Optimal Pricing and Energy Scheduling with Adaptive Grouping Based on Trading Contribution Evaluation in Smart Grid

With the development of future smart city, the structure of smart grid is undergoing fundamental changes, where the acquiring and scheduling of local energy become more flexible due to the massive and various access to renewable energy, such as solar energy from residential users. It is urgent to find a measure for efficient local energy management with the influx of a large amount of renewable energy. This paper proposes a peer-to-peer electricity scheduling algorithm with adaptive grouping based on the trading contribution evaluation to maximize local energy consumption. By modeling the electricity demand and the supply of prosumers, the electricity scheduling process is completed, which follows the principle of intra-group priority trading and inter-group auxiliary trading. In the adaptive grouping progress, for highlighting the influence of user engagement on the transaction factor of each user, a trading contribution evaluation is creatively defined to reflect the trading inspiring process. In addition, considering the incomplete sharing of information among prosumers in the electricity trading process, Bayesian game is adopted for the electricity pricing strategy to obtain the optimal balance between the economic benefits and energy transformation under linear strategic equilibrium maximizing the utility of both parties of electricity scheduling. Simulation results show that the proposed algorithm can effectively improve the system prosumer benefit and the local consumption rate of renewable energy.