it is obtained from Svenska kraftnät [61] and it is shown in Figure 19.

Figure 19: Nuclear production over the years 2013-2020 3.2.1.7 Lagged electricity prices

The historical prices of electricity and the average electricity price of the past days influence the electricity price and there has been a lot of short term electricity price forecasting modelling based on the previous day electricity price [32] [35] [33]. Hence it was also considered in the model to identify if it can improve the accuracy of the model, but the number of previous days electricity prices which can be considered are vast. Therefore, the lagged electricity price and lagged average electricity price have not been assigned a specific number of variables as different variations can be fed like the prices from the previous day to anytime in the past. Hence, an evaluation of the model has been done with varying the number of lagged prices and lagged average electricity price and the model with the best accuracy is selected accordingly for final future electricity price prediction. 3.2.2 Data Pre-processing Once the data selection is done the data needs to be pre-processed before feeding it into the model. The data pre-processing is an important step as it can reduce the computational time and help in avoiding overfitting of data. It includes data checks and data scaling and missing data handling. The data check is used to identify outliers (random sudden huge increase or decrease of a value), missing data points, the duplicity of data points and scaling of data. The identification of outliers in the data can be seen through visual means. Pre-processing of electricity price data just by visual means shows us that the prices have outliers and at times the outliers are of very large magnitude (more than 2 000 SEK/MWh during times when the production of some energy source was reduced drastically). There have been three data points where the prices of electricity have been greater than 2 000 SEK/MWh as observed from Figure 20.

Figure 20: Day-ahead electricity prices in SE3 from 2013 to 2019 depicting the extremities in price The extremes in the prices are due to the shortage of some energy resources. The magnitude of the extreme prices would make the model give more preference for the specific energy resource at that time than it normally should. By doing so the accuracy of the model is reduced. Due to this, the upper limit of the electricity price was set to 2 000 SEK/MWh. Once the upper limit of the data was set, non-linear scaling of data by using a natural logarithm was performed to the data before feeding it to the model. Then missing data points and duplicity of data points were checked. If there has been more than one value for a specific data point, then the average of the data before and after the missing data point is used in the model. Apart from this, if there exists any data point without any value, the average value of the data points at previous and the next hours is used. The values of the variables which are considered are in different scales, i.e., the values have different units and need to be scaled appropriately before feeding into the model [91]. The scaling can be done by several methods like:  Data Normalization: In this method, the data is converted into values between 0 and 1. This method is susceptible to outliers (values which are very high or low and not in the normal range).  Data Standardization: In this method, the data is converted into values so that the mean is 0 and the standard deviation is 1. This method is susceptible to outliers.  Non-Linear Relationships: In this method, if there exist non-linear relationships between the input and output values, then the data pre-processing includes implementing the non-linear relationship (e.g., Logarithms) to the input variable before feeding into the machine learning model. This is also used when the data has outliers as it is not susceptible to outliers.  Min-max Scaler: In this method, the data are scaled to a given range. This method is susceptible to outliers.

 Robust Scaler: This method is used when there are outliers in the data and this method scales the data according to the quantile range which is specified by the user without the influence of the mean and standard deviation of the data. In FEPFM, non-linear scaling of data using natural logarithms has been used as it performs better in terms of outliers. Once the data have been pre-processed the next step, which is data split, is carried out. 3.2.3 Data Split In all machine learning algorithms, the historical data are split into two different sets:  Training set.  Testing set. The training set is composed of 70 % of the entire historical data and the remaining 30 % is present in the testing set. This splitting up of data is done randomly. The data are split into two different sets to train the model and test the accuracy of the trained model. Once the data are split, training the model is the next step. 3.2.4 Training the machine learning model The training of the model is done by implementing different machine learning algorithms as explained in sections 3.1.1 to 3.1.5, on the training set of historical data. Each machine learning algorithm has its optimization function, as explained in the previous sections, which are then used to find out relationships between the inputs and the output. 3.2.5 Testing the machine learning model Once the model is trained the input values of the testing set are fed onto the machine learning model to predict the output values. The accuracy of the model is checked by finding the deviation of the predicted value to its actual value which is already known. There are different accuracy measurement techniques:  Square Error (SE): 푛 2 푆퐸 = ∑ 휖푖 푖 4-9  Mean Square Error (MSE):

푛 1 푀푆퐸 = ∗ ∑ 휖 2 푛 푖 푖 4-10  Root Mean Square Error (RMSE):

푛 1 푅푀푆퐸 = √ ∗ ∑ 휖 2 4-11 푛 푖 푖  Absolute Error (AE)

푛 퐴퐸 = ∑ |휖푖| 푖 4-12  Mean Absolute Percentage Error (MAPE)

푛 100% 휖 푀퐴푃퐸 = ∗ ∑ | 푖| 푛 푦푖 푖 4-13

Where ϵi is the error = the difference between the actual value and predicted value yi is the actual value n is the total number of predicted values From the above equations of the various errors, we can see that the SE, MSE, RMSE use the square of the error term. By doing this, if there are any outliers in the dataset then the value of these errors would be large. AE on the other hand just uses the summation of all the values of absolute errors, which may not be a good evaluation parameter when there are any outliers present in the dataset. MAPE is a good comparison since its scale is in percentage and the outliers are handled better. This is a preferred evaluation technique since the dataset of electricity prices has lots of outliers, but still, RMSE and MSE are calculated for a better understanding and comparison. Once the best model is identified which yields the least error, it is then used to predict future values. 3.2.6 Predicting future values Before moving on to predict future output i.e., future electricity price in FEPFM, the future inputs should be predicted as well. To predict the future inputs three different scenarios were formulated which is used to capture the uncertainty in the future.

Table 10: Scenario summary

This scenario is a business as usual scenario in which the nuclear Scenario 1 production is completely eradicated from the Swedish electricity mix by the year 2040. This scenario is an alteration of scenario 1 in which the nuclear Scenario 2 production is decreasing over the years but not completely eradicated. This scenario is based on the installed nuclear capacity present in 2020 Scenario 3 and this installed capacity will remain the same until 2040.

In all three scenarios, the load and total production of electricity for a specific year are set to be constant. The electricity production from wind power, solar power and CHP are increased over the years from 2020 to 2040 in-order to meet the increasing demands and also to fill the void created by removing nuclear powerplants in scenario 1 and 2. 3.2.6.1 Scenario 1

In this scenario, Sweden achieves its targets for 2040 of 100 % renewable energy production, with a complete phase-out of nuclear energy. In this scenario, the production from renewable energy should be only present in the grid and the contribution from each energy source can be

seen in 2040 in Table 13 following a report from Svensk Vindenergi, which predicts the energy production in the year 2040 [92]. To achieve this a linear increase in the production from wind energy and CHP were considered and for nuclear energy, one power plant is decommissioned every third to fourth year to achieve complete phase-out by 2040 in a linear fashion and this plan is shown in Table 12.

Table 11: Scenario 1 - 2040 Production

Wind 90 TWh Hydro 70 TWh CHP 38 TWh Nuclear 0 TWh Solar 10 TWh Total Production 208 TWh

The order by which the plants are decommissioned is based on the year of connection of the powerplants and the plan to decommission the nuclear powerplants in scenario 1 is shown in Table 11. Hydro production is almost the same compared to the current times. So, the hydro reservoir amount is kept constant throughout the years.

Table 12: Decommissioning of nuclear powerplants in scenario 1

Nuclear Capacity Reactor Year of Reactor in Grid after Capacity Decommissioning Name decommissioning (MWe) [MWe] 2020 Ringhals 1 881 6 872 2023 Forsmark 1 990 5 882 2026 Forsmark 2 1 118 4 764 2029 Ringhals 3 1 062 3 702 2033 Ringhals 4 1 130 2 572 2037 Forsmark 3 1 172 1 400 2040 Oskarshamn 3 1 400 0

3.2.6.2 Scenario 2

In this scenario, it was considered that nuclear energy is not completely phased out, but there are only two nuclear plants in the grid by the year 2040. This scenario is modelled to analyse the importance of nuclear power in terms of price stability. Due to the presence of nuclear energy in the grid by 2040, the production from other renewables is to be reduced compared to Table 11. The production from each source for scenario 2 is shown in Table 13. Similar to scenario 1, the production from Wind energy and CHP is considered to be increased linearly to achieve the 2040 goals of Scenario 2.

Table 13: Scenario 2 - 2040 Production

Wind 82 TWh Hydro 70 TWh CHP 33 TWh Nuclear 18 TWh Solar 5 TWh Total Production 208 TWh

The phasing out of nuclear in scenario 2 can be seen in Table 14. Hydro reservoir amount is again kept constant over the years and the changes are only seen in other sources of energy.

Table 14: Decommissioning of nuclear powerplants in scenario 2 Nuclear Capacity Reactor Year of Reactor in Grid after Capacity Decommissioning Name decommissioning (MWe) [MWe] 2020 Ringhals 1 881 6 872 2024 Forsmark 1 990 5 882 2028 Forsmark 2 1 118 4 764 2032 Ringhals 3 1 062 3 702 2036 Ringhals 4 1 130 2 572

3.2.6.3 Scenario 3

In this scenario, nuclear energy is not phased out and is considered to be the same as in 2020. The increase in renewable energy production apart from hydro is relatively less when compared with the other two scenarios. The production from different energy sources for this scenario is shown in Table 15. Due to no phase-out of nuclear as of 2040, the nuclear energy in the grid is 6872 MWe after the already decided decommissioning of the nuclear powerplant Ringhals 1 in the year 2020.

Table 15: 2040 Production - Scenario 3

Wind 60 TWh Hydro 70 TWh CHP 27 TWh Nuclear 48 TWh Solar 3 TWh Total Production 208 TWh

All the input variables either have defined values (day, season, and hour) or have the targets for 2040. From 2019, a linear approach is used to achieve the mentioned targets of electricity production from different sources.

Once the target values in every year for different input variables for all the scenarios are identified, the future inputs can be predicted. The procedure to predict future inputs is discussed below. 3.2.6.4 Electrical Load

The load is one of the important inputs which is to be identified for the future. The prediction of future loads is split into four steps: Step 1: To identify the total electricity load every year: The electric load variation over the years has no trend. Sweden has been a net exporter of electric energy in the past but there has been no significant pattern for the same as shown in Table 16.

Table 16: Electricity production and load in Sweden from 2013 to 2019

Total Net Load Production Export (TWh) (TWh) Percentage 2013 144 138 4,27 2014 145 134 7,98 2015 153 135 12,09 2016 146 138 5,36 2017 154 138 10,02 2018 152 138 9,36 2019 158 136 13,81 Average 150 137 8,98

The imports and exports in Sweden depend on the availability or lack of electricity in all the countries which are interconnected to Sweden for electricity. It is based on the policies for energy development in these interconnected countries and it is hard to determine an exact value of how much imports/exports Sweden would have in the future. With no proper trend being observed from Table 16 and in order to not add more uncertainty in the model, an average of export from 2013 to 2019 which is 8,98 % is considered to be fixed as an assumption and based on it, the load is predicted in the future. The total production for 2040 is understood to be 208 TWh [92]. Hence the load for the year 2040 is 198 TWh with a net 8,98 % export. Considering a linear increase from the years 2019 to 2040 the total load every year is identified. Step 2: Identification of a generic load profile throughout the year: The load varies throughout the day based on season, day and hour. The average load every day from 2013 to 2019 is shown in Figure 21. It can be observed that the pattern of the average

load is close to a sine wave and an equation can be identified to find the average load every day in a year which can be seen in Figure 21.

Figure 21: Average load per day from 2013 to 2019 Hence by finding the equation of the sine curve, the average electricity load on any day can be computed. Step 3: Identification of generic load profile for different days in different seasons: To identify the load profile in different days in different seasons the following steps were used:  The data have been split based on seasons.  The data are further divided into workdays and weekends.  For every subset of data, the value of load every hour is divided by the average load on that day.  The average value of this ratio is calculated for workdays and weekends for all the seasons and is shown in Figure 22.

Figure 22: Ratio of the load to the average load per day in different seasons Step 4: Predict the loads in the future: Once the generic profile in different seasons was identified, the prediction of future loads can be done by the following steps:  The ratio of load to the average load per day from step 3 is multiplied with the average load per day which is obtained from the equation from step 2. This provides the annual load profile and is considered as the baseload profile.  With the baseload profile, the total generation is calculated per year by taking a summation of it and it is scaled up accordingly based on annual values obtained from step 1. 3.2.6.5 Hydro Reservoir Amount

The hydro reservoir amount is a parameter which has inputs changing every week. It represents how much hydropower can produce based on the capacity of the hydropower plants and water availability. This factor is kept constant throughout the years because it is influenced by rain, snow, temperature and production from other sources of energy. An average value of hydro reservoir amount from 2013 to 2019 was considered for the FEPFM and it is shown in Figure 23.

Figure 23: Average Hydro Reservoir amount in the future [63] 3.2.6.6 Wind Energy Production

The production of wind energy is dependent on weather conditions. It is very complex with a lot of uncertainties in how the weather is predicted and using it to predict wind energy production. But a constant seasonal pattern in wind energy can be observed in Figure 17. The amount of wind power plants installed every year is identified and the ratio of wind energy produced every hour to the wind installed capacity would give a profile that can be used to scale it up in the future based on the installed capacity. The power factor of wind power is assumed to be constant from the historical data. The steps to identify future wind energy production is:  The ratio of wind energy production in Sweden to the installed wind energy capacity in Sweden in the specific year is calculated to identify the production per installed capacity of wind power.  The average value of the ratio from the previous step, throughout the year from 2013 to 2019 is calculated and this is the base wind energy production profile shown in Figure 24  The total energy produced in one year per installed capacity is calculated by adding the averages every hour from the base profile from the previous step.  Based on the required energy production in the future year based on the specific scenario, the base profile is scaled up accordingly.

Figure 24: Wind energy production for future - Base profile 3.2.6.7 Biomass CHP Production

CHP powerplants in Sweden are operated based on the heat demands and hence the generation is high during the winter months and low during the summer months. CHP production pattern is observed to be close to a sine wave and it is almost constant throughout the day from Figure 25. Hence an identical approach that was carried out to predict the future load is followed to predict future CHP production. The steps to identify future CHP production are:

Step 1: Identification of a generic load profile throughout the year. The CHP production varies throughout the year based on season, day, and hour. The CHP production every day from 2013 to 2019 is shown in Figure 25.

Figure 25: Average CHP production per day from 2013 to 2019 Step 2: Identification of generic CHP production profile for different days in different seasons. To identify the CHP production profile in different days in different seasons the following steps were used: It can be observed that the pattern of the average CHP production is close to a sine wave and an equation can be identified to find the average CHP production every day in a year. The equation fit, for the average CHP production per day, can be observed in Figure 25.  The data were split based on seasons.  The data were further divided into workdays and weekends.  For every subset of data, the value of CHP production every hour is divided by the average CHP production in that day.  The average value of this ratio is calculated for workdays and weekends for all the seasons and is shown in Figure 26.

Figure 26: Ratio of CHP production to average CHP production per day in different seasons. Step 3: Predict the CHP production in the future. Once the generic profile in different seasons was identified, the prediction of future CHP production can be done by the following steps:  The ratio of CHP production to average CHP production per day from step 2 is multiplied with the average CHP production per day which is obtained from the equation from step 1. This provides the annual CHP production profile and is considered as the base CHP production profile.  With the base CHP production profile, the total generation is calculated per year by taking a summation of it and it is scaled up accordingly in an annual trend based on the CHP production for different years in different scenarios.  It is assumed that the CHP power plants would still be operated in the future based on the heat demands and the possibility for CHP to act as a base load throughout the year in the future is neglected. 3.2.6.8 Nuclear Energy Production

The nuclear powerplants are operated around their full capacity from the end of autumn to the beginning of spring. During the other months of the year, the nuclear powerplants have a stop of two to four months where the powerplants are under maintenance. The production of electricity from nuclear power throughout the year is observed in Figure 19. A similar approach to that of predicting wind energy is used to find out nuclear energy production.

The steps to predict the future Nuclear energy is:  The targets for nuclear energy in 2040 are known for every scenario. Nuclear powerplants are decommissioned accordingly in an even manner to achieve the targets. Then the installed capacity of nuclear power is identified for the future year.  The ratio of nuclear production to the installed capacity of nuclear is calculated every year and an average over the years is calculated. This is the base nuclear production in the future as shown in Figure 27.  This base profile when multiplied with the installed nuclear capacity gives the annual nuclear production in the future year.

Figure 27: Nuclear energy production per installed capacity - nuclear base profile Once the future profile for each input variable is identified, it can be fed into FEPFM to give out the predicted electricity prices. With the predicted future electricity prices, the BPM model is employed to schedule the combined battery and hydropower plant system as a whole.

3.3 BATTERY PROFITABILITY MODEL (BPM) To model the operations of hydropower plant operating together with the battery energy storage system, the interactions between system components, the interaction of the system with the electricity grid, and the constraints in the operation of the power plants are identified. Considering a power plant to be a system, it can be broken down into four subsystems: water flow, turbine and generator, transmission and connection point, and BESS. The interactions between the subsystems and the system with the external systems are shown in Figure 28.

Figure 28: Block diagram for the functioning of a run-off river hydropower plants with BESS 3.3.1 System Interactions 3.3.1.1 Water Flow

Hydropower plants being barriers in the river’s water flow, the water upstream with higher potential energy tries to reach its stable state by crossing the hydropower plant and reaching downstream. The water crosses the hydropower plant through the subsystem water flow. This subsystem interacts with the external environment and encounters mass and energy exchange. All the water flowing in the river enters this subsystem and exits this subsystem through the turbines and spillways. The amount of water entering and exiting the system is governed by the law of conservation of mass. The water entering the system can exit only through the turbines, spillways and overflow. Turbines and spillways have a maximum flow limitation based on their design.

Table 17: Maximum flow in the powerplants in Oreälven Maximum Flow (m3/s) Powerplant Turbines Spillways Skattungbyn 24 280 Unnån 46 440 Hansjö 40 460

3.3.1.2 Turbine

This subsystem is responsible for energy conversion to electricity and heat. Heat is a form of loss produced due to friction and is neglected in this subsystem. The kinetic energy in the flowing water is converted to rotational mechanical energy by the turbines. The subsystem interacts with water flow and the energy transfer takes place. The mechanical energy is further converted to electrical energy by the generators connected to the turbine. The subsystem is constrained by the law of conservation energy. Kinetic energy enters this subsystem and exits as electrical energy and mechanical losses. Skattungbyn and Unnån have two turbines each, in which anyone turbine operates when the flow is lower than half of the maximum combined turbine flow capacity and both the turbines run simultaneously when the flow is higher. Hansjö has only one turbine which operates for any flow within its maximum permissible limit. The electrical power generation in the subsystem is dependent on the water flow in the turbine. The relation between different flow points and electrical power is defined by a flow vs power graph for the turbine and is turbine specific. The data points are determined by testing the turbine and acquiring data from measurement devices. An equation is obtained by linear curve- fitting on the data points which is used to compute the power production for any flow in the given range of operation. The best-fit curve equation from the graph is linear which might not pass through all the data points. To compute the power output for different flow points more accurately, a piecewise linear function is used. In this, the data gap between two known data points is considered as a linear curve as shown in Figure 29, and for any point which lies between the known data points, the corresponding power for the flow value can be computed with the linear equation for that data interval.

Figure 29: Piece-wise linear function for the Kaplan turbine in Skattungbyn powerplant To understand the functioning of a piecewise linear function, random flow of water is considered. In the example shown in Figure 30, the flow data is considered to be Q = 7 m3/s 3 and the P is to be determined. This point lies between the known data points Q1, P1 (6 m /s) 3 and Q2, P2 (8 m /s). A linear curve with slope “m” is created between the points Q1, P1 and Q2, 푃2−푃1 P2 by using the two-point theorem. The slope of the linear curve is 푚 = . The P value for 푄2−푄1 the corresponding Q is found by 푃 = 푃1 + (푄 − 푄1) ∗ 푚. This method is used to find any operating point of the turbine.

Figure 30: Point selection in the piece-wise linear function

3.3.1.3 Connection Point

In this subsystem the exchange of electricity with the grid takes place. A jumper from the busbar in the substation operating at the grid voltage connects the overhead lines of the grid and the busbar in the powerplant to which generators and batteries are connected. The electrical power from the generators and batteries are transmitted through cables and the voltage is stepped up to the grid voltage by transformers. The net energy of the subsystem is conserved by electricity entering from generators and discharging batteries and exiting the system through the grid connection, charging batteries and electrical losses. Unnån and Hansjö are connected to the same busbar in a substation near Hansjö as these power plants are close by each other and Skattungbyn is connected to a different substation. 3.3.1.4 Battery Energy Storage System (BESS)

In this subsystem, BESS is connected to the busbar where the hydropower plant is connected in the substation. The electricity generated in the power plant is used to charge the battery and the battery discharges to supply electricity to the grid. The charging and discharging is scheduled based on the production planning of the power plant. As Unnån and Hansjö are connected to the grid at the same location it is more feasible to place a single battery for both the power plants. In the case of Skattungbyn, separate battery storage is placed. This system has a limited operation life based on different factors. The life of the battery is determined by the capacity degradation of the battery. The capacity degradation can be classified into two: calendar degradation and cycling degradation, based on its operation. The factors which influence the degradation are as follows: 3.3.1.4.1 Calendar Degradation

 Ambient temperature – It has a significant influence on cell operation and degradation for prolonged storing times. The ideal ambient temperature for lithium-ion and lead- acid batteries are +20 ℃. In most of the utility application of batteries, the battery room is combined with a HVAC system to maintain the ambient temperature.  State of charge – SOC of battery influences the battery degradation on prolonged storage. The higher the SOC, the higher the degradation. This degradation is more significant only if the battery is left idle for long periods, which is not considered in this thesis. 3.3.1.4.2 Cycling Degradation

 Depth of discharge (DoD) – It directly influences the number of charge-discharge cycles the battery can achieve throughout its operational life. The number of cycles directly influences the degradation of the battery. The higher the depth of discharge, the higher the battery degradation. It is an important factor to consider in the model as it is dependent on the operation of the charge-discharge cycle of the battery.  Charging rate – The rate at which the battery is charged or discharged is the rate at which the electrochemical reaction in the cells is happening. The rate of reaction is directly proportional to battery degradation. Charging rate has a significant impact on battery degradation, so it is considered as a factor in the model.

 Operating temperature – Electrochemical reactions in the battery generate heat and increase the internal temperature of the battery. At higher temperatures than the ideal operating temperature of +20 ℃, the battery has a higher degradation rate. The degradation rate due to operating temperature is monitored and kept as minimum as possible by the battery cell cooling or heating system. End of life of a battery – Battery degrades on cycling and ageing as mentioned above. The level of degradation at which the battery reaches its end of service for that specific application is known as the end of life of the battery. 3.3.2 Components of BPM BPM is used to identify the cash flow by implementing BESS with the hydropower plants in focus based on the day-ahead electricity prices. In BPM, a linear programming optimization is considered as it is relatively less computationally demanding and also the equations in the BPM can be linearized. The optimization model requires a set of inputs to produce a set of outputs for an optimal solution. The optimization can be classified based on inputs and solutions.  Qualitative model: Logical optimal decisions derived as a solution from unquantifiable inputs and constraints.  Quantitative model: Measurable optimal solution derived from quantifiable inputs and constraints. The BPM takes measurable inputs and obtains a quantifiable solution, so the BPM is a quantitative model. The quantitative model is a mathematical optimization model which consists of the following components:  Parameters – They are the numerical coefficients and constants that do not depend on the optimization model but is rather used in the objective functions and constraints of the model.  Decision variables – They are the variables that help to decide the outcome of the model. They can vary in a range to produce different outcomes and it is used to optimize the model.  Constraints – They are the bounds and limitations in the operation of the system that influences the optimization. It is an equality or inequality which defines the limitations on decisions. Constraints can be a limitation in the system, physical law or contractual obligation.  Objective function – It is the component that specifies the objective of the model i.e. it specifies the function on which the model should base the optimization. In linear programming, the objective function is the real-valued function whose value is to either minimize or maximize over the set of feasible alternatives. The working of the linear optimization model is shown in Figure 31.

Figure 31: Block diagram for working of a Linear Optimization Model The BPM is further divided into two interdependent parts:  Turbine Modelling  Battery Modelling The turbine modelling is used to model the water flow through the turbine and the spillways and compute the corresponding power production. The battery modelling deals with the charging, discharging and storage level in the battery. It monitors the depth of discharge of the BESS, degradation of the BESS, and costs incurred related to it. The parameters, decision variables and constraints which interlink both the models in BPM are: 3.3.2.1 Parameters

푡 ∀ 1: 24 푡푖푚푒 푖푛 ℎ표푢푟푠 푖푛 표푛푒 푑푎푦

Turbine Modelling

휆푡 = 퐷푎푦 푎ℎ푒푎푑 푒푙푒푐푡푟푖푐푖푡푦 푝푟푖푐푒 푓표푟 푆퐸3 푟푒푔푖표푛 푎푡 푡푖푚푒 ′푡′ 3 푄푡 = 퐹푙표푤 푖푛 푡ℎ푒 푟푖푣푒푟 푎푡 푡푖푚푒 ′푡′ (푚 /푠) 3 푄̅̅푆̅ = 푀푎푥푖푚푢푚 푓푙표푤 표푓 푤푎푡푒푟 푡표 푡ℎ푒 푠푝푖푙푙푤푎푦 (푚 /푠) 3 푄̅̅̅푇̅ = 푀푎푥푖푚푢푚 푓푙표푤 표푓 푤푎푡푒푟 푡표 푡ℎ푒 푡푢푟푏푖푛푒 (푚 /푠)

푃̅퐺 = 푀푎푥푖푚푢푚 푝표푤푒푟 푔푒푛푒푟푎푡푖표푛 푖푛 푡푢푟푏푖푛푒 (푀푊) 푗 ∀ 푠푒푔푚푒푛푡 푖푛 푃표푤푒푟 푣푠 퐹푙표푤 퐶푢푟푣푒 3 푄̅̅̅푗 = 푀푖푛푖푚푢푚 푊푎푡푒푟 퐹푙표푤 푎푡 푠푒푔푚푒푛푡 ′푗′ (푚 /푠)

푃̅푗 = 푀푖푛푖푚푢푚 푃표푤푒푟 푓표푟 푎 푠푒푔푚푒푛푡 ′푗′ (푀푊) ̅ ̅ 푃푗+1−푃푗 ′ ′ ′ 푚푗 = = 푠푙표푝푒 표푓 푃표푤푒푟 푣푠 퐹푙표푤 푓표푟 푠푒푔푚푒푛푡 푗 푎푛푑 푗 + 1′ 푄̅푗+1−푄̅푗

Battery Modelling 푆̅ = 퐵푎푡푡푒푟푦 퐶푎푝푎푐푖푡푦 퐶푆̅̅̅̅ = 푀푎푥푖푚푢푚 푐ℎ푎푟푔푖푛푔 푝표푤푒푟 푓표푟 푡ℎ푒 푏푎푡푡푒푟푦 (푀푊) 퐷푆̅̅̅̅ = 푀푎푥푖푚푢푚 푑푖푠푐ℎ푎푟푔푖푛푔 푝표푤푒푟 푓표푟 푡ℎ푒 푏푎푡푡푒푟푦 (푀푊) 푘 ∀ 푠푒푔푚푒푛푡 푖푛 푐표푠푡 푖푛푐푢푟푟푒푑 푓표푟 푎 푐ℎ푎푟푔푒 − 푑푖푠푐ℎ푎푟푔푒 푐푦푐푙푒 푣푠 푑푒푝푡ℎ 표푓 푑푖푠푐ℎ푎푟푔푒

푁̅푘 = 퐶표푠푡 푖푛푐푢푟푟푒푑 푑푢푟푖푛푔 푐푦푐푙푒 푑푒푔푟푎푑푎푡푖표푛 푓표푟 푎 푠푝푒푐푖푓푖푐 퐷푂퐷 푠푒푔푚푒푛푡 ′푘′

퐷푂퐷̅̅̅̅̅̅푘 = 푆푡푒푝 푐ℎ푎푛푔푒 푖푛 푑푒푝푡ℎ 표푓 푑푖푠푐ℎ푎푟푔푒 푖푛 푐표푠푡 푖푛푐푢푟푟푒푑 푓표푟 푎 푐ℎ푎푟푔푒 − 푑푖푠푐ℎ푎푟푔푒 푐푦푐푙푒 푣푠 푑푒푝푡ℎ 표푓 푑푖푠푐ℎ푎푟푔푒 푎푡 푠푒푔푚푒푛푡 ′푘′

푚푁 푘 = 푆푙표푝푒 표푓 푡ℎ푒 푐표푠푡 푖푛푐푢푟푟푒푑 푓표푟 푎 푐ℎ푎푟푔푒 − 푑푖푠푐ℎ푎푟푔푒 푣푠 푑푒푝푡ℎ 표푓 푑푖푠푐ℎ푎푟푔푒 푔푟푎푝ℎ 푓표푟 푠푒푔푚푒푛푡 ′푘′ 푁̅ − 푁̅ = 푘+1 푘 퐷푂퐷̅̅̅̅̅̅푘

3.3.2.2 Decision Variables

Turbine Modelling 3 푄푇 푡 = 퐹푙표푤 표푓 푤푎푡푒푟 푡ℎ푟표푢푔ℎ 푡ℎ푒 푡푢푟푏푖푛푒 푎푡 푡푖푚푒 ′푡′ (푚 /푠) 3 푄푆 푡 = 퐹푙표푤 표푓 푤푎푡푒푟 푡ℎ푟표푢푔ℎ 푡ℎ푒 푠푝푖푙푙푤푎푦 푎푡 푡푖푚푒 ′푡′(푚 /푠) 3 푄푇 푗,푡 = 퐹푙표푤 표푓 푤푎푡푒푟 푡ℎ푟표푢푔ℎ 푡ℎ푒 푡푢푟푏푖푛푒 푎푡 푡푖푚푒 ′푡′ 푓표푟 푠푒푔푚푒푛푡 ′푗′ (푚 /푠)

푃퐺 푡 = 푃표푤푒푟 푠푢푝푝푙푖푒푑 푡표 푡ℎ푒 푔푟푖푑 푎푡 푡푖푚푒 ′푡′ (푀푊)

푃푇 푡 = 푃표푤푒푟 푝푟표푑푢푐푒푑 푖푛 푡ℎ푒 푡푢푟푏푖푛푒 푎푡 푡푖푚푒 ′푡′ (푀푊)

푍푗,푡 = 퐵푖푛푎푟푦 푣푎푟푖푎푏푙푒 푓표푟 푝푖푒푐푒 푤푖푠푒 푙푖푛푒푎푟 푝표푤푒푟 푓푙표푤 푐푢푟푣푒 푖푛 푠푒푔푚푒푛푡 ′푗′ 푎푡 푡푖푚푒 ′푡′

Battery Modelling 푆푡 = 푆푡표푟푎푔푒 푖푛 푡ℎ푒 푏푎푡푡푒푟푦 푎푡 푒푛푑 표푓 푡푖푚푒 ′푡′ (푀푊ℎ)

퐶푆푡 = 푃표푤푒푟 푠푝푒푛푡 푖푛 푐ℎ푎푟푔푖푛푔 푡ℎ푒 푏푎푡푡푒푟푦 푖푛 푡푖푚푒 ′푡′ (푀푊)

퐷푆푡 = 푃표푤푒푟 푠푝푒푛푡 푖푛 푑푖푠푐ℎ푎푟푔푖푛푔 푡ℎ푒 푏푎푡푡푒푟푦 푖푛 푡푖푚푒 ′푡′ (푀푊)

푢퐶푆 푡 = 퐶ℎ푎푟푔푒 푠푡푎푡푒 푣푎푟푖푎푏푙푒 푎푡 푡푖푚푒 ′푡′

푢퐷푆 푡 = 퐷푖푠푐ℎ푎푟푔푒 푠푡푎푡푒 푣푎푟푖푎푏푙푒 푎푡 푡푖푚푒 ′푡′

퐷푡표푡 푡 = 푇표푡푎푙 푑푖푠푐ℎ푎푟푔푒 푒푛푒푟푔푦 푎푡 푒푛푑 표푓 푡푖푚푒 ′푡′ 푓표푟 표푛푒 푐푦푐푙푒(푀푊ℎ) 56

퐷푂퐷푡 = 퐷푒푝푡ℎ 표푓 푑푖푠푐ℎ푎푟푒 푎푡 푒푛푑 표푓 푐푦푐푙푒 푎푡 푡푖푚푒 ′푡′ (푀푊ℎ)

퐷푂퐷푘,푡 = 퐷푒푝푡ℎ 표푓 푑푖푠푐ℎ푎푔푒 푎푡 푡푖푚푒 ′푡′ 푓표푟 푠푒푔푚푒푛푡 ′푘′ (푀푊ℎ)

푁푡 = 퐶표푠푡 표푓 푑푒푔푟푎푑푎푡푖표푛 표푓 푡ℎ푒 푏푎푡푡푒푟푦 푓표푟 푎 푐푦푐푙푒 푖푛 푡푖푚푒 ′푡′ (푆퐸퐾)

푍퐷푂퐷 푘,푡 = 퐵푖푛푎푟푦 푉푎푟푖푎푏푙푒 푓표푟 푝푖푒푐푒 푤푖푠푒 푙푖푛푒푎푟 푐푢푟푣푒 표푓 푐표푠푡 푖푛푐푢푟푟푒푑 푓표푟 푎 푐ℎ푎푔푒 − 푑푖푠푐ℎ푎푟푔푒 푐푦푐푙푒 푣푠 푑푒푝푡ℎ 표푓 푑푖푠푐ℎ푎푟푔푒 푖푛 푠푒푔푚푒푛푡 ′푘′ 푎푡 푡푖푚푒 ′푡′

3.3.2.3 Objective Function

4-14 푀푎푥푖푚푖푧푒 (∑(푃퐺 푡 ∗ 휆푡 − 푁푡)) 푡 The objective function defined in the equation 4-14 is to maximize the revenue of the model. Revenue can be increased by increasing the cash inflow and decreasing the expenses. The cash inflow is increased by increasing the power supplied to the grid at high price hours which is given by 푃퐺 푡 ∗ 휆푡. The expenses are reduced by decreasing the cost incurred which is given by −푁푡. 3.3.2.4 Constraints

Turbine Modelling

The hourly electrical power balance in the system is given by equation 4-15. Power enters the system from the turbine PT t and discharging of the battery DSt , exits the system by feeding electricity into the grid PG t and charging of the battery CSt.

−푃퐺 푡 + 푃푇 푡 − 퐶푆푡 + 퐷푆푡 = 0 ; ∀ 푡 4-15

The power fed into the grid PG t is constrained as nonnegative variable in equation 4-16 as the power is only supplied to the grid and not bought from the grid. The upperbound is defined in equation 4-17 which is the maximum power (푃̅퐺) that can be fed into the grid.

푃퐺 푡 ≥ 0 4-16

푃퐺 푡 ≤ 푃̅퐺 4-17

Conservation of mass flow in the system is given by the equation 4-18. The mass flow enters the system by the flow in the river upstream Qt and is equal to the sum of turbine QT t and the spillway QS t.

푄푇 푡 + 푄푆 푡 = 푄푡; ∀ 푡 4-18

The flow through turbine QT t and spillway QS t are lowerbounded by constraining them as nonnegative variables in equation 4-19 and 4-20. The maximum permissible flow in the turbine and spillway is defined as the upperbound in the equations 4-21 and 4-22.

푄푇 푡 ≥ 0 4-19

푄푆 푡 ≥ 0 4-20

푄푇 푡 ≤ 푄̅푇 4-21

푄푆 푡 ≤ 푄̅푆 4-22

To optimize the power production from the water flow in the river, the piecewise linear function is used to determine the turbine operation point with known operation data points. Equation 4-23 determines in which segment the operating point lies, in the piece wise linear function. Equation 4-24 determines the power output for the operating flow point in the turbine. 4-23 푄푇 푡 = ∑(푄̅푗 ∗ 푧푗,푡 + 푄푇 푗,푡) ; ∀ 푡 푗 4-24 푃푇 푡 = ∑(푃̅푗 ∗ 푧푗,푡 + 푚푗 ∗ 푄푇 푗,푡) ; ∀ 푡 푗 Equations 4-25 to 4-26 are used to implement piecewise linear function. 4-25 ∑ 푧푗,푡 = 1 ; ∀ 푡 푗 푄푇 푗,푡 ≤ 푧푗,푡 ∗ 푄̅푗 ; ∀ 푡, ∀푗 4-26

Battery Modelling

The battery storage’s hourly enegy conservation is defined in the equation 4-27. The previous hour’s remaining storage St-1 is carried forward to the current hour St. Charging of the battery CSt is an input to the system and the discharging of the battery DSt is the output of the system. The charging and discharging efficiencies of the battery system is included in the equation.

푆푡 = 푆푡−1 + (휂퐶푆 ∗ 퐶푆푡) − (퐷푆푡⁄휂퐷푆) ; ∀ 푡 > 1 4-27

Equations 4-28 to 4-31 constraint the lower bound and upper bound of the charging rate CSt and discharging rate DSt of the battery. 퐶푆푡 ≤ 푢퐶푆 푡 ∗ 퐶푆̅̅̅̅; ∀ 푡 4-28

퐶푆푡 ≥ 0; ∀ 푡 4-29

퐷푆푡 ≤ 푢퐷푆 푡 ∗ 퐷푆̅̅̅̅; ∀ 푡 4-30

퐷푆푡 ≥ 0; ∀ 푡 4-31

The lower bound and upper bound of the decision variable storage level is defined in the equations 4-32 to 4-33.

푆푡 ≤ 0,95 ∗ 푆 ̅ 4-32

푆푡 ≥ 0,2 ∗ 푆̅ 4-33

The state of the battery can be binary, charging uCS t or discharging uDS t, which is defined by the equation 4-34.

푢퐶푆 푡 + 푢퐷푆 푡 = 1; ∀ 푡 4-34

The total discharge of a discharge cycle Dtot t is summed in the equation 4-35 to determine the depth of discharge in a cycle. Equation 4-36 and 4-41 are used to piecewise linearly determine the cost incurred in the operating the cycle for the DODt.

퐷푡표푡 푡 = 퐷푡표푡 푡−1 + 퐷푆푡 − 퐷푂퐷푡; ∀ 푡 > 1 4-35

4-36 퐷푂퐷푡 = 푆̅ ∗ ∑(퐷푂퐷̅̅̅̅̅̅푘 ∗ 푍퐷푂퐷 푘,푡 + 퐷푂퐷푘,푡) ; ∀ 푡 푘 4-37 푁푡 = ∑(푁̅푘 ∗ 푍퐷푂퐷 푘,푡 + 퐷푂퐷푗,푡 ∗ 푚푁 푘) ; ∀ 푡 푘 4-38 ∑ 푍퐷푂퐷 푘,푡 ≤ 1; ∀ 푡 푘 퐷푂퐷푘,푡 ≤ 푍퐷푂퐷 푘,푡 ∗ 퐷푂퐷̅̅̅̅̅̅푘; ∀푡, ∀푘 4-39

퐷푡표푡 푡 ≤ 푢퐷푆 푡 ∗ 푆̅; ∀ 푡 4-40

퐷푂퐷 푡 ≤ 푢퐶푆 푡 ∗ 푆̅; ∀ 푡 4-41

The block diagram for the working of the BPM is shown in Figure 32. .

Figure 32: Block diagram for working of BPM Once the combined hydropower and battery system is production optimized, based on the predicted future electricity prices, the revenue from the operation of the combined system is calculated. To compare various cases in this thesis, Net Present Value (NPV) is used as a measurement tool. NPV is one of the best methods to use to identify the profitability of the implemented battery in the hydropower plants as it considers the discounted cash flows and includes the initial cost and the risk inherited by making projections for the future. The formula for NPV is as shown below:

푛 퐶푎푠ℎ 푓푙표푤 4-42 푁푃푉 = ∑ 푖 − 퐼푛푖푡푖푡푎푙 푖푛푣푒푠푡푚푒푛푡 (1 + 푟)푖 푖=1 Where i is the year n is the total years of operation i.e. 20 years r is the discount rate and is fixed to 6 %

3.3.3 Case Study Data The technical specifications of the hydropower plants are shown below:

Table 18: Specifications of the hydropower plants

Skattungbyn Unnån Hansjö Head (m) 4,8 – 7,5 6,5 10,6 Turbine type Kaplan Kaplan Kaplan Number of turbines 2 (0,7 and 0,7 MVA) 2 (1,4 and 1,4 MVA) 1 (3,3 MVA)

From Table 18, the highest-rated capacity is Hansjö having 3,3 MVA and the lowest rated capacity is Skattungbyn with 1,4 MVA. The turbines in the powerplants are Kaplan type turbines. The head for Skattungbyn varies depending on the water level in the reservoir Skattungen. Skattungbyn has a minimum discharge of 8 m3/s after spring flood till the end of October or as long as water supply allows and 6 m3/s from the first of November until the spring flow occurs. Since Unnån and Hansjö are RoR type of hydropower plants, they cannot control the discharge. The flow rates in Unnån is typically higher than that of Skattungbyn, due to the presence of tributaries between them. There is some flow from the river Unnån and Ämån which increases the water flow rate at the Unnån powerplant as shown in Figure 33. There are no other tributaries in-between Unnån and Hansjö, and they typically have the same flow rate apart from the short time lag of around 10 minutes, which is the time the water needs to reach Hansjö. The hydropower plants in lower Oreälven are shown in Figure 33.

Figure 33: Hydropower plants in lower Oreälven The hydropower plants are connected to the local electricity grid in SE3 which is owned by Ellevio. Electricity agreements are signed for all three hydropower plants which specify the limit up-to which electricity can be fed into the grid and the limit up-to which electricity can be consumed from the grid in the hydropower plants for auxiliary needs. The current electricity agreement from each hydropower plant, is shown in Table 19.

Table 19: Electricity agreement values in the hydropower plants

Hydropower plant Maximum Electricity Maximum electricity which can be supplied to which can be consumed the grid (MW) from the grid (MW) Skattungbyn 1,4 0,1 Unnån 2,9 0,06 Hansjö 3,3 0,07

Ellevio is responsible for maintaining and operating the grid to which the hydropower plants are connected. The current grid agreement costs for these power plants can be observed in Table 20.

Table 20: Grid agreement costs [76]

Charges Units Fixed Electricity Network Charge 8 500 SEK / year Electrical Measurement Charge 4 500 SEK / year Annual Power Charge 36 SEK / kW / year Transmission Line Charge 0,74 SEK / kWh / km

Various cases were formulated in BPM based on varying the following factors:  Electricity Price: three different future electricity price scenarios were obtained from FEPFM.  Water Flow: Three different flow scenarios of water were obtained based on current restrictions and seasonal patterns. In Figure 34, flow at the Skattungbyn reservoir and in Figure 35, flow at Unnån and Hansjö are defined as three scenarios: low flow, medium flow and high flow. The low flow scenario corresponds to a dry year, high flow scenario corresponds to a wet year and medium flow scenario corresponds to a year in-between dry and wet year. The low flow at Skattungbyn, Unnån and Hansjö satisfies the minimum flow required in the river based on the environmental regulation. The high flow at Skattungbyn, Unnån and Hansjö, the water flow in the river throughout the year is equivalent to the turbine’s maximum flow capacity.

Figure 34: Skattungbyn flow

Figure 35: Unnån and Hansjö flow

 Battery Connection Point: The battery can be implemented in two possible ways. One battery BESS for Skattungbyn separately and one battery for Unnån and Hansjö together. The reason for considering two different batteries is that the connection point of the hydropower plant and the grid is separate for Skattungbyn and the other hydropower plants.  Battery Size: Six different battery sizes were selected to compare which battery size would yield higher revenues and the sizes of batteries considered are shown in Table 21.

Table 21: Battery sizes considered

Connection Point Battery Size (MWh) 0,75 1 1,25 Skattungbyn 1,50 1,75 2 2,75 3,25 3,75 Unnån and Hansjö 4,5 5 5,5

 Grid Agreement Extension: Considering the current grid agreement restrictions, a case was developed for an increased grid agreement to analyse the effect of extending the grid agreement by which more power can be supplied to the grid at a period of time and would that be more profitable for the battery. Hence both the existing grid agreement and an increased grid agreement by 1 MW were considered. With increased grid Skattungbyn can supply 2,4 MW, Unnån can supply 3,9 MW and Hansjö can supply 4,3 MW.

 Battery Cost: the cost of the battery was varied from 100 % to 0 % of its current cost in steps of 2 % to calculate the break-even point for battery CAPEX. By varying all the aforementioned parameters, 11 016 different cases were formulated, and they were then fed into the BPM. Certain parameters were fixed and assumed to be constant or obtained from research papers in the BPM. They are as follow:  The entire BPM is evaluated from the beginning of the year 2020 to the end of the year 2040 based on the electricity price obtained throughout the years from FEPFM.  The discount rate which was considered for all the cases were set to 6 %.  After the end of the lifetime of the battery, another battery is replaced when it is degraded to 60 % of its initial capacity.  The grid agreement extension cost amounts to 36 SEK / kW / year from Table 20.  Different battery sizes were considered for the different connection point, due to the current technical constraints present in the connection point to the electrical grid.  The current BESS costs were obtained from NREL for utility size battery in the year 2019 including the costs of power electronics and project development costs which amounts to 3 640 SEK/kWh. The battery alone amounts to 55 % of the total costs. [93]  The cost of second life of a battery is assumed to be zero.

4 RESULTS AND DISCUSSION

This chapter explains the outcomes and discussion from FRFPM and BPM models. 4.1 RESULTS FROM FEPFM As explained in the section 3.2.5, a comparison between the MAPE, RMSE and MPE is used to identify the most accurate model. The input data for the model used were started with 35 input variables without using the lagged price and this is treated as input data 1. Then the input data are being revised every time to identify the best combination of historical time-lagged variables which yields the best accuracy of the model. There have been four variations of the time-lagged variable which are considered in the model shown in Table 20. These four variables have been selected based on previous research articles [32] [35] [33] and the dataset which yields higher accuracy and most significant changes. If the price of electricity at the time ‘t’ at the day ‘d’ is to be forecasted, then the lagged time variables considered for different input data in the model are shown in Table 22.

Table 22: Time lagged variables for different input datasets

Input Data Time lagged variables considered Input data 1 - Input data 2 Pavg,1 Input data 3 Pavg,1, Pavg,2 Input data 4 Pt-24, Pavg,1, Pavg,2 Input data 5 Pt-24, Pt-48, Pavg,1, Pavg,2

Where Pt is the price of electricity at the time ‘t’ at the day ‘d’

Pavg,i is the average price of electricity at day ‘d-i’ By using different input data, the best dataset which provides the most accurate result is considered for predicting future electricity prices. The results by using different models and input data are shown in Table 23.

Table 23: Results for different inputs using a different machine learning algorithm

Algorithm MAPE MPE RMS Input data MSE Used (%) (%) E Input data 1 32.17 -10.33 15984.2 126.43 Input data 2 15.57 -4.1 4181.86 64.75 OLS Input data 3 15.38 -3.96 4033.41 63.51 Regression Input data 4 14.24 -3.54 3700.17 60.83 Input data 5 14.13 -3.47 3702.36 60.84 Input data 1 32.18 -10.35 15992.4 126.46 Input data 2 15.58 -4.1 4175.53 64.62 Ridge Input data 3 15.36 -3.95 4025.88 63.45 Regression Input data 4 14.23 -3.55 3695.71 60.79 Input data 5 14.12 -3.47 3696.74 60.8 Input data 1 32.17 -10.34 15985.5 126.43 Input data 2 15.56 -4.1 4178.4 64.64 Lasso Input data 3 15.34 -3.96 4028.97 63.47 Regression Input data 4 14.22 -3.54 3695.72 60.79 Input data 5 14.1 -3.47 3698.3 60.81 Input data 1 32.61 -13.39 15510.1 124.54 Support Input data 2 15.55 -5.74 4192.36 64.75 Vector Input data 3 15.235 -5.455 23996.7 63.22 Regression Input data 4 13.77 -4.211 3634.88 60.3 Input data 5 13.68 -4.11 3625.37 60.21 Input data 1 28 -8 12868.4 113.43 Artificial Input data 2 14.7 -3.6 3913.85 62.56 Neural Input data 3 14.1 -3.4 3535.37 59.45 Network Input data 4 13.0486 -2.95 3254.94 57.05 Input data 5 13.31 -2.98 3357.45 57.9

The values of MAPE and MPE are in percentage and RMSE and MSE are the absolute square root of the error as explained in the previous section 3.2.5. The accuracy of the model is high if the errors are low. From the above tables, it can be seen that the value of errors has been significantly decreasing as a time-lagged variable is included in the input data, i.e., from input data 1 to input data 2. Hence, it can be said that the time-lagged variables affect the model accuracy. Also, there has been a significant decrease when the average price of two previous days is considered, i.e., from input data 3 to input data 4. All the models perform better when the prices before 24 and 48 hours are considered, i.e., from input data 4 to input data 5, except for neural network; it performs better only when the prices before 24 hrs were considered i.e., input data 4. It can also be seen that the accuracy of regression is almost same in all the models with the same input but the lowest value of MAPE and MPE are obtained using Artificial Neural Network (ANN) for the input dataset 4, i.e., having the lagged electricity price of the previous day, average electricity prices of the previous day and the day before the previous day. Hence ANN was considered as the best model and it is then further used for predicting the electricity prices in different scenarios. 66

Before using this model to predict the electricity prices in different scenarios, a sensitivity analysis is performed. Sensitivity analysis helps in identifying the key variables which influence the output and provides a better understanding between the inputs and the output. The future being uncertain, it is essential to perform the sensitivity analysis as it can explain the changes in output if the assumed future values differ. Hence a sensitivity analysis was performed by varying the inputs individually from -75 % to +75 % to check the influence of inputs on the electricity price. The input data for the year 2020 in Scenario 1 was used for this analysis. The results of this analysis are shown in Figure 36.

Figure 36: Sensitivity analysis of inputs From the above sensitivity analysis, it can be seen that load and CHP production are directly related to the price, i.e. if the load increases then the electricity price increases, and similarly if CHP increases the electricity price increases. There is a direct relation with load and electricity price as mentioned in the previous sections and an identical relation can be observed for the CHP production and electricity price. This can be explained by two reasons:  CHP production is similar to that of the load. It increases during winter and decreases during summer and a similar equation fit for CHP production is calculated similarly to that of the load.  The prices of electricity have been low in summer where CHP production is low and high in winter where CHP production is high. On the other hand, nuclear, wind production and hydro reservoir amount are indirectly proportional to the electricity price, if it increases, the electricity price decreases. The most significant influence corresponds to load and wind energy production. A 75 % increase in load, increases the average electricity price by 110 % and for a 75 % decrease in wind energy production, the average electricity price is increased by 100 %. In addition to that, it is important to note that the production of wind energy depends on the wind availability and similarly hydro reservoir amount depends on the precipitation. This sensitivity analysis was performed to encounter the influence of different parameters on the electricity price and it is not used to compare among the parameters. The influence of every input parameter is identified

and now the results from predicting the future electricity prices in different scenarios are discussed below. 4.1.1 Scenario 1 The best model of FEPFM was used to forecast scenario 1. The input variables are calculated as mentioned in the previous section based on the 2040 targets for the scenario. Then the inputs are fed into the model to predict future electricity prices. The variation of electricity prices throughout the year, every four years from 2020 to 2040 is shown in Figure 37.

Figure 37: Scenario 1 electricity price predictions

From the above figure, it can be observed that the variations in electricity prices have been steadily increasing over the years. But the lower limit of the prices has been more or less the same over the years occurring in the summer months. The prices of electricity have been relatively higher at the start of the year than during the summer months. The electricity prices have been the highest during February and March throughout the years. There is a typical seasonal pattern that can be observed throughout all the years. The high prices in February and March can be explained with the decreasing nuclear production and decreasing hydro reservoir amount. The low prices in summer are due to low load and low CHP production. The effect of nuclear is seen when it is completely phased out in 2040, the prices have huge volatility and a steady increase throughout the years with the constant removal of nuclear. But the increase in prices also constitutes the increase in the load and CHP production over the years which can be explained in Figure 36. The wind energy production is also steadily increased which would help in reducing the prices but, this is not significant enough and it is outweighed by the increase in load, increasing CHP production and decreasing nuclear energy. Until 2039 the presence of nuclear power in the grid reduces the volatility of the electricity prices. In 2040 the volatility in electricity prices has significantly increased due to the absence of nuclear power. The volatility in the prices can be ideal for the BESS to charge at a lower electricity price and sell the electricity at a higher price making this scenario the ideal case for the combined BESS and hydropower plants. 4.1.2 Scenario 2 Similarly, the inputs for scenario 2 are identified and are fed into the FEPFM to predict future electricity prices. The variation of electric prices for scenario 2 throughout the year, every four years from 2020 until 2040 is shown in Figure 38. The trend of prices of electricity is the same throughout the years with the maximum prices occurring in February or March and the lower in the summer with the same reasons as for scenario 1. The effect of increasing load, increasing CHP production, and decommissioning of nuclear powerplants over the years outweighs the increasing wind energy production to reduce the price. This is explained by the steady increase in price over the years. As nuclear energy acts as baseload and brings stability in the prices, the volatility of prices in scenario 2 is relatively less when compared to scenario 1. This scenario seems like the achievable scenario due to dependency of the Swedish grid on nuclear energy and suitable alternatives needs to be considered to replace it in the future as the base load in the energy mix.

Figure 38: Scenario 2 electricity price predictions

4.1.3 Scenario 3 The predicted electricity price for scenario 3 throughout the year, every four years from 2020 to 2040 is shown in Figure 39. The price of electricity has a seasonal pattern similar to that of the other scenarios along with the same reasons. The main change in scenario 3, when compared with that of scenario 2, is the electricity prices are relatively less in scenario 3 due to the presence of more nuclear power energy in the grid. The prices have been rising from 2020 due to the increased loads and increased CHP production. The volatility of the prices is least in scenario 3 and thus it can be concluded that the presence of nuclear in the energy mix reduces the price volatility significantly.

Figure 39: Scenario 3 electricity price predictions

From the above three scenarios, it can be seen that the trend of electricity prices has been raising and one major contribution to this is from the increasing load. Scenario 1 is observed to have the biggest price volatility, the prices take a huge hit and there is no stability in the prices. There needs to be some other source of energy either CHP or energy storage technology to fill up the void left by nuclear in the baseload. The CHP powerplants currently operated in the electric grid are based to follow the heat demand. The electricity produced in CHP powerplants is a by- product from the heat generation. In order for CHP powerplants to replace nuclear energy as the baseload would require the CHP powerplants to run throughout the day even when there is minimal to zero heat demand. This means that the unused heat energy is just spilled without utilization thus making it unprofitable to produce. There must be improvements in CHP technology where the extra heat which is to be spilled can be utilized or stored to produce electricity throughout the day. By making the CHP plants can run as baseload powerplants throughout the year, the volatility of the electricity price can be reduced. Scenario 2 seems to be an achievable and realistic scenario in which nuclear energy is not completely phased out but still is in the energy mix and provides stability to the system. Scenario 3 is observed to have the least price volatility due to the presence of a larger share of nuclear energy in the energy mix. Hence, the prices in scenario 3 are the most stable and scenario 1 is the most volatile. With the results of predicted electricity prices in different scenarios, they are used in the BPM to assess the profitability of the BESS. 4.2 RESULTS FROM BPM The production planning of the combined hydropower plant and battery system can be observed in Figure 40. The prices from scenario 1 and river flow from the medium flow scenario was selected along with a 5 MWh battery in Unnån and Hansjö. The week starts from Monday and ends with Sunday in Figure 40.

Figure 40: Production planning of the combined system In the Figure 40, the battery energy storage system charges from the turbine’s power generation and increases the storage level when the price of electricity is low and discharges to the grid and decreases the storage level when the price of electricity is high, i.e., the battery discharges when the prices are relatively high, thus generating revenue. During the weekend, the prices of electricity are relatively low compared to the workdays and the volatility of the electricity price is also low. Due to this the operation of the battery is restricted since the profits yielded by operating the battery does not cover the levelized cost per cycle of the battery. During the

workdays, the electricity prices have clear peaks as explained in section 3.2.1. The battery charges and discharges throughout the workdays based on the electricity price as observed in Figure 40. With this operation of battery, various cases based on varying battery size, battery connection point, alternate grid agreements and future electricity prices were simulated, and techno-economic feasibility of the system was analysed. 4.3 RESULTS FROM ECONOMIC ANALYSIS 4.3.1 NPV Analysis of BESS and Hydropower plant The economic analysis uses NPV to evaluate the different cases. Due to the presence of two different battery connection point, a separate battery in Skattungbyn and a separate battery for Hansjö and Unnån together were analysed. The results from different cases for the battery in Skattungbyn for short term regulation until the year 2040 is shown in Figure 41.

Figure 41: Results from BPM for Skattungbyn The NPV using different electricity price scenarios is shown. It can be observed in Figure 41 that the NPV is vastly negative for the majority of the cases. As the battery costs per MW are varied from its current costs 100 % to 0 %, the NPV increases as the investment decreases. There is not much of a difference between the NPV among the different scenarios, but scenario 1 has a slightly higher NPV than scenarios 2 and 3. Scenario 3 has the least NPV among the price scenarios. A break-even line is also seen in the graph which depicts when the investment is either profitable or unprofitable. The intersection of this line typically happens when the battery cost is less than 0,4 million SEK / MWh which is 90 % less than the current cost of battery which is 3,6 Million SEK / MWh. Similarly, the results from BPM for a battery for both Unnån and Hansjö together for short term regulation until the year 2040 is shown in Figure 42.

Figure 42: Results from BPM for Unnån and Hansjö The cost of the battery to achieve the breakeven point in this location is around 0,5 Million SEK / MWh. The NPV is less for the battery in Unnån and Hansjö when compared to the NPV for the battery in Skattungbyn. From both the graphs, it can be observed that the NPV is negative for the current battery costs and thus concluding that short term regulation in these hydropower plants is not profitable. The battery costs must significantly reduce to 12 % of its current costs to attain the breakeven point. 4.3.2 Second Life of EV Batteries As mentioned in the section 2.7, the second life of a battery can be a possible solution to reduce the high investment costs of battery technology. Hence, it was considered in this thesis and further analysis of NPV was calculated based on the second life of batteries. In this analysis, the cost of power electronics and the balance of system was considered as 45 % of the total battery costs [94]. Considering the current battery costs of 3 640 SEK / kWh then the cost of power electronics amounts to 1 638 SEK / kWh. In this analysis, NPV is computed for different battery sizes along with the changing the grid limitation by varying the grid agreement are computed. The grid agreements are varied from their existing values and to an increase of 1 MW to their existing value. The results for different cases using the second life of the battery in Skattungbyn are shown in Figure 43.

Figure 43: Second life of the battery in Skattungbyn From Figure 43, it can be observed that in all the cases NPV is negative. By utilizing the second life of battery, the initial investments are significantly reduced. The blue points represent the cases where the increased grid agreement is, and the yellow points represent the cases which have the existing grid agreement. For the same battery size, the NPV from increasing the grid agreement is lower than the NPV from the existing grid agreement. This can be reasoned by returns from implementing BESS for short-term regulation is not able to make up for the grid agreement fees. By increasing the battery size, the NPV is still negative even with the increased grid agreements. This can be reasoned by the revenues incurred by the combined system does not make up for the cost of balance of the system of the BESS. The highest NPV which can be obtained by using the second life of the battery in Skattungbyn is -0,3 Million SEK.

Figure 44: Second life of the battery in Unnån and Hansjö Figure 44 shows the results of the second life of the battery in Unnån and Hansjö. Similar to the second life of the battery in Skattungbyn, by the increased grid agreement for the same battery size, the NPV is reduced. The pattern of results from different battery sizes and grid agreements is the same for both Skattungbyn and Unnån and Hansjö together. The NPV from the battery in Unnån and Hansjö is less profitable than that in Skattungbyn and has an NPV of around -3 Million SEK.

Table 24: Best case using the second life of the battery

Battery Connection Battery Size Grid Agreement NPV (Million SEK) Point (MWh) Skattungbyn Existing 0,75 -0,3 Unnån and Hansjö Existing 2,75 -3

Table 24 shows the best battery size and grid agreement conditions for each connection point to obtain the highest NPV using short term regulation at the hydropower plants with the help of the second life of the battery. Overall, it can be inferred that even with the second life of a battery, the short-term regulation is still not profitable. Hence other forms of revenues must be identified and used if BESS is implemented in the hydropower plants. 4.4 DISCUSSION The electricity price has been a difficult entity to forecast over the years. With most of the research for forecasting electricity prices falling under the short term, there has not been a significant amount of research which can be used to compare the results from this thesis. The prices of electricity have a daily, weekly and a seasonal pattern over the years. The price of electricity is significantly influenced by production from different energy sources, hydro reservoir amount and previous days’ electricity prices. One of the important factors which can improve the long-term forecasting model is including the imports and exports of electricity to and from Sweden. As Sweden being part of a connected electricity market along with other countries, the price of electricity is also influenced by the electricity demand and production from the other countries participating in the same electricity market. But due to the uncertainty in imports/exports and the difficulty to predict it in an hourly fashion from 2020 to 2040, it was not considered in the forecasting model. The most influencing factors on the electricity price from FEPFM, are the load and the wind energy production. The load influences the electricity price directly and the wind energy production indirectly. The electrical load in Sweden is supposed to be increasing tremendously over the coming years, with the increasing population and the demand for necessities. With more demand from electrification in the transport and industrial sectors, the demand is supposed to increase even with more efficient devices and processes being developed. The wind energy production is also set to increase over the years. With current subsidies for wind energy in Sweden, makes wind energy one of the most profitable forms of renewable energy investments. The increase in wind energy would help drive down the electricity price over the years. The role of nuclear power in the Swedish electricity mix is noteworthy. In Scenarios 1 and 2, the influence of removing nuclear energy can be observed. Nuclear energy acts as a baseload in the Swedish electric system and helps bring stability. With the complete phase-out of nuclear would cause the price to escalate and makes it more volatile. The prices are supposed to increase in the future with the increase in demand and phasing out of nuclear and its effect on the electricity price surpasses the effect of renewable energy production in the electricity mix. The forecasted electricity prices are one of the most critical inputs for techno-economic optimization. As discussed in section 3.3.3, multiple cases were developed based on varying the parameters. The parameters which were varied were battery cost, battery capacity, battery

connection point, changing the grid agreement and electricity prices from different scenarios of FEPFM. From analysing the data points in the NPV graphs plotted in Figure 41 and Figure 42, it was observed that the change in battery cost has the highest effect on the NPV followed by battery size, changing the grid agreement, electricity price scenarios and flow scenarios. It was observed from the results that changing battery size does not have an impact on the revenue but had an inverse impact on NPV as the cost changes. It was identified that grid agreement limits the battery size as it determines the amount of power injected to the grid, which further limits the power that can be sold in the electricity market to generate revenue. Technical limitation from the electrical system and the cost for increasing the grid agreement with the local grid operator limits the battery size and operation as well. The results from the economic analysis show that the current costs of new batteries are significantly high and implementing such batteries in the hydropower plants in lower Oreälven is not profitable for short-term regulation. The main limitation of implementing the batteries are electrical restrictions posed by the grid agreements with the grid operator, high capital costs of the batteries and the size of the powerplants. The grid agreements can be altered but it incurs a significant cost which is subsequently removed from the revenues from the combined system. The powerplants in focus are small and have a relatively small capacity. This restricts the battery charging. Also, the powerplants in focus are operated like RoR hydropower plants as if they had no reservoir storage, due to the water rights permit limitations for Skattungen. If the electricity agreements can be altered without incurring costs, then the combined system would yield high returns. There need to be other sources of revenue from the BESS which can help in increasing the revenues and justifying the implementation of the battery. With constant developing technology and high demand for batteries, the specific costs of batteries have been significantly decreasing over the years and is expected to follow the same trend for many years still to come. Along with some subsidy schemes from the government, batteries would attract a lot of attention and potential investors. More and more batteries being employed in the market make the electrical grid more stable and reduce the dependencies on non-renewable forms of energy and would aid in reducing the price of batteries. When the price of batteries is significantly lower than current costs, the implementation of battery for short term regulation as described in this thesis would be able to yield profits and would justify the investment. 4.5 SUSTAINABILITY ASPECT The research in the thesis aims to contribute towards the understanding of the environmental problems posed by rapid changes in flow rates of water in hydropower plants and an opportunity created here complying with the environmental laws by incorporating a battery energy storage system. Implementing a battery in said location can help the existing powerplants to produce more electricity than the usual during the peak hours and help in reducing the dependency on non-renewable sources of energy in the grid. The proposed model is based on renewable energy production from the hydropower plants and maximizes the use of renewable energy. Additionally, it also helps in grid stability and peak- shaving, thus reducing the stress on the local grid and increases the reliability of renewable energy productions.

The inclusion of discarded EV batteries in the hydropower plants for storage would further enhance the resource efficiency. By doing so, the carbon footprint of such batteries is drastically reduced, and its capacity is used to its maximum possible extent. 4.6 FUTURE WORK With long-term forecasting, predicting the future imports and exports, one would need to do a detailed analysis of connected countries’ energy policies for the future and their grid development to assess the impact on future imports. Also, the study based on different prices of batteries in the future may lead to more feasible results, due to the anticipated reduction of costs of battery storage in the future. With the increasing share of renewable energy in the electricity mix, the volatility of prices would increase giving the combined system to operate effectively to yield better returns. The falling battery price as lithium-ion technology matures will also make the investments in BESS feasible in the future [93]. A separate analysis of implementing the BESS in various future years might lead to a potential improvement in NPV. Vehicle to grid and grid to vehicle technologies are still in their pilot phases and have not been implemented large scale. With these two technologies, the load can be stabilized more, and the volatility of electricity prices may be reduced. Again, this can be implemented in a future study. The BESS connected to the hydropower plant is in operation throughout its life of service. The usage of the battery is analysed from the results of the BPM, which is shown in the Figure 45 for BESS at Unnån and Hansjö connection point. The figure shows each day’s revenue from BESS throughout its operation. The days when the revenue is zero means that the battery is not being used.

Figure 45: Daily revenue from BESS at Unnån and Hansjö In the Figure 45, it can be seen that the revenue is zero for a period of days that repeat every year, which correspond to the summer days when the market electricity price variation in a day is not enough to cover the levelized cost per cycle of BESS and electricity losses in charging and discharging. To increase the utilization of the battery during such periods, the regulation market can be a possible solution. Figure 46 shows the daily average regulation market price of 2019 and revenue from BESS of 2020. It can be noted that from June 20 to August 27 the revenue from the BESS is zero, but the regulation market does not have many changes in summer compared to the rest of the year.

Figure 46: Comparison of revenue from BESS and up-regulation market price This opportunity can be used, and the combined BESS and hydropower plant can be employed in the regulation market. By doing so, the revenue from the combined system would be significantly increased and yielding a higher NPV than before but a detailed study on the future of the regulation market will provide the exact concrete results of the increase in revenue and NPV.

5 CONCLUSION

The feasibility of short-term battery regulation in the three hydropower plants in lower Oreälven was analysed both in technical and economic terms in this thesis. The day-ahead electricity market was analysed first. The factors which influence the day-ahead electricity prices were analysed and were used as an input to the various machine learning algorithm models. By employing different combinations of input data on various machine learning algorithms, it was found that artificial neural network (ANN) yielded the best results with input data of hour, day, season, electrical load, hydro reservoir amount, wind energy production, CHP production, nuclear energy production and lagged prices of electricity. The MAPE of the selected machine learning algorithm was 13 % and it was then employed to predict the future electricity prices based on the Swedish electricity mix in the future. Three possible scenarios were developed based on the nuclear energy phase-out plan. The first scenario is designed based on the Swedish energy targets of 2040. In this scenario, nuclear energy is completely phased out and there is only renewable energy in the Swedish electricity mix. The second scenario is designed in a way that there are two nuclear powerplants still operating in the electricity mix of Sweden in 2040. The third scenario is designed with Sweden having the same nuclear capacity as of 2020 in the year 2040. The ANN with the best result is used on different scenarios to predict the future day-ahead electricity prices. The first scenario had very high prices with high volatility. The second scenario had less prices with reduced volatility when compared with scenario one and the third scenario had the lowest price with relatively less volatility. The combined BESS and hydropower plants production optimization was modelled in python, using mixed-integer linear programming. The parameters which influence the production planning were identified as the water flow in the river, battery size, levelized cost per charge cycle of BESS and day-ahead electricity market price. The major factor was identified as the volatility in electricity market price within a day, which gives an opportunity for the battery to operate. Various cases were studied and analysed by varying the cost of the battery, size of the battery, connection point of the battery, grid agreement extension and future electricity market scenarios. Based on the results, the NPV of implementing a BESS in the hydropower plants in lower Oreälven for short term regulation in the day-ahead electricity market is negative and thus non-profitable. The non-profitability can be explained by the high CAPEX of battery and the low return on investment, as the fluctuations in the day-ahead electricity market price is not enough to generate the breakeven revenue. The grid agreement and battery size co-dependency limit the BESS to operate effectively. The second life of battery which utilises the discarded EV batteries was considered and evaluated as well. The costs of such batteries are significantly low but the usage of such batteries as a stationary energy storage system is still in its pilot phase and the cost of integrating them and the power electronics required for it is still uncertain. Even with employing such discarded batteries as BESS in the hydropower plants in lower Oreälven, the NPV is still negative. The most profitable case was a battery size of 0,75 MWh for Skattungbyn with the current grid agreements and a battery size of 2,75 MWh for Unnån and Hansjö with the current grid agreement. To conclude, the implementation of BESS in a hydropower plant provides an opportunity to short term regulate the combined system based on the day-ahead electricity prices. The current

costs of batteries are high and implementing the system would not yield a profitable investment. The second life of the battery is a possible alternative that can diminish the significant initial investment of the BESS. When such batteries are employed, the implementation of the BESS in the hydropower plants in lower Oreälven for short-term regulation in the day-ahead electricity market is not profitable and it is not significant enough to justify the investment. Alternate sources of revenues need to be explored and one potential usage is to employ the said BESS in the regulation market during the summer months. With additional sources of revenue, the combined system would yield a higher return but needs to be properly investigated if it is to be implemented.

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