Congfei Li, Jiayi Liu, Tian Ding, Xi Liu, Zhenyu Zhou, Zhongwei Sun
{"title":"考虑光伏负荷不确定性的光伏-储能集成 5G 基站的聚合调节与协调调度","authors":"Congfei Li, Jiayi Liu, Tian Ding, Xi Liu, Zhenyu Zhou, Zhongwei Sun","doi":"10.1016/j.ijepes.2024.110306","DOIUrl":null,"url":null,"abstract":"<div><div>Photovoltaic (PV)-storage integrated 5G base station (BS) can participate in demand response on a large scale, conduct electricity transaction and provide auxiliary services, thus reducing the high electricity consumption of 5G BSs and increasing the flexibility resource capacity of the distribution network. However, the flexible resource regulation of PV-storage integrated 5G BSs still faces problems such as many regulators, ignored PV-load uncertainty, and poor adaptability of existing regulatory frameworks and scheduling methods, which leads to greater security operation risks in resource regulation decisions and affects the exploitation of the 5G BSs scheduling potential. Aiming at the above problems, this paper proposes an aggregated regulation and coordinated scheduling method of PV-storage integrated 5G BSs considering PV-load uncertainty. Firstly, a hierarchical cluster-cooperative aggregated regulation framework for the scale PV-storage integrated 5G BSs is established, and a regional communication operator (RCO) schedulable capability model and an information gap decision theory (IGDT) based PV-load uncertainty model are built. Next, a two-stage joint optimization problem is proposed for maximizing the RCO income while reducing the BS cluster operation cost. Then, the first-stage day-ahead transaction optimization problem in the electricity market is solved, and the reliable operation planning and economic operation planning strategies are proposed based on IGDT to adapt to the regulation demand under different uncertainty risks; the second-stage BS cluster real-time operation optimization problem is solved based on the adaptive consensus algorithm considering scheduling preferences (ACSP), achieving distributed real-time coordinated scheduling of multiple agents in the BS cluster. Finally, the effectiveness of the proposed method is verified by simulation examples, which show that the aggregated regulation and coordinated scheduling of PV-storage integrated 5G BSs can achieve mutual benefits for the distribution network and communication operators.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"162 ","pages":"Article 110306"},"PeriodicalIF":5.0000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Aggregated regulation and coordinated scheduling of PV-storage integrated 5G base stations considering PV-load uncertainty\",\"authors\":\"Congfei Li, Jiayi Liu, Tian Ding, Xi Liu, Zhenyu Zhou, Zhongwei Sun\",\"doi\":\"10.1016/j.ijepes.2024.110306\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Photovoltaic (PV)-storage integrated 5G base station (BS) can participate in demand response on a large scale, conduct electricity transaction and provide auxiliary services, thus reducing the high electricity consumption of 5G BSs and increasing the flexibility resource capacity of the distribution network. However, the flexible resource regulation of PV-storage integrated 5G BSs still faces problems such as many regulators, ignored PV-load uncertainty, and poor adaptability of existing regulatory frameworks and scheduling methods, which leads to greater security operation risks in resource regulation decisions and affects the exploitation of the 5G BSs scheduling potential. Aiming at the above problems, this paper proposes an aggregated regulation and coordinated scheduling method of PV-storage integrated 5G BSs considering PV-load uncertainty. Firstly, a hierarchical cluster-cooperative aggregated regulation framework for the scale PV-storage integrated 5G BSs is established, and a regional communication operator (RCO) schedulable capability model and an information gap decision theory (IGDT) based PV-load uncertainty model are built. Next, a two-stage joint optimization problem is proposed for maximizing the RCO income while reducing the BS cluster operation cost. Then, the first-stage day-ahead transaction optimization problem in the electricity market is solved, and the reliable operation planning and economic operation planning strategies are proposed based on IGDT to adapt to the regulation demand under different uncertainty risks; the second-stage BS cluster real-time operation optimization problem is solved based on the adaptive consensus algorithm considering scheduling preferences (ACSP), achieving distributed real-time coordinated scheduling of multiple agents in the BS cluster. 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Aggregated regulation and coordinated scheduling of PV-storage integrated 5G base stations considering PV-load uncertainty
Photovoltaic (PV)-storage integrated 5G base station (BS) can participate in demand response on a large scale, conduct electricity transaction and provide auxiliary services, thus reducing the high electricity consumption of 5G BSs and increasing the flexibility resource capacity of the distribution network. However, the flexible resource regulation of PV-storage integrated 5G BSs still faces problems such as many regulators, ignored PV-load uncertainty, and poor adaptability of existing regulatory frameworks and scheduling methods, which leads to greater security operation risks in resource regulation decisions and affects the exploitation of the 5G BSs scheduling potential. Aiming at the above problems, this paper proposes an aggregated regulation and coordinated scheduling method of PV-storage integrated 5G BSs considering PV-load uncertainty. Firstly, a hierarchical cluster-cooperative aggregated regulation framework for the scale PV-storage integrated 5G BSs is established, and a regional communication operator (RCO) schedulable capability model and an information gap decision theory (IGDT) based PV-load uncertainty model are built. Next, a two-stage joint optimization problem is proposed for maximizing the RCO income while reducing the BS cluster operation cost. Then, the first-stage day-ahead transaction optimization problem in the electricity market is solved, and the reliable operation planning and economic operation planning strategies are proposed based on IGDT to adapt to the regulation demand under different uncertainty risks; the second-stage BS cluster real-time operation optimization problem is solved based on the adaptive consensus algorithm considering scheduling preferences (ACSP), achieving distributed real-time coordinated scheduling of multiple agents in the BS cluster. Finally, the effectiveness of the proposed method is verified by simulation examples, which show that the aggregated regulation and coordinated scheduling of PV-storage integrated 5G BSs can achieve mutual benefits for the distribution network and communication operators.
期刊介绍:
The journal covers theoretical developments in electrical power and energy systems and their applications. The coverage embraces: generation and network planning; reliability; long and short term operation; expert systems; neural networks; object oriented systems; system control centres; database and information systems; stock and parameter estimation; system security and adequacy; network theory, modelling and computation; small and large system dynamics; dynamic model identification; on-line control including load and switching control; protection; distribution systems; energy economics; impact of non-conventional systems; and man-machine interfaces.
As well as original research papers, the journal publishes short contributions, book reviews and conference reports. All papers are peer-reviewed by at least two referees.