Optimization of Electricity Retail Packages Under the Spot Market Mode Accounting for Customer Response

With China’s electricity spot market about to enter a new stage of “official operation” and large-scale renewable energy connected to the power system, the risk of volatility in spot market prices on the wholesale side of the electricity market is becoming more prominent. To address the issue that the current retail package design of electric power retailers cannot solve the risk of spot electricity price, this paper proposes an optimization approach for retail electricity packages considering customer response., in which the demand response model of retail users and the retail package optimization model are constructed and the DDPG reinforcement learning algorithm is applied to solve the model. Finally, simulation analysis is performed based on historical data and the results show that the optimization method of electricity retail packages taking into account user response effectively mitigates the negative impact of spot electricity price fluctuations and achieves the improvement of economic efficiency; electric power retailers appropriately reduce the pricing level of retail packages to attract new user groups to bring long-term potential revenue and achieve the increase of total revenue; The DDPG reinforcement learning algorithm is used to solve the model, which has an excellent convergence utility and can achieve the dynamic decision-making of electricity retail packages continuously, thereby guiding users to respond promptly.