分布式能源的渗透率不断提高给配电网带来更加复杂的电压质量及安全稳定问题,单一的无功或储能优化配置难以满足现代化电网日渐多元化的需求。提出一种储能与无功相结合的配置策略,用以满足新能源配电网在有功调节与无功补偿两方面的需求,实现灵活性、电压质量、经济性三方面的权衡优化,适应不同渗透率新能源的影响。首先,以灵活性需求、无功需求两方面为目标建立配电网安全稳定评价体系;其次,考虑规划层与运行层的耦合性,建立配电网储能-无功优化配置双层模型;之后,使用小概率随机突变法改进标准粒子群算法,减小计算过程中陷入局部最优的可能。最后,使用改进的IEEE 33节点配电系统进行仿真测试,验证所提方案的合理性、有效性。仿真结果表明,所提方案能够有效改善配电网的经济性与安全稳定,且在不同的新能源渗透率下均能得到多目标兼顾的最优配置方案。

Increasing the permeability of distributed energy results in a greater number of complex problems related to voltage quality, safety, and stability in distribution networks. Achieving a single optimal configuration of reactive power or energy storage to satisfy the increasingly diverse requirements of modern power grids is difficult. In this study, a configuration strategy that combines energy storage and reactive power is proposed to meet the requirements of new energy distribution networks in both active-power regulation and reactive-power compensation. The strategy also realizes a balanced optimization of flexibility, voltage quality, and economy, and adapts to the influence of new energy under different permeabilities. First, a safety and stability evaluation system of the distribution network is established based on flexibility and reactive-power demands. Second, based on the coupling between the planning and operating layers, a two-layer model of optimal energy-reactive power allocation in the distribution network is established. A small-probability random mutation method is then used to improve the standard particle swarm optimization algorithm to reduce the possibility of falling into local optima. Finally, an improved IEEE-33 node distribution system is used in a simulation test to verify the rationality and effectiveness of the scheme. Simulation results show that the proposed scheme can effectively improve the economy, security, and stability of the distribution network, and a multi-objective optimal configuration scheme can be obtained under new energy permeabilities.

未来电网的演化将由负荷驱动的单一模式转变为源荷双驱动模式,亟需多样化灵活性资源多元互动发展实现高比例可再生能源愿景,而演化驱动的有效辨识及演化路径的综合优选可明确未来电网的发展方向及指导具体实施路径的构建。文章首先从技术成熟度及潜力、能源成本方面分析电网演化面临的不确定性,提出海量演化路径生成方法。然后提出数据驱动的演化路径分析方法,包括路径降维与可视化、基于时变模式的驱动因素辨识、基于帕累托前沿的最优路径建议生成。最终以我国华北地区为例分析了高比例可再生能源电网的演化路径。分析结果表明,未来华北地区光伏逐步超越风电成为最主要发电资源,2060年碳排放量较2030年将下降81%;影响演化各层面的主要因素基本相同,但各因素之间相对重要程度略有不同,在经济和环境层面,最重要的影响因素是煤炭价格,电池储能最大可投资容量是技术层面的主要影响因素。为实现兼顾低成本和低碳排的演化路径,应努力降低可再生能源和电池储能单位投资、煤炭价格,提高电池储能配置上限。

Power grids in the future will evolve from a single mode driven by a load to a dual-driven mode of a power source and a load. Diversified and flexible resources are urgently required for multiple interactive developments to realize a scenario with a high proportion of renewable energy. The effective identification of evolution-driven paths and the comprehensive optimization of evolution paths have an important guiding significance for clarifying the development direction of future power grids and constructing specific implementation paths. This study analyzed the uncertainty faced by power grid evolution from the aspects of technological maturity, potential, and energy cost and proposed a method for generating massive evolution paths. Subsequently, a data-driven evolution path analysis method was proposed, including path dimensionality reduction and visualization, driving factor identification based on time-varying patterns, and optimal path proposal generation based on the Pareto frontier. Finally, the evolution path of a high-proportion renewable energy system was analyzed using North China as an example. The analysis results indicated that photovoltaics in North China will gradually surpass wind power to become the most important power generation resource in the future and that carbon emissions in 2060 will be 81% lower than those in 2030. The relative importance of each factor differed marginally. At the economic and environmental levels, the most important factor was the price of coal, while the maximum investable capacity of battery energy storage was the main factor at the technical level. Efforts should be made to reduce unit investment in renewable and battery energy storage and coal prices and increase the upper limit of battery energy storage allocation to achieve an evolutionary path that considers both low cost and low carbon emissions.