LAPSE:2023.11718
Published Article

LAPSE:2023.11718
An Annual Electric Energy Trade Scheduling Model under the Dual Track Mode
February 27, 2023
Abstract
The annual electricity trade scheduling is the basis of long-term power generation scheduling. In recent decades, the ratio of new energy generation in China has increased annually, and the electricity market has operated under the “market electricity” and “planned electricity” double track mode in recent years. However, the existing annual electricity trade scheduling methods are extensive and cannot adapt to the new situation of “market electricity” and large-scale new energy generation. The annual scheduled energy of the power units is set as a decision variable, and a novel annual energy scheduling optimization model based on Gini coefficient of fairness is presented in this paper. In this model, “market electricity” capacity is conversed monthly, considering peaking reserve demand and monthly characteristics of new energy generation. The fairness constraint set based on Gini coefficient is introduced into the optimization model to solve various fairness problems. Simulation results show that the introduction of the Gini coefficient and the optimization model considering the monthly conversion of marketing electricity capacity can obtain more accurate and reasonable electricity distribution results, and the peaking demand can be considered more fairly and effectively. The proposed method provides a feasible solution to the annual electric energy scheduling for dual-track operation country such as China.
The annual electricity trade scheduling is the basis of long-term power generation scheduling. In recent decades, the ratio of new energy generation in China has increased annually, and the electricity market has operated under the “market electricity” and “planned electricity” double track mode in recent years. However, the existing annual electricity trade scheduling methods are extensive and cannot adapt to the new situation of “market electricity” and large-scale new energy generation. The annual scheduled energy of the power units is set as a decision variable, and a novel annual energy scheduling optimization model based on Gini coefficient of fairness is presented in this paper. In this model, “market electricity” capacity is conversed monthly, considering peaking reserve demand and monthly characteristics of new energy generation. The fairness constraint set based on Gini coefficient is introduced into the optimization model to solve various fairness problems. Simulation results show that the introduction of the Gini coefficient and the optimization model considering the monthly conversion of marketing electricity capacity can obtain more accurate and reasonable electricity distribution results, and the peaking demand can be considered more fairly and effectively. The proposed method provides a feasible solution to the annual electric energy scheduling for dual-track operation country such as China.
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Keywords
annual electric energy trade scheduling, double-track operation, high penetration of clean energy
Subject
Suggested Citation
Zhang N, Zhang M, Sun L, Hu J, Li J, Li W. An Annual Electric Energy Trade Scheduling Model under the Dual Track Mode. (2023). LAPSE:2023.11718
Author Affiliations
Zhang N: State Grid Liaoning Economic Research Institute, Shenyang 110015, China
Zhang M: State Grid Liaoning Economic Research Institute, Shenyang 110015, China
Sun L: School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China; State Grid Liaoning Electric Power Company Limited, Shenyang 110006, China
Hu J: State Grid Liaoning Economic Research Institute, Shenyang 110015, China
Li J: State Grid Liaoning Economic Research Institute, Shenyang 110015, China
Li W: School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China
Zhang M: State Grid Liaoning Economic Research Institute, Shenyang 110015, China
Sun L: School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China; State Grid Liaoning Electric Power Company Limited, Shenyang 110006, China
Hu J: State Grid Liaoning Economic Research Institute, Shenyang 110015, China
Li J: State Grid Liaoning Economic Research Institute, Shenyang 110015, China
Li W: School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China
Journal Name
Energies
Volume
15
Issue
14
First Page
5075
Year
2022
Publication Date
2022-07-12
ISSN
1996-1073
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Original Submission
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PII: en15145075, Publication Type: Journal Article
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LAPSE:2023.11718
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https://doi.org/10.3390/en15145075
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