Coordinated Electric Vehicle Demand Management in the Unit Commitment Problem Integrated with Transmission Constraints

Dimitrios Stamatakis () and Athanasios I. Tolis ()
Additional contact information
Dimitrios Stamatakis: Industrial Engineering Laboratory, Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, 157 72 Zografou, Greece
Athanasios I. Tolis: Industrial Engineering Laboratory, Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, 157 72 Zografou, Greece

Energies, 2025, vol. 18, issue 16, 1-48

Abstract: Advancements in battery technology, marked by reduced costs and enhanced efficiency, are steadily making electric vehicles (EVs) more accessible to consumers. This trend is fueling global growth in EV fleet sizes, allowing EVs to compete directly with internal combustion engine vehicles. However, this rapid growth in EV numbers is likely to introduce challenges to the power grid, necessitating effective load management strategies. This work proposes an optimization method where EV load management is integrated into the Transmission Constrained Unit Commitment Problem (TCUCP). A Differential Evolution (DE) variant, enhanced with heuristic repair sub-algorithms, is employed to address the TCUCP. The heuristic sub-algorithms, adapted from earlier approaches to the simpler Unit Commitment Problem (UCP), are updated to incorporate power flow constraints and ensure the elimination of transmission line violations. Additionally, new repair mechanisms are introduced that combine priority lists with grid information to minimize violation. The proposed formulation considers EVs as both flexible loads and energy sources in a large urban environment powered by two grid nodes, accounting for the vehicles’ daily movement patterns. The algorithm exhibits exceptionally fast convergence to a feasible solution in fewer than 150 generations, despite the nonlinearity of the problem. Depending on the scenario, the total production cost is reduced by up to 45% within these generations. Moreover, the results of the proposed model, when compared with a MILP algorithm, achieve values with a relative difference of approximately 1%.

Keywords: differential evolution; optimized electric vehicle charging; heuristic repair algorithm; transmission constraints; unit commitment problem (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: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/16/4293/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/16/4293/ (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:18:y:2025:i:16:p:4293-:d:1722697

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 ().