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Utility-scale renewable hybrid power plants (HPPs) have emerged as promising electricity generation resources by combining multiple renewable generation technologies and storage. However, due to overplanting and co-location, storage size is usually smaller than that of renewable resources, which imposes challenges for HPP in providing reliable balancing services. This paper presents a novel model for optimizing the offering and operation of HPPs in hour-ahead manual frequency restoration reserve (mFRR) energy activation markets, with a focus on guaranteed service provision. The model takes into account uncertainties from wind power generation as decision-independent uncertainties, and considers the uncertainties related to the activation of mFRR to be influenced by the offering decisions, leading to decision-dependent uncertainties. The proposed model utilizes a robust two-level optimization approach, where the first level focuses on hour-ahead offering and operation, and the second level handles generation re-scheduling. Then, to ensure the computational efficiency with 15 min resolution, a modified column and constraint generation algorithm is proposed to solve the model. A comparative analysis reveals that the HPP with the proposed model can deliver upward and downward mFRR in 94% and 99% of the activated time, respectively. It meets transmission system operators' required 90% reliability.

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.

The increasing trend of small-scale residential photovoltaic (PV) system installation in low-voltage (LV) distribution networks poses challenges for power grids. To quantify these impacts, hosting capacity has become a popular framework for analysis. However, previous studies have mostly focused on small-scale or test feeders and overlooked uncertainties related to rooftop azimuth and tilt. This paper presents a comprehensive evaluation of city-level PV hosting capacity using data from over 300 real LV systems in Varberg, Sweden. A previously developed rooftop azimuth and tilt model is also applied and evaluated. The findings indicate that the distribution systems of the city, with a definition of PV penetration as the percentage of houses with 12 kW installed PV systems, can accommodate up to 90\% PV penetration with less than 1\% risk of overvoltage, and line loading is not a limiting factor. The roof facet orientation modeling proves to be suitable for city-level applications due to its simplicity and effectiveness. Sensitivity studies reveal that PV size assumptions significantly influence hosting capacity analysis. The study provides valuable insights for planning strategies to increase PV penetration in residential buildings and offers technical input for regulators and grid operators to facilitate and manage residential PV systems.

The increasing share of intermittent and non-dispatchable power sources such as wind and solar photovoltaic (PV) power in the electrical energy generation mix pose operational challenges in the electric power system and corresponding markets. Co-locating wind and PV power parks, forming utility-scale hybrid power parks (HPPs), means that the power sources can share grid connection, land, permitting procedures as well as operation and maintenance work. According to the results of the thesis, the power output of co-located wind and PV power parks are generally negatively correlated, which results in a smoothed aggregated power output. The seasonal and diurnal time scales contribute the most to the negative correlation, where wind power parks are likely to be more negatively correlated than any randomly chosen site. The smoothing effect as a result of aggregation is also studied in terms of probabilistic forecasting, which corresponds to estimating the uncertainty of power production predictions by means of a probabilistic distribution. By forecasting co-located wind and PV power production, the probabilistic forecasts can be improved, which is explained by the aggregated time series being smoother and therefore more straightforward to predict. The value of improved forecasts is also realized in the day-ahead market, where sharper and more reliable probabilistic forecasts improve decision making by lowering imbalance costs. Furthermore, when trading energy from HPPs with storage, probabilistic forecasts reduce the energy throughput of the battery and is preferable over a deterministic model when the regulating prices are more difficult to forecast than the spot-prices, and when the battery energy capacity is low. Finally, a techno-economic simulation model to assess and forecast the potential to retrofit existing wind power parks with PV power parks was developed. Retrofitting means that a PV power park is connected behind the same point of interconnection to the electricity grid as an existing wind power park. Results show that the curtailment losses from retrofitting are small (max. 3.5% of PV power generation with over 100% added capacity) due to the complementary characteristics of the power sources. On top of this, the most influential resource-related site characteristics for a profitable investment from retrofitting are, in their order of importance; high PV power capacity factor, low wind power capacity factor, and strong negative correlation between PV and wind power production. By estimating these three variables, a forecast of the expected income from retrofitting at any given site can be estimated using a simple regression model.

To evaluate the effects of different energy retrofit scenarios on the residential building sector, in this study, an urban building energy model (UBEM) was developed from open data, calibrated using energy performance certificates (EPCs), and validated against hourly electricity use measurement data. The calibrated and validated UBEM was used for implementing energy retrofit scenarios and improving the energy performance of the case study city of Varberg, Sweden. Additionally, possible consequences of the scenarios on the electricity grid were also evaluated in this study. The results showed that for a calibrated UBEM, the MAPE of the simulated versus delivered energy to the buildings was 26 %. Although the model was calibrated based on annual values from some of the buildings with EPCs, the validation ensured that it could produce reliable results for different spatial and temporal levels than calibrated for. Furthermore, the validation proved that the spatial aggregation over the city and temporal aggregation over the year could considerably improve the results. The implementation of the energy retrofit scenarios using the calibrated and validated UBEM resulted in a 43 % reduction of the energy use in residential buildings renovated based on the Passive House standard. If this was combined with the generation of on-site solar energy, except for the densely populated areas of the city, it was possible to reach near zero (and in some cases positive) energy districts. The results of grid simulation and power flow analysis for a chosen low-voltage distribution network indicated that energy retrofitting of buildings could lead to an increase in voltage by a maximum of 7 %. This particularly suggests that there is a possibility of occasional overvoltages when the generation and use of electricity are not in perfect balance.

Renewable hybrid power parks (HPPs) that combine wind power, solar photovoltaic (PV) power and storage have emerged as promising electricity generation resources. However, HPPs face operational challenges due to the uncertainty in power production and electricity prices, which is why probabilistic forecasts that capture the uncertainty associated with forecast errors have gained attention. While the research community has proposed several methods to improve the accuracy of probabilistic forecasts, the question on how these forecasts can improve decision-making over deterministic forecasts is rarely quantified. This study assesses the value of probabilistic forecasts and analyze the improvement compared to deterministic forecasts in day-ahead markets. The value is quantified using almost two years of data from an operational HPP in Sweden. Results show that: (i) high grid connection capacities leverage the value of probabilistic models, (ii) a deterministic model is preferable for parks with a ratio of battery energy capacity to installed nominal power of the renewable power park equal to 0.6 MWh/MW, (iii) a probabilistic model allows utilizing the energy storage more effectively by reducing the energy throughput of the battery with 61%–87%, and (iv) a probabilistic model increases the unit profit when the forecast errors of the regulating price are higher than the spot price, (v) a simple probabilistic benchmark model, which is worse in terms of forecast accuracy, increases the unit profit compared to the analyzed deterministic models, and (vi) the more advanced probabilistic model analyzed in this study does not provide a significant improvement over a simple probabilistic benchmark model.

Renewable energy sources (RES) and electric vehicles (EVs) are two promising technologies that are widely recognized as key components for achieving sustainable cities. However, intermittent RES generation and increased peak load due to EV charging can pose technical challenges for the power systems. Many studies have shown that improved load matching through energy system optimization can minimize these challenges. This paper assesses the optimal urban-scale energy matching potentials in a net-zero energy city powered by wind and solar energy, considering three EV charging scenarios: opportunistic charging, smart charging, and vehicle-to-grid (V2G). This paper takes a city on the west coast of Sweden as a case study. The smart charging and V2G schemes in this study aim to minimize the mismatch between generation and load and are formulated as quadratic programming problems. Results show that the optimal load matching performance is achieved in a net-zero energy city with the V2G scheme and a wind-PV electricity production share of 70:30. The load matching performance is increased from 68% in the opportunistic charging scenario to 73% in the smart charging scenario and to 84% in the V2G scenario. It is also shown that a 2.4 GWh EV battery participating in the V2G scheme equals 1.4 GWh stationary energy storage in improving urban-scale load matching performance. The findings in this paper indicate a high potential from EV flexibility in improving urban energy system performance. 

This paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling framework where we post-process numerical weather predictions to calibrated probabilistic production forecasts, which is a prerequisite when placing optimal bids in the day-ahead market. The results show that aggregation improves forecast accuracy in terms of continuous ranked probability score, interval score and quantile score when compared to wind or PV power forecasts alone. The optimal aggregation ratio is found to be 50%–60% wind power and the remainder PV power. This is explained by the aggregated time series being smoother, which improves the calibration and produces sharper predictive distributions, especially during periods of high variability in both resources, i.e., most prominently in the summer, spring and fall. Furthermore, the daily variability of wind and PV power generation was found to be anti-correlated which proved to be beneficial when forecasting the aggregated time series. Finally, we show that probabilistic forecasts of co-located production improve trading in the day-ahead market, where the more accurate and sharper forecasts reduce balancing costs. In conclusion, the study indicates that co-locating wind and PV power parks can improve probabilistic forecasts which, furthermore, carry over to electricity market trading. The results from the study should be generally applicable to other co-located parks in similar climates.

An important aspect in designing co-located wind and solar photovoltaic hybrid power plants is the sizing of the energy converters to achieve as efficient power smoothening as possible. In this study, the ratio of wind- and photovoltaic energy converters in a hybrid power plant is determined by minimizing the overall stored energy that is needed to facilitate constant power output. Using Fourier transform the variability is isolated at predefined time scales that are relevant for grid integration. For the investigated time scales, energy and power ratings for energy storages are determined to counteract the variability. The resulting configuration is the one that is able to achieve constant power output with minimum stored energy. It is shown that co-locating wind- and photovoltaic energy converters smoothen seasonal energy generation, and reduce the energy storage need in both the diurnal and seasonal time scales. A case study for south-eastern Sweden is presented where the wind- \& solar hybrid plant configuration that minimizes the energy storage need and therefore most closely resembles constant output power is determined. It is found that a ratio of approximately 40-45\% wind power in the hybrid power plant yields the lowest need for energy storage. The presented method is valid for any number of co-located energy sources, and can also be extended to sizing of hybrid power systems.