Research On Models For Optimal Energy Management Of Microgrids

As for now, there is a global problem:the traditional energy resources are reducing with increasing energy demand. It is important to improve energy efficiency and make sustainable energy development. In this context, distributed generation, smart grid and micro-grid become the new direction for future power development. Because they can make the most effective balance between energy generation, storage, and electricity demand, while reducing the losses of distance distribution.On this basis, the economic dispatch of power generation equipment, storage systems, and the load demand is a crucial problem as for micro-grid energy management. While taking into account the relevant constraints, it is necessary to reduce the operating costs and achieve optimal management of the energy. Energy management is the use of a micro-grid central controller (MCC) realized by tertiary, secondary and primary controls. Tertiary control of the management as the first step is one day ahead to determine two goals. The first goal is to determine the economic optimization running program based on the economic dispatch and generator sets. The second goal is to take full advantage of electric vehicles-to-grid systems (V2G) and grid interconnection with grid charging and discharging functions, and improving the profitability to get the corresponding energy scheduling one-day before. Secondary control is based on the tertiary control to adjust the power output. Secondary control plays the role of the correction of the output power of generator sets. Finally, primary controls can regulate the flow of real-time energy, ensuring proper operation and solving any unexpected problems, although we don’t consider the real-time control here in this paper.With different scenarios, the energy management system has been tested. The test scenario is about a micro-grid system with a PV module, a micro-wind turbine and an electric car charging point. Household load can be divided into three different categories:critical load, controllable load and offset load. According to the load forecast and generation forecasts, the case under different scenarios can be analyzed. Further, in the extended case, the micro-grid system is added with a micro-turbine and a storage device, and the corresponding energy management is researched. This study can be seen through the case study, the validity of the model for the smart micro-grid energy optimization is tested.