Optimal Dispatching Method of Demand-Side Resources with Load Aggregator Participation

doi:10.11930/j.issn.1004-9649.202407085

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202407085

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

Figures/Tables 13

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

Fig.2 Game solving flowchart

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

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

Fig.3 Prediction curves of load and new energy output

Fig.4 Electric load curve of the operating entity 3

Fig.5 Load curve before and after clustering

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

Fig.7 Optimized results of electricity pricing strategy

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

Fig.8 Market signal decomposition results

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

Fig.9 Optimal dispatch results of various stakeholders' resources

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