A Novel Hybrid Optimization-Based Controller for AGC in Deregulated Environments With Electric Vehicle Loads

Abstract

This paper proposes an IGSA-BPSO optimized hybrid PIDNPDN controller for Automatic Generation Control in a deregulated multisource power system with Electric Vehicle load participation. The hybrid power system considered comprises thermal, hydro, wind, diesel units, and aggregated EV fleets, with physical nonlinearities such as Generation Rate Constraint, Governor Dead Band, and Boiler Dynamics. To overcome the limitations of classical controllers in frequency regulation, an improved hybrid optimization technique—Improved Gravitational Search Algorithm–Binary Particle Swarm Optimization (IGSA-BPSO)—is employed to tune controller gains by minimizing the Integral of Time multiplied Absolute Error. The system is evaluated under Poolco, Bilateral, and Contract Violation Transactions, where the proposed controller consistently demonstrates faster dynamic response, minimal overshoot, and improved robustness against disturbances. The results highlight that the proposed IGSA-BPSO tuned PIDNPDN controller achieves significantly reduced settling times (ΔF₁: 2.0048 s, ΔF₂: 1.2261 s, ΔP_tie: 1.6051 s in Poolco transactions) compared to PID, PIDN, and TIDF controllers. Furthermore, the inclusion of Redox Flow Battery (RFB) and Unified Power Flow Controller in coordination with EV loads yields superior damping of oscillations and frequency stability and demonstrates that integrating EV fleets enhances system flexibility by compensating uncontracted demand during contract violations, thereby improving reliability and environmental sustainability. Overall, the study confirms that the integration of EV loads with IGSA-BPSO optimized hybrid controllers offers a promising pathway for achieving resilient and sustainable AGC in deregulated power systems.