Joint optimization of energy trading and consensus mechanism in blockchain-empowered smart grids: a reinforcement learning approach

Abstract

Under the trend of green development, the traditional fossil fuel and centralized energy management models are no longer applicable, and distributed energy systems that can efficiently utilize clean energy have become the key to research in the energy field nowadays. However, there are still many problems in distributed energy trading systems, such as user privacy protection and mutual trust in trading, how to ensure the high quality and reliability of energy services, and how to motivate energy suppliers to participate in trading. To solve these problems, this paper proposes a blockchain-based smart grid system that enables efficient energy trading and consensus optimization, enabling electricity consumers to obtain high-quality, reliable energy services and electricity suppliers to receive rich rewards, and motivating all parties to actively participate in trading to maintain the balance of the system. We propose a reputation value assessment algorithm to evaluate the reputation of electricity suppliers to ensure that electricity consumers receive quality energy services. To minimize the cost, maximize the benefit for the electricity suppliers and optimize the system, we present an algorithm based on reinforcement learning DDPG to determine the power supplier, power generation capacity, and consensus mechanism between nodes to obtain power trading rights in each round. Simulation results show that the proposed energy trading scheme has good performance in terms of rewards.