| 陈朝艺,杨永标,徐青山,庄重.高耗能工业负荷调控灵活性量化及需求响应报价策略[J].电力需求侧管理,2026,28(2):15-21 |
| 高耗能工业负荷调控灵活性量化及需求响应报价策略 |
| Quantification of flexibility in high energy-consuming industrial load control and demand response pricing strategy |
| 投稿时间：2025-12-15 修订日期：2026-01-30 |
| DOI：10.3969/j.issn.1009-1831.2026.02.003 |
| 中文关键词: 高耗能工业用户 调控灵活性 G1-熵权-TOPSIS法 需求响应市场 报价策略 |
| 英文关键词: high-energy-consuming industrial users regulatory flexibility G1-entropy-weight-TOPSIS method demand response market quotation strategy |
| 基金项目:国家电网有限公司科技项目资助(5400-202418217A-1-1-ZN) |
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| 中文摘要: |
| 针对高耗能工业用户负荷调节潜力多元化、差异化显著的特点,为实现其参与多品种需求响应市场的精准投标,提出一种负荷调控灵活性量化评价方法及跨时间参与需求响应报价的策略。首先,构建基于序关系分析法-熵权法-利用优劣解距离法(technique for order preference by similarity to ideal solution,TOPSIS)法的多时间尺度负荷调控灵活性评估指标体系,量化负荷调控灵活性差异;其次,设计了计及负荷调控灵活性的跨时间负荷资源与多类型需求响应品种的动态匹配机制,建立需求响应总收益最大化的跨时间报价模型;最后,采用粒子群算法求解某工业用户在参与约定需求响应(agreed demand response,ADR)、快速避峰响应(fast peak avoidance response,FPAR)、实时需求响应(real-time demand response,RDR)时的最优功率分配预案与报价组合,案例表明该策略可使用户参与各类型需求响应的总收益均达到最大。 |
| 英文摘要: |
| In view of the diversification and significant differentiation of load regulation potential of high-energy-consuming industrial users, in order to realize accurate bidding in the multivvariety demand response market, a quantitative evaluation method considering the flexibility of load regulation and a strategy of cross-time participation in demand response quotation are proposed. Firstly, a multi-time scale load control flexibility evaluation index system based on the order relation analysis method-entropy weight method-TOPSIS is constructed to quantify the difference in load control flexibility. Secondly, a dynamic matching mechanism between cross-time load resources and multiple types of demand response varieties considering the flexibility of load regulation is proposed, and a cross-time quotation model considering the maximization of total revenue of demand response is established. Finally, the PSO algorithm is used to solve the optimal power allocation plan and quotation combination of an industrial user when participating in the agreed demand response (ADR), fast peak avoidance response (FPAR) and real-time demand response (RDR), and the case study shows that the strategy can maximize the total benefit of the user's participation in various types of demand response. |
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