A comprehensive techno-economic analysis for a PHEV-integrated microgrid system involving wind uncertainty and diverse demand side management policies

• Diverse DSM strategies which include load shifting, load curtailing and novel load shifting cum curtailing policies have been implemented on a low voltage MG system facilitating PHEV charging facilities. • The generation costs have been minimized for all the cases and compared with the base case to analyse the technical and economical aspects of the MG system. • Effect of varying SOC during arrival time and smart charging policy were also implemented to investigate the economic aspect of the MG system. The aim of demand side management (DSM) is to optimize a distribution system's performance and operation economically. For reducing the system’s peak demand, DSM integrates load curtailment and shifting methods. In the load curtailment technique, customers are offered incentives to take part in lowering the load during peak hours and hence are referred to as incentive-based demand response (IBDR). The load shifting method, which is optimization-based, fills in the valleys to lower the peak by shifting the elastic loads to hours whenever the electricity price is low. The objective of this novel work is to optimize the scheduling of distributed generations (DG) in a low-voltage microgrid (MG) system by implementing amalgamated load curtailment and shifting techniques to minimize the total costs of generation. The MG system provides the provision to charge plug-in hybrid electric vehicles (PHEV), which brings complexities to the work. Consequently, a smart charging strategy based on the electricity market price imposed by utilities is recommended for PHEV charging integration to lower the daily charging cost as a whole. Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) methods are integrated in this approach. The research investigation employed the Differential Evolution (DE) algorithm as an optimization technique. Numerical results show that the total operating cost (TOC) of the MG system reduced from $25,575 during the base load model to $24,521 when the proposed hybrid DSM was implemented. It is to be noted that this TOC also includes a $28 incentive to be awarded to the MG customers for participating in DSM policies. Some of the additional benefits of implementing the hybrid DSM strategy were a 24 % reduction in peak load demand, a 22 % improvement in load factor, and 50 kWh of energy saved at the end of the day. Furthermore, inclusion of smart PHEV charging brought down the TOC to $24,443, thereby proving that the combination of hybrid DSM strategy with smart PHEV charging is the most economical method of operation for the MG system.

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Publication Details

Journal
Results in Engineering
Published
2025-05-03
DOI
https://doi.org/10.1016/j.rineng.2025.105176
Citations
6
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
7.29
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article

A comprehensive techno-economic analysis for a PHEV-integrated microgrid system involving wind uncertainty and diverse demand side management policies

Laishram Khumanleima Chanu, Gulshan Sharma, Pitshou Ntambu Bokoro, Bishwajit Dey
6 citations
Results in Engineering
Microgrid Control and Optimization
7.29
article

A comprehensive techno-economic analysis for a PHEV-integrated microgrid system involving wind uncertainty and diverse demand side management policies

Laishram Khumanleima Chanu, Gulshan Sharma, Pitshou Ntambu Bokoro, Bishwajit Dey
article en
6 citations

Abstract

• Diverse DSM strategies which include load shifting, load curtailing and novel load shifting cum curtailing policies have been implemented on a low voltage MG system facilitating PHEV charging facilities. • The generation costs have been minimized for all the cases and compared with the base case to analyse the technical and economical aspects of the MG system. • Effect of varying SOC during arrival time and smart charging policy were also implemented to investigate the economic aspect of the MG system. The aim of demand side management (DSM) is to optimize a distribution system's performance and operation economically. For reducing the system’s peak demand, DSM integrates load curtailment and shifting methods. In the load curtailment technique, customers are offered incentives to take part in lowering the load during peak hours and hence are referred to as incentive-based demand response (IBDR). The load shifting method, which is optimization-based, fills in the valleys to lower the peak by shifting the elastic loads to hours whenever the electricity price is low. The objective of this novel work is to optimize the scheduling of distributed generations (DG) in a low-voltage microgrid (MG) system by implementing amalgamated load curtailment and shifting techniques to minimize the total costs of generation. The MG system provides the provision to charge plug-in hybrid electric vehicles (PHEV), which brings complexities to the work. Consequently, a smart charging strategy based on the electricity market price imposed by utilities is recommended for PHEV charging integration to lower the daily charging cost as a whole. Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) methods are integrated in this approach. The research investigation employed the Differential Evolution (DE) algorithm as an optimization technique. Numerical results show that the total operating cost (TOC) of the MG system reduced from $25,575 during the base load model to $24,521 when the proposed hybrid DSM was implemented. It is to be noted that this TOC also includes a $28 incentive to be awarded to the MG customers for participating in DSM policies. Some of the additional benefits of implementing the hybrid DSM strategy were a 24 % reduction in peak load demand, a 22 % improvement in load factor, and 50 kWh of energy saved at the end of the day. Furthermore, inclusion of smart PHEV charging brought down the TOC to $24,443, thereby proving that the combination of hybrid DSM strategy with smart PHEV charging is the most economical method of operation for the MG system.

Results in EngineeringVol. 26
University of Johannesburg (ZA), National Institute of Technology Manipur (IN), Manipal University Jaipur
Affordable and clean energy
Openalex Percentile: Top 3%
Microgrid Control and Optimization
7.29
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