An Optimization Coal Procurement and Inventory Model for Power Generation Enterprises Based on Data-driven Chance Constraints

Optimization of coal procurement and inventory for power generation enterprises are of great significance for guaranteeing power supply and ensuring generation income. The requirements for safe coal inventory level have been clearly put forward by the energy administrative authority of our country....

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Main Authors: Li YAO, Haifeng ZHENG, Baoguo SHAN, Xiandong TAN, Chuanlong XU, Zhicheng XU
Format: Article
Language:zho
Published: State Grid Energy Research Institute 2023-06-01
Series:Zhongguo dianli
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Online Access:https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202210036
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author Li YAO
Haifeng ZHENG
Baoguo SHAN
Xiandong TAN
Chuanlong XU
Zhicheng XU
author_facet Li YAO
Haifeng ZHENG
Baoguo SHAN
Xiandong TAN
Chuanlong XU
Zhicheng XU
author_sort Li YAO
collection DOAJ
description Optimization of coal procurement and inventory for power generation enterprises are of great significance for guaranteeing power supply and ensuring generation income. The requirements for safe coal inventory level have been clearly put forward by the energy administrative authority of our country. However, no existing research has ever focused on the probabilistic model and corresponding optimization strategy for the violation risk of inventory caused by the uncertainties of power generation and transportation capacity. Aiming at this problem, this paper presents an optimization coal procurement and inventory model for power generation enterprises based on data-driven chance constraints and proposes a corresponding solution method. Firstly, with consideration of the uncertainty of power generation and transportation capacity, the data-driven chance constraints for inventory are established and converted to soluble constraints of conditional value at risk (CVaR). Furthermore, based on the convexity of CVaR to decision variables, a piecewise linear approximation method for CVaR constraints is proposed. A power generation enterprise which owns 10 coal power plants is selected for case study. The optimization results show that with consideration of the chance constraints, the violation risk of power coal inventory is restricted within the allowable range; the proposed piecewise linear approximation method for CVaR constraints can make the model scalable and reduce the model’s scale with a high accuracy.
format Article
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issn 1004-9649
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publishDate 2023-06-01
publisher State Grid Energy Research Institute
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spelling doaj-art-154cda103ff84df785f59697b0cc25772025-08-20T02:52:41ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492023-06-0156617618410.11930/j.issn.1004-9649.202210036zgdl-56-04-yaoliAn Optimization Coal Procurement and Inventory Model for Power Generation Enterprises Based on Data-driven Chance ConstraintsLi YAO0Haifeng ZHENG1Baoguo SHAN2Xiandong TAN3Chuanlong XU4Zhicheng XU5State Grid Energy Research Institute Co., Ltd., Beijing 102209, ChinaState Grid Energy Research Institute Co., Ltd., Beijing 102209, ChinaState Grid Energy Research Institute Co., Ltd., Beijing 102209, ChinaState Grid Energy Research Institute Co., Ltd., Beijing 102209, ChinaState Grid Energy Research Institute Co., Ltd., Beijing 102209, ChinaState Grid Energy Research Institute Co., Ltd., Beijing 102209, ChinaOptimization of coal procurement and inventory for power generation enterprises are of great significance for guaranteeing power supply and ensuring generation income. The requirements for safe coal inventory level have been clearly put forward by the energy administrative authority of our country. However, no existing research has ever focused on the probabilistic model and corresponding optimization strategy for the violation risk of inventory caused by the uncertainties of power generation and transportation capacity. Aiming at this problem, this paper presents an optimization coal procurement and inventory model for power generation enterprises based on data-driven chance constraints and proposes a corresponding solution method. Firstly, with consideration of the uncertainty of power generation and transportation capacity, the data-driven chance constraints for inventory are established and converted to soluble constraints of conditional value at risk (CVaR). Furthermore, based on the convexity of CVaR to decision variables, a piecewise linear approximation method for CVaR constraints is proposed. A power generation enterprise which owns 10 coal power plants is selected for case study. The optimization results show that with consideration of the chance constraints, the violation risk of power coal inventory is restricted within the allowable range; the proposed piecewise linear approximation method for CVaR constraints can make the model scalable and reduce the model’s scale with a high accuracy.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202210036power supply guaranteeelectric coal inventorydata-drivenchance-constrained programmingconditional value at risk (cvar)
spellingShingle Li YAO
Haifeng ZHENG
Baoguo SHAN
Xiandong TAN
Chuanlong XU
Zhicheng XU
An Optimization Coal Procurement and Inventory Model for Power Generation Enterprises Based on Data-driven Chance Constraints
Zhongguo dianli
power supply guarantee
electric coal inventory
data-driven
chance-constrained programming
conditional value at risk (cvar)
title An Optimization Coal Procurement and Inventory Model for Power Generation Enterprises Based on Data-driven Chance Constraints
title_full An Optimization Coal Procurement and Inventory Model for Power Generation Enterprises Based on Data-driven Chance Constraints
title_fullStr An Optimization Coal Procurement and Inventory Model for Power Generation Enterprises Based on Data-driven Chance Constraints
title_full_unstemmed An Optimization Coal Procurement and Inventory Model for Power Generation Enterprises Based on Data-driven Chance Constraints
title_short An Optimization Coal Procurement and Inventory Model for Power Generation Enterprises Based on Data-driven Chance Constraints
title_sort optimization coal procurement and inventory model for power generation enterprises based on data driven chance constraints
topic power supply guarantee
electric coal inventory
data-driven
chance-constrained programming
conditional value at risk (cvar)
url https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202210036
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