Integral Performance Criteria Based Analysis of Load Frequency Control in Bilateral Based Market

Performance index based analysis is made to examine and highlight the effective application of Particle Swarm Optimization (PSO) to optimize the Proportional Integral gains for Load Frequency Control (LFC) in a restructured power system that operates under Bilateral based policy scheme. Various Integral Performance Criteria measures are taken as fitness function in PSO and are compared using overshoot, settling time and frequency and tie-line power deviation following a step load perturbation (SLP). The motivation for using different fitness technique in PSO is to show the behavior of the controller for a wide range of system parameters and load changes. Error based analysis with parametric uncertainties and load changes are tested on a two-area restructured power system. The results of the proposed PSO based controller show the better performance compared to the classical Ziegler-Nichols (Z-N) tuned PI and Fuzzy Rule based PI controller.

Real-time vehicle-to-grid control for frequency regulation with high frequency regulating signal

The large-scale popularization of electric vehicles(EVs)brings the potential for grid frequency regulation.Considering the characteristics of fast response and adjustment of EVs,two control strategies of automatic generation control(AGC)with EVs are proposed responding to two high frequency regulating signals extracted from area control error(ACE)and area regulation requirement(ARR)by a digital filter,respectively.In order to dispatch regulation task to EVs,the capacity of regulation is calculated based on maximum V2G power and the present V2G power of EVs.Finally,simulations based on a two-area interconnected power system show that the proposed approaches can significantly suppress frequency deviation and reduce the active power output of traditional generation units.

An Interactive Operating Demand Response Approach for Hybrid Power Systems Integrating Renewable Energy Sources

The increased deployment of renewable energy in existing power networks has jeopardized rotational inertia,resulting in system degradation and insta-bility.To address the issue,this paper proposes a demand response strategy for ensuring the future reliability of the electrical power system.In addition,a modified fuzzy logic control topology-based two-degree-of-freedom(fractional order proportional integral)-tilt derivative controller is designed to regulate the frequency within a demand response framework of a hybrid two-area deregulated power system.The test system includes thermal power plants,renewable energy sources(such as wind,parabolic trough solar thermal plant,biogas),and electric vehicle assets.To adaptively tune the controller’s coefficients,a quasi-opposition-based harris hawks optimization(QOHHO)algorithm is developed.The effectiveness of this algorithm is compared to other optimization algorithms,and the stability of the system is evaluated.The results demonstrate that the designed control algorithm significantly enhances system frequency stability in various scenarios,including uncertainties,physical constraints,and high penetration of renewables,compared to existing work.Additionally,an experimental assessment through OPAL-RT is conducted to verify the practicality of the proposed strategy,considering source and load intermittencies.