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| DC loop-closed reconfiguration for distribution networks considering prediction error uncertainty and power complementarity |
| DOI：10.19783/j.cnki.pspc.240218 |
| Key Words:DC loop-closed distribution network distributed PV uncertainty of prediction error Gaussian mixture model power complementarity second-order cone-convex optimization |
| Author Name | Affiliation | | CHENG Long1 | 1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of
Education (Northeast Electric Power University), Jilin 132012, China
2. Construction Branch,
State Grid Jilin Electric Power Company Limited, Changchun 130021, China | | LI Guoqing1 | 1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of
Education (Northeast Electric Power University), Jilin 132012, China
2. Construction Branch,
State Grid Jilin Electric Power Company Limited, Changchun 130021, China | | WANG Chong1 | 1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of
Education (Northeast Electric Power University), Jilin 132012, China
2. Construction Branch,
State Grid Jilin Electric Power Company Limited, Changchun 130021, China | | WANG Zhenhao1 | 1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of
Education (Northeast Electric Power University), Jilin 132012, China
2. Construction Branch,
State Grid Jilin Electric Power Company Limited, Changchun 130021, China | | MA Hongbo2 | 1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of
Education (Northeast Electric Power University), Jilin 132012, China
2. Construction Branch,
State Grid Jilin Electric Power Company Limited, Changchun 130021, China |
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| Abstract:To accurately quantify the fluctuation of photovoltaic power and alleviate the risk of voltage exceeding limits caused by distributed photovoltaic integration into distribution networks, a DC loop-closed reconfiguration method for distribution networks considering the uncertainty of prediction error and power complementarity is proposed. By introducing the concept of probability power flow, Gaussian mixture model (GMM)-based probability distribution models for photovoltaic power prediction errors and resultant voltage deviation of buses in distribution networks are established. Then the high-risk buses with voltage exceeding the limit are defined, and the feasibility of any two buses’ loop-closed reconfiguration is analyzed according to the conditional probability distribution of the buses’ voltage deviation. By calculating the power to be balanced between the loop-closed buses, the power support capability to alleviate voltage exceeding limits is quantified. Thus a DC loop-closed reconfiguration model for distribution networks is proposed with the feasibility of loop-closed and power support capability as the main constraints, and the optimization objective is to minimize comprehensive investment costs, operational losses, and bus voltage deviations. The effectiveness and superiority of the proposed method are verified through IEEE 33-bus test examples on the Matlab 2020a platform. |
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