Optimizing Virtual Power Plant Management: A Novel MILP Algorithm to Minimize Levelized Cost of Energy, Technical Losses, and Greenhouse Gas Emissions

Alain Aoun (), Mehdi Adda, Adrian Ilinca (), Mazen Ghandour and Hussein Ibrahim
Additional contact information
Alain Aoun: Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski (UQAR), Rimouski, QC G5L 3A1, Canada
Mehdi Adda: Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski (UQAR), Rimouski, QC G5L 3A1, Canada
Adrian Ilinca: Département de Génie Mécanique, Ecole de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
Mazen Ghandour: Faculty of Engineering, Lebanese University, Beirut 1003, Lebanon
Hussein Ibrahim: Centre National Intégré du Manufacturier Intelligent (CNIMI), Université du Québec à Trois-Rivières (UQTR), Drummondville, QC J2C 0R5, Canada

Energies, 2024, vol. 17, issue 16, 1-23

Abstract: The modern energy landscape is undergoing a significant transformation towards cleaner, decentralized energy sources. This change is driven by environmental and sustainability needs, causing traditional centralized electric grids, which rely heavily on fossil fuels, to be replaced by a diverse range of decentralized distributed energy resources. Virtual power plants (VPPs) have surfaced as a flexible solution in this transition. A VPP’s primary role is to optimize energy production, storage, and distribution by coordinating output from various connected sources. Relying on advanced communication and control systems, a VPP can balance supply and demand in real time, offer ancillary services, and support grid stability. However, aligning VPPs’ economic and operational practices with broader environmental goals and policies is a challenging yet crucial aspect. This article introduces a new VPP management and optimization algorithm designed for quick and intelligent decision-making, aiming for the lowest levelized cost of energy (LCOE), minimum grid technical losses, and greenhouse gas (GHG) emissions. The algorithm’s effectiveness is confirmed using the IEEE 33-bus grid with 10 different distributed power generators. Simulation results show the algorithm’s responsiveness to complex variables found in practical scenarios, finding the optimal combination of available energy resources. This minimizes the LCOE, technical losses, and GHG emissions in less than 0.08 s, achieving a total LCOE reduction of 16% from the baseline. This work contributes to the development of intelligent energy management systems, aiding the transition towards a more resilient and sustainable energy infrastructure.

Keywords: virtual power plant (VPP); MILP; optimization; LCOE minimization; distributed energy resources; energy 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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/16/4075/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/16/4075/ (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:17:y:2024:i:16:p:4075-:d:1457747

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 ().