Hourly Congestion Management by Adopting Distributed Energy Storage System using Hybrid Optimization

In this paper, the optimal location and sizing of DESS are proposed for transmission congestion management. Transmission Congestion Cost (TCC) is used to find the optimal location of DESS, whereas hybrid optimization based on Flower Pollination Algorithm (FPA) and Differential Evolution (DE) is prop...

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Main Authors: VATSALA SHARMA, PRATIMA WALDE, ANWAR SHAHZAD SIDDIQUI
Format: Article
Language:English
Published: ESRGroups 2020-06-01
Series:Journal of Electrical Systems
Subjects:
Online Access:http://journal.esrgroups.org/jes/papers/16_2_10.pdf
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spelling doaj-9c2f54e02a884b51bc6c3f76e630b81d2020-11-25T03:08:38ZengESRGroupsJournal of Electrical Systems1112-52091112-52092020-06-01162257275Hourly Congestion Management by Adopting Distributed Energy Storage System using Hybrid OptimizationVATSALA SHARMA0PRATIMA WALDE1ANWAR SHAHZAD SIDDIQUI 2School of Electrical, Electronics and Communication Engineering, Galgotias University, Greater Noida, Uttar Pradesh-226001, IndiaSchool of Electrical, Electronics and Communication Engineering, Galgotias University, Greater Noida, Uttar Pradesh-226001, IndiaDepartment of Electrical Engineering, JamiaMilliaIslamia (A Central University), New Delhi-110025, IndiaIn this paper, the optimal location and sizing of DESS are proposed for transmission congestion management. Transmission Congestion Cost (TCC) is used to find the optimal location of DESS, whereas hybrid optimization based on Flower Pollination Algorithm (FPA) and Differential Evolution (DE) is proposed for optimal sizing of DESS. The methodology considering Solar PV and Energy Storage System (ESS) as sources of energy. 24 hours real temperature and solar irradiance data of Delhi are taken to mathematically model the generation from Solar PV to manage congestion throughout the 24 hours. ESS is used to store surplus energy. The proposed approach is tested on IEEE-30 and IEEE-57 bus system, and 24 hours demand is assumed to follow the hourly load shape (summer season) of the IEEE reliability test system. The performance of the proposed approach is validated by comparing the results obtained through hybrid (FPA-DE) based hybrid optimization with the results obtained through DE optimization. It is observed from the experiment that both the optimization techniques (DE, Hybrid) performed well in managing congestion. However, DE has a higher consumption of resources that lead to shortage of resources at the end of the day; hence fail to manage congestion the next day, when solar irradiance is not available. In contrast, hybrid optimization provides very encouraging results, and at the end of the day saves approx. 39% of ESS, thus can participate in congestion management for the next day in the absence of solar irradiancehttp://journal.esrgroups.org/jes/papers/16_2_10.pdfenergy storage systemtransmission congestion costlocational marginal priceflower pollination algorithmdifferential evolutionhybrid optimization
collection DOAJ
language English
format Article
sources DOAJ
author VATSALA SHARMA
PRATIMA WALDE
ANWAR SHAHZAD SIDDIQUI
spellingShingle VATSALA SHARMA
PRATIMA WALDE
ANWAR SHAHZAD SIDDIQUI
Hourly Congestion Management by Adopting Distributed Energy Storage System using Hybrid Optimization
Journal of Electrical Systems
energy storage system
transmission congestion cost
locational marginal price
flower pollination algorithm
differential evolution
hybrid optimization
author_facet VATSALA SHARMA
PRATIMA WALDE
ANWAR SHAHZAD SIDDIQUI
author_sort VATSALA SHARMA
title Hourly Congestion Management by Adopting Distributed Energy Storage System using Hybrid Optimization
title_short Hourly Congestion Management by Adopting Distributed Energy Storage System using Hybrid Optimization
title_full Hourly Congestion Management by Adopting Distributed Energy Storage System using Hybrid Optimization
title_fullStr Hourly Congestion Management by Adopting Distributed Energy Storage System using Hybrid Optimization
title_full_unstemmed Hourly Congestion Management by Adopting Distributed Energy Storage System using Hybrid Optimization
title_sort hourly congestion management by adopting distributed energy storage system using hybrid optimization
publisher ESRGroups
series Journal of Electrical Systems
issn 1112-5209
1112-5209
publishDate 2020-06-01
description In this paper, the optimal location and sizing of DESS are proposed for transmission congestion management. Transmission Congestion Cost (TCC) is used to find the optimal location of DESS, whereas hybrid optimization based on Flower Pollination Algorithm (FPA) and Differential Evolution (DE) is proposed for optimal sizing of DESS. The methodology considering Solar PV and Energy Storage System (ESS) as sources of energy. 24 hours real temperature and solar irradiance data of Delhi are taken to mathematically model the generation from Solar PV to manage congestion throughout the 24 hours. ESS is used to store surplus energy. The proposed approach is tested on IEEE-30 and IEEE-57 bus system, and 24 hours demand is assumed to follow the hourly load shape (summer season) of the IEEE reliability test system. The performance of the proposed approach is validated by comparing the results obtained through hybrid (FPA-DE) based hybrid optimization with the results obtained through DE optimization. It is observed from the experiment that both the optimization techniques (DE, Hybrid) performed well in managing congestion. However, DE has a higher consumption of resources that lead to shortage of resources at the end of the day; hence fail to manage congestion the next day, when solar irradiance is not available. In contrast, hybrid optimization provides very encouraging results, and at the end of the day saves approx. 39% of ESS, thus can participate in congestion management for the next day in the absence of solar irradiance
topic energy storage system
transmission congestion cost
locational marginal price
flower pollination algorithm
differential evolution
hybrid optimization
url http://journal.esrgroups.org/jes/papers/16_2_10.pdf
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