Two-Stage Stochastic Scheduling of Cascaded Hydropower–Wind–Photovoltaic Hybrid Systems Considering Contract Decomposition and Spot Market

Yang Li (), Ni Fang, Shengming He, Feng Wu (), Outing Li, Linjun Shi and Renshan Ding
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Yang Li: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Ni Fang: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Shengming He: Yalong River Hydropower Development Company Ltd., Chengdu 610051, China
Feng Wu: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Outing Li: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Linjun Shi: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Renshan Ding: Yalong River Hydropower Development Company Ltd., Chengdu 610051, China

Sustainability, 2024, vol. 16, issue 3, 1-19

Abstract: With the advancement of China’s electricity markets and the continuous development of renewable energy sources (RESs), it is of great importance to investigate the strategic behavior of RESs in electricity markets. In this paper, a two-stage stochastic optimization model is proposed for a hybrid energy system composed of cascade hydropower plants, wind farms, and photovoltaic stations. Firstly, typical scenarios are generated based on Latin hypercube sampling (LHS) and the K-means clustering algorithm to represent uncertainties of wind–photovoltaic power outputs. Then, with an analysis of China’s electricity market structure, a two-stage coordinated scheduling model of hydropower–wind–photovoltaic hybrid systems in electricity markets is established with the objective of maximizing total revenues considering bilateral contract decomposition, the day-ahead energy market, and the real-time balance market. In addition, the proposed model is transformed into a mixed-integer linear programming (MILP) problem for computational convenience. As shown in an analysis of case studies, cascade hydropower plants can compensate for the fluctuation in wind and photovoltaic power outputs to reduce financial risks caused by uncertainties of wind and photovoltaic power generation. Simulation results show that compared with uncoordinated operation, the coordinated operation of hydropower–wind–photovoltaic hybrid systems increases total revenue by 1.08% and reduces the imbalance penalty by 29.85%.

Keywords: hydropower–wind–photovoltaic hybrid systems; electricity markets; two-stage stochastic optimization; bilateral contract decomposition; spot market; mixed-integer linear programming (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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