A Method for Formulating Delivery Curves of Clean Energy Bases Considering Load Demand of Receiving Provinces

Against the backdrop of China’s dual carbon goals, cross-regional low-carbon power transmission from large-scale clean energy bases is a pivotal direction for energy transition. Formulating their power delivery curves requires precise alignment with the load demand characteristics of receiving provinces and the coordinated operation of hydropower, wind power, photovoltaic (PV) power, and pumped-storage hydropower (PSH). To address the limitations of existing methods, such as the lack of linearized modeling for core operational constraints, low solution efficiency and inadequate integration of multi-energy coupling constraints, this paper proposes a tailored linearized optimization modeling approach. By adopting auxiliary variables, binary variables and the Big M method, core constraints including PSH pumping power supply, stepwise power delivery and multi-energy coordinated operation are linearized. A monthly rolling linear optimization model is constructed with triple objectives: minimizing the renewable curtailment rate and the absolute error between delivery and load curves, and maximizing delivered electricity volume. Multi-objective coordinated optimization is realized via the linear weighted summation method, and the model is solved with the Gurobi solver. Case validation on an integrated hydro–wind–solar clean energy base in Southwest China and its corresponding receiving provincial power grid shows that the proposed method effectively improves the curve matching degree, controls the wind–PV curtailment rate within around 12% (engineering tolerance), and strictly meets engineering safety constraints such as PSH operation and HVDC transmission requirements. Comprehensive optimization of the three objectives is achieved when the weight coefficients for curtailment rate, load matching error and delivered electricity volume are set to 0.3–0.8, 0.1–0.2 and 0.1–0.6, respectively. This method resolves the problems of traditional nonlinear models being disconnected from engineering practice and low solution efficiency, providing a reliable technical reference for the refined dispatching of cross-regional power transmission and scientific formulation of power delivery curves for clean energy bases.

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Publication Details

Journal
Energies
Published
2026-05-19
DOI
https://doi.org/10.3390/en19102445
Primary Topic
Integrated Energy Systems Optimization
Type
article
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A Method for Formulating Delivery Curves of Clean Energy Bases Considering Load Demand of Receiving Provinces

Xiao Qin, Zenghai Zhao, Jiayan Zhang, Chuntian Cheng et al.
Energies
Integrated Energy Systems Optimization
article

A Method for Formulating Delivery Curves of Clean Energy Bases Considering Load Demand of Receiving Provinces

Xiao Qin, Zenghai Zhao, Jiayan Zhang, Chuntian Cheng, Jie Gao, Yue Zhao, Xu Han
article en

Abstract

Against the backdrop of China’s dual carbon goals, cross-regional low-carbon power transmission from large-scale clean energy bases is a pivotal direction for energy transition. Formulating their power delivery curves requires precise alignment with the load demand characteristics of receiving provinces and the coordinated operation of hydropower, wind power, photovoltaic (PV) power, and pumped-storage hydropower (PSH). To address the limitations of existing methods, such as the lack of linearized modeling for core operational constraints, low solution efficiency and inadequate integration of multi-energy coupling constraints, this paper proposes a tailored linearized optimization modeling approach. By adopting auxiliary variables, binary variables and the Big M method, core constraints including PSH pumping power supply, stepwise power delivery and multi-energy coordinated operation are linearized. A monthly rolling linear optimization model is constructed with triple objectives: minimizing the renewable curtailment rate and the absolute error between delivery and load curves, and maximizing delivered electricity volume. Multi-objective coordinated optimization is realized via the linear weighted summation method, and the model is solved with the Gurobi solver. Case validation on an integrated hydro–wind–solar clean energy base in Southwest China and its corresponding receiving provincial power grid shows that the proposed method effectively improves the curve matching degree, controls the wind–PV curtailment rate within around 12% (engineering tolerance), and strictly meets engineering safety constraints such as PSH operation and HVDC transmission requirements. Comprehensive optimization of the three objectives is achieved when the weight coefficients for curtailment rate, load matching error and delivered electricity volume are set to 0.3–0.8, 0.1–0.2 and 0.1–0.6, respectively. This method resolves the problems of traditional nonlinear models being disconnected from engineering practice and low solution efficiency, providing a reliable technical reference for the refined dispatching of cross-regional power transmission and scientific formulation of power delivery curves for clean energy bases.

EnergiesVol. 19(10)
Dalian University of Technology (CN), China Institute of Water Resources and Hydropower Research (CN), Beijing Solar Energy Research Institute (CN), Ministry of Water Resources of the People's Republic of China (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 8%
Integrated Energy Systems Optimization
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