现阶段配电网中量测设备覆盖率较低,只有部分节点的负荷数据可以实时采集得到,因此在配电网中进行实时无功优化时无法使用基于潮流计算的优化方法。考虑到以上情况,文章提出了一种基于数据驱动的非全实时观测配电网无功优化方法。该方法基于历史运行数据使用最优潮流离线生成无功优化策略,并通过训练神经网络构建可实时量测节点负荷数据和无功优化策略间的映射关系,实现对非全实时观测配电网的实时无功优化。最后基于改造的IEEE 33节点系统,将所提方法与传统九区图无功优化方法作对比,验证了所提方法的有效性。

At present, the coverage of measuring equipment in distribution network is low, so only part of the nodes’ load data can be collected in real time. This situation makes it impossible to use the optimization based on power flow calculation in the real-time reactive power optimization of distribution network. Considering the above situation, this paper proposes a data-driven reactive power optimization method based on partial real-time visible distribution network. According to the historical operation data, the optimal power flow is used to generate the reactive power optimization strategy offline, and the mapping between the real-time measured node load data and the reactive power optimization strategy is established by training the neural network to realize the real-time reactive power optimization of the partial real-time visible distribution network. Finally, in the modified IEEE 33-bus system, the proposed method is compared with the 9-zone diagram method to verify the effectiveness of the proposed method.

随着风、光渗透率不断提高，其多重时序波动与不确定性给主动配电网优化调度工作带来了挑战。为提高主动配电网应对不确定性扰动的能力，提出一种考虑综合承载力的主动配电网优化调度方法。首先，从主动配电网资源充裕度和运行安全性两方面提出了综合承载力指标体系。然后，计及多种主动管理资源的协调调度，构建了考虑综合承载力的主动配电网优化调度模型。其次，引入图论“破圈法”对网架编码策略进行改进，并采用混合编码粒子群算法对模型进行求解。最后，基于改进IEEE 33系统设计算例验证了所建模型的科学性和有效性。

With the increasing penetration of wind and solar power， the uncertainties bring great challenges to the optimal scheduling of active distribution network. In order to improve the ability of active distribution network to deal with uncertain disturbances， an optimal scheduling method is proposed to enhance the comprehensive carrying capacity of active distribution network. Firstly， comprehensive carrying capacity is proposed from the aspects of resource adequacy and operation safety of active distribution network. Then， based on the coordinated scheduling of various active management resources， an optimal scheduling model of active distribution network considering comprehensive carrying capacity is constructed. Secondly， the graph theory “broken circle” method is introduced to improve the encoding strategy of the grid， and the hybrid encoding particle swarm optimization algorithm is used to solve the model. Finally， an improved IEEE 33-node system is taken as an example to verify the validity of the model.

The high proportion of renewables added to the electricity system and the “dual carbon target” require more adjustable resources to keep the power system running smoothly. The flexible resource of demand side is an important adjustable resource, which can help the safe and reliable operation of power system. This paper considers the way that demand-side resources participate in load regulation through the power market, discusses the types and characteristics of demand-side resources, analyses the trading mechanism of demand-side resources participating in various types of markets, and puts forward the benefit calculation methods under different scenarios, which has important reference significance.

由于风电的不确定性与随机性导致风电出力难以精确预测，进而导致依据风电预测值所建立的日前调度模型存在一定的局限性，为解决此问题并提升风电消纳比例，提出一种计及风电预测误差的柔性负荷日内调度模型。首先，对储能和工业高载能负荷等可控柔性负荷进行建模；然后，考虑风电预测误差的概率分布特性，并基于日前调度计划建立以风电消纳量最高为目标的日内调度模型；最后，在保证系统安全可靠基础上，通过基于MATLAB的遗传算法进行优化。仿真结果表明：与日前调度计划相比，日内调度模型风电消纳比例提升了1.83%，验证了所提调度模型的有效性；通过对柔性负荷的超短期调度，可有效解决仅依据日前计划进行调度易出现电力系统功率不平衡的问题，提高电力系统稳定性。

Due to the uncertainty and randomicity of wind power, it is difficult to accurately predict wind power, which leads to certain limitations of day-ahead dispatch model established based on wind power prediction. In order to solve this problem and improve wind power accommodation ratio, a flexible load day-ahead dispatch model considering wind power prediction error was proposed. Firstly, the controllable flexible loads, such as energy storage and industrial high load load, were modeled. Secondly, the probabilistic distribution characteristics of wind power prediction errors were considered, and a day-ahead dispatch model aiming at the maximum wind power accommodation was established. Finally, based on ensuring the safety and reliability of the system, the dispatch model was optimized by genetic algorithm based on MATLAB. The simulation results show that compared with the day-ahead dispatch model, the wind power accommodation is increased by 1.83%, and the efficiency of the proposed dispatch model is verified. Moreover, the imbalance problems of power system power is solved effectively by the short-term scheduling of the flexible load. The stability of power system is improved.

针对热/电联合运行下多种柔性负荷协调优化不足以及面临的风、光出力和柔性负荷等多种不确定性,文章提出了一种考虑柔性负荷下的虚拟电厂(virtual power plant,VPP)热电联合鲁棒优化调度方法。充分考虑可削减、可平移、可转移柔性负荷之间的协调互补性,分别以热/电负荷曲线标准差和系统综合运行成本最低为目标,建立了虚拟电厂热电联合双层协调优化运行模型,提高了多种柔性负荷协调优化效果;并采用鲁棒优化方法处理风、光出力和柔性负荷预测误差的不确定性,提高热电系统运行的鲁棒性。多种柔性负荷的协调优化增强了系统的灵活调节能力,使经济性更优,不同鲁棒系数下柔性负荷优化效果的对比,为适应不同需求情景下的决策者提供参考依据。算例分析验证了所提模型的有效性。

Aiming at the lack of coordination and optimization of multiple flexible loads under combined heat and power operation and the uncertainties faced by wind, solar power, and flexible loads, a robust optimization scheduling method for virtual power plant(VPP)combined heat and power considering flexible loads is proposed. Fully considering the coordination and complementarity among the reduction, translation and transferable flexible loads, the standard deviations of the thermal and electric load curves and the lowest comprehensive operating cost of the system are chosen to be the goals, and a dual-layer coordinated optimization operation model of virtual power plant combined heat and power is established, which may improve the coordination and optimization effect of multiple flexible loads. The prediction error uncertainty of wind and solar power and flexible load is dealt with by the robust optimization method to improve the system operation robustness. The coordination and optimization of multiple flexible loads enhance the system's flexible adjustment capabilities and make the economy better. The comparison of the optimization effects of flexible loads under different robustness coefficients provides a reference for decision makers in different demand scenarios. The analysis of an example verifies the effectiveness of the proposed model.

针对以生物质联供机组为核心的综合能源系统(integrated energy system,IES)在实际运行过程中存在的生物质燃料收集困难、供应不稳定等问题,文章提出了一种考虑生物质供给价格弹性的综合能源系统规划方法。首先,基于生物质收购价格弹性理论建立了秸秆收集成本模型,并将其引入工业园区综合能源系统双层规划中。其次,上层模型以设备规划容量及秸秆收购价格为优化变量,以年化综合费用最小为优化目标;下层模型以设备逐时出力及秸秆消耗量为优化变量,以考虑价格弹性的秸秆收集成本在内的年运行成本最小为优化目标,并采用粒子群算法和CPLEX工具箱进行联合求解。最后,以吉林省某典型工业园区为算例验证了所提规划方法更贴合工程实际情况,具有更加优越的经济性。

Because of the difficulties of biomass fuel collection and unstable supply in the actual operation of the integrated energy system (IES) centered on biomass cogeneration units, this paper proposes an IES planning method that considers the price elasticity of biomass supply. Firstly, according to the biomass purchase price elasticity theory, a straw collection cost model is established and introduced into the two-level planning of the industrial park IES. Secondly, the upper-level model uses equipment planning capacity and straw purchase price as optimization variables to pursue the minimum annualized comprehensive cost; the lower-level model uses equipment hourly output and straw consumption as optimization variables to pursue the minimum annual operating cost that takes into account the price flexibility of straw collection costs, and the particle swarm optimization and the CPLEX toolbox are used to solve the problem. Finally, a typical industrial park in Jilin Province is taken as an example to verify that the proposed planning method is more suitable for the actual situation of the project and has a more superior economic efficiency.