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1.内蒙古电力(集团)有限责任公司阿拉善供电分公司,内蒙古自治区阿拉善盟 750300
2.华中科技大学电气与电子工程学院,湖北省武汉市 430074
Received:19 December 2023,
Revised:2024-08-22,
Published Online:12 October 2024,
Published:10 March 2025
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马鹏,齐军,熊逸等.考虑响应可靠性与经济性的需求侧精准调峰策略[J].电力系统自动化,2025,49(5):176-186.
MA Peng,QI Jun,XIONG Yi,et al.Precise Peak-shaving Strategy at Demand Side Considering Response Reliability and Economy[J].Automation of Electric Power Systems,2025,49(5):176-186.
马鹏,齐军,熊逸等.考虑响应可靠性与经济性的需求侧精准调峰策略[J].电力系统自动化,2025,49(5):176-186. DOI: 10.7500/AEPS20231219004.
MA Peng,QI Jun,XIONG Yi,et al.Precise Peak-shaving Strategy at Demand Side Considering Response Reliability and Economy[J].Automation of Electric Power Systems,2025,49(5):176-186. DOI: 10.7500/AEPS20231219004.
需求侧调峰资源是新型电力系统调峰资源的重要组成部分。然而,受用户行为不确定性和设备状态异常等内外因素影响,部分负荷往往无法可靠响应。针对工业园区需求侧资源在实际调峰中响应可靠性低的问题,提出了一种兼顾负荷响应可靠性与聚合商调度经济性的需求侧精准调峰策略。首先,建立直控中央空调负荷与工业负荷的调控模型,结合多样化特性负荷,分析影响其响应可靠性的相关因素。然后,定义负荷响应可靠性指数来定量刻画内外因素对负荷响应可靠性的影响程度,与负荷调控成本共同作为负荷聚合商决策变量,通过迭代算法求解需求侧调峰响应优化方案。最后,基于美国Open Energy Data Initiative (OEDI)数据集内的中央空调负荷数据与中国湖北省某工业园区负荷数据,以经济激励为唯一手段,将所提精准调峰策略与仅考虑经济性的调峰策略进行对比仿真,并以负荷聚合商调度总成本、实际响应率作为评价指标进行分析,验证了所提策略的有效性。
The peak shaving resource at demand side is an important part of peak shaving resources in new power systems. However
due to the internal and external factors such as the uncertainty of user behaviors and abnormal equipment status
some loads often fail to respond reliably. Aiming at the low response reliability of demand-side resources in industrial parks during the actual peak shaving
a precise peak shaving strategy at demand side is proposed considering load response reliability and aggregator scheduling economy. Firstly
a regulation model is established for the direct control of central air conditioning load and industrial load
and the relevant factors that affect its response reliability are analyzed by combining diversified characteristic loads. Secondly
the load response reliability index is defined to quantitatively characterize the influence degree of internal and external factors on load response reliability. Together with load regulation cost
it is used as a decision variable for load aggregation
and the peak shaving response optimization scheme at demand side is solved through iterative algorithms. Finally
based on the central air conditioning load data from the Open Energy Data Initiative (OEDI) dataset of America and the load data from an industrial park in Hubei Province
China
the proposed precise peak shaving strategy is compared and simulated with the economic-only peak shaving strategy by using economic incentives as the only means
and the total scheduling cost and actual response ratio of load aggregators are analyzed as evaluation indicators to verify the effectiveness of the proposed strategy.
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