This study endeavours an effective frequency control of renewable‐based isolated two‐area interconnected microgrid (ICμG) without battery, incorporating wind power generation in area‐1, dish‐Stirling solar power generation system in area‐2 and other common distributed generation systems such as diesel engine driven generator, plug‐in hybrid electric vehicle, heat pump and freezer in both the areas. A recently developed heuristic optimisation technique called water cycle algorithm (WCA) is applied to optimally tune the parameters of the non‐integer fractional‐order proportional‐integral‐derivative (FOPID) controllers employed with ICμG. Application of WCA‐based FOPID controllers in frequency control two‐area ICμG without battery is a novel work. The comparative performance analysis of proportional‐integral‐derivative (PID), PID with filter and non‐integer order‐based FOPID controller with their gains tuned by particle swarm optimisation (PSO), improved PSO, firefly algorithm and WCA algorithms indicates the superiority of WCA‐based FOPID controller's under different case studies (considering different load disturbances, wind speed variation with real data, and solar irradiance) in terms of frequency deviation, tie‐line power, and objective function. Furthermore, the sensitivity analysis contemplates that WCA‐optimised FOPID controller can withstand ±25% change in synchronising coefficient, +20% change in loading condition and ±25% change in frequency bias constant without resetting the gain values.

This work presents a maiden approach of coordinated frequency control of novel solar tower (ST)‐Archimedes wave energy conversion (AWEC)‐geothermal energy conversion (GEC)‐biodiesel driven generator (BDDG)‐energy storage (ES) units and direct current (DC) links based independent three‐area interconnected microgrid system (ImGS). A recent metaheuristic technique, named butterfly optimisation algorithm (BOA) is applied to obtain the optimal gains of the controllers employed with the ImGS and system participation factors. The dynamic performance of proportional–integral derivative (PID), PID with filter (PIDN), proportional–fractional‐order integral derivative (PFOID) controllers with their gains tuned by different algorithms such as particle swarm optimisation (PSO), firefly algorithm (FA), whale optimisation algorithm (WOA), and BOA have been compared. Further, the effect of ES units and DC links in all the areas is analysed first time in ImGS. The results have established the superiority of the BOA‐based PFOID controllers under different real‐world scenarios in terms of frequency deviation, tie‐line power, and objective functions. Finally, rigorous sensitivity analysis has been conducted to evaluate the superiority of BOA‐optimised PFOID controller towards preserving system stability of ImGS with ±25% change in synchronising tie‐line coefficients and bias values, and +20% change in loading condition without resetting the nominal condition gain values.

This study proposes an earliest approach toward coordinated frequency stabilisation of wind turbine driven generator‐tidal power generation‐biodiesel driven generator‐micro‐turbine generator‐based islanded two‐area interconnected microgrid system with demand response support (DRS) mechanism. A recent bio‐inspired optimisation technique, named yellow saddle goatfish algorithm (YSGA) is employed to optimally tune the controller gains. The comparative dynamic performance of conventional proportional–integral–derivative (CPID), fractional order (FO) PID, dual‐stage PIFOD‐one plus PI [PIFOD‐(1 + PI)] controllers’ parameters optimised by several algorithmic tools such as particle swarm optimisation, firefly algorithmic tool, salp swarm technique and YSGA clearly designates the superiority of YSGA‐PIFOD‐(1 + PI) controller under different scenarios (considering the real‐time recorded wind and load data) in terms of change in frequency, tie‐line power fluctuation and objective function. Furthermore, the impact of the DRS mechanism in both areas is analysed first time under real‐time wind and load disturbances. Finally, the rigorous sensitivity analysis of YSGA‐optimised PIFOD‐(1 + PI) controller has been conducted with the variation of wind turbine driven generator gain, ±30% change in synchronising tie‐line factor, frequency bias value, microgrid system time constant and + 30% change in loading magnitude without retuning the optimal base condition values.

During the past few decades, there has been significant growth in the renewable energy market because of increased concern over global warming and the continuous depletion of fossil fuel resources. There is a promising solar thermal technology that utilizes low-temperature heat to generate electricity. The conversion process of thermal energy to electricity is based on the principle of an organic Rankine cycle (ORC). This study investigated a novel islanded hybrid power system consisting of an ORC low temperature solar thermal system, wind (WTG), diesel generation (DEG) set, and combined application of an energy storage system (ESS), such as a battery (BESS), super magnetic energy storage (SMES), and an ultracapacitor (UC) unit. Furthermore, the hybrid system was employed with a single controller (one of proportional-integral (PI), PI with derivative (PID), two-degree-of-freedom (2DOF) PI, and 2DOF PID controllers) with proportionate gains to the DEG, and the ESS, which is another unique aspect of this work. Moreover, a comparative performance assessment of the flower pollination algorithm (FPA) to tune the PI, PID, 2DOF PI, and 2DOF PID controllers was carried out. Finally, the performance of the above hybrid system was compared with different ESS combinations, namely, (i) only BESS, (ii) BESS + UC, and (iii) BESS + SMES. The simulation results indicated that a renewable integrated isolated power system with BESS + SMES provided a better response than the other ESS combinations. In fact, the presence of comparative dynamic responses verified the superiority of an FPA-tuned 2DOF PID compared with other FPA-tuned controllers.

Striving for the suppression of greenhouse emissions, the modern power network is facing fundamental changes with the utilization of renewable energies (REs) for the future carbon-free society. The utilization of intermittent renewable-green power needs a better power management system and virtual power plant (VPP) can be a vital candidate that meets this demand. This study investigates a coordinated control grid integrated virtual power plant (VPP) in the presence of Central Receiver Solar Thermal System (CRSTS), Wind Turbine Generator (WTG), and Electric Vehicle (EV). To this end, CRSTS employed with thermal storage acts as a dispatchable renewable generating unit and coordinated control of the system units are achieved using the available control strategy on interconnected microgrids in the modified form, employing communication time delay. The proposed control strategy employs the proportional-integral (PI) and PI-derivative (PID) controller. Coordinated power control with real-time communication delay in grid integrated VPP in presence of CRSTS, WTG, and EV is a novel approach. Genetic algorithm (GA), Particle Swarm Optimization (PSO), Slap Swarm Algorithm (SSA), and recent Butterfly Optimization Algorithm (BOA) are used for tuning the necessary control parameters. The results establish the superiority of the BOA over SSA and PSO in suppressing system frequency deviations and tie line power deviation. The analysis of the dynamic response reveals that the consideration of the communication delay in the system expressively impedes the stable operation of the power system.

Smart grid technology enables active participation of the consumers to reschedule their energy consumption through demand response (DR). The price-based program in demand response indirectly induces consumers to dynamically vary their energy use patterns following different electricity prices. In this paper, a real-time price (RTP)-based demand response scheme is proposed for thermostatically controllable loads (TCLs) that contribute to a large portion of residential loads, such as air conditioners, refrigerators and heaters. Wind turbine generator (WTG) systems, solar thermal power systems (STPSs), diesel engine generators (DEGs), fuel cells (FCs) and aqua electrolyzers (AEs) are employed in a hybrid microgrid system to investigate the contribution of price-based demand response (PBDR) in frequency control. Simulation results show that the load frequency control scheme with dynamic PBDR improves the system’s stability and encourages economic operation of the system at both the consumer and generation level. Performance comparison of the genetic algorithm (GA) and salp swarm algorithm (SSA)-based controllers (proportional-integral (PI) or proportional integral derivative (PID)) is performed, and the hybrid energy system model with demand response shows the supremacy of SSA in terms of minimization of peak load and enhanced frequency stabilization of the system.