This paper presents a new structure for non-isolated and non-inverting DC-DC converters with high voltage gain harnessing the fundamentals of the voltage lift technique. The proposed topology is a suitable structure for low voltage applications. The operation principles, the steady-state relations, and different switching strategies to further improve the voltage gain performance of the proposed converter are described. A hybrid utilization of complementary switching approach and simultaneous switching of two switches is proposed to achieve the highest voltage gain in different duty cycles. Furthermore, a theoretical analysis of power losses is provided. The suggested DC-DC converter architecture features high voltage gain, high efficiency, and low stress on semiconductor devices. In order to demonstrate these advantages, the structure is compared with some recently-presented high step-up converters in terms of efficiency, voltage gain, and voltage stress. Moreover, A 200W laboratory prototype is developed with experiments carried out to validate the given theories and feasibility of the proposed converter topology.

Since the last decade, partial shading conditions (PSCs) and its adverse influences on photovoltaic (PV) system performance have received due attention. It motivates researchers to explore methods to diminish/disperse the shading effects and/or novel PV array configurations to sustain under PSCs. To diminish the effects of PSCs, this paper presents a comprehensive review of various PV array configuration models for PV systems and metaheuristic approaches for shade dispersion effectively. Different PV array modeling approaches are identified, emphasizing their benefits, inadequacies and categorized according to vital features such as shade dispersion and improved performance in terms of efficiency; fill factor (FF), and maxima power, minimized power losses (PL) primarily. Besides these various PV array configurations such as hybrid, reconfigured, mathematical/game puzzle based advanced configurations are uniquely discussed with the existing configurations. In the current scenario, the metaheuristic algorithms are explored and widely accepted by researchers due to the long wire length requirement for PV array reconfiguration. This paper discusses and deliberates recent developments in methods of solar PV performance enhancement that deserves further study. Overall, the present study is helpful for academicians and researchers in the committed solar power installation area. INDEX TERMS Photovoltaic system, partial shading condition (PSC), game puzzle, total cross-tied (TCT), honey comb, bridge link, metaheuristic algorithm.

Today's electrical power system became more complex interconnected network that is expanding every day. The transmission lines of the power system are more severely loaded than ever before. Hence, the power system is facing many problems such as power losses increasing, voltage instability, line overloads, etc. The optimization of real and reactive powers due to the installation of energy resources at appropriate buses can minimize the losses and improve the voltage profile especially, for congested networks. As a result, the optimal power flow problem (OPF) is considered more important tool for the processes of planning and operation of power systems. OPF is a very significant tool for power system operators to meet the electricity demand of the consumers efficiently, and for the reliable operation of the power system. However, the incorporation of renewable energy sources (RESs) into the electrical grid is a very challenging problem due to their intermittent nature. In this paper, the proposed power flow model contains three different types of energy sources: thermal power generators representing the conventional energy sources, wind power generators (WPGs), and solar photovoltaic generators (SPGs) representing RESs. Uncertain output powers from WPGs and SPGs are forecasted with the aid of Weibull and lognormal probability distribution functions (PDF), respectively. The under and overestimation output powers of RESs are taken into consideration while formulating the objective function through adding a penalty and reserve cost, respectively. Moreover, carbon tax is imposed to the main objective function to help in reducing carbon emissions. A jellyfish search optimizer (JS) is employed to reach optimization in the modified IEEE 30-bus test system to validate its feasibility. To examine the effectiveness of the proposed JS algorithm, its simulation results are compared with the results of four other nature-inspired global optimization algorithms. The developed OPF algorithm considers several practical cases such as generation uncertainty of renewable energy sources, timevarying load and the ramp rate limits of thermal generators. The simulation results show the effectiveness of the JS algorithm in solving the OPF problem in terms of minimization of total generation cost and solution convergence.

This work presents a new robust control technique which combines a model predictive control (MPC) and linear quadratic gaussian (LQG) approach to support the frequency stability of modern power systems. Moreover, the constraints of the proposed robust controller (MPC-LQG) are fine-tuned based on a new technique titled Chimp optimization algorithm (ChOA). The effectiveness of the proposed robust controller is tested and verified through a multi-area power system (i.e., single-area and two-area power systems). Each area contains a thermal power plant as a conventional generation source considering physical constraints (i.e. generation rate constraint, and governor dead band) in addition to a wind power plant as a renewable resource. The superiority of the proposed robust controller is confirmed by contrasting its performance to that of other controllers which were used in load frequency control studies (e.g., conventional integral and MPC). Also, the ChOA's ingenuity is verified over several other powerful optimization techniques; particle swarm optimization, gray wolf optimization, and ant lion optimizer). The simulation outcomes reveal the effectiveness as well as the robustness of the proposed MPC-LQG controller based on the ChOA under different operating conditions considering different load disturbances and several penetration levels of the wind power.

This paper deals with power generation through solar photovoltaic (SPV) system and its implementation as grid‐tied and stand‐alone systems. The major setbacks of SPV system of low conversion efficacy, that can be enhanced through the maximum power point tracking (MPPT) algorithm. A modified incremental conductance (MIC), termed as error based incremental conductance MPPT, has been proposed, and its behaviour is comprehensively compared with classical perturb and observe (P&O) and incremental conductance (IC) techniques. Dealing with the on‐grid application of SPV system, maximum power obtained through MIC is fed to a three‐phase grid operating at unity power factor (UPF), and the quality of grid current is monitored. Further, the power obtained through MIC finds its application in designing an SPV‐diesel generator (DG) based hybrid renewable energy system (HRES) for areas either not connected to the grid or have insufficient fossils. The stochastic nature of source or uncertain load demand leads to deviation in system frequency. This paper proposes an intelligent, comprehensive supervisory optimal‐fuzzy‐proportional‐integral‐derivative (O‐F‐PID) controller for load frequency control (LFC). The performance of proposed O‐F‐PID controller has been vividly compared with the designed optimal‐PID (O‐PID) and conventional PID (C‐PID) controllers under varying source and load demand conditions.