Robust Load Frequency Control of Hybrid Solar Power Systems Using Optimization Techniques

It is necessary to predict solar photovoltaic (PV) output and load profile guarantee the security, stability, reliability of hybrid power systems. Severe frequency fluctuations in systems are expected due intermittent nature unexpected variation load. This paper proposes designing a PID controller along with integration battery energy storage system (BESS) plug-in electric vehicle (PHEV) for damping system. The PV predicted high accuracy using artificial neural networks (ANN) given that irradiance cell temperature inputs model. also forecasted considering factors affecting ANN. Optimum values have been found genetic algorithm, particle swarm optimization, bee colony, firefly algorithm integral absolute error (IAE), square (ISE), time (ITAE) objective functions. IAE, ISE, ITAE, Rise time, settling peak overshoot maximum deviation measured comparison effectiveness. transient behavior has further improved by utilizing from BESS/PHEV results demonstrate efficacy suggested design control method ISE function compared those obtained conventional, techniques.