Optimization of a battery energy storage system : For utilization of peak shaving and fast frequency reserve

• Main Author: Sundgren, Robert
• Format: Bachelor Thesis
• Language: English
• Published: Umeå universitet, Institutionen för tillämpad fysik och elektronik 2020
• Subjects: battery energy stroage system optimization; Other Electrical Engineering; Electronic Engineering; Information Engineering; Annan elektroteknik och elektronik; Northern Sweden
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172786

As Sweden switches to increasing renewable electricity production the demand on the energy grid and energy market will become higher. Since a bigger part of the electricity consumption will come from flowing energy sources the production will become less stable and harder to plan with the consumption. The inertia of the electrical system will also decrease since solar and wind power are not synchronously connected to the electrical system which will make the system more sensitive to interference. In order to keep the short-term balance so that the frequency remains at 50 ????????, Svenska kraftnät has several reserves at their disposal. As of summer 2020, Svenska kraftnät will launch a new reserve called Fast frequency reserve (FFR) with the purpose to deal with rapid imbalances. By supplementing a wind farm with a battery energy store system (BESS), it becomes possible to even out the wind farm's intermittent electricity production by applying peak shaving and lower the grid costs for the wind farm. Because a BESS can provide power within a fraction of a second and is therefore is suitable to provide FFR. To study the profitability and determine what capacity and power a BESS needs for peak shaving and FFR with a wind farm, an optimization model was built in MATLAB to study the profitability of a BESS with multiple power and capacity combination. In addition, the cycling of the BESS and the limitation of peak shaving was also studied to get deeper knowledge about the limitations. The optimization model is using hourly generation data from a wind farm in northern Sweden. Besides the BESS optimization, a separate optimization model was built in order regulate the output power to minimize the generation cost by prolonging the service life of a wind turbine (WTG). The purpose of this optimization was to study if regulating the output power could lower the generation cost, more for the WTG. In addition of the net income the loss of electricity was also studied. The optimization used hourly data during one time period ...