In order to replace fossil fuels with the use of renewable energy resources, unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To resolve this problem, a reinforcement learning (RL) technique is introduced in this paper. For RL, graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters based on cooperative game theory. The flexible and reliable DC nanogrid is suitable to integrate renewable energy for distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid clusters, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of electric vehicle (EV) is performed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as deep Q-learning network (DQN), deep recurrent Q-learning network (DRQN), Bi-DRQN, proximal policy optimization (PPO), GCN-DQN, GCN-DRQN, GCN-Bi-DRQN, and GCN-PPO, are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit utilizing the time of use (ToU) tariff-based electricity cost and system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on the distribution test feeder in real-time and proposed GCN-PPO technique reduces the electricity cost of nanogrid clusters by 36.7%.
翻译:为了用可再生能源资源取代化石燃料,间歇性风能和光电(PV)电力的不平衡资源生产是同行对等电价交易的关键问题。为了解决这一问题,本文件引入了强化学习技术。对于RL,图形连动网络和双向长期短期存储(Bi-LSTM)网络在基于合作游戏理论的纳米电网组间P2P电力交易中共同应用。灵活和可靠的DC纳米电网适合于将可再生能源整合到分销系统。每个本地纳米电网组都占据了标本价格的位置,同时侧重于电力生产和消费。对于纳米电网集群的电力管理,将多目标优化应用于与Times(IOT)技术的互联网的每个本地纳米电网集群。考虑到风能和光电发电生产之间的间歇性特征,基于合作游戏理论的网络(DQN)、深度CN-N交易网络(DRQN)、深度的网络(DR-PO-POF) 电流-电流-GDRM(G-PR-F) 电流测试G-S-S-S-ML 技术的使用成本、GDRDR-M-M-GDR-G-G-G-M-M-G-G-M-M-G-M-M-F-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-IG-ID-IG-IG-ID-ID-IT-ID-F-S-ID-IT-S-S-S-S-S-S-S-S-IT-IT-I-IT-I-IT-S-S-S-S-S-S-S-S-S-IL-IL-IL-IL-IL-IL-IL-S-IL-IL-IT-S-I-I-I-I-IT-IT-IT-S-IT-IT-IT-I-I-I-I-I-I-I-I-I-I