Optimal operation of integrated energy systems based on multi-energy complementarity

Optimal operation of integrated energy systems based on multi-energy complementarity

Автор: Kang Chuanzhi, Zhang Zongnan, Kudashev Sergei, Liu Meinan, Zhang Qianwei, Pan Jiashuang

Журнал: Бюллетень науки и практики @bulletennauki

Рубрика: Технические науки

Статья в выпуске: 8 т.8, 2022 года.

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Complementary multi-energy operation is an inevitable trend in the development of integrated energy systems, and the construction of a fair and reasonable distribution method among multiple stakeholders is the key to realizing complementary multi-energy operation. Integrated energy systems have important applications for achieving sustainable energy development and building a green, low-carbon society, while the complex internal energy structures and equipment coupling relationships pose challenges for their operational optimization. Taking advantage of the interactive and complementary relationship between power and heat on both the supply and demand sides, energy storage devices are used on the supply side to realize the thermo-electrolytic coupling of the combined supply equipment and to enhance the multi-energy supply capacity of the system through each energy conversion device. On the demand side, load types are classified, and the elasticity of electrical loads and the diversity of system heating methods are used to construct a comprehensive energy demand response model with time shifting and curtailment responses of electrical loads and responses of heating load supply methods, and to propose a response compensation mechanism. On this basis, with the objective of minimizing the sum of system operation cost and response compensation cost, the mathematical model for optimal operation of integrated energy systems based on multi-energy complementarity is established, taking into account the equipment operation and dispatchable load resource constraints on both the supply and demand sides. Simulation results and comparative analyses based on arithmetic examples show that: synergistic optimization of both supply and demand, taking into account multi-energy complementarities, can effectively improve the flexibility and operational economy of the system.

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Multi-energy complementarity, integrated energy systems, thermal electrolytic coupling, integrated demand response

Короткий адрес: https://sciup.org/14125301

IDR: 14125301   |   DOI: 10.33619/2414-2948/81/37

Список литературы Optimal operation of integrated energy systems based on multi-energy complementarity

  • Li, H., Liu, D., & Yao, D. Y. (2021). Analysis and reflection on the development of power system towards the goal of carbon emission peak and carbon neutrality. Proc. CSEE, 41, 6245-6259.
  • Li, X., Ai, X., Hu, J., Zhou, B., & Lin, Z. (2019). Three-stage combined peak regulation strategy for nuclear-thermal-virtual power plant considering carbon trading mechanism. Power Syst. Technol, 43, 2460-2470.
  • Wu, C., Lin, S., Xia, C., & Guan, L. (2020). Distributed optimal dispatch of microgrid cluster based on model predictive control. Power Syst. Technol., 44(2), 530-538.
  • Khan, M. R. B., Jidin, R., & Pasupuleti, J. (2016). Multi-agent based distributed control architecture for microgrid energy management and optimization. Energy Conversion and Management, 112, 288-307. https://doi.org/10.1016Zj.enconman.2016.01.011
  • Dou, C., Jia, X., & Heng, L. I. (2016). Multi-agent-system-based market bidding strategy for distributed generation in microgrid. Power System Technology, 40(2), 579-586.
  • Lee, J., Guo, J., Choi, J. K., & Zukerman, M. (2015). Distributed energy trading in microgrids: A game-theoretic model and its equilibrium analysis. IEEE Transactions on Industrial Electronics, 62(6), 3524-3533. https://doi.org/10.1109/TIE.2014.2387340
  • Jadhav, A. M., & Patne, N. R. (2017). Priority-based energy scheduling in a smart distributed network with multiple microgrids. IEEE Transactions on Industrial Informatics, 13(6), 3134-3143. https://doi.org/10.1109/TII.2017.2671923
  • Jadhav, A. M., Patne, N. R., & Guerrero, J. M. (2018). A novel approach to neighborhood fair energy trading in a distribution network of multiple microgrid clusters. IEEE Transactions on Industrial Electronics, 66(2), 1520-1531. https://doi.org/10.1109/TIE.2018.2815945
  • Zhong, W., Xie, S., Xie, K., Yang, Q., & Xie, L. (2020). Cooperative P2P energy trading in active distribution networks: An MILP-based Nash bargaining solution. IEEE Transactions on Smart Grid, 12(2), 1264-1276. https://doi.org/10.1109/TSG.2020.3031013
  • Sheng, W., Wu, M., Ji, Y., Kou, L., Pan, J., Shi, H., ... & Wang, Z. G. (2019, April). Key techniques and engineering practice of distributed renewable generation clusters integration. In Proceedings of the CSEE (Vol. 39, No. 8, pp. 2175-2186).
  • Mu, C., Ding, T., Dong, J., Ning, K., Dong, X., & He, Y. (2021). Development of decentralized peer-to-peer multi-energy trading system based on private blockchain technology. Journal of Proceedings of the CSEE..
  • Doan, H. T., Cho, J., & Kim, D. (2021). Peer-to-peer energy trading in smart grid through blockchain: A double auction-based game theoretic approach. Ieee Access, 9, 49206-49218. https://doi.org/10.1109/ACCESS.2021.3068730
  • Yang, Z., Peng, S., Liao, Q., Liu, D., Xu, Y., & Zhang, Y. J. (2018). Non-cooperative trading method for three market entities in integrated community energy system. Autom. Electr. Power Syst, 42(14), 32-39.
  • Park, S., Lee, J., Bae, S., Hwang, G., & Choi, J. K. (2016). Contribution-based energy-trading mechanism in microgrids for future smart grid: A game theoretic approach. IEEE Transactions on Industrial Electronics, 63(7), 4255-4265. https://doi.org/10.1109/TIE.2016.2532842
  • Liu, N., Cheng, M., Yu, X., Zhong, J., & Lei, J. (2018). Energy-sharing provider for PV prosumer clusters: A hybrid approach using stochastic programming and stackelberg game. IEEE Transactions on Industrial Electronics, 65(8), 6740-6750. https://doi.org/10.1109/TIE.2018.2793181
  • Cui, S., Wang, Y. W., Shi, Y., & Xiao, J. W. (2020). Community energy cooperation with the presence of cheating behaviors. IEEE Transactions on Smart Grid, 12(1), 561-573. https://doi.org/10.1109/TSG.2020.3022792
  • Mei, S., Liu, F., & Wei, W. (2016). Engineering game theory and power system application [M]. Beijing, the Science Publishing Compan.
  • Paudel, A., Chaudhari, K., Long, C., & Gooi, H. B. (2018). Peer-to-peer energy trading in a prosumer-based community microgrid: A game-theoretic model. IEEE Transactions on Industrial electronics, 66(8), 6087-6097. https://doi.org/10.1109/TIE.2018.2874578
  • Liu, N., Yu, X., Wang, C., Li, C., Ma, L., & Lei, J. (2017). Energy-sharing model with price-based demand response for microgrids of peer-to-peer prosumers. IEEE Transactions on Power Systems, 32(5), 3569-3583. https://doi.org/10.1109/TPWRS.2017.2649558
  • Xu, Y., Liao, Q., Liu, D., Peng, S., Yang, Z., Zou, H., & Zhang, L. (2019). Multi-player intraday optimal dispatch of integrated energy system based on integrated demand response and games. Power System Technology, 43(7), 2506-2518.
  • Tushar, W., Saha, T. K., Yuen, C., Morstyn, T., Poor, H. V., & Bean, R. (2019). Grid influenced peer-to-peer energy trading. IEEE Transactions on Smart Grid, 11(2), 1407-1418.
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