An MINLP Optimization Method to Solve the RES-Hybrid System Economic Dispatch of an Electric Vehicle Charging Station
Abstract
1. Introduction
- An economic dispatch is proposed for grid-tied solar PV energy management approaches for EVCS to meet electrical demand using MINLP;
- The evaluation of charging station daily demand in an integrated solar PV, BESS, and grid connection while considering a nonlinear cost function algorithm for estimating PV generation power;
- EVCS energy demands are met while maintaining optimal grid, PV, and wind energy costs through a simulation environment to handle the supply-and-demand mismatch of a grid-tied RES-hybrid energy management system.
2. Materials and Methods
2.1. Materials
2.2. Methods
2.2.1. Optimization Problem Formulation for Energy Management of a Solar PV-Hybrid System Economic Dispatch
2.2.2. Constraints and Variable Limits
- : Power supplied to (discharging) or stored in (recharging) the battery at time t;
- : Coefficient for pollution treatment cost;
- : Maintenance coefficient;
- : Value depreciation coefficient;
- : RES’s social cost.
2.2.3. EMS MINLP Classical Algorithm (Figure 3)
- Step 1—Input grid settings to optimize usage of energy storage Ppv, N, EVCSload, dt, Cost, Einit, EWeight, MinMaxbattery)
- N—Number of discrete steps horizon
- dt—Optimization calls time [s]
- Ppv—Solar PV power [W]
- EVCSload—Grid load power [W]
- Einit—Battery initial energy [J]
- EbattV—Battery voltage [V]
- Cost—Grid charge cost [$/kWh]
- EWeight—Energy storage weight
- MinMaxbattery—Battery min/max
- Step 2—Confirm battery/grid power differential (d) = EVCSload − Ppv
- Step 3—Minimize grid energy cost from the objective optimization calls time * grid charge cost * Pgrid − Energy storage weight * Battery voltage
- Step 4—Battery input/output power Optimconstr(N) = constraints. energyBalance
- Step 5—Power from PV, grid, and battery Ppv + PgridV + PbattV − EVCSload = constraints. load Balance
- Step 6—Battery SOC constraints
- Step 7—Perform linear programming optimization
- Step 8—Sub-matrices for optimization constraints
- Step 9—Optimoptions(prob.optimoptions,) = Options for Linear Program
- Step 10—Parsing the optimization results
2.2.4. RES-Hybrid System of an Electric Vehicle Charging Station
2.2.5. Energy Management Strategies at an Electric Vehicle Charging Station
3. EMS MINLP Simulation Results and Discussion
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
EVCS on-grid transmission line spinning reserve operating cost | |
PV ith and wind mth on-grid transmission line | |
PV ith and wind mth operating cost | |
battery charging and discharging maximum cost | |
, | battery charging and discharging cost |
grid transmission line spinning reserve operating cost | |
cost of operating the fuel cell at time t | |
cost of operating the wind turbines at time t | |
total grid operating cost at time t with both wind turbines and fuel cells | |
, | grid, battery, RES social cost |
battery model equation | |
battery storage capacity | |
, | minimum and maximum generated power |
ramp-up and -down generated power | |
, | PV ith output and wind mth power at time t horizon |
battery charging and discharging power | |
energy storage system charging or discharging power | |
total power generation of the system | |
real power generation of grid transmission line spinning | |
cost of distribution network and operation cost at time t | |
intra-hour voltage dispatch | |
battery charging and discharging efficiencies | |
spinning reserve operating cost |
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Ref. | Power System Considered and Contribution | Optimization Technique | Objective Function |
---|---|---|---|
[19] | Integrated electric vehicle charging stations with photovoltaic uncertainties and battery power limits. | A new stochastic scheduling optimization approach. | Maximize the mean operational profit (μOP) under various scheduling scenarios for the PV–BESS–EVCS integrated system. |
[20] | Energy demand management for optimal scheduling of electric vehicle charging and discharging. | Four algorithms (DE, PSO, WOA, and GWO) provide efficient, grid-friendly V2G strategies. | Reduce EV customers’ day-to-day costs and energy demand management difficulties in smart networks. |
[21] | EVCI infrastructure sizing and placement. | Modified teaching–learning-based optimization (TLBO). | Minimize the power loss index and cost, while maximizing the voltage stability and reliability index. |
[22] | EVCS operation efficiency of battery energy storage systems. | Gorilla troop optimizer (GTO) algorithm. | Power loss and total voltage deviation minimization. |
[23] | Fast electric vehicle charging station optimization. | Grey wolf optimization. | Minimization of land cost, power loss, and electric vehicle population. |
[24] | Co-optimization of energy losses and transformer operating costs based on smart charging algorithm for plug-in electric vehicle parking lots. | Nonlinear programming (NLP). | Minimize energy loss and transformer operating cost, with voltage profile and power factor maximization. |
Data [47] | Electric Car | Electric SUV | Electric Van | Electric Pickup Truck |
---|---|---|---|---|
EV Class | Compact | Economy | Mid-Size Van | Light Truck |
BCap, kWh | 8–12 | 10–14 | 14–18 | 19–23 |
EC, kWh/km | 15–25 | 25–40 | 40–55 | 55–60 |
Consumption | 90 | 105 | 120 | 120 |
km for 15 kWh | 1480 | 80 | 48 | 8 |
Authors | Optimization | Power Curtailment Rate (%) |
---|---|---|
Proposed work | MINLP | 51.29 |
Dong et al., 2025 [19] | Stochastic optimization | 17.83 |
Altaf et al., 2025 [51] | Particle swarm optimization (PSO) | 58.33 |
Bagherzadeh et al., 2024 [52] | Salp swarm algorithm (SSA) | 54.54 |
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© 2025 by the authors. Published by MDPI on behalf of the World Electric Vehicle Association. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Adenuga, O.T.; Krishnamurthy, S. An MINLP Optimization Method to Solve the RES-Hybrid System Economic Dispatch of an Electric Vehicle Charging Station. World Electr. Veh. J. 2025, 16, 266. https://doi.org/10.3390/wevj16050266
Adenuga OT, Krishnamurthy S. An MINLP Optimization Method to Solve the RES-Hybrid System Economic Dispatch of an Electric Vehicle Charging Station. World Electric Vehicle Journal. 2025; 16(5):266. https://doi.org/10.3390/wevj16050266
Chicago/Turabian StyleAdenuga, Olukorede Tijani, and Senthil Krishnamurthy. 2025. "An MINLP Optimization Method to Solve the RES-Hybrid System Economic Dispatch of an Electric Vehicle Charging Station" World Electric Vehicle Journal 16, no. 5: 266. https://doi.org/10.3390/wevj16050266
APA StyleAdenuga, O. T., & Krishnamurthy, S. (2025). An MINLP Optimization Method to Solve the RES-Hybrid System Economic Dispatch of an Electric Vehicle Charging Station. World Electric Vehicle Journal, 16(5), 266. https://doi.org/10.3390/wevj16050266