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There is a potential dual usage of Autonomous Electric Vehicles (AEVs) for ride-hailing and electricity market participation in a vehicle-to-market (V2M) setting. In this study a model for early adoption of AEVs is developed based on sequential optimization at each time-step for dual market participation choosing between ride-hailing and energy arbitrage market participation via Vehicle-to-Grid (V2G). Simulations with the model shows that there are, depending on ride-hailing rate and electricity price cost, yearly revenue estimations of upwards of $45k in extreme cases, with considerable revenue from in particular AEV ride-hailing payoff. Simulations also show that the activation fraction of AEV ride-hailing is dependent on both ridehailing rate and electricity price. This dual market participation model thus reveals a potential demand-side management for V2G interaction, and ride-hailing, via for example time-based energy price tariffs.

The increasing share of intermittent and non-dispatchable power sources, such as wind and solar photovoltaic (PV) power in the electrical energy mix, poses challenges to power system stabil-ity. One possible way to dampen fluctuations during frequency instability situations or remove bottlenecks across the power system is to install battery energy storage systems (BESSs) or to enable demand side management via electric vehicle (EV) batteries. The BESS can operate in the power system transmission or distribution or behind-the-meter (BTM), mitigating power system challenges and electricity market inefficiencies. Although BTM BESS or aggregating EVs fleets can operate similarly to utility-scale BESS systems, a significant common challenge is to determine the specific services these systems should provide to maximize their profits. This thesis investigates the techno-economics of operating these technologies in different con-texts: 1) economics of participation on spot and ancillary services markets for utility-scale solar PV power plant for a case-study in Sweden, 2) the most profitable markets and sizes of BESS combined with utility-scale solar PV power plants using techno-economic analysis frameworks applied in Swedish and German contexts, 3) techno-economics of adding peak shaving (PS) and participation on local flexibility markets if using, BTM BESS for a Swedish church pow-ered with PV system, and 4) techno-economics of minimizing monthly cost of electricity for residential buildings with both rooftop PV system and EV smart charging. The input data for all four studies are historical market prices and measured frequency, PV power, and electricity consumption data. For case study 1), operation on ancillary service markets increases the profits by 20% compared to only participating on the spot market for a utility-scale PV power plant in Sweden. For case study 2), adding a utility-scale BESS to an existing PV park does not result in a lower payback period than if implementing a stand-alone BESS. However, the payback period differs between Sweden and Germany, being 1.8 and 6.8 years, respectively, using the market prices from 2023. This is explained by the lower frequency market prices for Germany compared to Sweden. For case study 3), the BTM BESS results do not show high profitability for adding the operation on the local energy market or performing PS in a church case study. Lastly, for case study 4), EV smart charging was shown to lower the yearly electricity cost by up to 15% on average for residential buildings powered by rooftop PV systems for the year 2021. This gain is higher when power-based networks are applied instead of the energy-based network tariffs relative to the immediate charging scenario. In conclusion, the thesis shows that the economic results demonstrate profitability for BESS, utility scale PV, and EVs smart charging in Sweden while alleviating some of the power system challenges. For future work, investigating the integration of forecasting methods with market models to optimize PV power usage with EVs smart charging would be interested to enhancing short-term trading strategies and ASMs participation.

A significant challenge is to determine the specific services Battery Energy Storage System (BESS) should provide to maximize profits. This study investigates the most profitable markets and sizes of BESS with utility-scale solar Photovoltaics (PV) power plants using techno-economic analysis frameworks. The objective is to maximize profitability in energy and frequency markets, focusing on primary regulation and day-ahead markets for Sweden and Germany. The inputs are historical market prices and frequency data, as well as real measurement PV power data. The results show that adding a BESS to an existing PV park does not result in a lower payback period than if implementing a stand-alone BESS. However, the payback period differs between Sweden and Germany during 2023, i.e., being 1.8 and 6.8 years, respectively. This is explained by the lower frequency market prices for Germany compared to Sweden. The technical results indicate that the BESS energy capacity after 10 years of operation is approximately 83% for Germany, whereas, for Sweden, it is around 87%. Also, combining the operating of BESS on primary regulation and day-ahead markets showed a 6-year payback period with a slight increase in loss of energy capacity (from 83 to 80%) for Germany. Moreover, combining various PV-BESS sizes showed a discrepancy in economic and technical metrics for the BESS in Germany, resulting in a best-case of a 6-year payback period. A sensitivity analysis, which examines a drop in the frequency control prices in the future relative to 2023 (by 20% and 50% for Germany and Sweden, respectively), reveals an increase in the payback period for both countries by approximately 1 year.

In Sweden, Svenska Kyrkan (the Church of Sweden) has over 3300 churches. A majority of the churches are electrically heated. Usage patterns of electrically heated buildings such as church buildings, creating problems for the grid and the church organization through increased grid fees. Simultaneously, interest in deploying Battery Energy Storage Systems (BESSs) is growing. A significant challenge is determining the specific services the BESS should provide to maximize profits for the owner. For church load profiles, with the help of a battery, the church consumption peaks can be shaved. Additionally, when the Battery Energy Storage System (BESS) is not used for this purpose, it can instead be employed to support the grid through participation in the frequency regulation market. Frequency control services are activated in response to changes in the electricity grid frequency, with the BESS providing support during frequency fluctuations. The objective of this study is to investigate the economic value of installing BESS in a church powered by a PV system. Various frequency regulation services, with a focus on primary reserve, are explored. The model operates on other energy markets, which are local flexibility and day-ahead markets. The inputs include selected services, feed-in and feed-out profiles, historical frequency data, and frequency regulation and energy market prices over the year 2023. The case study involves measured data from Kila Church, which has a 60 kWp solar power system and is located in mid-western Sweden. The economic metrics are net present value and payback period, whereas technical and environment metrics are the battery degradation and CO2 emission equivalents, respectively. This study indicates that the investment in BESS is profitable if the BESS operates on frequency stability services together stacked with Peak Shaving (PS). The results show a 1.6-year payback period for a 120 kWh/60 kW BESS. A sensitivity analysis explores future changes in prices of the frequency regulation market and BESS shows that FCR-D Up has more sensitivity for a drop in the prices in the future. Nevertheless, FCR-D Down has more economic potential value. Conclusively, BESS would be a beneficial investment for churches and facilities with similar load and PV power generation profiles, both from an economic and societal perspective.

As interest in deploying Battery Storage systems (BSSs) grows, a significant challenge is to determine the specific services that the BSS should provide to maximize profits. This study aims to determine the most profitable strategy and size of integrated grid-connected BSS with and without PV park for participating in Day-Ahead Market (DAM) and Frequency Regulation Market (FRM). The Frequency control services activate in response to changes in the electricity grid frequency, with BSS supporting during frequency fluctuations. The focus of this study is on the primary regulation within FRM. In this study, a BSS operation algorithm is evaluated in economic terms. The algorithm imports inputs like market prices, fees, tariffs, PV production, and chosen BSS service. Economic metrics include Net Present Value (NPV) and Internal Rate of Return (IRR). Real-world data from a Swedish PV park was used for case studies across three categories: BSS stand-alone, PV park alone, and PV-BSS combination. Results highlight that stand-alone BSS scenarios are superior to PV-BSS combination cases, showing a 73% Internal Rate of Return (IRR) for a 1000 kWh/400 kW BSS configuration. PV park alone participation in FRM and DAM shows marginal benefits compared to only acting on the spot market. The sensitivity analysis examining changes in prices for both DAM and FRM relative to 2022 reveals a significant negative change in revenue in 2020, which is explained by the higher and more fluctuating electricity prices. Lastly, the sensitivity analysis explores changes in the acceptance rate of bids in the future relative to 2022, as FCR products will be procured at a marginal price. These analyses indicate potential negative changes that may occur as the acceptance rate may decrease.

Grid-connected photovoltaic (PV) parks are increasing in number and size. For local optimal battery control, electricity market participation and generally for delivering ancillary services to the grid from PV parks, it is important to be able to forecast PV park power generation. This study investigates short-term probabilistic forecasts and scenario-based forecasts on PV park clear-sky index for photovoltaics with two Markov-chain mixture distribution (MCM) models, Persistence Ensemble (PeEn) and Climatology. The models were trained on, and used to forecast, a 5 minute resolution data set of PV park power generation for two years from Vasakronan AB’s PV park in Uppsala, Sweden. The study shows that the MCM models outperform the PeEn and Climatology for five minute ahead forecasts in terms of continuous ranked probability score and in terms of point forecast MAE. It is also concluded that PeEn outperforms the Climatology, which despite lack of accuracy has highest similarity in result output. In terms of scenario-forecasting, where the two MCM models are compared to outputs from the Climatology, all models have similar CDF goodness-of-fit. In terms of autocorrelation, the MCM models are superior. Based on the results, the MCM model, regardless of setting, is recommended as advanced benchmark for very short-term probabilistic PV park power production forecasts.