{"title":"先进的能源管理战略在可再生能源社区灵活运行能源方面的作用","authors":"Antonio Gallo, Alfonso Capozzoli","doi":"10.1016/j.enbuild.2024.115043","DOIUrl":null,"url":null,"abstract":"<div><div>Renewable Energy Communities (REC) can largely contribute to building decarbonization targets and provide flexibility through the adoption of advanced control strategies of the energy systems. This work investigates how the role of flexibility sources will be impacted by shifting towards advanced control strategies under a high penetration of variable Renewable Energy Sources, in the following years. A large residential area with diverse energy systems, building envelope configurations, and energy demand patterns is modeled with the simulation environment RECsim, a virtual testbed for the implementation of energy management strategies in REC. Photovoltaic (PV) panels, Battery Energy Storage and Thermal Energy Storage (TES) of different sizes for each household provide a realistic description of a REC which includes both consumers and prosumers.</div><div>This study explores a scenario in which advanced controllers based on Deep Reinforcement Learning (DRL) replace existing Rule-Based Controllers in building energy systems across a significant number of buildings. These control policies are simulated under three different scenarios that consider consumers with different pricing schemes and TES penetration.</div><div>Efficient control strategies, have demonstrated significant potential, regardless of the presence of thermal storage and ToU pricing schemes, in reducing energy demand by 12.6%, cutting energy costs by 20.8%, and enhancing self-sufficiency and self-consumption, with minimal impact on Shared Energy. Implementing a flat tariff scheme under DRL enables consumers to increase their energy demand during periods of PV generation, which is particularly advantageous in a REC. Also, this approach lowers overall energy demand by 12.6% and boosts self-sufficiency, and it also decreases electricity exports from the REC to the grid by 18.2% compared to a ToU tariff scheme. When using ToU tariffs, thermal storage can be used to achieve cost savings, but total Shared Energy decreases, as do self-sufficiency and self-consumption of the REC. The results indicate that in a REC with high variable renewable energy and decentralized control, consumers using TES and ToU tariffs with peak prices during high irradiance periods may not be beneficial for the grid compliance.</div><div>In conclusion, the coupling between DRL and thermal storage should be supported by more innovative pricing schemes for RECs and/or coordinated energy management, although it requires advanced communication and monitoring infrastructure.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"325 ","pages":"Article 115043"},"PeriodicalIF":6.6000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The role of advanced energy management strategies to operate flexibility sources in Renewable Energy Communities\",\"authors\":\"Antonio Gallo, Alfonso Capozzoli\",\"doi\":\"10.1016/j.enbuild.2024.115043\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Renewable Energy Communities (REC) can largely contribute to building decarbonization targets and provide flexibility through the adoption of advanced control strategies of the energy systems. This work investigates how the role of flexibility sources will be impacted by shifting towards advanced control strategies under a high penetration of variable Renewable Energy Sources, in the following years. A large residential area with diverse energy systems, building envelope configurations, and energy demand patterns is modeled with the simulation environment RECsim, a virtual testbed for the implementation of energy management strategies in REC. Photovoltaic (PV) panels, Battery Energy Storage and Thermal Energy Storage (TES) of different sizes for each household provide a realistic description of a REC which includes both consumers and prosumers.</div><div>This study explores a scenario in which advanced controllers based on Deep Reinforcement Learning (DRL) replace existing Rule-Based Controllers in building energy systems across a significant number of buildings. These control policies are simulated under three different scenarios that consider consumers with different pricing schemes and TES penetration.</div><div>Efficient control strategies, have demonstrated significant potential, regardless of the presence of thermal storage and ToU pricing schemes, in reducing energy demand by 12.6%, cutting energy costs by 20.8%, and enhancing self-sufficiency and self-consumption, with minimal impact on Shared Energy. Implementing a flat tariff scheme under DRL enables consumers to increase their energy demand during periods of PV generation, which is particularly advantageous in a REC. Also, this approach lowers overall energy demand by 12.6% and boosts self-sufficiency, and it also decreases electricity exports from the REC to the grid by 18.2% compared to a ToU tariff scheme. When using ToU tariffs, thermal storage can be used to achieve cost savings, but total Shared Energy decreases, as do self-sufficiency and self-consumption of the REC. The results indicate that in a REC with high variable renewable energy and decentralized control, consumers using TES and ToU tariffs with peak prices during high irradiance periods may not be beneficial for the grid compliance.</div><div>In conclusion, the coupling between DRL and thermal storage should be supported by more innovative pricing schemes for RECs and/or coordinated energy management, although it requires advanced communication and monitoring infrastructure.</div></div>\",\"PeriodicalId\":11641,\"journal\":{\"name\":\"Energy and Buildings\",\"volume\":\"325 \",\"pages\":\"Article 115043\"},\"PeriodicalIF\":6.6000,\"publicationDate\":\"2024-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy and Buildings\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378778824011599\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and Buildings","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378778824011599","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
The role of advanced energy management strategies to operate flexibility sources in Renewable Energy Communities
Renewable Energy Communities (REC) can largely contribute to building decarbonization targets and provide flexibility through the adoption of advanced control strategies of the energy systems. This work investigates how the role of flexibility sources will be impacted by shifting towards advanced control strategies under a high penetration of variable Renewable Energy Sources, in the following years. A large residential area with diverse energy systems, building envelope configurations, and energy demand patterns is modeled with the simulation environment RECsim, a virtual testbed for the implementation of energy management strategies in REC. Photovoltaic (PV) panels, Battery Energy Storage and Thermal Energy Storage (TES) of different sizes for each household provide a realistic description of a REC which includes both consumers and prosumers.
This study explores a scenario in which advanced controllers based on Deep Reinforcement Learning (DRL) replace existing Rule-Based Controllers in building energy systems across a significant number of buildings. These control policies are simulated under three different scenarios that consider consumers with different pricing schemes and TES penetration.
Efficient control strategies, have demonstrated significant potential, regardless of the presence of thermal storage and ToU pricing schemes, in reducing energy demand by 12.6%, cutting energy costs by 20.8%, and enhancing self-sufficiency and self-consumption, with minimal impact on Shared Energy. Implementing a flat tariff scheme under DRL enables consumers to increase their energy demand during periods of PV generation, which is particularly advantageous in a REC. Also, this approach lowers overall energy demand by 12.6% and boosts self-sufficiency, and it also decreases electricity exports from the REC to the grid by 18.2% compared to a ToU tariff scheme. When using ToU tariffs, thermal storage can be used to achieve cost savings, but total Shared Energy decreases, as do self-sufficiency and self-consumption of the REC. The results indicate that in a REC with high variable renewable energy and decentralized control, consumers using TES and ToU tariffs with peak prices during high irradiance periods may not be beneficial for the grid compliance.
In conclusion, the coupling between DRL and thermal storage should be supported by more innovative pricing schemes for RECs and/or coordinated energy management, although it requires advanced communication and monitoring infrastructure.
期刊介绍:
An international journal devoted to investigations of energy use and efficiency in buildings
Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.