信息物理融合的智慧能源系统多级对等协同优化

针对能源电力系统的优化管理与控制问题，提出了一种信息物理融合的智慧能源系统（Intelligent energy systems，IES）多级对等协同优化方法.在信息物理融合能源系统（Cyber-physical energy systems，CPES）的基础上，构建了智慧能源系统的局域和广域两级协同优化架构.综合考虑产消者能源实体对等交互过程中的社会福利、供求平衡和需求意愿等因素，基于Stone-Geary函数和双向拍卖机制构建了智慧能源系统能量优化模型，给出了通过收敛判定域引导的全局随机寻优与区域定向寻优策略，有效地提高了算法的局部搜索能力.此外，通过双向拍卖机制的理性定价以及智能合约的辅助服务，有效地实现了用户友好的对等交易模式.仿真实例表明，在社会福利最大化的前提下可获得产消者电力资源最优分配结果，进一步验证了本文方法的有效性和可行性.

Multilevel Peer-to-Peer Co-optimization for Cyber-physical Intelligent Energy Systems

A multilevel peer-to-peer co-optimization method for cyber-physical intelligent energy systems (IES) is proposed to analyze the optimal control and management problem of energy power systems. On the basis of the cyber-physical energy system (CPES), a co-optimization architecture of local and wide-area levels for intelligent energy system is constructed. With the help of Stone-Geary utility function and double auction mechanism, an energy optimization model for intelligent energy system is constructed in consideration of social welfare, supply-demand balance and demand willingness in the peer-to-peer interaction process of prosumers. At the same time, the local search ability of the intelligent optimization algorithm is further improved by the guidance of convergence judgment domain as well as the combination strategy of global random search and directional search. In addition, the user-friendly peer-to-peer trading mode is effectively realized through rational pricing of double auction mechanism and the auxiliary services of smart contract. Simulation results show that the optimal allocation of power resources can be obtained under the premise of maximizing social welfare, which further illustrates the effectiveness and feasibility of this method.