综合能源系统(integrated energy system,IES)是未来能源消费方式的重要发展方向,对提升能源的综合用能能效和可再生能源的消纳将发挥越来越重要的作用。然而,由于IES中能源系统的高度耦合以及源-网-荷-储各个环节存在的不确定性,使得IES的动态过程极其复杂,给IES的优化运行带来巨大挑战。针对IES的优化运行问题,首先简要介绍了IES的概念,分析了其主要特点;其次总结和概括了IES运行基础信息感知、混合时间尺度动态协调优化调度体系及其求解算法三个方面的国内外研究现状,并指出了现有研究的不足之处;最后展望了IES优化运行未来的研究方向,为该方向的后续研究提供参考。

Integrated energy system (IES) is an important development direction of energy consumption in the future, and will play an increasingly important role in improving the energy efficiency of integrated energy use and the consumption of renewable energy. However, due to the high coupling of energy system in IES and the uncertainty in each link of source-network-load-storage, the dynamic process of IES is extremely complex, which brings great challenges to the optimal operation of IES. This paper aims at the optimal operation of IES. Firstly, the concept of IES is introduced and its main characteristics are analyzed. Secondly, the research status in home and abroad on the basic information perception of IES operation, the dynamic coordinated optimization scheduling system with hybrid time scales and its solution algorithm are summarized, and the shortcomings of the existing research are pointed out. Finally, the future research direction is prospected. The results of this paper will provide a reference for the follow-up research of integrated energy system.

A cluster system consisting of interconnected microgrids represents a novel form of smart grid architecture. However, the uncontrolled access of a large number of electric vehicles (EVs) poses challenges to system operation, including increased operational costs and threats to safe and stable grid performance. To address these issues, this paper proposes a Multi-Factor Oriented Model strategy (MOM strategy) and improved bald eagle search (BES) algorithm to optimize the multi-objective day-ahead scheduling of microgrid clusters. Firstly, building upon an established disordered charging model, roulette strategy is employed to integrate photovoltaic redundancy and time-of-use tariffs, thereby constructing an orderly charging and discharging model for EVs. Secondly, a multi-objective day-ahead scheduling model for microgrid cluster with multiple charging and discharging strategies for electric vehicles is established. The multi-microgrid scheduling problem is solved within the proposed optimization framework using an improved bald eagle search algorithm that integrates the Levy flight strategy and the golden sine strategy through an updating factor. Finally, through case study of a park-based microgrid cluster, the scheduling results under various scenarios and EV strategies are analyzed. The results demonstrate that the proposed improved algorithm outperforms other algorithms in practical applications. The multi-factor oriented ordinal model strategy effectively reduces operational costs, minimizes power fluctuations in the tie lines between microgrids, and mitigates the adverse effects of large-scale EV integration on the economic and stable operation of the cluster system. This study provides a scientific reference for addressing the scheduling challenges of EVs at different scales in day-ahead.

为深入挖掘电动汽车的可调度潜力,缓解含高比例新能源微电网的供能压力,结合多元需求响应技术,提出一种考虑电动汽车资源参与的微电网多时间尺度优化调度模型。在日前调度阶段,一部分电动汽车资源与价格型需求响应技术相结合,以用户的综合满意度为目标进行优化。微电网基于价格型电动汽车资源的调度计划,以经济低碳成本最低与灵活性满足度最大为目标进行优化,确定各侧可调资源的调度安排。在日内调度阶段,另一部分电动汽车资源与激励型需求响应技术相结合。微电网能量管理中心作为领导者,以运营成本最小为目标,激励型电动汽车群作为跟随者,以用电成本最小为目标,构建微电网日内主从博弈模型进行滚动优化,双方基于补贴价格与用能策略进行博弈。最后,基于某微电网场景进行仿真验证,结果表明所提模型能够降低用户用电成本,减少负荷曲线的峰谷差,实现新能源的全消纳。

To exploit the schedulable potential of electric vehicles (EVs) efficiently and relieve the energy supply pressure of microgrids with a high proportion of new energy sources, a multi-time-scale optimization scheduling model of the microgrid is proposed, considering the participation of EV resources combined with the multi-demand response technology. In the day-ahead scheduling stage, some EV resources are combined with the price-based demand response technology to optimize comprehensive user satisfaction. Based on the scheduling plan of the EV resources based on price, the microgrid is optimized focusing on minimizing economic cost, ensuring low-carbon expenditure, and maximizing flexibility satisfaction, and the scheduling arrangement of the adjustable resources on each side is determined. In the intra-day scheduling phase, another portion of the EV resources is combined with the incentive demand response technology. The microgrid energy management center, as the leader, aims to minimize the operating costs, and the incentive EV group, as the follower, aims to minimize the electricity costs. The intra-day master-slave game model of the microgrid was constructed for rolling optimization, and the two sides played the game based on a subsidized price and energy use strategy. Finally, a simulation verification was conducted based on a microgrid scenario, and the results show that the proposed model can reduce the electricity cost of users, reduce the peak-valley difference of the load curve, and realize the full absorption of new energy.

大型风电场的动态等值建模是研究风电并网问题的基础，然而基于聚类的风电场等值模型无法高精度拟合其动态特性，且应用时泛化能力差是聚类等值的固有缺陷。针对该问题，提出一种基于粒子群算法-长短期记忆神经网络-误差校正模型（particle swarm optimization-long short term memory neural network-error correction model，PSO-LSTM-ECM）的风电场等值建模方法。首先，利用K-means聚类算法和容量加权法对风电场风电机组进行分群，构建风电场聚类等值模型；然后，基于详细模型和聚类等值模型的暂态响应误差构建ECM，通过PSO优化的LSTM网络训练得到校正模型，将网络的输出值补偿给聚类等值模型；最后，在PSCAD和Matlab平台进行联合仿真，对风电场详细模型、聚类等值模型和所提出模型进行对比分析，结果验证了提出模型的有效性与优越性。

Dynamic equivalent modeling of large-scale wind farms is the foundation for studying wind power grid integration，while the clustering-based equivalent model of wind farms cannot fit the dynamic output characteristics with high accuracy，and the poor generalization ability in its application is an inherent defect of clustering based model. Aiming at this problem，this paper proposes a wind farm equivalent modeling method based on particle swarm optimization-long short term memory neural network-error correction model （PSO-LSTM-ECM）. Firstly，K-means clustering algorithm and capacity weighting method are used to cluster wind turbines in wind farms，and a clustering equivalent model of the wind farms is constructed； Then，ECM is constructed based on the transient response errors of the detailed model and the clustering equivalent model，and the correction model is obtained through the LSTM neural network training optimized by PSO，and the output value of the network is compensated to the clustering equivalent model； Finally，a joint simulation is conducted on PSCAD and Matlab platforms to compare and analyze the detailed wind farm model，clustering equivalent model，and the model proposed in this paper. The result proves the effectiveness and superiority of the proposed model.

针对新能源发电接入以及考虑需求响应背景下的微电网优化调度问题，建立微电网模型；以考虑需求响应带来的用户用电不舒适度和系统的运行成本构建目标函数，调整用户可转移负荷。根据风光出力具有的随机性、波动性等特点，采用模糊K-means算法对风光出力数据进行聚类，得到典型的风光出力曲线。对哈里斯鹰优化(Harris hawks optimization， HHO)算法种群分布不均以及易陷入局部最优的问题进行改进：首先，在初始化种群阶段引入Tent映射，使得初始种群覆盖更全面，避免在早期陷入局部最优解；然后，在搜索阶段引入Levy飞行函数，增强算法的全局搜索能力，再将改进哈里斯鹰优化(improved HHO， IHHO)算法应用于寻优，并将其与经典算法进行对比。最终结果验证了所提策略的有效性以及IHHO算法的优越性。

A microgrid model is established to address the optimization and scheduling of microgrid in the context of new energy generation access and demand response. The objective function is constructed to consider the user's electricity discomfort caused by demand response and the operating cost of the system， and the user's transferable load is adjusted. Based on the randomness and volatility of wind and solar power output， the fuzzy K-means algorithm is used to cluster the wind and solar power output data and obtain typical wind and solar power output curves. Next， this paper improves the Harris hawks optimization (HHO) algorithm to address the issues of uneven population distribution and susceptibility to local optima. Firstly， Tent mapping is introduced in the initialization stage of the population to make the initial population coverage more comprehensive and avoid falling into local optima in the early stage. Then， Levy flight function is introduced in the search stage to enhance the global search ability of the algorithm. Finally， improved HHO (IHHO) algorithm is applied to optimization and compared with classical algorithms. The final results validate the effectiveness of the proposed strategy and the superiority of the IHHO algorithm.

Fossil energy is becoming increasingly scarce, and technological innovation to promote clean energy consumption and achieve the “dual carbon” goal has increasingly become the focus of discussion. Compared with the full coverage thermoelectric module design scheme, the optimized layout scheme uses fewer thermoelectric generation (TEG) modules, thus confirming that the more TEG modules that are not arranged, the better. This research provides a possible way to improve the output power of TEG system. This paper proposed a bald eagle search algorithm (BES) scheme to optimize centralized TEG array reconfigures, which has not been previously employed to achieve a fast and effective tracking of the maximum power point tracking (MPPT) under non-uniform temperature difference (NTD) condition. In order to efficiently seek the global MPP (GMPP) under NTD condition, a BES algorithm is adopted to TEG reconfigures arrays to evidently improve the global searching ability of BES algorithm through the previous searching results. Furthermore, the main method in this paper is preliminarily verified on MATLAB. Both simulation and experimental results show that BES algorithm can significantly improve the convergence accuracy and output power.