An attempt is made in this paper to present the application, design, and performance analysis of a novel optimal controller (OC) for automatic generation control (AGC) of interconnected two-area electrical power systems in a deregulated power environment with energy storage units. The OC is designed via full state vector feedback strategy to carry out the study. Swift acting energy storage units such as redox flow batteries (RFBs) are integrated into the power system models, and their efficacy in boosting AGC performance is executed and compared. Initially, the efficacy of OC is investigated in a restructured two-area single-source thermal system, and then, the study is extended to a proposed more realistic restructured two-area multi-source thermal-hydro-diesel-gas power system. It is observed that OC is able to satisfy the AGC requirement under different power contracts in the open electricity market and shows better performance in comparison to a recently published artificial cooperative search algorithm optimized proportional integral controller. MATLAB simulation results demonstrate the improvements in the dynamic performance of the system in the presence of RFB. Sensitivity analysis reveals the robustness of OC under ample variations in the system parameters, initial loading and size and positions of uncontracted load power demands.

This paper offers three easy-to-use metaphor-less optimization algorithms proposed by Rao to solve the optimal power flow (OPF) problem. Rao algorithms are parameter-less optimization algorithms. As a result, algorithm-specific parameter tuning is not required at all. This quality makes these algorithms simple to use and able to solve various kinds of complex constrained optimization and engineering problems. In this paper, the main aim to solve the OPF problem is to find the optimal values of the control variables in a given electrical network for fuel cost minimization, real power losses minimization, emission cost minimization, voltage profile improvement, and voltage stability enhancement, while all the operating constraints are satisfied. To demonstrate the efficacy of Rao algorithms, these algorithms have been employed in three standard IEEE test systems (30-bus, 57-bus, and 118-bus) to solve the OPF problem. The OPF results of Rao algorithms and the results provided by other swarm intelligence (SI)/evolutionary computing (EC)-based algorithms published in recent literature have been compared. Based on the outcomes, Rao algorithms are found to be robust and superior to their competitors.

A new hybrid meta-heuristic approach Jaya–PPS, which is the combination of the Jaya algorithm and Powell’s Pattern Search method, is proposed in this paper to solve the optimal power flow (OPF) problem for minimization of fuel cost, emission and real power losses and total voltage deviation simultaneously. The recently developed Jaya algorithm has been applied for the exploration of search space, while the excellent local search capability of the PPS (Powell’s Pattern Search) method has been used for exploitation purposes. Integration of the local search procedure into the classical Jaya algorithm was carried out in three different ways, which resulted in three versions, namely, J-PPS1, J-PPS2 and J-PPS3. These three versions of the proposed hybrid Jaya–PPS approach were developed and implemented to solve the OPF problem in the standard IEEE 30-bus and IEEE 57-bus systems integrated with distributed generating units optimizing four objective functions simultaneously and IEEE 118-bus system for fuel cost minimization. The obtained results of the three versions are compared to the Dragonfly Algorithm, Grey Wolf Optimization Algorithm, Jaya Algorithm and already published results using other methods. A comparison of the results clearly demonstrates the superiority of the proposed J–PPS3 algorithm over different algorithms/versions and the reported methods.

Optimal power flow solutions are required for the cost-effective operation and control of an existing power system as well as for its future expansion planning. In this paper, a hybrid meta-heuristic Jaya-Powell’s Pattern Search method is proposed to solve the optimal power flow problem integrated with distributed generating units. Powell’s Pattern Search method has been incorporated into the Jaya algorithm in three different ways, resulting in three variants namely; Jaya-PPS1, Jaya-PPS2, and Jaya-PPS3. Performance of these variants are evaluated by solving optimal power flow in IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. In all the cases, Jaya-PPS1 is found to provide a lower objective function value with a smaller deviation over multiple runs than other algorithms. For example, the combined objective function found by Jaya-PPS1 for IEEE 30-bus system is 937.340, with a reduction of 2.86% of the original system. This benefit increases further with system size.

For planning the economic operation and control of an existing electricity grid and its future extension planning, the optimal power flow (OPF) results are essential. In this paper, a hybrid algorithm is proposed, which is based on a sine-cosine mutation operator and a modified Jaya (SCM-MJ) algorithm to solve the OPF problems. The efficacy of the SCM-MJ algorithm is primarily evaluated using thirteen (unimodal and multimodal) mathematical benchmark functions. Later, the SCM-MJ algorithm is applied on the Algerian 59-bus system and IEEE 118-bus test system to handle the OPF problems. The SCM-MJ has successfully achieved a minimum value of an objective function over several runs than other modern meta-heuristic optimization approaches in all the thirteen mathematical benchmark functions as well as in OPF case studies. For example, in the Algerian 59-bus system, the quadratic fuel cost obtained by SCM-MJ is 1688 $/hour, with a saving of 13.12% of the initial fuel cost. This benefit increases further with the size of the system. The SCM-MJ algorithm has provided high-quality solutions for mathematical benchmark functions and OPF problems quickly and efficiently. The comparison of numerical outcomes demonstrates that the suggested SCM-MJ algorithm dominates over other approaches for solving the OPF problem.