Abstract
The crisis of energy depletion is a common problem faced by countries around the world, and renewable energy is gradually gaining popularity among researchers, and the proportion of new energy penetration in distribution networks is increasing. However, the resulting problem of new energy consumption is a major challenge. For a more in-depth analysis of the problem, This paper analyzes the method to solve the voltage crossing limit of distribution network nodes caused by distributed power sources based on the demand response dispatching model with the participation of multiple types of customer-side loads. Firstly, the user-side load models of distributed power generation, residential users, commercial users and electric vehicle users are established respectively, and then by constructing a customer satisfaction evaluation model, taking into account the economy and satisfaction of multiple types of customers participating in demand response, and finally verifies the validity of the model by simulation analysis. The validity of the model is verified by calculate examples simulation analysis.
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Zhigang, Z., Chongqing, K.: Challenges and prospects of constructing new power system under carbon neutrality target. China Elect. Eng. Report 42(08), 2806–2819 (2022). https://doi.org/10.13334/j.0258-8013.pcsee.220467
Hamza-Ebtsam, A., Sedhom-Bishoy, E., Badran-Ebrahim, A.: Impact and assessment of the overvoltage mitigation methods in low-voltage distribution networks with excessive penetration of PV systems: a review. Int. Trans. Elect. Energy Syst. 31(12), 1–16 (2021)
Ying, C., Jianfeng, L., Jinxia, G.: Wind-solar consumption scheduling optimization model for load aggregators. Renew. Energy 36(4), 563–567 (2018)
Yang, S., Jingye, Z., Lei, W., et al.: Research on multi-time scale scheduling of photovoltaic consumption considering forecast deviation. Power Eng. Technol. 37(1), 58–64 (2018)
Ruelens, F., Claessens, B., Quaiyum, S., et al.: Reinforcement learning applied to an electric water heater : form theory to practice. IEEE Trans. Smart Grid. 1 (2015)
Liang, S., Jian, S., Cheng, Z., et al.: Day-ahead scheduling and quantitative evaluation of micro-energy network considering demand-side response. Chinese J. Solar Energy 42(9), 461–469 (2021)
Tian Biyuan, X., Haiqi, Z.X., et al.: Day-ahead optimal dispatch method for microgrid considering electricity price incentive demand response. Elect. Demand Side Manage. 22(6), 45–50 (2020)
Tang, C., Zhang, F., Zhang, N., et al.: Day-ahead economic dispatch of power systems considering randomness and demand response of renewable energy. Autom. Electric Power Syst. 43(15), 18–25+63 (2019)
Clement-Nyns, K., Haesen, E., Driesen, J.: The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans. Power Syst. 25(1), 371–380 (2010)
Sortomme, E., Hindi, M.M., MacPherson, S.D.J., et al.: Coordinated charging of plug-in hybrid electric vehicle to minimize distribution system losses. IEEE Trans. Smart Grid. 2(1), 186–193 (2011)
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Wang, L., Zhang, L., Zhang, J., Tang, W., Zhang, X. (2022). Day-Ahead Scheduling of PV Consumption in Distribution Networks Based on Demand Response of Multiple Types of Customer-Side Loads. In: Fan, W., Zhang, L., Li, N., Song, X. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2022. Communications in Computer and Information Science, vol 1713. Springer, Singapore. https://doi.org/10.1007/978-981-19-9195-0_18
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DOI: https://doi.org/10.1007/978-981-19-9195-0_18
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