Optimal centralized scheduling strategy for bidirectional charging of PEV fleets in low-voltage microgrids

• Proposes a centralized, linear programming-based strategy for optimal bidirectional scheduling of PEV fleets in low-voltage microgrids. • Integrates forward-backward sweep load flow analysis to ensure real-time voltage stability during G2V and V2G operations. • Demonstrates significant energy cost reduction (up to 50 %) and voltage improvement through Time-of-Use tariffs and PV integration. • Considers realistic operational constraints including SoC limits, charger ratings, voltage bounds, and feeder power capacities. • Provides a scalable and adaptable framework for demand-side management applicable to future smart grids and real-time control systems. Rapid growth of plug-in electric vehicles (PEVs) is reshaping demand in low-voltage microgrids where voltage stability and power-quality margins are tight. Uncoordinated charging deepens evening peaks, stresses feeder limits, and constrains renewable hosting. This paper proposes a centralized, optimization-based scheduling strategy for bidirectional charging coordinating grid-to-vehicle (G2V) and vehicle-to-grid (V2G) dispatch to jointly minimize energy cost and enhance voltage stability. A linear programming (LP) model optimizes charging/discharging over discrete intervals subject to realistic constraints: charger power limits, state-of-charge (SoC) bounds, nodal-voltage regulation, and line-flow limits. The optimization is embedded in a forward-backward sweep load-flow loop to respect feeder physics. Using the IEEE European LV 8-bus system, we evaluate five scenarios single tariff, time-of-use (ToU) tariff, holiday load growth, ToU under holiday load, and photovoltaic (PV) integration. Relative to an uncontrolled baseline, the centralized strategy shifts demand off-peak, reduces peaks by up to 40% (12.0 to 7.2 kW), lowers energy cost by up to 25% (₹192.0 to ₹144.0), and improves minimum node voltages to 400–407 V; with PV, energy cost reaches ₹96.0 and minimum voltage rises to 412 V, all within EN 50,160 (±10%) bounds. These results validate a practical, scalable demand-side management (DSM) approach that improves reliability, reduces operating cost, and facilitates renewable integration; extensions to real-time, data-driven, or decentralized variants for larger fleets are outlined.

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Publication Details

Journal
e-Prime - Advances in Electrical Engineering Electronics and Energy
Published
2025-11-26
DOI
https://doi.org/10.1016/j.prime.2025.101136
Citations
1
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.73
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article

Optimal centralized scheduling strategy for bidirectional charging of PEV fleets in low-voltage microgrids

Binod Kumar Sahu, Indu Sekhar Samanta, Mohit Bajaj, Vojtěch Blažek et al.
1 citations
e-Prime - Advances in Electrical Engineering Electronics and Energy
Electric Vehicles and Infrastructure
0.73
article

Optimal centralized scheduling strategy for bidirectional charging of PEV fleets in low-voltage microgrids

Binod Kumar Sahu, Indu Sekhar Samanta, Mohit Bajaj, Vojtěch Blažek, Subhasis Panda, Cansu Ayvaz Güven, Lukáš Prokop, Buddhadeva Sahoo, Pravat Kumar Rout
article en
1 citations

Abstract

• Proposes a centralized, linear programming-based strategy for optimal bidirectional scheduling of PEV fleets in low-voltage microgrids. • Integrates forward-backward sweep load flow analysis to ensure real-time voltage stability during G2V and V2G operations. • Demonstrates significant energy cost reduction (up to 50 %) and voltage improvement through Time-of-Use tariffs and PV integration. • Considers realistic operational constraints including SoC limits, charger ratings, voltage bounds, and feeder power capacities. • Provides a scalable and adaptable framework for demand-side management applicable to future smart grids and real-time control systems. Rapid growth of plug-in electric vehicles (PEVs) is reshaping demand in low-voltage microgrids where voltage stability and power-quality margins are tight. Uncoordinated charging deepens evening peaks, stresses feeder limits, and constrains renewable hosting. This paper proposes a centralized, optimization-based scheduling strategy for bidirectional charging coordinating grid-to-vehicle (G2V) and vehicle-to-grid (V2G) dispatch to jointly minimize energy cost and enhance voltage stability. A linear programming (LP) model optimizes charging/discharging over discrete intervals subject to realistic constraints: charger power limits, state-of-charge (SoC) bounds, nodal-voltage regulation, and line-flow limits. The optimization is embedded in a forward-backward sweep load-flow loop to respect feeder physics. Using the IEEE European LV 8-bus system, we evaluate five scenarios single tariff, time-of-use (ToU) tariff, holiday load growth, ToU under holiday load, and photovoltaic (PV) integration. Relative to an uncontrolled baseline, the centralized strategy shifts demand off-peak, reduces peaks by up to 40% (12.0 to 7.2 kW), lowers energy cost by up to 25% (₹192.0 to ₹144.0), and improves minimum node voltages to 400–407 V; with PV, energy cost reaches ₹96.0 and minimum voltage rises to 412 V, all within EN 50,160 (±10%) bounds. These results validate a practical, scalable demand-side management (DSM) approach that improves reliability, reduces operating cost, and facilitates renewable integration; extensions to real-time, data-driven, or decentralized variants for larger fleets are outlined.

e-Prime - Advances in Electrical Engineering Electronics and EnergyVol. 14
Al-Ahliyya Amman University (JO), National Institute of Technology Warangal (IN), VSB - Technical University of Ostrava (CZ), Siksha O Anusandhan University (IN), Sri Sri University (IN), Yalova University (TR), Graphic Era University (IN)
Openalex Percentile: Top 21%
Electric Vehicles and Infrastructure
0.73
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