Peer-to-Peer trading with Demand Response using proposed smart bidding strategy

The electricity demand is increasing rapidly among residential consumers due to the utilization of many smart appliances. The renewable-based Distributed Generation (DGs) and Electric Vehicles (EV) investment is also rising among residential consumers. Consumers with renewable-based DGs and smart appliances are considered prosumers in this work. The main objective of this work is to reduce the electricity cost of the smart home by scheduling the smart appliances with Demand Response (DR) using Binary Particle Swarm Optimization (BPSO) algorithm and Peer-to-Peer (P2P) trading using a smart bidding strategy. The smart home consists of two prosumers and two consumers with different Distributed Generation (DG) availability, battery, EV, and smart appliances (thermal and electrical loads). The smart appliances are scheduled based on Real-Time Pricing (RTP), DGs and storage devices availability. The available excess power in the prosumers after self-consumption is traded to the neighbouring consumers to reduce the grid dependency. The novelty of this article lies in the trading decision and trading cost determination for P2P trading, which is called the proposed smart bidding strategy in this work. The proposed smart P2P trading algorithm involves the double auction mechanism, where the bidding occurs between the prosumers and consumers based on the supply-demand ratio (SDR) and RTP. The trading cost calculated is beneficial for both prosumers and consumers in reducing their electricity costs. The electricity cost of consumer 1, consumer 2, prosumer 1, and prosumer 2 is reduced by (sic)58.073, (sic)20.37, (sic)51.656, and (sic)20.37, respectively, as compared with grid tariff under the category of normal condition. Similarly, the electricity cost of consumer 1, consumer 2, prosumer 1, and prosumer 2 is reduced by (sic)63.514, (sic)3.208, (sic)98.155, and (sic)34.049, respectively, as compared with grid tariff under the category of EV uncertainty in both consumer & prosumer premises. The simulation results proved that the proposed smart bidding strategy effectively reduced the electricity cost of the prosumers and consumers under normal and uncertain conditions compared to the grid tariff.