考虑需求侧响应的商业用户风光储自备电源配置方法OA
Planning Strategy for Commercial User-Sited Wind,Photovoltaic,and Storage Power Sources Based on Demand-Side Response
[目的]为降低商业用户用能总成本,满足可再生能源电力消纳责任权重,提高新能源利用率,缓解配电变压器反向重过载,构建了价格型、准线型、基线型需求侧响应(demand response,DR)下的风光储自备电源配置策略,计及反向输电约束,对比评估其参与三类DR降低用能总成本的效果.[方法]以最小化用能总成本为目标,构建规划阶段自备电源配置、运行阶段负荷基线交替迭代的双层优化模型.上层模型考虑多条新能源预测曲线各时刻最大值,建立了自备电源配置模型,求解新能源容量、储能功率和标称放电时间;下层模型基于上层模型结果和多条新能源预测曲线概率,求解用能总成本的数学期望,并以输电曲线数学期望作为负荷基线反馈至上层模型,直至连续两次自备电源配置结果和负荷基线相同.按允许反向输送不超过20%的光伏发电量设定输电约束,并分析分时电价、基准激励价格和负荷准线的调整对经济性指标的影响.以我国沿海某省某商业综合体为例进行计算评估,从降低用能总成本方面验证策略的有效性.[结果]从数学期望看,相较于无自备电源的对照方案,采用所提模型方案的用能总成本降低2%~10%;参与基线型DR的相似度为98%~100%,显著高于参与准线型DR的82%~86%,参与基线型DR相比准线型DR用能总成本降低6%~10%.反向输送的光伏电量占比为0~10%.[结论]在满足省级电网可再生能源电力消纳责任权重需求下,所提策略求解的负荷基线和输电曲线相似度高,设定的反向输电约束增加了自备电源配置规模,显著降低了商业用户用能总成本.
[Objective]To reduce the total energy costs for commercial entities,satisfy renewable energy consumption mandates,improve the utilization rate of new energy,and alleviate reverse overload of distribution transformers,this study develops a configuration strategy for user-sited power sources including wind,solar and energy storage.The strategy accounts for time-of-use pricing curve,load guideline and load baseline demand response(DR).Furthermore,the constraints allowing reverse power transmission are incorporated,and the effectiveness of participating in three types of DR to minimize total energy costs is evaluated and compared.[Methods]A two-layer optimization model was established with the objective of minimizing total energy costs.This model involves alternating iterations between the configuration of user-sited power sources during the planning phase and the determination of the load baseline during the operational phase.The upper-level model considers the maximum values of multiple predicted new energy curves at any given time to establish a user-sited power source configuration model,and solves the capacity of new energy,energy storage,and nominal discharge time.The lower-level model calculates the mathematical expectation of total energy costs based on the outputs of the upper-level model and the probability of multiple predicted new energy curves.The mathematical expectation of transmission curves is used as the load baseline and fed back to the upper-level model.This iterative process continues until the results of two consecutive user-sited power source configuration and the load baseline are identical.By setting a reverse transmission constraint(not exceeding 20%of photovoltaic generation)and adjusting time-of-use pricing,benchmark incentive prices,and load guidelines,the influence on economic indicators was analyzed.A large commercial complex in a coastal province of China served as a case study for calculation and evaluation to verify the strategy's effectiveness from the perspective of reducing total energy costs.[Results]From the perspective of mathematical expectation,the proposed model reduced total energy costs by 2%to 10%compared to control schemes without user-sited power sources.The similarity when participating in baseline DR ranged from 98%to 100%,significantly higher than the 82%to 86%observed with guideline DR.Compared to guideline DR,participating in baseline DR reduced total energy costs by an additional 6%to 10%.Furthermore,the proportions of photovoltaic reverse transmission remained within 0%to 10%.[Conclusions]The strategy proposed in this paper satisfies the renewable energy consumption weights mandated provincial power grids while achieving high similarity between the load baseline and the transmission curve.The inclusion of reverse transmission constraints allows for an increased scale of user-sited power sources,significantly reducing the total energy costs for commercial users.
游沛羽;王智冬;杨卫红;杨晓东;武诚;彭丽
国网经济技术研究院有限公司,北京市 102209国网经济技术研究院有限公司,北京市 102209国网经济技术研究院有限公司,北京市 102209国网福建省电力有限公司,福州市 350003国网山东省电力公司,济南市 250001华电电力科学研究院有限公司,北京市 100039
信息技术与安全科学
风光储自备电源用能总成本需求侧响应(DR)反向输电负荷准线负荷基线
user-sited wind,photovoltaic and storage power sourcestotal energy costdemand response(DR)reverse transmissionload guidelineload baseline
《电力建设》 2026 (6)
68-81,14
智能电网国家科技重大专项(2025ZD0805001) This work is supported by Smart Grid National Science and Technology Major Project of China(No.2025ZD0805001).
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