Renewable energy sources, such as wind and solar power, are increasingly being integrated into smart grid systems. However, when compared to traditional energy resources, the unpredictability of renewable energy generation poses significant challenges for both electricity providers and utility companies. Furthermore, the large-scale integration of distributed energy resources (such as PV systems) creates new challenges for energy management in microgrids. To tackle these issues, we propose a novel framework with two objectives: (i) combating uncertainty of renewable energy in smart grid by leveraging time-series forecasting with Long-Short Term Memory (LSTM) solutions, and (ii) establishing distributed and dynamic decision-making framework with multi-agent reinforcement learning using Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed framework considers both objectives concurrently to fully integrate them, while considering both wholesale and retail markets, thereby enabling efficient energy management in the presence of uncertain and distributed renewable energy sources. Through extensive numerical simulations, we demonstrate that the proposed solution significantly improves the profit of load serving entities (LSE) by providing a more accurate wind generation forecast. Furthermore, our results demonstrate that households with PV and battery installations can increase their profits by using intelligent battery charge/discharge actions determined by the DDPG agents.
翻译:可再生能源,如风能和太阳能,正日益被纳入智能电网系统,但与传统能源相比,可再生能源生产的不可预测性对电力供应商和公用事业公司都构成重大挑战。此外,大规模整合分布式能源(如光电系统)给微电网的能源管理带来了新的挑战。为了解决这些问题,我们提出了一个新的框架,其中有两个目标:(一) 利用长期短期内存(LSTM)解决方案的时间序列预测,消除智能电网中可再生能源的不确定性,以及(二) 建立分布式和动态决策框架,利用深层确定性政策梯度算法(DPG)多剂强化学习,建立分配式和动态决策框架,同时进行多剂强化学习。拟议框架认为这两个目标同时充分整合,同时考虑批发市场和零售市场,从而在存在不确定和分布式可再生能源的情况下进行高效的能源管理。通过广泛的数字模拟,我们证明拟议的解决方案通过提供更准确的风力生成预测,大大提高了负载实体的利润。此外,我们的结果表明,拥有光电和电池装置的家庭能够通过智能电压/气压公司确定D行动增加利润。</s>