Energy storage is of increasing interest as an enabler of incorporating renewable intermittent power in the power systems globally. There are several technologies for energy storage, and this thesis focuses on battery energy storage systems (BESS). Previous research has shown that it is difficult to install BESS with a payback time within the battery lifetime, making it a challenge to realise profitable investments. The complexity of developing an optimal control of the battery is also documented in research as another challenge. Optimal sizing of the BESS could be a solution to the challenge of reaching profitability. The thesis is identifying and analysing some important technical and energy-related parameters affecting the performance of BESS installations. Identification and analysis of parameters affecting the performance will help build insight into the optimization of BESS and help enable the development of more efficient sizing and operation. By developing an algorithm simulating the BESS when controlled using two different strategies, this thesis additionally contributes to the research by displaying the complexity of battery control, which is realised by the energy management system (EMS). Thereby the thesis is adding to the research base for the future development of smarter and more optimal EMS. The main research methodologies used in the thesis was a literature study and a case study. The results suggested that the energy management strategy used in the battery control was gravely affecting the performance in terms of economic profitability, self-sufficiency and environmental impact. It was also implied that it is difficult to develop an efficient battery control to reach the full potential of the storage system. The main conclusions in this paper are that the most important parameters to consider when implementing a battery storage in a residential multifamily building are battery technology, battery capacity, building load, renewable energy generation, energy management strategy as well as the electricity prices and investment cost. The energy management strategy most favourable for the case building studied was found to be a combination of optimizing the self-sufficiency and performing peak shaving. It would also be preferable to further develop the battery control to also take electricity prices and balance services into consideration. For this, AI and machine learning could be integrated in the control of the system. According to the case study results, the lithium ion battery technology had better potential for reaching economic profitability while the nickel metal hydride technology showed better potential in terms of environmental performance. The choice of battery technology and energy management strategy should however be adjusted to the customer specific demands and prerequisites.