optimal risk-constrained peer-to-peer energy trading strategy for a smart microgrid

Nowadays, encouraging consumers to use renewable resources and generate electricity locally in a microgrid is very important that has attracted much attention. in this paper, an optimal strategy is proposed to model energy trading among the photovoltaic (pv) prosumers in a smart microgrid. a prosumer is considered to be able to exchange energy with other prosumers through a peer-to-peer (p2p) energy trading mechanism. moreover, they could have contracts with the utility grid to purchase or sell electricity as well. for this purpose, first, a new energy pricing model based on the production and consumption of each prosumer is presented that shows how consumers interact with the utility grid as well as other consumers. the price-based demand response (dr) programs is used to increase the profitability of each consumer and reduce the microgrid dependency to the utility grid. the uncertainty of pv systems generation is taken into account through forecasting by deep learning method. for this purpose, the long short-term memory (lstm) model based on time series information is used. moreover, the risk associated with the generation uncertainties is modeled by downside risk constraint (drc). the classical optimization method is employed to minimize the total incurred costs. simulation analysis and results show that not only the costs of energy trading will be decreased using the proposed model, but also the willingness of the prosumers to participate in the p2p energy trading will be increased significantly.