The increasing demand for renewable energy sources to meet climate targets and reduce carbon emissions poses challenges to the power grid due to their intermittent nature. One potential solution to maintain grid stability is by implementing Hybrid Energy Systems (HESs) that incorporate a Battery Energy Storage System (BESS). To achieve the most favorable outcome in terms of both technical feasibility and profitability of a BESS, it is essential to employ models for simulating and optimizing the control of system components.

This thesis focuses on the analysis of energy and revenue streams in a HES consisting of a BESS, photovoltaics (PVs), and an energy load including a fast charging station for electric vehicles (EVs). The objective is to optimize the system based on revenue generation by comparing the control techniques of peak shaving, energy arbitrage, and the integration of ancillary services within the Swedish energy market. The research questions explore the optimal utilization of the BESS and assess the impact of the different control techniques.

A model is created in Python with the package CasADi where data from an ongoing installation of a HES in southern Sweden is combined with data from literature research. The model includes an objective function that minimizes the total cost of power from the grid based on the day-ahead price, battery degradation, and monthly peak power. 

To answer the research questions, four different scenarios are simulated. The first scenario is a base for comparison, the second one focuses on peak shaving and energy arbitrage, the third on participation in the ancillary service FCR-D upwards regulation, and the last one is a combination of peak shaving, energy arbitrage, and the ancillary service FCR-D. The results show that the remuneration from the ancillary service FCR-D is comparably much higher than the revenues generated from peak shaving and energy arbitrage, providing more than 500% of revenue compared to the same system but without a BESS. The scenario with peak shaving and energy arbitrage shows an increase in revenue of 29% but with more cycling of the battery which could cause losses in performance in the long term. To validate the results, sensitivity analyses are conducted by evaluating weighting in the objective function, implementing Model Predictive Control (MPC), and reviewing price variations. 

In conclusion, efficient control techniques can enhance system performance, minimize losses, and ensure optimal utilization of different energy sources, leading to improved feasibility and profitability. The optimal usage of a BESS involves finding a balance between maximizing revenue generation and minimizing battery degradation. This can be achieved through control strategies that optimize the charging and discharging patterns of the BESS based on electricity price signals, demand patterns, and battery health considerations.