Load Management and Optimal Sizing of Special-Purpose Microgrids Using Two Stage PSO-Fuzzy Based Hybrid Approach
Fawad Azeem,
Ashfaq Ahmad,
Taimoor Muzaffar Gondal,
Jehangir Arshad,
Ateeq Ur Rehman (),
Elsayed M. Tag Eldin (),
Muhammad Shafiq () and
Habib Hamam
Additional contact information
Fawad Azeem: Energy Research Center, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
Ashfaq Ahmad: Department of Computer Science, COMSATS University Islamabad, Lahore 54000, Pakistan
Taimoor Muzaffar Gondal: Department of Electrical Engineering, Superior University, Lahore 54600, Pakistan
Jehangir Arshad: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54700, Pakistan
Ateeq Ur Rehman: Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan
Elsayed M. Tag Eldin: Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt
Muhammad Shafiq: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
Habib Hamam: Faculty of Engineering, Universite de Moncton, Moncton, NB E1A3E9, Canada
Energies, 2022, vol. 15, issue 17, 1-19
Abstract:
The sizing of microgrids depends on the type of load and its operational hours. The significance of understanding the load operational characteristics in special purpose islanded microgrids is much needed for economic system sizing. The load operation of special-purpose microgrids often consumes high power for a short duration and remains idle most of the time, thus reducing the load factor. The inclusion of such loads in microgrid sizing causes huge capital costs making islanded microgrids an unfeasible solution. The islanded microgrid under study is an agricultural microgrid in a village having a small Crab Processing Plant (CPP) and a Domestic Sector (DS). The CPP constitutes the major power consumption. The community has a unique load consumption trend that is dependent on the highly uncertain parameter of availability of the crabs. Interestingly, crab availability is an independent parameter and cannot be accurately scheduled. The existing system sizing of the microgrid is performed based on the conventional methods that consider the CPP for full-day operation. However, the microgrid sources, especially the storage system may be reflected as oversized if the crab processing plants do not operate for several days due to the uncertain behavior of CPP causing enormous power wastage. In this paper, an integrated fixed and operational mode strategy for uncertain heavy loads is formulated. The proposed algorithm is based on the optimal sizing methodology aided by the load scheduling control strategy. The Particle Swarm Optimization technique is used for the optimal sizing integrated with the fuzzy logic controller to manage the available load. The membership functions are available excess power and the state of the charge of storage that defines the operational conditions for CPP. Based on input membership functions, the fuzzy controller decides on power dispatch in DS or CPP, keeping considerable SoC available for night hours. The simulation result shows that the time-dependent fuzzy controller approach manages to provide power to both sectors under optimal sizing while reducing the overall cost by 24% less than the existing microgrid.
Keywords: load factor; special-purpose microgrid; economic dispatch; fuzzy logic; load management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/1996-1073/15/17/6465/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/17/6465/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:17:p:6465-:d:906757
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().