As the connection between the upgraded power grid and users, the microgrid can not only control the output of the local distributed power but also realize a low-carbon transition of users in order to achieve the carbon reduction target. With the aim of reducing operating costs and carbon emissions at the same time, this paper proposes a sustainable economic scheduling strategy combining a ladder-type reward and punishment carbon trading mechanism with generalized energy storage. Initially, the charge and discharge process is analyzed in detail for the energy storage system, and mathematical models relating the actual energy storage system with the virtual energy storage system in the microgrid are established. The composition of the energy supplied to the microgrid and the emission intensity of different power supply sources are analyzed, and a multi-ladder carbon trading mechanism with rewards and penalties is proposed. Subsequently, the typical wind power and photovoltaic power generation cases are simulated by an improved generative adversarial network, which considers the randomness of renewable energy. Eventually, based on MATLAB, a simulation verification is carried out on the proposed scheduling scheme, and the experimental results show that the proposed scheme can effectively reduce operating costs and carbon emissions by 2.03% and 14.08%, respectively, which improves the economy and sustainability of the microgrid.
Citation: Qingze Pan, Haipeng Chen, Zhiwei Li. Economic scheduling optimization of microgrids combining generalized energy storage and reward and penalty ladder carbon trading[J]. AIMS Energy, 2026, 14(1): 185-211. doi: 10.3934/energy.2026008
As the connection between the upgraded power grid and users, the microgrid can not only control the output of the local distributed power but also realize a low-carbon transition of users in order to achieve the carbon reduction target. With the aim of reducing operating costs and carbon emissions at the same time, this paper proposes a sustainable economic scheduling strategy combining a ladder-type reward and punishment carbon trading mechanism with generalized energy storage. Initially, the charge and discharge process is analyzed in detail for the energy storage system, and mathematical models relating the actual energy storage system with the virtual energy storage system in the microgrid are established. The composition of the energy supplied to the microgrid and the emission intensity of different power supply sources are analyzed, and a multi-ladder carbon trading mechanism with rewards and penalties is proposed. Subsequently, the typical wind power and photovoltaic power generation cases are simulated by an improved generative adversarial network, which considers the randomness of renewable energy. Eventually, based on MATLAB, a simulation verification is carried out on the proposed scheduling scheme, and the experimental results show that the proposed scheme can effectively reduce operating costs and carbon emissions by 2.03% and 14.08%, respectively, which improves the economy and sustainability of the microgrid.
| [1] |
Shi H, Chai J, Lu Q, et al. (2022) The impact of China's low-carbon transition on the economy, society, and energy in 2030 based on CO2 emissions drivers. Energy 239: 122336. https://doi.org/10.1016/j.energy.2021.122336 doi: 10.1016/j.energy.2021.122336
|
| [2] |
Kuang H, Akmal Z, Li F (2022) Measuring the effects of green technology innovations and renewable energy investment for reducing carbon emissions in China. Renewable Energy 197: 1–10. https://doi.org/10.1016/j.renene.2022.06.091 doi: 10.1016/j.renene.2022.06.091
|
| [3] |
Pan Y, Ju L, Yang S, et al. (2024) A multi-objective robust optimal dispatch and cost allocation model for microgrids-shared hybrid energy storage system considering flexible ramping capacity. Appl Energy 369: 123565. https://doi.org/10.1016/j.apenergy.2024.123565 doi: 10.1016/j.apenergy.2024.123565
|
| [4] |
Yin L, Cai Z (2024) Multimodal hierarchical distributed multi-objective moth intelligence algorithm for economic dispatch of power systems. J Cleaner Prod 434: 140130. https://doi.org/10.1016/j.jclepro.2023.140130 doi: 10.1016/j.jclepro.2023.140130
|
| [5] |
Dong H, Fu Y, Jia Q, et al. (2022) Optimal dispatch of integrated energy microgrid considering hybrid structured electric-thermal energy storage. Renewable Energy 199: 628–639. https://doi.org/10.1016/j.renene.2022.09.027 doi: 10.1016/j.renene.2022.09.027
|
| [6] |
Ji BX, Liu HH, Cheng P, et al. (2024) Phased optimization of active distribution networks incorporating distributed photovoltaic storage system: A multi-objective coati optimization algorithm. J Energy Storage 91: 112093. https://doi.org/10.1016/j.est.2024.112093 doi: 10.1016/j.est.2024.112093
|
| [7] |
Li LL, Ji BX, Li ZT, et al. (2025) Microgrid energy management system with degradation cost and carbon trading mechanism: A multi-objective artificial hummingbird algorithm. Appl Energy 378: 124853. https://doi.org/10.1016/j.apenergy.2024.124853 doi: 10.1016/j.apenergy.2024.124853
|
| [8] |
Zhang J, Liu Z (2024) Low carbon economic scheduling model for a park integrated energy system considering integrated demand response, ladder-type carbon trading and acceptable utilization of hydrogen. Energy 290: 130311. https://doi.org/10.1016/j.energy.2024.130311 doi: 10.1016/j.energy.2024.130311
|
| [9] |
Gao J, Meng Q, Liu J, et al. (2024) Thermoelectric optimization of integrated energy system considering wind-photovoltaic uncertainty, two-stage power-to-gas, and ladder-type carbon trading. Renewable Energy 221: 119806. https://doi.org/10.1016/j.renene.2023.119806 doi: 10.1016/j.renene.2023.119806
|
| [10] |
Zhan Z, Xu J, Zhang T, et al. (2024) Optimal scheduling of integrated wind-photovoltaic-hydrogen energy system considering hydrogen application and waste heat recovery. Energy Rep 11: 3684–3694. https://doi.org/10.1016/j.egyr.2024.03.034 doi: 10.1016/j.egyr.2024.03.034
|
| [11] |
Liu L, Zhai R, Hu Y (2023) Multi-objective optimization with advanced exergy analysis of a wind-solar-hydrogen multi-energy supply system. Appl Energy 348: 121512. https://doi.org/10.1016/j.apenergy.2023.121512 doi: 10.1016/j.apenergy.2023.121512
|
| [12] |
Moazzen F, Hossain MJ (2025) A two-layer strategy for sustainable energy management of microgrid clusters with embedded energy storage system and demand-side flexibility provision. Appl Energy 377: 124659. https://doi.org/10.1016/j.apenergy.2024.124659 doi: 10.1016/j.apenergy.2024.124659
|
| [13] |
Zhang Y, Zhao H, Qi Z, et al. (2024) A two-stage low-carbon economic coordinated dispatching model for generation-load-storage resources considering flexible supply-demand balance. Appl Energy 373: 123981. https://doi.org/10.1016/j.apenergy.2024.123981 doi: 10.1016/j.apenergy.2024.123981
|
| [14] |
Cui Y, Wang Y, Xu Y, et al. (2023) Low-carbon economic dispatching of microgrid considering generalized integrated demand response and nonlinear conditions. Energy Rep 9: 1606–1620. https://doi.org/10.1016/j.egyr.2022.12.049 doi: 10.1016/j.egyr.2022.12.049
|
| [15] |
Zhu D, Cui J, Wang S, et al. (2024) An optimization strategy for intra-park integration trading considering energy storage and carbon emission constraints. J Cleaner Prod 447: 141031. https://doi.org/10.1016/j.jclepro.2024.141031 doi: 10.1016/j.jclepro.2024.141031
|
| [16] |
Dong Z, Li G, Xie F, et al. (2025) Hierarchical collaborative optimization of generalized shared energy storage system connected to distribution system: A master-slave game based on deep reinforcement learning approach. Energy Rep. https://doi.org/10.2139/ssrn.5297368 doi: 10.2139/ssrn.5297368
|
| [17] |
Dong H, Shan Z, Zhou J, et al. (2023) Refined modeling and co-optimization of electric-hydrogen-thermal-gas integrated energy system with hybrid energy storage. Appl Energy 351: 121834. https://doi.org/10.1016/j.apenergy.2023.121834 doi: 10.1016/j.apenergy.2023.121834
|
| [18] |
Jia J, Li H, Wu D, et al. (2024) Multi-objective optimization study of regional integrated energy systems coupled with renewable energy, energy storage, and inter-station energy sharing. Renewable Energy 225: 120328. https://doi.org/10.1016/j.renene.2024.120328 doi: 10.1016/j.renene.2024.120328
|
| [19] |
Wang C, Fu S, Zhang L, et al. (2023) Optimal control of source-load-storage energy in DC microgrid based on the virtual energy storage system. Energy Rep 9: 621–630. https://doi.org/10.1016/j.egyr.2022.12.002 doi: 10.1016/j.egyr.2022.12.002
|
| [20] |
Xu Y, Mu Y, Qi H, et al. (2023) Optimal dispatch approach for rural multi-energy supply systems considering virtual energy storage. Global Energy Interconnect 6: 675–688. https://doi.org/10.1016/j.gloei.2023.11.002 doi: 10.1016/j.gloei.2023.11.002
|
| [21] |
Ma Y, Zhang M, Yang H, et al. (2023) Decentralized and coordinated scheduling model of interconnected multi-microgrid based on virtual energy storage. Int J Electr Power Energy Syst 148: 108990. https://doi.org/10.1016/j.ijepes.2023.108990 doi: 10.1016/j.ijepes.2023.108990
|
| [22] |
Guo R, Ye H, Zhao Y (2022) Low carbon dispatch of electricity-gas-thermal-storage integrated energy system based on stepped carbon trading. Energy Rep 8: 449–455. https://doi.org/10.1016/j.egyr.2022.09.198 doi: 10.1016/j.egyr.2022.09.198
|
| [23] |
Kok JBW, Haselhoff EA (2023) Thermodynamic analysis of the thermal and exergetic performance of a mixed gas-steam aero derivative gas turbine engine for power generation. Heliyon 9: e18927. https://doi.org/10.1016/j.heliyon.2023.e18927 doi: 10.1016/j.heliyon.2023.e18927
|
| [24] |
Jia K, Liu C, Li S, et al. (2023) Modeling and optimization of a hybrid renewable energy system integrated with gas turbine and energy storage. Energy Convers Manage 279: 116763. https://doi.org/10.1016/j.enconman.2023.116763 doi: 10.1016/j.enconman.2023.116763
|
| [25] |
Wang Y, Li Y, Zhang Y, et al. (2024) Optimized operation of integrated energy systems accounting for synergistic electricity and heat demand response under heat load flexibility. Appl Therm Eng 243: 122640. https://doi.org/10.1016/j.applthermaleng.2024.122640 doi: 10.1016/j.applthermaleng.2024.122640
|
| [26] |
Gao L, Fei F, Jia Y, et al. (2024) Optimal dispatching of integrated agricultural energy system considering ladder-type carbon trading mechanism and demand response. Int J Electr Power Energy Syst 156: 109693. https://doi.org/10.1016/j.ijepes.2023.109693 doi: 10.1016/j.ijepes.2023.109693
|
| [27] |
Zhu G, Gao Y, Liu H (2024) Optimal scheduling of an integrated energy system considering carbon trading mechanism and multi-energy supply uncertainties in a real-time price environment. Sustainable Energy, Grids Networks 38: 101351. https://doi.org/10.1016/j.segan.2024.101351 doi: 10.1016/j.segan.2024.101351
|
| [28] |
Yang M, Shen X, Huang D, et al. (2025) Fluctuation classification and feature factor extraction to forecast very short-term photovoltaic output powers. CSEE J Power Energy Syst 11: 661–670. https://doi.org/10.17775/CSEEJPES.2022.03760 doi: 10.17775/CSEEJPES.2022.03760
|
Figures(18) / Tables(6)
Qingze Pan, Haipeng Chen, Zhiwei Li. Economic scheduling optimization of microgrids combining generalized energy storage and reward and penalty ladder carbon trading[J]. AIMS Energy, 2026, 14(1): 185-211. doi: 10.3934/energy.2026008
DownLoad: