DataSheet1_Demand-response oriented multi-dimension refined portrait of adjustable resources based on load and survey data fusion.docx

Adjustable resources on the demand side of power system plays a vital role to improve operational flexibility of future low-carbon power system integrated with high-penetration renewable generations. While, these demand-side resources may underperform their expected potentials, due to the lack of understanding on consumers’ refined behaviors. Facing the flexibility improvement of future power system, refined portrait structure of single user combining load characteristics and subjective behavior, is constructed with multi-dimension label system from 4 aspects, including energy consumption and load characteristics, adjustable potential, behavioral awareness and user’s nature. Aiming at supplying demand response service, several key indexes are selected and further evaluated here, via data-driven load character analysis and social-survey-driven user’s subjective consciousness mining based on comprehensive evaluation with combination weighting approach. For practical application to demand response decision making, large-scale user adjustable resource is evaluated and classified based on multivariate density-based clustering algorithm. Numerical results show the feasibility and rationality of the proposed assessment method.

在未来的低碳电力系统中，与高渗透率可再生能源集成，电力系统需求侧的可调节资源在提高运行灵活性方面发挥着至关重要的作用。然而，由于对消费者精细行为的理解不足，这些需求侧资源可能无法发挥其预期的潜力。面对未来电力系统的灵活性提升需求，通过结合负荷特性和主观行为，构建了单个用户的精细画像结构，该结构采用多维度标签系统，从四个方面进行构建，包括能源消耗和负荷特性、可调节潜力、行为意识和用户本质。为了提供需求响应服务，在此选取了几个关键指标，并通过对数据驱动的负荷特性分析和基于综合评价与组合加权方法的社交调查驱动的用户主观意识挖掘进行进一步评估。为了实际应用于需求响应决策，基于多变量密度聚类算法，对大规模用户可调节资源进行了评估和分类。数值结果表明，所提出的评估方法具有可行性和合理性。