摘要
【目的】随着分布式电源、新型储能、充电设施等规模化接入,配电网的物理形态、数字形态和商业形态发生深刻变革。传统基于人工决策的规划方法难以解决要素海量、结构复杂、设备繁多的配电网组网优化问题,人工智能技术为突破配电网规划技术瓶颈提供了可行的解决路径。【方法】在此背景下,文章对配电网规划流程与新形势下源荷多时空精准预测、电力电量概率平衡、源网荷储规划协同、数字化智能化赋能赋效等方面面临的挑战进行分析,并围绕知识图谱构建、源荷场景生成、电力电量平衡、规划需求推演以及智能组网规划等关键环节,详细阐述了基于人工智能的配电网规划研究现状。【结果】对基于人工智能的配电网规划技术所存在的非/半结构化数据处理难、场景适用单一、需求推演精度低、缺乏可解释性以及规划方案求解维度高等问题进行了总结与分析,并给出了基于图学习、迁移学习、多模态融合、增强可解释性以及人机混合智能增强等配电网规划技术演进方向的展望。【结论】相比于传统配电网规划方法,基于人工智能的配电网规划具有泛化性强、适用性强、扩展性强等明显优势,但也存在模型精度不高、生成方案质量差等一些关键性问题。在未来的工作中,将继续深入研究基于人工智能的配电网规划方法,解决其涉及的关键性问题,为新型电力系统下配电网规划技术体系发展和数智化转型提供参考和借鉴。
[Objective]With large-scale access to distributed power sources,new energy storage,charging facilities,etc.,the physical,digital,and commercial forms of distribution networks have undergone profound changes.The traditional planning method based on manual decision-making hinders distribution network optimization due to massive factors,complex structures,and numerous pieces of equipment.Artificial intelligence technology provides a feasible solution for overcoming the technical bottlenecks of distribution network planning.[Methods]In this context,this study analyzes the challenges faced by the distribution network planning process under new circumstances,including the precise spatiotemporal prediction of source-load,probabilistic balance of power and energy,coordinated planning of source-grid-load-storage,and empowerment of digitalization and intelligence.It elaborates on the current research status of artificial intelligence-based distribution network planning,focusing on key aspects such as knowledge graph construction,source-load scenario generation,power-energy balance,planning demand reduction,and intelligent network planning.[Results]This study summarizes and analyzes the issues in artificial intelligence-based distribution network planning technologies,including difficulties in processing unstructured and semi-structured data,limited scenario applicability,low accuracy in demand deduction,lack of interpretability,and high-dimensional solution spaces for planning schemes.It proposes potential solutions in technical research,such as graph learning,transfer learning,multimodal fusion,enhanced interpretability,and human-machine hybrid intelligence enhancement.[Conclusions]Compared with traditional distribution network planning methods,artificial intelligence-based distribution network planning demonstrates significant advantages of strong generalization,applicability,and scalability.However,it still faces critical issues,such as insufficient model accuracy and poor quality of generated solutions.In future work,we will continue to investigate artificial intelligence-based distribution network planning methods based on technical prospects,aiming to address the key challenges involved.This will provide references and insights for the development and digital-intelligent transformation of distribution network planning technology systems under the new power system framework.
作者
李敬如
李红军
马良
姜世公
穆朝絮
司晨怡
LI Jingru;LI Hongjun;MA Liang;JIANG Shigong;MU Chaoxu;SI Chenyi(State Grid Economic and Technological Research Institute Co.,Ltd.,Beijing 102209,China;School of Electrical Automation and Information Engineering,Tianjin University,Tianjin 300072,China)
出处
《电力建设》
北大核心
2025年第4期1-15,共15页
Electric Power Construction
基金
国家电网公司科技项目(5400-202456175A-1-1-ZN)。
关键词
人工智能
配电网规划
分布式电源
研究现状
技术展望
artificial intelligence
distribution network planning
distributed generation
research status
technology prospects
作者简介
李敬如(1969),女,硕士,教授级高级工程师,主要研究方向为配电网规划、分布式电源、人工智能等,E-mail:lijingru@chinasperi.sgcc.com.cn;李红军(1971),男,博士,教授级高级工程师,主要研究方向为交直流配电网规划运行、储能等,E-mail:lihongjun@chinasperi.sgcc.com.cn;通信作者:马良(1991),男,博士,高级工程师,主要研究方向为微电网、人工智能等,E-mail:maliang@chinasperi.sgcc.com.cn;姜世公(1983),男,博士,高级工程师,主要研究方向为城市配电网规划运行等,E-mail:jiangshigong@chinasperi.sgcc.com.cn;穆朝絮(1984),女,博士,教授,主要研究方向为智能无人系统优化、强化学习、新型电力系统运行规划与优化控制等,E-mail:cxmu@tju.edu.cn;司晨怡(1998),男,博士研究生,主要研究方向为强化学习、配电网规划等,E-mail:sichenyi2021@163.com。
