Real-time Joint Energy Management For Battery Swapping Station And Household Loads In A Smart-Community Microgrid

Energy storage is an important flexible resource in a high-penetrated power system.However,the cost of battery-based energy storage needs to be further reduced currently.It is important to make the full use of the flexible performance of batteries and improve the economics of their application.Compared to the real-time charging mode,a battery swapping station(BBS)is a new mode of electric vehicle(EV)power supply.As a distributed energy storage and demand side response resources,the full utilization of energy storage resources in the BBS will play a key role in improving the proportion of renewable energy consumption and solving the technical and economic problems caused by real-time charging of electric EVs.Therefore,this paper focuses on real-time energy management for distributed energy storage and renewable energy generation in smart microgrids.The main work is summarized as follows:(1)A real-time energy management method for distributed energy storage and renewable energy generation in smart micro-grids is proposed.A power supply optimization model for dual-purposes is established.The aim of the proposed model is to charge the batteries in the BSS using the electricity generated from the renewable energy sources(RES)and supply the conventional load and EV users.Under the premise of ensuring the reliability of the critical load and the quality of the BSS service,the long-term average power supply cost is minimized.The results of the case study show that the proposed method can improve the renewable energy consumption and provides a new idea for future distributed battery applications.(2)An optimization algorithm based on Lyapunov optimization technique(LOT)and queuing theory is proposed.It is not necessary to assume the future distribution of stochastic processes or input any prediction information,e.g.renewable energy generation,BSS battery demand,household load demand,electricity price,etc.The results of the case study show that compared with other benchmark algorithms assuming prediction information or even actual distribution,the proposed algorithm obtains better results in a short period of time,improving the optimization accuracy and efficiency.