考虑需求侧灵活性资源（简称“资源”）配置,开展网-荷-储联合规划是提升配电网灵活性以适应新能源出力波动的关键途径。为此提出一种灵活性缺失场景筛选与不确定性分析方法改进相结合的配电网规划研究模型：首先,立足资源视角进行配电网灵活性供需关系建模;其次,基于影子价格理论提出配电网灵活性缺失场景筛选策略,建立筛选指标以描述规划资源、运行约束、场景筛选间闭环关系;然后,将该策略嵌入配电网-多资源两阶段联合规划模型,一阶段考虑投资成本最优,二阶段协调运行及灵活性综合成本期望;将规划模型重构并于改进场景概率驱动型分布鲁棒优化（ISPD-DRO）框架下求解,其优势在于实现了概率优化求解与场景动态更新的有机统一;最后经算例分析验证所提模型在提升决策经济性及配电网灵活性层面的优势。
Abstract
Considering the allocation of demand-side flexible resources （referred to as “resources”）, the joint planning of grid-load-storage is a critical way to improve the flexibility of the distribution network to adapt to the fluctuation of new energy output. Given this,we propose a research model for distribution network planning combining scenario selection for lack of flexibility and improvement of uncertainty analysis method. Firstly,the distribution network's flexible supply and demand relationship is modeled from the perspective of resources. Secondly, based on shadow price theory, a scenario selection strategy for distribution network flexibility lackness is proposed, and screening indicators are established to describe the closed-loop relationship between planning resources, operation constraints and scenario screening. Then, the strategy is embedded in the distribution network-multi-resource two-stage joint planning model, the first stage considers the optimal investment cost, and the second stage coordinates operation and flexibility comprehensive cost expectations. The planning model is reconstructed and solved under the framework of improved distributionally robust optimization based on scenario probability driven （ISPD-DRO）, which has the advantage of realizing the organic unity of probability optimization solution and scene dynamic update. Finally, combined with the case analysis, the advantages of the proposed model in improving decision-making economy and distribution network flexibility are verified.
关键词
需求侧灵活性资源 /
影子价格理论 /
场景筛选 /
改进场景概率驱动型分布鲁棒优化 /
配电网规划
Key words
demand side flexible resources /
shadow price theory /
scenario selection /
improved distributionally robust optimization based on scenario probability driven /
distribution network planning
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