| 引用本文: | 杜晓东,赵建利,刘科研,詹惠瑜.基于数字孪生的光伏高比例配电网过载风险预警方法[J].电力系统保护与控制,2022,50(9):136-144.[点击复制] |
| DU Xiaodong,ZHAO Jianli,LIU Keyan,ZHAN Huiyu.Digital twin early warning method study for overload risk of distribution network with a high proportion of photovoltaic access[J].Power System Protection and Control,2022,50(9):136-144[点击复制] |
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| 摘要: |
| 在构建以新能源为主体的新型电力系统背景下，分布式光伏将呈现极高比例并网趋势。高比例光伏出力的随机性会加剧配电网潮流的不确定变化，给配电设备带来严重的过载风险，载荷安全问题突出。基于此，提出一种基于数字孪生的含高比例光伏配电网载荷安全分析和预警方法。论述了孪生配电网过载风险安全预警的实现机理和功能方案。根据相关标准分析设备过载耐受能力，并建立不确定场景下载荷安全预警等级。基于历史运行数据建立配电网源荷随机行为的马尔科夫模型，采用吉布斯算法对该模型进行蒙特卡洛随机抽样。根据配电网当前运行状态，结合孪生配电网超实时计算能力，对配电网未来随机场景快速仿真并计算过载风险指标，评判载荷安全性。算例分析验证了所提分析方法的有效性及合理性。算例结果表明，该方法能够得出高比例光伏配电网在分析时段的载荷安全态势，为电力系统调度人员提供了一定的参考依据。 |
| 关键词: 配电网 分布式光伏 载荷安全 数字孪生 马尔科夫链 吉布斯抽样 |
| DOI：DOI: 10.19783/j.cnki.pspc.211422 |
| 投稿时间：2021-10-24修订日期：2021-12-08 |
| 基金项目:河北省重点研发计划项目资助(21312102D)；国网河北省电力有限公司科技项目资助(kj2020-084) |
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| Digital twin early warning method study for overload risk of distribution network with a high proportion of photovoltaic access |
| DU Xiaodong,ZHAO Jianli,LIU Keyan,ZHAN Huiyu |
| (1. State Grid Hebei Electric Power Co., Ltd. Research Institute, Shijiazhuang 050021, China;
2. Power Distribution Technology Center, China Electric Power Research Institute Co., Ltd., Beijing 100192, China) |
| Abstract: |
| With reference to building a new power system with new energy as the main body, distributed photovoltaic (PV) will show a trend of extremely high levels of access. The randomness of PV output given its high proportion will aggravate the uncertain change of power flow in the distribution network. It brings serious overload risks to the distribution equipment, and the problem of load safety is prominent. Based on this, a digital twin based load security analysis and warning method of the distribution network with a high proportion of PV is proposed. The realization mechanism and function scheme of overload risk warning in twin distribution network are presented. The overload tolerance of equipment is analyzed according to relevant standards, and the load safety early warning levels in uncertain scenarios are established. Based on the historical operational data, Markov models of the random behavior of sources and loads in the distribution network are established. The Gibbs sampling algorithm is employed for random Monte Carlo sampling to the Markov models. Based on the current states, by means of the ultra-real-time computing capacity of the digital twins to quickly simulate the afterward random scenes of the distribution network, an overload risk index is calculated and evaluated. Numerical cases verify the effectiveness and rationality of the method. The case study demonstrates that the proposed method can analyze the load security situation of a distribution network with a high proportion of photovoltaic access in the analysis period, providing a certain reference for power system dispatchers.
This work is supported by the Key Research and Development Program of Hebei Province (No. 21312102D). |
| Key words: distribution network distributed photovoltaic load safety digital twins Markov chain Gibbs sampling |
