| 张振芳,撤奥洋,贾元峥,张智晟.计及时空差异性的屋顶分布式光伏选址定容规划[J].电力需求侧管理,2026,28(3):97-103 |
| 计及时空差异性的屋顶分布式光伏选址定容规划 |
| Optimal sizing and siting planning of rooftop distributed photovoltaic considering spatiotemporal difference |
| 投稿时间：2025-12-31 修订日期：2026-01-29 |
| DOI：10.3969/j.issn.1009-1831.2026.03.014 |
| 中文关键词: 屋顶分布式光伏 时空差异性 电动汽车充电负荷 多目标麻雀搜索算法 选址定容 |
| 英文关键词: rooftop distributed photovoltaic spatiotemporal difference electric vehicle charging load multi-objective spar-row search algorithm optimal sizing and siting |
| 基金项目:国网山东省电力公司科技项目(520602220004) |
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| 中文摘要: |
| 近年来, 可再生能源规模呈现快速增长态势, 分布式光伏的无序开发和分散并网等问题逐渐显露, 使得配电网的安全运行和可持续发展都面临巨大挑战。针对屋顶分布式光伏功率时空差异和光伏系统区域经济指标差异, 提出了一种计及时空差异性的屋顶分布式光伏选址定容规划模型。首先, 基于新型智能配电网中多元源荷时空尺度不确定性的特点, 构建了光-荷时空差异模型。其次, 综合考虑用户侧经济效益、节点电压偏差和系统网络损耗, 并采用多目标麻雀搜索算法对模型进行求解。最后, 以IEEE-33节点配电系统为例进行仿真验证。结果表明, 所提模型对于提高屋顶分布式光伏的经济价值和配电网承载能力具有实际意义, 并为新型智能配电网的规模发展提供了有益的思路。 |
| 英文摘要: |
| In recent years, the scale of renewable energy is characterized by rapid growth, while issues such as the disorderly development of distributed PV and decentralized grid integration are gradually revealed, posing significant challenges to the safe operation and sustainable development of distribution networks. A planning model for rooftop distributed PV site selection and capacity determination that accounts for spatiotemporal differences is proposed, based on the spatiotemporal variability in rooftop distributed PV output and regional economic indicators of PV systems. First, considering the characteristics of spatiotemporal uncertainty of multiple sources and loads in new smart distribution networks, a PV-load spatiotemporal difference model is constructed. Next, the model is solved using a multi-objective sparrow search algorithm, taking into account the economic benefits on the user side, node voltage deviations, and system network losses. Finally, simulation verification is conducted using the IEEE 33-node distribution system as an example. It is demonstrated that the proposed model has practical significance for improving the economic value of rooftop distributed PV and the hosting capacity of distribution networks, while also providing valuable insights for the large-scale development of new smart distribution networks. |
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