Active-reactive power optimization of active distribution networks considering dynamic wind-solar renewable uncertainty and demand response

doi:10.11930/j.issn.1004-9649.202501063

Abstract

Abstract:

To address the impact of peak loads on voltage security in active distribution networks, an active-reactive power optimization strategy is proposed based on price-guided demand-side response. Firstly, this strategy incorporates multi-scenario stochastic optimization and proposes a dynamic renewable scenario generation method based on nonparametric kernel density estimation and sequential sampling from standardized multivariate normal distributions, yielding a probabilistic set of typical daily wind-solar power profiles. Secondly, considering demand-side response and grid interaction to mitigate grid operational risks, this study proposes an optimization strategy coordinating time-of-use price-guided load shifting and reactive power compensation. The optimization objective is constructed based on multiple scenario probabilities to minimize voltage deviation and economic operation costs in the distribution networks. Finally, to address the complex characteristics of this model, a multi-objective hybrid multi-population collaborative optimization algorithm is proposed, yielding optimal reactive power compensation output and demand-side load scheduling strategy. Verification results show that the proposed strategy can effectively reduce the risk of voltage violations, minimize network losses, and achieve safe and economic operation of the distribution networks.

Key words: scenario analysis, demand-side response, reactive power compensation, multi-objective hybrid multi-population collaborative optimization algorithm, voltage violation

JI Hongli, XU Lei, YANG Jiahui, DOU Chunxia. Active-reactive power optimization of active distribution networks considering dynamic wind-solar renewable uncertainty and demand response[J]. Electric Power, 2026, 59(2): 47-60.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202501063

https://www.electricpower.com.cn/EN/Y2026/V59/I2/47

Figures/Tables 23

Fig.1 Method for generating dynamic wind-solar scenarios

Fig.2 Schematic of active distribution network dispatching

Fig.3 Active-reactive power optimization of active distribution networks considering dynamic wind-solar uncertainty and demand response

Table 1 Distribution network operating cost parameters

运行成本	单价
主网购电/(元·(kW·h)^–1)	0.6
网损/(元·(kW·h)^–1)	0.9
CB/(元·组^–1)	25
SVC/(元·(kV·A)^–1)	0.5
IL/(元·(kW·h)^–1)	0.4

Table 2 Parameters of the elastic tariff matrix

自弹性系数$\varepsilon $	交叉弹性系数$\varepsilon $
自弹性系数$\varepsilon $	峰-平	峰-谷	平-谷
–0.1	0.01	0.01	0.01

Fig.4 IEEE 33 node topology

Fig.5 Comparison of two scenario-generation methods

Fig.6 SSE and SC indicators

Fig.7 Scenario probability of 4 wind-solar power output clusters

Fig.8 Load fluctuation curve

Fig.9 Comparison of global minimum voltage time-series

Fig.10 Comparison of active power network loss time-series

Table 3 Comparison of metrics between two strategies

策略	平均电压偏移/(p.u.)	网损/MW	运行成本/万元
1	0.0160	2.630	5.553
2	0.0144	2.310	5.196

Fig.11 Power output time-series of reactive power compensation device in strategy 1

Fig.12 Power output time-series of reactive power compensation device in strategy 2

Fig.13 Curves of shifting load and time-of-use price

Fig.14 Interruptible load response

Table 4 Time-of-use tariff delineation

峰谷类型	时间段	电价/(元·(kW·h)^–1)
峰	18:00—24:00	0.89
平	08:00—12:00；17:00—18:00 00:00—12:00	0.61
谷	02:00—08:00；12:00—17:00	0.45

Table 5 Comparison of three algorithms

案例	经济成本/万元	电压偏移/(p.u.)	运行时间/s
1	5.196	113.88	1233.84
2	5.485	107.38	1462.28
3	5.483	104.46	1373.36

Fig.15 Optimal Pareto frontier solution for three algorithms

Fig.16 Voltage variation of node 33 at time 21

Table 6 Comparison of two scenario-generation methods

方法	SVC出力/(MV·A)	CB动作次数	用户舒适度/%
静态场景生成	5.503	12	94.02
动态场景生成	5.305	9	94.24

Table 7 Scheduling results for different numbers of clusters

聚类数	经济成本/万元	电压偏移/(p.u.)	运行时间/s
1	5.499	121.41	310.46
2	5.443	120.02	620.92
4	5.196	113.88	1233.84
6	5.125	110.33	1853.84
8	5.119	107.28	2478.58

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