Unit Commitment Accommodating Large Scale Green Power

As more clean energy sources contribute to the electrical grid, the stress on generation scheduling for peak-shaving increases. This is a concern in several provinces of China that have many nuclear power plants, such as Guangdong and Fujian. Studies on the unit commitment (UC) problem involving the...

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Main Authors: Yuntao Ju, Jiankai Wang, Fuchao Ge, Yi Lin, Mingyu Dong, Dezhi Li, Kun Shi, Haibo Zhang
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/9/8/1611
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spelling doaj-f672417fdd694e569fa205f26cfcf8c22020-11-25T02:16:03ZengMDPI AGApplied Sciences2076-34172019-04-0198161110.3390/app9081611app9081611Unit Commitment Accommodating Large Scale Green PowerYuntao Ju0Jiankai Wang1Fuchao Ge2Yi Lin3Mingyu Dong4Dezhi Li5Kun Shi6Haibo Zhang7College of Information and Electrical Engineering, China Agricultural University, Haidian District, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Haidian District, Beijing 100083, ChinaLinyi Power Supply Company, State Grid Shandong Electric Power Company, Linyi 276000, ChinaPower Planning Department State Grid Fujian Economic Research Institute, Fuzhou 350000, ChinaBeijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique, Beijing 100192, ChinaChina Electric Power Research Institute, Haidian District, Beijing 100192, ChinaBeijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique, Beijing 100192, ChinaThe State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaAs more clean energy sources contribute to the electrical grid, the stress on generation scheduling for peak-shaving increases. This is a concern in several provinces of China that have many nuclear power plants, such as Guangdong and Fujian. Studies on the unit commitment (UC) problem involving the characteristics of both wind and nuclear generation are urgently needed. This paper first describes a model of nuclear power and wind power for the UC problem, and then establishes an objective function for the total cost of nuclear and thermal power units, including the cost of fuel, start-stop and peak-shaving. The operating constraints of multiple generation unit types, the security constraints of the transmission line, and the influence of non-gauss wind power uncertainty on the spinning reserve capacity of the system are considered. Meanwhile, a model of an energy storage system (ESS) is introduced to smooth the wind power uncertainty. Due to the prediction error of wind power, the spinning reserve capacity of the system will be affected by the uncertainty. Over-provisioning of spinning reserve capacity is avoided by introducing chance constraints. This is followed by the design of a UC model applied to different power sources, such as nuclear power, thermal power, uncertain wind power, and ESS. Finally, the feasibility of the UC model in the scheduling of a multi-type generation unit is verified by the modified IEEE RTS 24-bus system accommodating large scale green generation units.https://www.mdpi.com/2076-3417/9/8/1611nuclear power generationwind power generationenergy storage systemunit commitmentGaussian mixture modelspinning reserve
collection DOAJ
language English
format Article
sources DOAJ
author Yuntao Ju
Jiankai Wang
Fuchao Ge
Yi Lin
Mingyu Dong
Dezhi Li
Kun Shi
Haibo Zhang
spellingShingle Yuntao Ju
Jiankai Wang
Fuchao Ge
Yi Lin
Mingyu Dong
Dezhi Li
Kun Shi
Haibo Zhang
Unit Commitment Accommodating Large Scale Green Power
Applied Sciences
nuclear power generation
wind power generation
energy storage system
unit commitment
Gaussian mixture model
spinning reserve
author_facet Yuntao Ju
Jiankai Wang
Fuchao Ge
Yi Lin
Mingyu Dong
Dezhi Li
Kun Shi
Haibo Zhang
author_sort Yuntao Ju
title Unit Commitment Accommodating Large Scale Green Power
title_short Unit Commitment Accommodating Large Scale Green Power
title_full Unit Commitment Accommodating Large Scale Green Power
title_fullStr Unit Commitment Accommodating Large Scale Green Power
title_full_unstemmed Unit Commitment Accommodating Large Scale Green Power
title_sort unit commitment accommodating large scale green power
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2019-04-01
description As more clean energy sources contribute to the electrical grid, the stress on generation scheduling for peak-shaving increases. This is a concern in several provinces of China that have many nuclear power plants, such as Guangdong and Fujian. Studies on the unit commitment (UC) problem involving the characteristics of both wind and nuclear generation are urgently needed. This paper first describes a model of nuclear power and wind power for the UC problem, and then establishes an objective function for the total cost of nuclear and thermal power units, including the cost of fuel, start-stop and peak-shaving. The operating constraints of multiple generation unit types, the security constraints of the transmission line, and the influence of non-gauss wind power uncertainty on the spinning reserve capacity of the system are considered. Meanwhile, a model of an energy storage system (ESS) is introduced to smooth the wind power uncertainty. Due to the prediction error of wind power, the spinning reserve capacity of the system will be affected by the uncertainty. Over-provisioning of spinning reserve capacity is avoided by introducing chance constraints. This is followed by the design of a UC model applied to different power sources, such as nuclear power, thermal power, uncertain wind power, and ESS. Finally, the feasibility of the UC model in the scheduling of a multi-type generation unit is verified by the modified IEEE RTS 24-bus system accommodating large scale green generation units.
topic nuclear power generation
wind power generation
energy storage system
unit commitment
Gaussian mixture model
spinning reserve
url https://www.mdpi.com/2076-3417/9/8/1611
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