Distributed energy trading management for renewable prosumers with HVAC and energy storage
Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distrib...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484721002080 |
| _version_ | 1819174657720320000 |
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| author | Qing Yang Hao Wang |
| author_facet | Qing Yang Hao Wang |
| author_sort | Qing Yang |
| collection | DOAJ |
| description | Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to leverage their power usage flexibility without compromising the users’ privacy. To achieve this goal, we design a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to automatically operate the HVAC system and batteries, which minimizes the energy costs of users. Specifically, we decouple the optimization problem into a primal subproblem and a dual subproblem. The primal subproblem is solved by the users, and the dual subproblem is solved by the grid operator. Unlike the existing centralized method, our approach only uses the users’ private information locally for solving the primal subproblem hence preserves the users’ privacy. Using real-world data, we validate our proposed algorithm through extensive simulations in Matlab. The results demonstrate that our method effectively incentivizes the energy trading among the users to reduce users’ peak load and reduce the overall energy cost of the system by 23% on average. |
| first_indexed | 2024-12-22T20:42:28Z |
| format | Article |
| id | doaj.art-c5aa48289eb14fceb269bce435e472cf |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-12-22T20:42:28Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-c5aa48289eb14fceb269bce435e472cf2022-12-21T18:13:18ZengElsevierEnergy Reports2352-48472021-11-01725122525Distributed energy trading management for renewable prosumers with HVAC and energy storageQing Yang0Hao Wang1College of Electronics and Information Engineering (CEI), Shenzhen University, Shenzhen, Guangdong Province, ChinaDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia; Corresponding author.Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to leverage their power usage flexibility without compromising the users’ privacy. To achieve this goal, we design a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to automatically operate the HVAC system and batteries, which minimizes the energy costs of users. Specifically, we decouple the optimization problem into a primal subproblem and a dual subproblem. The primal subproblem is solved by the users, and the dual subproblem is solved by the grid operator. Unlike the existing centralized method, our approach only uses the users’ private information locally for solving the primal subproblem hence preserves the users’ privacy. Using real-world data, we validate our proposed algorithm through extensive simulations in Matlab. The results demonstrate that our method effectively incentivizes the energy trading among the users to reduce users’ peak load and reduce the overall energy cost of the system by 23% on average.http://www.sciencedirect.com/science/article/pii/S2352484721002080Transactive energyEnergy tradingSmart gridSmart homeHeating, ventilation, and air-conditioning (HVAC)Distributed optimization |
| spellingShingle | Qing Yang Hao Wang Distributed energy trading management for renewable prosumers with HVAC and energy storage Energy Reports Transactive energy Energy trading Smart grid Smart home Heating, ventilation, and air-conditioning (HVAC) Distributed optimization |
| title | Distributed energy trading management for renewable prosumers with HVAC and energy storage |
| title_full | Distributed energy trading management for renewable prosumers with HVAC and energy storage |
| title_fullStr | Distributed energy trading management for renewable prosumers with HVAC and energy storage |
| title_full_unstemmed | Distributed energy trading management for renewable prosumers with HVAC and energy storage |
| title_short | Distributed energy trading management for renewable prosumers with HVAC and energy storage |
| title_sort | distributed energy trading management for renewable prosumers with hvac and energy storage |
| topic | Transactive energy Energy trading Smart grid Smart home Heating, ventilation, and air-conditioning (HVAC) Distributed optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2352484721002080 |
| work_keys_str_mv | AT qingyang distributedenergytradingmanagementforrenewableprosumerswithhvacandenergystorage AT haowang distributedenergytradingmanagementforrenewableprosumerswithhvacandenergystorage |