Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT

Zhiwei Liao (), Bowen Wang, Wenjuan Tao, Ye Liu and Qiyun Hu
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Zhiwei Liao: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Bowen Wang: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Wenjuan Tao: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Ye Liu: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Qiyun Hu: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China

Energies, 2024, vol. 17, issue 7, 1-21

Abstract: In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency.

Keywords: dual-layer decision optimization model; cost optimization for coal power plants; McCormick envelopes; uncertainty risk measurement (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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