Grid Integration of Electrical Vehicles for Economic Benefits

Electric Vehicles (EVs) are no longer only transportation devices, they are emerging as flexible and distributed energy resources that can actively support modern power systems. By intelligently coordinating EV charging and discharging with the power grid, multiple stakeholders in the electricity sector can obtain significant economic and operational benefits.

This article explains how EVs are integrated into the grid and how economic benefits are created for different participants such as generation companies, distribution operators, aggregators, and end users, based on a comprehensive review of recent research.

Concept of EV Grid Integration

The core idea is to treat EVs as controllable electrical loads and, in some cases, as mobile energy storage systems through Vehicle-to-Grid (V2G) operation.
Depending on the market structure and control architecture, EVs can be coordinated by an aggregator or directly by system operators to provide services such as:

• demand shifting,
• peak reduction,
• reserve support,
• and renewable energy balancing.

The overall framework links all major power-system participants to a common objective: economic benefit creation.

Overall Framework for Economic Benefits

The following diagram is illustrates how economic benefits are generated through coordination among the main actors.

Description of the diagram

• GENCO (Generation Company)
  Applies scheduling and operational tools such as:
  • Unit Commitment (UC)
  • Optimal Power Flow (OPF)
  • Integration of Renewable Energy Sources (RES)
• DSO (Distribution System Operator)
  Focuses on:
  • peak shaving and peak shifting
  • loss minimization
  • network constraint management
• Aggregator
  Acts as an intermediate entity that coordinates a large number of EVs and provides grid services.
• End User (EV owner)
  Targets:
  • charging cost minimization
  • total electricity cost reduction

All these entities contribute to and benefit from the central objective of economic benefits.

The grid-integration framework connects four main actors generation companies, distribution operators, aggregators, and end users through coordinated charging and vehicle-to-grid operation. Economic benefits are achieved when EV flexibility is used to optimize generation scheduling, reduce network stress, support renewable energy sources, and minimize charging and operational costs across the system.

Economic Benefits for Generation Companies (GENCOs)

For generation companies, coordinated EV charging and V2G operation improve generation scheduling and reduce operating costs.
Major benefit mechanisms include:

• improved unit commitment decisions,
• better utilization of renewable generation,
• reduced reliance on expensive peak generators,
• improved spinning reserve management.

Table I Grid integration of EV: economic benefits for GENCO

Performance objective	Solving method / algorithm used	UC	Integration of RES	DSM	OPF	V2G capability
Generation cost and emission minimization	Particle swarm optimization	Yes	Yes	No	No	No
Total operation cost and emission minimization	Fireworks algorithm	Yes	Yes	No	No	Yes
Total cost minimization	Mixed-integer linear programming	Yes	Yes	No	No	Yes
Grid operation cost minimization	Mixed-integer programming	SCUC	Yes	No	No	Yes
Total cost of generation minimization	Non-convex optimization	Yes	No	No	No	Spinning reserve
Generation cost and emission minimization	Mixed-integer linear programming	Yes	Yes	No	No	Spinning reserve
Total cost of system minimization	Simulated annealing algorithm	Yes	No	No	No	Spinning reserve
Generation cost minimization	Game theory	No	No	Valley filling	No	No
Optimal control for valley filling	Convex optimization	No	No	Valley filling	No	No
Generation cost minimization	Maximum sensitivities selection optimization	No	No	Valley filling	No	No
Demand fulfillment in microgrid	Game theory	No	Yes	Load shifting	No	Yes
Generation cost minimization	Convex optimization	No	No	Valley filling	Yes	No
Generation cost minimization	Lagrange multiplier	No	No	No	Security-constrained OPF (SCOPF)	No
System-wide generation cost minimization	Linear optimization	No	No	Valley filling	Yes	No
Frequency control	Simulation study	No	Yes	No	No	Frequency regulation
Total operation cost minimization	Monte-Carlo simulations	No	Yes	No	No	Yes
Financial cost of supply minimization	Evolutionary optimization	No	Yes	No	No	Yes
Wind integration cost minimization	Rolling-horizon algorithm	Yes	Yes	Yes	No	No
Wind power mismatch and V2G cost minimization	Genetic algorithm coupled with Monte-Carlo simulation	No	Yes	No	No	Yes
Expected cost minimization	Stochastic programming	No	Yes	No	No	Yes
Generation cost and emission minimization	Linear optimization	No	Yes	No	No	Frequency regulation
Viability VPP formation	Linear programming	No	Yes	No	No	Yes
Frequency control	Simulation study	No	Yes	No	No	Yes

By allowing EVs to absorb excess renewable generation and supply power during high-price periods, GENCOs can lower fuel costs and reduce start-up and shut-down operations of thermal units.

For generation companies, coordinated EV operation helps improve unit commitment and optimal power flow decisions. EVs absorb excess renewable energy and reduce the need for expensive peak generation, which lowers fuel consumption, reduces generator start-ups and shutdowns, and improves the overall efficiency and profitability of power generation.

Economic Benefits for Distribution System Operators (DSOs)

Distribution operators benefit mainly through reduced network congestion, lower technical losses, improved voltage profiles, and better utilization of existing infrastructure. Smart and coordinated charging enables peak shaving and peak shifting, which delays costly network upgrades and improves the reliability of the distribution system. Distribution networks are highly affected by uncoordinated EV charging. Research shows that controlled charging and V2G significantly improve network performance.

Table II Grid integration of EV: economic benefits for DSO

Performance objective	Solving method / algorithm used	Loss minimization	DSM	Maximum power transfer	V2G capability
Grid energy loss minimization	Maximum sensitivities selection optimization	Yes	Valley filling	No	No
Voltage profile improvement	MATLAB based algorithm	Yes	Peak shaving	No	No
Charging maximization and cost minimization	Linear optimization	Yes	No	No	–
PHEV impact minimization	Heuristic or sequential method	Yes	No	Yes	No
Power loss and charging cost minimization	Multi-objective particle swarm optimization	Yes	No	No	No
PEV charging impact estimation	General algebraic modelling system	Yes	No	No	No
Total energy consumption and PAR minimization	Game theory	No	Peak shaving	No	No
Peak power demand minimization	Linear and convex optimization	No	Peak shaving	No	Yes
Peak demand minimization	Two-stage V2G control algorithm	No	Peak shaving	No	Yes
Electricity demand cost minimization	Proposed control algorithm	No	Peak shaving	No	No
Total energy cost and peak demand minimization	Game theory	No	Peak shaving	No	Yes
EWH power consumption control	MATLAB simulation	No	Peak shaving	No	No
PHEV impact assessment	MATLAB simulation	No	Peak shifting, load shedding	No	No
Distribution transformer utilization improvement	Proposed control algorithm	No	Peak shifting	No	No
Load curve flattening of LVT	Convex optimization	No	Peak shaving	No	No
Cost minimization	Heuristic-based	No	Load shedding	No	No
Maximize power delivered to EV	Linear programming	No	No	Yes	No
Grid congestion minimization	Sequential quadratic programming	No	No	Yes	No
Congestion prevention	Lagrange multiplier	No	No	Yes	No

Main benefits for DSOs include:

• reduction of feeder and transformer overloading,
• minimization of distribution losses,
• voltage profile improvement,
• peak shaving and peak shifting.

Through coordinated charging strategies, DSOs can defer expensive grid reinforcement investments while maintaining reliable operation integration.

Economic Benefits for Aggregators

Aggregators manage large fleets of EVs and participate in electricity and ancillary service markets on behalf of EV owners. By optimally scheduling charging and discharging, aggregators can earn revenue from energy trading, frequency regulation, and reserve services, while also ensuring that users’ mobility requirements are satisfied. An aggregator manages a fleet of EVs and participates in electricity and ancillary service markets on their behalf.

Table III Grid integration of EV: economic benefits aggregator

Performance objective	Solving method / algorithm used	V2G capability	Ancillary service
Aggregator revenue maximization	Linear programming	Yes	Frequency regulation
Aggregator revenue maximization	Linear programming	Yes	Spinning reserve and frequency regulation
Aggregator profit maximization	Mixed-integer linear programming	Yes	Frequency regulation
Aggregator profit maximization	Stochastic linear programming	Yes	Frequency regulation
Online scheduling of EV	Convex optimization	Yes	Frequency regulation
Regulation quality improvement	Convex optimization	Yes	Frequency regulation
Energy trading profile maximization	Scheduling and dispatching algorithm	No	No
Aggregator revenue maximization	MILP model and heuristic algorithm	Yes	Frequency regulation
Aggregator revenue maximization	Dynamic programming	Yes	Frequency regulation
Charging discharging cost minimization	Linear and quadratic optimization	Yes	No
Charging cost minimization of PHEV	Mixed-integer linear programming	No	No
Energy trading cost minimization	Mixed-integer programming	Yes	No
Total electricity cost minimization	Linear programming	Yes	No
Aggregator revenue risk management	Lagrange relaxation	Yes	No
Aggregator revenue maximization	Linear programming	Yes	Frequency regulation
Aggregator profit maximization and charging cost minimization	Heuristic dynamic optimization	Yes	No
EV user cost minimization	Quadratic programming	Yes	Frequency regulation
Social welfare maximization	Dynamic programming	No	No
Cost of electricity for PHEV minimization	k-nearest neighbors (kNN) classification	No	No
Aggregator revenue maximization	Mixed-integer linear programming	Yes	No

Key benefits include:

• revenue from frequency regulation and reserve markets,
• profit from energy arbitrage,
• optimized charging schedules across multiple EV owners.

Advanced scheduling and bidding strategies allow aggregators to maximize revenue while respecting user mobility constraints and battery limitations.

Economic Benefits for End Users (EV Owners)

End users mainly benefit through reduced charging costs and lower total electricity bills. By charging during low-price periods and participating in vehicle-to-grid programs, EV owners can receive financial incentives without affecting their daily travel needs, provided that smart charging strategies are applied. From the user perspective, EV grid integration mainly focuses on reducing charging and energy costs.

Table IV Grid integration of EV: economic benefits for end user

Performance objective	Solving method / algorithm used	V2G capability
PEV charging impact estimation	General algebraic modelling system	No
Charging cost minimization	Heuristic method	No
Charging cost minimization	Linear and quadratic approximation	No
Charging cost minimization	Quadratic programming	No
Total cost of fuel and electricity minimization	Multi-objective genetic algorithm	No
Total charging cost minimization	Convex optimization	Yes
Total charging cost minimization	Electricity price based control algorithm	Yes
EV profit maximization	Non-linear programming	Yes
Total charging cost minimization	Proposed price based algorithm	Yes
Daily electricity cost minimization	Dynamic programming	Yes
Spinning reserve and user cost optimization	Proposed algorithm	Yes
EV scheduling considering battery wear cost	Mixed-integer linear problem	Yes

Important advantages are:

• charging during low-tariff periods,
• participation in V2G programs for additional income,
• reduced overall electricity bills through smart scheduling.

Well-designed charging strategies ensure that users’ mobility needs are preserved while still enabling economic participation in grid services.

Key Enabling Technologies

Several technologies enable the practical realization of this framework:

• smart charging infrastructure,
• real-time communication and control platforms,
• advanced optimization and forecasting algorithms,
• secure data exchange between EVs, aggregators and grid operators.

These technologies allow real-time coordination and large-scale deployment of EV-based grid services

Open Challenges and Future Research Directions

Major challenges include large-scale commercialization of vehicle-to-grid systems, cybersecurity and privacy protection, upgrading charging and distribution infrastructure, and designing suitable regulatory and market mechanisms. Future research must focus on scalable control methods, battery degradation modeling, and fair market participation frameworks for EV owners.

Conclusion

Large-scale integration of electric vehicles increases electricity demand and can stress existing power system infrastructure. However, smart and optimal EV charging and scheduling can reduce generation cost, support renewable energy integration, minimize distribution network losses, and improve demand-side management. At the same time, aggregators and EV users can earn economic benefits by optimizing charging and providing grid support services. Overall, multi-objective EV integration strategies help multiple stakeholders achieve economic and operational benefits simultaneously.