Siting and Sizing of Distributed Generation in Distribution Transformer Areas Using Multi-objective Bi-level Optimization

Abstract

Figures/Tables 19

References 29

doi:10.11930/j.issn.1004-9649.202505080

Abstract:

In recent years, with the continuous implementation of the "county-wide rooftop distributed photovoltaic (DPV) development program" promoted by the National Energy Administration, the integration scale of PV in China has steadily expanded, enhancing the utilization efficiency of renewable energy. However, the large-scale integration of PV users, particularly at the ends of low-voltage distribution networks. has led to issues such as localized voltage violations and abnormal power losses, thereby compromising power system's operational security and economic efficiency. To address the aforementioned issues, this paper proposes a bi-level optimal allocation method for distributed generation siting and sizing based on second-order cone programming relaxation. Firstly, to balance system's operational security and economic efficiency, a bi-level optimization model is established with the dual objectives of minimizing the system's daily comprehensive operating cost and maximizing PV utilization, which takes account such factors as the aging, maintenance costs and operational safety of distributed energy storage systems. Then, the second-order cone programming relaxation technique is employed to transform the power flow model, converting the original non-convex model into a convex one to reduce computational complexity, and the multi-objective particle swarm optimization algorithm is used to solve the model. Finally, using the improved IEEE 33-bus system and an actual distribution network in Hebei for case study, a comparison with the traditional planning approaches is conducted to verify the superiority of the proposed method. The results indicate that the proposed method can enhance power quality, reduce network losses, and increase the DPV hosting capacity while ensuring economic efficiency of distribution networks.

Key words: distributed generation, voltage violation, second-order cone programming, multi-objective bi-level optimization, distributed photovoltaic

WANG Deshun, LEI Jie, HU Anping, WANG Bing, FENG Xinzhen, WANG Zhihui. Siting and Sizing of Distributed Generation in Distribution Transformer Areas Using Multi-objective Bi-level Optimization[J]. Electric Power, 2025, 58(10): 136-146.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202505080

https://www.electricpower.com.cn/EN/Y2025/V58/I10/136

Fig.1 Model framework for DERs siting and sizing

Fig.2 Branch equivalent circuit model

Fig.3 Bi-level model solution flowchart

Fig.4 Typical source-load scenario

Fig.5 IEEE 33-bus network topology

Fig.6 The impact of penetration rate on node voltage

Fig.7 The impact of penetration rate on node voltage

Fig.8 The impact of grid-connected nodes on voltage

Table 1 Optimal DPV configuration results for distribution transformer area A

方案	接入节点	配置容量/kW	经济成本/元	光伏利用率/%
一	2，8，16，32，20	40，60，60，70，40	192	73.3
二	2，6，15，21，31	50，50，70，60，40	189	74.9

Fig.9 Changes in branch network losses before and after DPV integration

Fig.10 Changes in bus voltages before and after DPV integration

Table 2 Optimal configuration results of DES

Table 3 DES Planning Indicators

方案	日均成本/元	线损率/%	光伏利用率/%	电压合格率/%
一	67.19	0.87	88.6	100
二	62.99	0.75	89.3	100

Fig.11 Topology distribution of Area A

Fig.12 Topology distribution of Area B

Table 4 Basic information of distribution transformer areas A and B before planning

台区	光伏利用率/%	线损率/%	电压合格率/%
A	71.1	3.92	79.17
B	65.9	9.32	6.94

Fig.13 Voltage distribution of distribution transformer areas on a PV peak generation day before energy storage planning

Table 5 Optimal configuration results of DES for distribution transformer areas A and B

台区	接入节点	配置容量/(kV·A)
A	6，10，12	50，70，10
B	5，12，17，22，27	30，50，50，20，50

Table 6 DES planning indicators for distribution transformer areas A and B

台区	日均投资成本/元	日均运行维护成本/元	线损率/ %	光伏利用率/%	电压合格率/%
A	48.59	5.31	1.26	82.1	100
B	74.75	9.48	3.72	79.5	100

配置容量/(kW·h)

配置容量/(kW·h)

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