论文标题
P2P能源交易的合作学习通过反优化和间隔分析
Cooperative Learning for P2P Energy Trading via Inverse Optimization and Interval Analysis
论文作者
论文摘要
点对点（P2P）能源系统最近成为将可再生能源和分布式能源集成到能源网格中以减少碳排放的有前途的方法。但是，解决最佳P2P能源管理问题所产生的市场清算能源价格和金额可能对同行/代理人而言并不令人满意。这是因为实际上，同行/代理人在参与P2P能源市场时如何设置其成本函数参数。为了解决此类缺点，本文提出了一种新颖的方法，其中为同龄人/代理人制定了反相反的问题，以合作学习其目标函数参数，因为它们的间隔是所需的能源价格和金额的间隔。结果是，如果他们的能源价格和金额的下限和上限进行分析计算的间隔，同行/代理可以设置其目标函数参数，如果其最大总买卖能量的比率在于他们的最高总买卖能量的比率，则在某种间隔中要学到的一定间隔。然后进行案例研究，以验证拟议方法的有效性。
Peer-to-peer (P2P) energy systems have recently emerged as a promising approach for integrating renewable and distributed energy resources into energy grids to reduce carbon emissions. However, market-clearing energy price and amounts, resulted from solving optimal P2P energy management problems, might not be satisfactory for peers/agents. This is because peers/agents in practice do not know how to set their cost function parameters when participating into P2P energy markets. To resolve such drawback, this paper proposes a novel approach, in which an inverse optimization problem is formulated for peers/agents to cooperatively learn to choose their objective function parameters, given their intervals of desired energy prices and amounts. The result is that peers/agents can set their objective function parameters in the intervals computed analytically from the lower and upper bounds of their energy price and amounts, if the ratio of their maximum total buying and selling energy amounts lies in a certain interval subject to be learned by them. A case study is then carried out, which validates the effectiveness of the proposed approach.
