The increasing integration of regional power grids, rising penetration of multigeneration sources, and day-ahead power agreements have raised the operational challenges on deregulated modern electric energy systems. Among various challenges, frequency regulation becomes more prominent in the restructured power system (RPS) due to increased uncertainties and intricacies among other operational challenges. A minor deviation in system frequency affects the safety, quality, stability, and operation of the interconnected power system (IPS). An appropriate control mechanism and energy storage device are essential to regulate the system's dynamic responses during continuously varying loading conditions. This study proposed a novel Salp swarm algorithm (SSA) optimized fuzzy-based proportional-integral-derivative filter (FPIDF) controller with redox flow battery (RFB) to regulate the frequency of a realistic multi-area multi-source (thermal-hydro-gas) interconnected power system. All probable contract transaction scenarios are simulated that can be possible in a deregulated power industry. Various nonlinearities such as time delay (TD), governor dead band (GBD), and generation rate constraint (GRC) are incorporated to resemble the realistic operational conditions of the IPS. The effectiveness of the suggested controller has been validated by comparing the system performances with a recently published sine-cosine algorithm (SCA) based proportional-integral (PI) controller and SSA-PID controller. The investigation has been further extended by incorporating RFB in the proposed system. The study reveals that the suggested controller provided superior system control under various system uncertainties in all contact scenarios of the competitive electricity market. Additionally, the system performances have been significantly enhanced due to quick response and precise control offered by RFB.

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The uncertain demeanour from wind generators and loads adversely affect the grid operational stability. Various control approaches have been explored to remedy the system uncertainties while maintaining generation and load demand balance. This study proposes a fuzzy‐based proportional–fractional integral–derivative with filter controller to sustain frequency stability in wind integrated power systems having different configurations. The controller parameters have been tuned using a recently developed coyote optimisation algorithm (COA). The proposed control approach is executed and validated on three distinct configurations of two‐area power systems. All test models are integrated with a doubly fed induction generator (DFIG) type wind turbine units (WTUs). Different case scenarios have been considered to analyse the efficacy of the proposed control strategy in the presence of WTU. Furthermore, the impact of inertial support delivered by the DFIG‐WTU and higher penetration of wind energy in the power system has been studied. The analysis reveals that the control scheme in coordination with WTU support reduces the stress on a wind turbine during the inertial control scheme and maintains the grid frequency stability under unexpected load disturbances. Stability and robustness analysis are also conducted to verify the validity of the introduced control approach.

Summary Mathematical model parameter estimation of solar photovoltaic (PV) cell, module and array represent a prodigious challenge in recent researches, where analytical, metaheuristic and hybrid techniques play a vital role for parameter extraction by using manufacturer's datasheet and experimental data, although the precise and highly reliable solution identification is still a complex problem to be solved. In this paper, a new Emperor Penguin Optimisation‐ (EPO) based parameter estimation approach for a single‐diode Rp solar PV model is presented that is cohesively analysed under different sets of temperature and irradiance (G). Validation of the proposed technique is perceived on the analysis of performance characteristics, that is, current‐voltage (I‐V) and power‐voltage (P‐V) of KC200GT, PWP201, and STP6 120‐36 PV modules under various simulation conditions. Moreover, estimated results are compared with the experimental data and several established estimation techniques in the literature for validation and demonstrate the proposed technique with highly precise outcomes having reduced computational cost for the PV model parameter extraction problem.