This paper summarizes a review of the distribution system state estimation (DSSE) methods, techniques, and their applications in power systems. In recent years, the implementation of a distributed generation has affected the behavior of the distribution networks. In order to improve the performance of the distribution networks, it is necessary to implement state estimation methods. As transmission networks and distribution networks are not similar due to variations in line parameters, buses, and measuring instruments, transmission state estimation cannot be implemented in distribution state estimation. So, some aspects, such as accuracy, computational time, and efficiency, should be taken into account when designing distribution state estimation methods. In this paper, the traditional methods are reviewed and analyzed with data-driven techniques in order to present the advantages and disadvantages of the various methods.

The manufacturers of photovoltaic (PV) panel give the data of three major points on I–V characteristics. However, this information alone is not sufficient to derive the five parameter and seven parameter models, i.e., single-diode and double-diode models. Hence, several population-based metaheuristic techniques are proposed in the literature. However, there is a need of well-balanced algorithm which is used for extracting the parameters of the diode model of PV panels from the datasheet. In this paper, a novel hybrid algorithm is proposed by combing the best features of a recently developed Marine Predators Algorithm (MPA) and Success History based Adaptive Differential Evolution (SHADE) algorithm. The principal algorithm for obtaining the optimum solution is MPA. However, during the search process to enhance the best solution region, self-adaptive DE based on the successive history of parameters is used. The derived objective function ensures the zero error at three important points of the I–V characteristics. Hence, the parameters extracted by using proposed method results in the I–V curves which are passing through the all three important points. Only three parameters out of five in single-diode model, and five parameters out of seven are optimized with the proposed algorithm and remaining are calculated analytically to reduce the burden on metaheuristic algorithm. MATLAB programming is used to test the proposed parameter extraction by using hybrid Marine Predators – Success History based Adaptive Differential Evolution (MP-SHADE) algorithm, and the results are compared with the other state-of-the-art metaheuristic techniques. The single-diode and double-diode models of three types of panels (monocrystalline, polycrystalline, and thin-film) are derived by using MP-SHADE algorithm.

Provision of secured and uninterrupted supply of power is an essential pre‐requisite for the electrical industry. Hence, FACTS devices need to be employed efficiently to ensure the security of the power system. This study proposes, a repetition of severity‐based strategy of placement of interline power flow controller (IPFC) using composite severity index (CSI) for contingency management. The formulated CSI is a combination of real power performance index) and line stability index L _mn . Thus, the CSI is expected to provide an exact measure of stress on the lines in terms of MW overloading and voltage instability. The IPFC is further tuned for a multi‐objective function using firefly algorithm. To verify the proposed methodology, it has been tested and implemented on IEEE 30 and Indian utility 62 bus system. The system has been further tested under highly stressful condition by increasing the load on the maximum loaded bus up to the critical value. The results have been presented and analysed to determine the effectiveness of IPFC for contingency management.

The study of power system planning, security, and control is crucial for determining optimal power flow (OPF), a highly nonlinear complicated issue. With the rapid use of renewable energy sources (RES), the power system is changing. The functioning of the power system remains complex when they are connected to the grid alongside traditional generators. The Optimal Power Flow control approach is intended to be used with these systems to execute energy management during RES and load demand fluctuations. In power systems, there are many objectives for better control, those are operating costs, emissions, active power losses, voltage collision, and stability. This review paper describes various linear, nonlinear, and metaheuristic techniques applicable to the power system networks accessible in the literature. The conventional optimization algorithms have been reintroduced while the power systems have been heavily predisposed over the last few years. The most crucial technical, adaptive and economic tool in this context is optimal power flow. In this paper, we compare OPF approaches related to objective functions. That aside from computational power, case study network, and publication dates for these algorithms are described. Finally, we will address some of the basic challenges that arise from the new OPF approach to the modern grid. Mainly, optimization techniques are compared in terms of reliability, accurateness, faster convergence, and applicability for single and multiple objective functions. Initially, the paper discusses on type of networks like AC or DC, constraints and its objectives on OPF, mathematical and Nonlinear programming (NLP) problems, various convex relaxation algorithms, and different optimization techniques reviewed. The heuristic and meta-heuristic algorithms like genetic algorithm, teaching-learning algorithm, honeybee mating algorithm, bat algorithm, imperialistic competition algorithm, black hole algorithm, artificial immune system algorithm, clonal selection algorithm, symbiotic organism algorithm, Jaya algorithm, invasive weed optimization algorithm were compared for various operational parameters. In this work, OPF with single or multi-objective functions like voltage stability, voltage deviation, emission, cost, and losses, consisting of conventional and renewable energy resources were considered. Additionally, Pseudo-code algorithms were presented for some recently evolved algorithms for ease of understanding of the readers.