负荷聚合商参与需求侧资源聚合优化调控方法

doi:10.11930/j.issn.1004-9649.202407085

摘要/Abstract

摘要：

需求响应技术是目前实现新能源消纳及“双碳”目标的重要手段，但由于需求资源分散特性和实际调度能力，独立需求侧主体缺乏有效参与调控的途径。为此，综合考虑多运营主体共识、多类型需求响应和碳排放，提出一种负荷聚合商参与的需求侧资源聚合优化调控方法。首先，分析了需求侧资源的基本管理模式；其次，综合经济成本和碳排放目标，建立了考虑碳排放和多类型需求响应的需求侧资源聚合优化调控模型；最后，利用麻雀算法进行仿真求解，验证了优化调度模型的有效性。仿真结果表明，所提模型可降低分散资源用能成本5.16%以上，明显改善需求侧资源主体的经济成本和碳排放量，提高需求侧资源管理水平，为低碳韧性电网的优化调控问题提供了一种有效手段。

关键词: 需求侧资源, 运营主体共识, 碳排放, 负荷聚合商, 优化调控

Abstract:

Demand response (DR) technology is an important means to realize new energy consumption and dual-carbon strategy. However, independent demand-side entities lack effective participation pathways in dispatch due to the decentralized characteristics of demand-side resources (DSRs) and limited actual dispatch capacities. For this reason, an optimal dispatch methodology for DSRs with load aggregator participation is proposed, with consideration of multi-operator consensus, multi-category DR and carbon emissions. Firstly, the basic management mode of DSRs was analyzed. Secondly, an optimal dispatch model for DSRs was established by integrating the economic cost and carbon emission objectives with consideration of carbon emissions and multi-category DR. Finally, the sparrow algorithm was used for simulation to verify the effectiveness of the optimal dispatch model. The simulation results show that the proposed model can effectively reduce the energy use cost of decentralized resources by 5%, significantly improve the economic cost and carbon emissions of DSRs entities, and enhance the DSRs management level, which provides an effective means for the optimal dispatch of low-carbon resilient power grid.

Key words: demand-side resources, multi-operator consensus, carbon emissions, load aggregator, optimal dispatch

傅成程, 张春雁, 刘键烨, 贾德香, 李丹, 王素. 负荷聚合商参与需求侧资源聚合优化调控方法[J]. 中国电力, 2025, 58(8): 1-11.

FU Chengcheng, ZHANG Chunyan, LIU Jianye, JIA Dexiang, LI Dan, WANG Su. Optimal Dispatching Method of Demand-Side Resources with Load Aggregator Participation[J]. Electric Power, 2025, 58(8): 1-11.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202407085

https://www.electricpower.com.cn/CN/Y2025/V58/I8/1

图/表 13

图 1 多级需求侧管理基本模式

Fig.1 Schematic diagram of multi-level demand-side management mode

图 2 博弈求解流程

Fig.2 Game solving flowchart

表 1 设备参数

Table 1 Equipment parameters

设备类型	$ \mathop P\nolimits_{\min } $/ kW	$ \mathop P\nolimits_{\max } $/ kW	$ \mathop R\nolimits_{{\text{down}}} $/ (kW·h^–1)	$ \mathop R\nolimits_{{\text{up}}} $/ (kW·h^–1)	$ \mathop \eta \nolimits_{\text{e}} $/%	$ \mathop \eta \nolimits_{\text{h}} $/%
MT	25	600	–200	200	0.35	0.6
GB	0	400	–100	100		0.9
P2G	0	200			1.90
ES	30	300	–90	90	0.99

表 2 用户电价弹性

Table 2 Customer price elasticity

时段	高峰	平段	低谷
高峰	–0.100	0.018	0.014
平段	0.018	–0.016	0.010
低谷	0.014	0.010	–0.100

图 3 负荷及新能源出力预测曲线

Fig.3 Prediction curves of load and new energy output

图 4 运营主体3电负荷曲线

Fig.4 Electric load curve of the operating entity 3

图 5 聚类前后负荷曲线

Fig.5 Load curve before and after clustering

图 6 需求响应前后负荷功率对比

Fig.6 Comparison of load power before and after demand response

图 7 电价策略优化结果

Fig.7 Optimized results of electricity pricing strategy

表 3 不同场景下各运营主体的总运营效益

Table 3 The total operational benefits of each operating entity in different scenarios 单位：元

场景	主体1	主体2	主体3	LA
1	–5009.9	5420.8	–10639.5
2	–3265.0	4082.9	–9326.6	–6304.3
3	–1927.6	9481.2	–8107.1	10279.9
4	–1050.7	7903.7	–7236.7	2280.0

图 8 市场信号分解结果

Fig.8 Market signal decomposition results

表 4 不同场景下各运营主体的CO2交易成本

Table 4 CO2 trading costs of various operating entities under different scenarios 单位：元

运营主体	场景1	场景2	场景3	场景4
1	–188.6	–188.6	–13.8	2.1
2	824.7	824.7	1074.7	1087.6

图 9 各利益主体的资源优化调控结果

Fig.9 Optimal dispatch results of various stakeholders' resources

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