Coordinated optimal operation of a joint virtual energy storage system based on an improved non-dominated sorting genetic and multi-objective particle swarm optimization algorithm
High renewable penetration intensifies the source–load temporal mismatch in building-integrated microgrids. This study introduces a joint virtual energy storage concept—combining building thermal inertia with electric-vehicle (EV) batteries—and formulates a multi-objective dispatch model to minimize...
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| Vydané v: | Energy reports Ročník 14; s. 3993 - 4005 |
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01.12.2025
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| ISSN: | 2352-4847, 2352-4847 |
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| Abstract | High renewable penetration intensifies the source–load temporal mismatch in building-integrated microgrids. This study introduces a joint virtual energy storage concept—combining building thermal inertia with electric-vehicle (EV) batteries—and formulates a multi-objective dispatch model to minimize operating cost and CO₂ emissions. An improved NSGA-MOPSO solver is employed to obtain well-distributed Pareto fronts with fast convergence under operational constraints. Case studies on a summer day in Beijing show that, relative to no virtual storage, EV-only storage reduces daily operating cost by 5.21 % and CO₂ emissions by 3.00 %. In comparison, the joint virtual storage achieves 13.33 % cost reduction and 17.08 % emission reduction. Coordinated scheduling of EVs lowers the peak-to-valley spread by 8.65 %, 20.1 %, and 24.9 % under progressively responsive user modes, while indoor temperature remains within 22–26 °C. The enhanced solver yields orders-of-magnitude improvements in convergence accuracy and stability on benchmark functions. These results demonstrate quantifiable economic and environmental gains and practical feasibility for low-carbon microgrids.
•Joint virtual storage combining building thermal inertia and electric vehicle batteries.•Improved NSGA-MOPSO for multi-objective microgrid optimization.•Raises efficiency and economy while reducing emissions.•Coordinated scheduling mitigates source–load temporal mismatch. |
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| AbstractList | High renewable penetration intensifies the source–load temporal mismatch in building-integrated microgrids. This study introduces a joint virtual energy storage concept—combining building thermal inertia with electric-vehicle (EV) batteries—and formulates a multi-objective dispatch model to minimize operating cost and CO₂ emissions. An improved NSGA-MOPSO solver is employed to obtain well-distributed Pareto fronts with fast convergence under operational constraints. Case studies on a summer day in Beijing show that, relative to no virtual storage, EV-only storage reduces daily operating cost by 5.21 % and CO₂ emissions by 3.00 %. In comparison, the joint virtual storage achieves 13.33 % cost reduction and 17.08 % emission reduction. Coordinated scheduling of EVs lowers the peak-to-valley spread by 8.65 %, 20.1 %, and 24.9 % under progressively responsive user modes, while indoor temperature remains within 22–26 °C. The enhanced solver yields orders-of-magnitude improvements in convergence accuracy and stability on benchmark functions. These results demonstrate quantifiable economic and environmental gains and practical feasibility for low-carbon microgrids.
•Joint virtual storage combining building thermal inertia and electric vehicle batteries.•Improved NSGA-MOPSO for multi-objective microgrid optimization.•Raises efficiency and economy while reducing emissions.•Coordinated scheduling mitigates source–load temporal mismatch. |
| Author | Zhang, Jianyu Liu, Chao Xing, Na Chen, Guowei Liu, Yongming Liu, Jun |
| Author_xml | – sequence: 1 givenname: Jianyu surname: Zhang fullname: Zhang, Jianyu email: zhangjianyu@jncec.com organization: Beijing Jingneng Clean Energy Co., Ltd., China – sequence: 2 givenname: Na surname: Xing fullname: Xing, Na email: xingna@jncec.com organization: Beijing Jingneng Clean Energy Co., Ltd., China – sequence: 3 givenname: Jun surname: Liu fullname: Liu, Jun email: 21452186@ncepu.edu.cn organization: Beijing Energy Holding Co., Ltd., China – sequence: 4 givenname: Yongming surname: Liu fullname: Liu, Yongming email: liuyongming@powerbeijing.com organization: Beijing Energy Holding Co., Ltd., China – sequence: 5 givenname: Guowei surname: Chen fullname: Chen, Guowei email: 51452025@ncepu.edu.cn organization: Beijing Jingneng Energy Technology Research Co., Ltd., China – sequence: 6 givenname: Chao surname: Liu fullname: Liu, Chao email: liuchao@powerbeijing.com organization: Beijing Jingneng Energy Technology Research Co., Ltd., China |
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| Cites_doi | 10.1109/TPWRS.2013.2266121 10.1109/TSG.2014.2318836 10.3390/en18010055 10.1109/PESGM.2016.7741879 10.1109/ISGT-Asia.2019.8881263 10.1016/j.energy.2017.06.003 10.1016/j.energy.2020.119674 10.1109/TSG.2016.2523504 10.1088/1742-6596/2404/1/012015 10.33619/2414-2948/81/38 10.3390/app7090943 10.1109/TSG.2018.2824308 10.1016/j.epsr.2022.108979 10.1016/j.enbuild.2022.112326 10.1016/j.enconman.2019.06.020 10.1080/01430750.2019.1630299 10.1016/j.buildenv.2019.106287 10.1016/j.est.2025.116592 10.3390/en18112905 10.1016/j.patrec.2025.05.008 10.1016/j.applthermaleng.2024.122499 10.1109/ACPEE51499.2021.9436856 |
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| Keywords | Integrated energy system Economy NSGA_MOPSO algorithm Joint virtual energy storage Environmental protection |
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| Title | Coordinated optimal operation of a joint virtual energy storage system based on an improved non-dominated sorting genetic and multi-objective particle swarm optimization algorithm |
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