| 引用本文: | 杨晓辉,邓福伟,张钟炼,等.基于自适应ε约束法考虑OLTC模糊控制的低碳配电网双层规划[J].电力系统保护与控制,2023,51(13):1-11.[点击复制] |
| YANG Xiaohui,DENG Fuwei,ZHANG Zhonglian,et al.Bi-level planning for low-carbon distribution networks based on an adaptive ε-constraintmethod considering OLTC fuzzy control[J].Power System Protection and Control,2023,51(13):1-11[点击复制] |
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| 摘要: |
| 新时代低碳发展目标给配电网带来巨大挑战，为保证供电时降低配电网碳排放，提出了一种考虑有载调压变压器(on-load tap changer, OLTC)模糊控制的低碳配电网双层规划模型。依据网损灵敏度确定微型燃气轮机(micro-turbine generator, MTG)、新能源、储能和电容器(capacitor banks, CB)选址，采用双层模型实现低碳配电网规划。上层规划层以综合成本最小为目标，考虑了微型燃气轮机、新能源、储能和电容器投资运行成本，采用改进鲸鱼算法求解。下层运行层以运行成本和电压偏移量最小为目标，考虑了OLTC模糊控制、电容器投切、新能源不确定性、微型燃气轮机和储能调度，采用自适应ε约束法多目标粒子群算法获得均匀帕累托前沿，利用TOPSIS决策法选取最优解。最后通过改进IEEE33节点系统算例验证表明，所提方法能够实现配电网低碳经济运行，改善潮流分布，提高电压质量，降低网损。 |
| 关键词: 低碳配电网 OLTC 双层规划 自适应ε约束法 机会约束规划 |
| DOI：10.19783/j.cnki.pspc.221840 |
| 投稿时间：2022-11-22修订日期：2023-01-08 |
| 基金项目:国家自然科学基金项目资助(61963026)；江西省研究生创新项目资助(YC2022—s022) |
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| Bi-level planning for low-carbon distribution networks based on an adaptive ε-constraintmethod considering OLTC fuzzy control |
| YANG Xiaohui,DENG Fuwei,ZHANG Zhonglian,TANG Chouyan,HU Yuyin,DENG Yunwei |
| (College of Information Engineering, Nanchang University, Nanchang 330031, China) |
| Abstract: |
| The low-carbon development goal brings huge challenges to the distribution network, and a bi-level planning model for low-carbon distribution network, considering on-load tap changer fuzzy control, is proposed to reduce the carbon emission while ensuring power supply. The location of micro gas turbines, new energy sources, energy storage and capacitors is determined by network loss sensitivity. The bi-level model is used to fulfill low carbon planning. The planning layer is aimed at minimizing the comprehensive cost, considering the investment and operation cost of micro gas turbines, new energy sources, energy storage and capacitors, and the model is analyzed by the improved whale algorithm. The operation layer is aimed at minimizing the operation cost and voltage offset, considering OLTC fuzzy control, capacitor dropout, new energy uncertainty, micro-gas turbine and energy storage scheduling. It adopts the adaptive ε-constraint method multi-objective particle swarm algorithm to obtain the uniform Pareto front and then uses TOPSIS decision method to select the optimal solution. The proposed method can achieve low carbon and economic operation, improve tide distribution, increase the voltage quality and reduce network loss. |
| Key words: low carbon distribution network OLTC bi-level planning adaptive ε constraint method opportunity constraint programming |
