Huang, Tzu-HanTzu-HanHuangTai, Chia-ShingChia-ShingTaiLI-CHEN FU2020-05-042020-05-042018https://scholars.lib.ntu.edu.tw/handle/123456789/488954Demand-side management (DSM) is a very important topic in recent years thanks to the growth of Electric Vehicle(EV) and the renewable energy nowadays. DSM ability in a residential area has been improved a lot under the enforcement of real-time pricing(RTP) mechanism. In this work, we aim to integrate the residential user and commercial user into one DSM system. By integrating them, we can utilize the energy more efficiency by sharing the surplus energy in residential area to the commercial area. On the other hand, optimization is always a crucially important process to be adopted in a DSM system, and particle swarm optimization (PSO) turns out to be a popular optimization method for solving DSM problem. In this work, we enhance the PSO by adding two popular concepts in deep learning and call it improved particle swarm optimization(IPSO). The simulation results reveal that the proposed DSM system has improved the energy efficiency in community, and at the same time, the energy costs paid by both residential and commercial users will be reduced. © 2018 IEEE.Demand-side management; energy sharing; particle swarm optimization; residential and commercial community; smart grid; user comfort[SDGs]SDG7Costs; Cybernetics; Deep learning; Demand side management; Electric utilities; Energy efficiency; Housing; Reactive power; Smart power grids; Energy sharings; Optimization method; Renewable energies; residential and commercial community; Residential areas; Residential users; Smart grid; User comforts; Particle swarm optimization (PSO)conference paper10.1109/SMC.2018.00213https://doi.org/10.1109/SMC.2018.00213