Open Access

ARTICLE

Bilevel Planning of Distribution Networks with Distributed Generation and Energy Storage: A Case Study on the Modified IEEE 33-Bus System

Haoyuan Li, Lingling Li^*

State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300130, China

* Corresponding Author: Lingling Li. Email:

Energy Engineering 2025, 122(4), 1337-1358. https://doi.org/10.32604/ee.2025.060105

Received 24 October 2024; Accepted 03 February 2025; Issue published 31 March 2025

Abstract

Rational distribution network planning optimizes power flow distribution, reduces grid stress, enhances voltage quality, promotes renewable energy utilization, and reduces costs. This study establishes a distribution network planning model incorporating distributed wind turbines (DWT), distributed photovoltaics (DPV), and energy storage systems (ESS). K-means++ is employed to partition the distribution network based on electrical distance. Considering the spatiotemporal correlation of distributed generation (DG) outputs in the same region, a joint output model of DWT and DPV is developed using the Frank-Copula. Due to the model’s high dimensionality, multiple constraints, and mixed-integer characteristics, bilevel programming theory is utilized to structure the model. The model is solved using a mixed-integer particle swarm optimization algorithm (MIPSO) to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality. The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system. The results demonstrate significant improvements, with the proposed method reducing the annual comprehensive cost by 41.65% and 13.98%, respectively, compared to scenarios without DG and ESS or with only DG integration. Furthermore, it reduces the daily average voltage deviation by 24.35% and 10.24% and daily network losses by 55.72% and 35.71%.

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Keywords

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