Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary Services

Distributed generators providing auxiliary service are an important means of guaranteeing the safe and economic operation of a distribution system. In this paper, considering an energy storage system (ESS), switchable capacitor reactor (SCR), step voltage regulator (SVR), and a static VAR compensato...

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Main Authors: Jian Zhang, Mingjian Cui, Yigang He
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Energies
Subjects:
CCP
Online Access:https://www.mdpi.com/1996-1073/14/16/4911
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spelling doaj-4af2b2bc85624727aabc5d744b456a802021-08-26T13:42:44ZengMDPI AGEnergies1996-10732021-08-01144911491110.3390/en14164911Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary ServicesJian Zhang0Mingjian Cui1Yigang He2School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaDepartment of Electrical Engineering and Computer Science,The University of Tennessee,Knoxville, TN 37996, USASchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaDistributed generators providing auxiliary service are an important means of guaranteeing the safe and economic operation of a distribution system. In this paper, considering an energy storage system (ESS), switchable capacitor reactor (SCR), step voltage regulator (SVR), and a static VAR compensator (SVC), a two-stage multi-period hybrid integer second-order cone programming (SOCP) robust model with partial DGs providing auxiliary service is developed. If the conic relaxation is not exact, a sequential SOCP is formulated using convex–concave procedure (CCP) and cuts, which can be quickly solved. Moreover, the exact solution of the original problem can be recovered. Furthermore, in view of the shortcomings of the large computer storage capacity and slow computational rate for the column and constraint generation (CCG) method, a method direct iteratively solving the master and sub-problem is proposed. Increases to variables and constraints to solve the master problem are not needed. For the sub-problem, only the model of each single time period needs to be solved. Then, their objective function values are accumulated, and the worst scenarios of each time period are concatenated. As an outcome, a large amount of storage memory is saved and the computational efficiency is greatly enhanced. The capability of the proposed method is validated with three simulation cases.https://www.mdpi.com/1996-1073/14/16/4911ancillary servicesCCPdistribution systemrobust optimizationsequential SOCP
collection DOAJ
language English
format Article
sources DOAJ
author Jian Zhang
Mingjian Cui
Yigang He
spellingShingle Jian Zhang
Mingjian Cui
Yigang He
Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary Services
Energies
ancillary services
CCP
distribution system
robust optimization
sequential SOCP
author_facet Jian Zhang
Mingjian Cui
Yigang He
author_sort Jian Zhang
title Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary Services
title_short Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary Services
title_full Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary Services
title_fullStr Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary Services
title_full_unstemmed Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary Services
title_sort multi-period fast robust optimization for partial distributed generators (dgs) providing ancillary services
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2021-08-01
description Distributed generators providing auxiliary service are an important means of guaranteeing the safe and economic operation of a distribution system. In this paper, considering an energy storage system (ESS), switchable capacitor reactor (SCR), step voltage regulator (SVR), and a static VAR compensator (SVC), a two-stage multi-period hybrid integer second-order cone programming (SOCP) robust model with partial DGs providing auxiliary service is developed. If the conic relaxation is not exact, a sequential SOCP is formulated using convex–concave procedure (CCP) and cuts, which can be quickly solved. Moreover, the exact solution of the original problem can be recovered. Furthermore, in view of the shortcomings of the large computer storage capacity and slow computational rate for the column and constraint generation (CCG) method, a method direct iteratively solving the master and sub-problem is proposed. Increases to variables and constraints to solve the master problem are not needed. For the sub-problem, only the model of each single time period needs to be solved. Then, their objective function values are accumulated, and the worst scenarios of each time period are concatenated. As an outcome, a large amount of storage memory is saved and the computational efficiency is greatly enhanced. The capability of the proposed method is validated with three simulation cases.
topic ancillary services
CCP
distribution system
robust optimization
sequential SOCP
url https://www.mdpi.com/1996-1073/14/16/4911
work_keys_str_mv AT jianzhang multiperiodfastrobustoptimizationforpartialdistributedgeneratorsdgsprovidingancillaryservices
AT mingjiancui multiperiodfastrobustoptimizationforpartialdistributedgeneratorsdgsprovidingancillaryservices
AT yiganghe multiperiodfastrobustoptimizationforpartialdistributedgeneratorsdgsprovidingancillaryservices
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