A TSP-CVaR Model for Day-ahead Schedule Optimization of Wind-PV-Hydro Hybrid Power System under Uncertainties

Based on TSP-CVaR risk aversion theory, this paper proposes an optimal dispatching model for wind-PV-hydro hybrid power system with complementarity characteristics. Under the framework of two-stage programming (TSP), the proposed method could effectively deal with the volatility of demand, the randomness of upstream traffic, and the complex connection of hydraulic resource in cascade hydropower station. Besides, conditional value at risk (CVaR) is introduced to characterize the risk aversion attitude of decision makers (DMs), and take full account of the minimum acceptable profit under certain confidence level. Finally, the feasibility and effectiveness of the proposed method is verified through a case study. The influence of the risk aversion attitude of DMs on the scheduling and operation plan of the multi-energy complementary power generation system is also analyzed and discussed.

[1]  Yanpeng Cai,et al.  Identification of optimal recourse strategies for power generation under a government-guided coal-pricing mechanism in north China , 2016 .

[2]  Xu Yi.  An inexact two-stage stochastic model for water resource management under uncertainty , 2012 .

[3]  Guohe Huang,et al.  GHG-mitigation oriented and coal-consumption constrained inexact robust model for regional energy structure adjustment – A case study for Jiangsu Province, China , 2018, Renewable Energy.

[4]  Ionel Vechiu,et al.  CVaR-based energy management scheme for optimal resilience and operational cost in commercial building microgrids , 2018, International Journal of Electrical Power & Energy Systems.

[5]  Hamed Kebriaei,et al.  An interval-based stochastic dominance approach for decision making in forward contracts of electricity market , 2018, Energy.

[6]  Guohe Huang,et al.  Explicit cost-risk tradeoff for renewable portfolio standard constrained regional power system expansion: A case study of Guangdong Province, China , 2017 .

[7]  Guohe Huang,et al.  Inexact Two-Stage Stochastic Programming for Water Resources Allocation under Considering Demand Uncertainties and Response—A Case Study of Tianjin, China , 2017 .

[8]  Ali Mohammad Ranjbar,et al.  A scenario-based optimization of Smart Energy Hub operation in a stochastic environment using conditional-value-at-risk , 2018 .

[9]  Ye Lin.  Optimization Configuration of Wind/Solar/Diesel Hybrid Power Generation System , 2011 .

[10]  Wu Jiekan.  Establishment and Solution of the Complementary Power Generation Model of Wind-Energy,Hydro-Energy and Natural Gas , 2014 .

[11]  Carlos Ruiz,et al.  Optimal sales-mix and generation plan in a two-stage electricity market , 2019, Energy Economics.

[12]  Yu Zhiyon.  Optimal Operation of the Complementary Micro-grid with Hybrid Wind-solar-hydro Power , 2014 .

[13]  Saeed Rahmani Dabbagh,et al.  Risk Assessment of Virtual Power Plants Offering in Energy and Reserve Markets , 2016, IEEE Transactions on Power Systems.

[14]  Guohe Huang,et al.  Development of an inexact risk-aversion optimization model for regional carbon constrained electricity system planning under uncertainty , 2015 .

[15]  Taras Bodnar,et al.  Minimum VaR and minimum CVaR optimal portfolios: Estimators, confidence regions, and tests , 2012 .

[16]  Fu Zhi-xin.  Economical and Environmental Benefits of Grid-Connected Small Hydropower-Wind Power-Photovoltaic Power , 2011 .

[17]  Naomi Miller,et al.  Risk-Averse Two-Stage Stochastic Linear Programming: Modeling and Decomposition , 2011, Oper. Res..

[18]  L. Ji,et al.  An inexact two-stage stochastic robust programming for residential micro-grid management-based on random demand , 2014 .