Sweden’s energy system is facing major changes in the near future in order to reducecarbon emissions and to switch to sustainable energy sources. PV systems havebecome a sensible alternative for homeowners that want to be a part of this changeand at the same time reduce the cost of their electricity bill. To further improve theutilization of their PV system and to handle the intermittent nature of solar power,battery storages have become an interesting system complement. This thesisinvestigates how batteries can provide smart services; load shift and peak price energyutilization to a household. This is done by developing an optimized battery algorithmmodel that can provide these smart services which is compared to a simple batteryalgorithm. The results show that the developed battery optimization model works asintended. It performs both load shift and peak price energy utilization. The economicanalysis shows that the most profitable PV system and battery configuration is a 20kW PV system with a 5 kWh battery. The system has an internal rate of return, IRR,of 2.3% which does not reach Vattenfall’s weighted average cost of capital, WACC, at7%. The results also show that the battery cost is an important factors for a system'sprofitability. A larger battery system is more expensive and the increased yield doesnot cover the increased cost. Further research is needed to implement the optimizedbattery as a functional application since the model has access to a perfect forecast andthus a method for forecasting PV production and load profile of the household arecrucial to get similar results.

In a deregulated electricity market, offering the appropriate amount of electricity at the right time with the right bidding price is of paramount importance. The forecasting of electricity market clearing price (MCP) is a prediction of future electricity price based on given forecast of electricity demand, temperature, sunshine, fuel cost, precipitation and other related factors. Currently, there are many techniques available for short-term electricity MCP forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. The mid-term electricity MCP forecasting focuses electricity MCP on a time frame from one month to six months. Developing mid-term electricity MCP forecasting is essential for mid-term planning and decision making, such as generation plant expansion and maintenance schedule, reallocation of resources, bilateral contracts and hedging strategies. Six mid-term electricity MCP forecasting models are proposed and compared in this thesis: 1) a single support vector machine (SVM) forecasting model, 2) a single least squares support vector machine (LSSVM) forecasting model, 3) a hybrid SVM and auto-regression moving average with external input (ARMAX) forecasting model, 4) a hybrid LSSVM and ARMAX forecasting model, 5) a multiple SVM forecasting model and 6) a multiple LSSVM forecasting model. PJM interconnection data are used to test the proposed models. Cross-validation technique was used to optimize the control parameters and the selection of training data of the six proposed mid-term electricity MCP forecasting models. Three evaluation techniques, mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square root error (MSRE), are used to analysis the system forecasting accuracy. According to the experimental results, the multiple SVM forecasting model worked the best among all six proposed forecasting models. The proposed multiple SVM based mid-term electricity MCP forecasting model contains a data classification module and a price forecasting module. The data classification module will first pre-process the input data into corresponding price zones and then the forecasting module will forecast the electricity price in four parallel designed SVMs. This proposed model can best improve the forecasting accuracy on both peak prices and overall system compared with other 5 forecasting models proposed in this thesis.