Optimal management of local energy trading in future smart microgrid via pricing

In this paper, we investigate optimal management of local energy trading in future smart micro-grid (SMG) via pricing. In SMG, energy consumers and providers, in addition to trading with utility company, can also perform local energy trading controlled by a local trading manager (LTM) for reaping benefits. We first quantify the benefits achieved by the consumers and providers from local trading and then formulate a two-layered optimization framework to investigate i) how the energy consumers and providers maximize their benefits via appropriately adjusting their local trading decisions in response to the LTM's pricing, and ii) how the LTM adjusts its price in local market to benefit the consumers and providers as much as possible while guaranteeing a required gain for itself. We propose two algorithms to solve the layered optimization problem and perform numerical experiments with practical data set to validate the proposed local trading model and the algorithms.