Analytical modeling of a multicluster special purpose system based on several monitoring scenarios

The article considers the problem and formulation of the task of modeling the optimal functioning of a multicluster special purpose system (MSPS), based on multi-scenario modeling. The problems associated with the uncertainty of sources and loads in the MSPS in the energy sector are becoming increasingly apparent due to the combination of large-scale renewable energy sources and multi-energy loads. Moreover, such scenarios pose great problems for the optimal functioning of the MSPS. The distributed MSPS in the energy sector is considered as an object of research, and a functioning model based on multi-scenario modeling is proposed to account for forecasting uncertainties arising in the case of distributed electricity generation and multi-energy loads. Traditional models for optimizing the work of the MSPS usually take into account only one deterministic work scenario, which can lead to certain limitations of work strategies. When optimizing, it is necessary to balance the problems with conservative optimization results caused by extreme scenarios and the high complexity of the model caused by the large sample size of the random sample scenario. To solve the above problems, an optimization model based on multi-scenario modeling is proposed for a load-side distributed MSPS in a multicluster system. The optimization model is also applicable to account for the uncertainties associated with distributed wind and solar energy sources and the randomness of load forecasting for cooling, heating and electricity needs.

1. Leng Q., Chen W.-J., Huang P.-C., Wei Y.-H., Mok A.K., Han S. Network Management of Multicluster RT-WiFi Networks. ACM Transactions on Sensor Networks. 2019;15(1). https://doi.org/10.1145/3283451

2. Wu C., Zhang X.-P., Sterling M.J.H. Global Electricity Interconnection With 100% Renewable Energy Generation. IEEE Access. 2021;9:113169–113186. https://doi.org/10.1109/ACCESS.2021.3104167

3. Yan X., Yu H., Liang M. Risk-benefit comparative analysis of different control methods for reservoirs under the water to electricity mode. Journal of Physics: Conference Series. 2024;2836(1). https://doi.org/10.1088/1742-6596/2836/1/012023

4. Hao L., Hu W., Wang C., Wang G., Sun Y., Chen J., Pan X. Coordinated restoration optimization of power-gas integrated energy system with mobile emergency sources. Global Energy Interconnection. 2023;6(2):205–227. https://doi.org/10.1016/j.gloei.2023.04.008

5. Reymov K.M., Turmanova G.M., Makhmuthonov S.K., Uzakov B.A. Mathematical models and algorithms of optimal load management of electrical consumers. In: E3S Web of Conferences: Volume 216: Rudenko International Conference "Methodological problems in reliability study of large energy systems" (RSES 2020), 21–26 September 2020, Kazan, Russia. EDP Sciences; 2020. https://doi.org/10.1051/e3sconf/202021601166

6. Fu X., Guo Q., Sun H., Zhang X., Wang L. Estimation of the failure probability of an integrated energy system based on the first order reliability method. Energy. 2017;134:1068–1078. https://doi.org/10.1016/j.energy.2017.06.090

7. He C., Liu T., Wu L., Shahidehpour M. Robust coordination of interdependent electricity and natural gas systems in day-ahead scheduling for facilitating volatile renewable generations via power-to-gas technology. Journal of Modern Power Systems and Clean Energy. 2017;5(3):375–388. https://doi.org/10.1007/s40565-017-0278-z

8. Wang C., Gao R., Wei W., Shafie-khah M., Bi T., Catalão J.P.S. Risk-Based Distributionally Robust Optimal Gas-Power Flow With Wasserstein Distance. IEEE Transactions on Power Systems. 2019;34(3):2190–2204. https://doi.org/10.1109/TPWRS.2018.2889942

9. Li Y., Liu W., Shahidehpour M., Wen F., Wang K., Huang Y. Optimal Operation Strategy for Integrated Natural Gas Generating Unit and Power-to-Gas Conversion Facilities. IEEE Transactions on Sustainable Energy. 2018;9(4):1870–1879. https://doi.org/10.1109/TSTE.2018.2818133

10. Chen Y., Wen J., Cheng S. Probabilistic Load Flow Method Based on Nataf Transformation and Latin Hypercube Sampling. IEEE Transactions on Sustainable Energy. 2013;4(2):294–301. https://doi.org/10.1109/TSTE.2012.2222680

11. Ren Z., Yan W., Zhao X., Li W., Yu J. Chronological Probability Model of Photovoltaic Generation. IEEE Transactions on Power Systems. 2014;29(3):1077–1088. https://doi.org/10.1109/TPWRS.2013.2293173

12. Bahrami A., Teimourian A., Okoye C.O., Khosravi N. Assessing the feasibility of wind energy as a power source in Turkmenistan; a major opportunity for Central Asia's energy market. Energy. 2019;183:415–427. https://doi.org/10.1016/j.energy.2019.06.108