Abstract
Against the backdrop of China’s implementation of the “dual carbon” target and carbon emissions trading policies, renewable energy generation technologies have matured and received support from related policies. Distributed power sources have played a crucial role in the power system, and aggregators have integrated a large number of distributed power sources with diverse characteristics, shielding the complex characteristics of the underlying distributed power sources from grid scheduling. This article introduces the revenue optimization of low-carbon integration to optimize the aggregator’s scheduling model, designs distributed renewable energy generation units, and studies the solution strategy based on quantum genetic algorithms for large-scale optimization scheduling problems. The aggregator’s optimization variables divide the entire optimization problem and consider low-carbon integration to achieve distributed management of green energy parks, providing a feasible theoretical framework for the further development of distributed power sources. It has important practical significance in energy conservation, emissions reduction, and ecological environmental protection.
Funding source: National Grid Shandong Provincial Power Company supports the marketing project “National Grid Shandong Provincial Power Company’s 2022 data services for distributed power microgrids based on blockchain”
Award Identifier / Grant number: 640633220018
-
Research ethics: The research is ethical.
-
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: The authors states no conflict of interest.
-
Research funding: This research is funded by National Grid Shandong Provincial Power Company supports the marketing project “National Grid Shandong Provincial Power Company’s 2022 data services for distributed power microgrids based on blockchain” (640633220018).
-
Data availability: Not applicable.
References
1. Zhigang, Z, Chongqing, K. Challenges and prospects of building new power systems under carbon neutrality [J]. Proc Chin Soc Electr Eng 2022;42:2806–19.Search in Google Scholar
2. Hui, L, Dong, L, Danyang, Y. Development and judgment of China’s electric power system for carbon peaking carbon neutrality [J]. Proc Chin Soc Electr Eng 2021;41:6245–59.Search in Google Scholar
3. Ming, L, Lin, Y, Kuang, S, He, X, Zhang, K, Zeng, M. Research on market transaction decision considering weight of renewable energy consumption. Power Demand Side Manag 2021;23:21–5+36.Search in Google Scholar
4. Wang, Y, Li, N, Pan, C, Li, Y, Qin, J, Li, T, et al.. Research on model optimization and dispatching strategy of multi-energy complementary energy system based on exergy analysis. Global Energy Interconnect 2021;4:249–63.Search in Google Scholar
5. Diao, H, Li, P, Lv, X, Liu, X, Li, X. Coordinated optimal allocation of energy storage in regional integrated energy system considering the diversity of multi-energy storage. Trans China Electrotech Soc 2021;36:151–65.Search in Google Scholar
6. Yi, Z, Li, Z. A combined heat and power dispatch strategy that takes into account the thermal inertia of heat storage and heating areas in the heating network. Power Syst Technol 2018;42:1378–84.Search in Google Scholar
7. Xu, H, Dong, S, He, Z, Xu, H, Dong, S, He, Z, Shi, Y. Multi-energy collaborative optimization of factory integrated energy system considering energy cascade utilization. Autom Electr Power Syst 2018;42:123–30.Search in Google Scholar
8. Zheng, F, Zhao, Y, Chen, Y. Exergy loss power analysis of gas engine heat pump system with combined cooling, heating and power supply. Therm Energy Power Eng 2009;24:53–9+142.Search in Google Scholar
9. Zhou, X, Sun, Y, Xie, D, Wang, J, Zhong, Y. Energy storage optimization configuration of regional integrated energy system considering electric heat flexible load. Autom Electr Power Syst 2020;44:53–63.Search in Google Scholar
10. Morstyn, T, McCulloch, MD. Multiclass energy management for peer-to-peer energy trading driven by prosumer preferences. IEEE Trans Power Syst 2018;34:4005–14. https://doi.org/10.1109/tpwrs.2018.2834472.Search in Google Scholar
11. Yan, M, Shahidehpour, M, Alabdulwahab, A, Abusorrah, A, Gurung, N, Zheng, H, et al.. Blockchain for transacting energy and carbon allowance in networked microgrids. IEEE Trans Smart Grid 2021;12:4702–14. https://doi.org/10.1109/tsg.2021.3109103.Search in Google Scholar
12. Jamil, F, Iqbal, N, Ahmad, IS, Kim, D. Peer-to-peer energy trading mechanism based on blockchain and machine learning for sustainable electrical power supply in smart grid. IEEE Access 2021;39:193–217. https://doi.org/10.1109/access.2021.3060457.Search in Google Scholar
13. Sorin, E, Bobo, L, Pinson, P. Consensus-based approach to peer-to-peer electricity markets with product differentiation. IEEE Trans Power Syst 2019;34:994–1004. https://doi.org/10.1109/tpwrs.2018.2872880.Search in Google Scholar
14. Sousa, T, Soares, T, Pierre, P, Moret, F, Baroche, T, Sorin, E. Peer-to-peer and community-based markets: a comprehensive review. Renew Sustain Energy Rev 2019;104:367–78. https://doi.org/10.1016/j.rser.2019.01.036.Search in Google Scholar
15. Ge, S, Li, J, He, X. Joint energy market design for local integrated energy system service procurement considering demand flexibility. Appl Energy 2021;297:1–10.10.1016/j.apenergy.2021.117060Search in Google Scholar
16. Chen, Y, Ma, Y, Zhen, G, Sun, Z, Chen, D, Gao, Y. Coordinated planning of thermo-electrolytic coupling for multiple CHP units considering demand response. Power Syst Technol 2022;46:821–3832.Search in Google Scholar
17. Xuan, Z, Jing, C, Ye, W, You, Y. New energy market mechanism considering flexible state of energy in energy storage. Power Syst Technol 2022;46:3810–32.Search in Google Scholar
18. Xu, Y, Liao, Q, Liu, D, Peng, S, Yang, Z, Zou, H, et al. Multi-player intraday optimal dispatch of integrated energy system based on integrated demand response and games. Power Syst Technol 2019;43:2506–16 (in Chinese).Search in Google Scholar
19. Jiang, Z, Ran, H, Qian, A. Interaction mechanism of industrial park based on multi-energy complementation[J]. Electr Power Autom Equip 2017;37:260–7.Search in Google Scholar
20. Zhang, Z, Chen, Y, Liu, F. Two-stage robust unit commitment model considering operation risk and demand response. Proc Chin Soc Electr Eng 2021;41:961–73.Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
