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In replacing fossil fuels with renewable energy resources for carbonneutrality, the unbalanced resource production of intermittent wind andphotovoltaic (PV) power is a critical issue for peer-to-peer (P2P) powertrading. To address this issue, a reinforcement learning (RL) technique isintroduced in this paper. For RL, a graph convolutional network (GCN) and abi-directional long short-term memory (Bi-LSTM) network are jointly applied toP2P power trading between nanogrid clusters, based on cooperative game theory.The flexible and reliable DC nanogrid is suitable for integrating renewableenergy for a distribution system. Each local nanogrid cluster takes theposition of prosumer, focusing on power production and consumptionsimultaneously. For the power management of nanogrid cluster, multi-objectiveoptimization is applied to each local nanogrid cluster with the Internet ofThings (IoT) technology. Charging/discharging of an electric vehicle (EV) isexecuted considering the intermittent characteristics of wind and PV powerproduction. RL algorithms, such as GCN- convolutional neural network (CNN)layers for deep Q-learning network (DQN), GCN-LSTM layers for deep recurrentQ-learning network (DRQN), GCN-Bi-LSTM layers for DRQN, and GCN-Bi-LSTM layersfor proximal policy optimization (PPO), are used for simulations. Consequently,the cooperative P2P power trading system maximizes the profit by consideringthe time of use (ToU) tariff-based electricity cost and the system marginalprice (SMP), and minimizes the amount of grid power consumption. Powermanagement of nanogrid clusters with P2P power trading is simulated on adistribution test feeder in real time, and the proposed GCN-Bi-LSTM-PPOtechnique achieving the lowest electricity cost among the RL algorithms usedfor comparison reduces the electricity cost by 36.7%, averaging over nanogridclusters.

Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources