One of the important tasks for increasing the efficiency of photovoltaic (PV) system is the development and improvement of the maximum power point tracking algorithms (MPPT). These MPPT algorithms lead to the ability to catch efficiently the global maximum power point of the partially shaded PV array. One of these trackers is the particle swarm optimization (PSO) algorithm which is one of the Soft computing techniques. The conventional PSO based trackers have many advantages such as the simplicity of hardware implementation and independence from the installed system. The actual problem of the practical application of PSO is the determination of its parameters to ensure high effectiveness of extracting the global MPP. Analysis of scientific papers devoted to the PSO algorithm has shown that there is currently no methodology for the optimal parameters' selection of PSO algorithm based maximum power trackers for the PV system. This paper aims to create a convenient and reasonable method for choosing the optimal parameters of the PSO algorithm, taking into account the topology and parameters of the DC-DC converter and the configuration of solar panels. A new method for selecting the parameters of a buck converter connected to a battery has been presented. The optimal value of the sampling time for the digital MPP controllers, providing their maximum performance; has been determined based on a new methodology. Matlab/Simulink software package is used as the main research tool. The prominent outcomes identify that the modified PSO and its designed parameters best meet the requirements of the MPPT controller for the PV system.

This article proposes a review map for comparative designs of wireless high-power transfer (WHPT) systems using single- and double-resonance blocks. In-depth analyses and key guidelines are provided to design high-efficiency WHPT systems while keeping zero-phase-angle/unity-power-factor and constant - current/voltage supply to the load. Basic single- and double-resonance blocks based on LC- resonant circuits are analyzed. Then, resonant S- and T- blocks are recommended as the best transmission blocks for competitive designs. The proposed approach is applied to map recent developments on the wireless charging technologies for electric vehicles (EVs), especially for weak-coupling systems and dynamic-charging technologies. The proposed design approach offers a systematic and effective methodology to quickly evaluate current technologies and solutions for WHPTs in EVs applications. In extension, this article indicates different control strategies for WHPTs, especially for optimal-efficiency tracking. Design guidelines and control strategies are provided to achieve the maximum efficiency at a standard resonance frequency against variations from loads and coupling coefficients in operation which can easily map to recent research works and future research directions. Experimental verifications of dominant designs are also presented to validate the proposed approach. The map for comparative designs and control structures presented in this article aims to serve as a guideline and to ease the initial steps for other researchers in this area.

In some countries and regions, water distribution and treatment consume a considerable amount of electric energy. This paper investigates the water network's potential ability to provide demand response services to the power grid for the management of renewable resources under the framework of a distribution-level water-energy nexus (micro WEN). In particular, the hidden controllable water loads, such as irrigation systems, were closely studied as virtual energy storage to improve the flexibility of electrical grids. An optimization model is developed for the demand-side management (DSM) of micro WEN, and the simulation results assert that grid flexibility indeed benefits from taking controllable water loads into account. Although the proposed optimal DSM model is a computationally intractable mixed-integer nonlinear programming (MINLP) problem, quasi-convex hull techniques were developed to relax the MINLP into a mixed-integer convex programming (MICP) problem. The numerical study shows that the quasi-convex hull relaxation is tight and that the resulting MICP problem is computationally efficient.

We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the drivers. Rather than one prediction (step, model, or variables) made by conventional LSTM models, three charging load (energy demand) predictions of EVs were made depending on different multi-feature inputs. Data fusion was used to combine and optimize the different charging load prediction results. The performance of the final implemented model was evaluated by the mean absolute prediction error of the forecast. The implemented model had a prediction error of 3.29%. This prediction error was lower than initial prediction results by the LSTM model. The numerical results indicate an improvement in the performance of the EV load forecast, indicating that the proposed model could be used to optimize and improve EV load forecasts for electric vehicle charging stations to meet the energy requirements of EVs.

Inverter-based distributed generators (DGs) have been customarily used for voltage unbalance (VU) mitigation in microgrids. The sole dependency on DGs for VU mitigation may not be justified, particularly in islanded microgrids. Demand side management (DSM) can be a potential candidate for VU mitigation in microgrids. Therefore, this paper presents a new VU mitigation scheme for an islanded microgrid by coordinating photovoltaic (PV) grid-tied inverters, and thermostatically controlled loads (TCLs). A negative sequence compensation loop working in parallel with a positive sequence compensation loop is designed for PV inverters for VU mitigation. Also, a voltage dependent model of TCLs, unlike the conventional one, along with the control strategy for VU mitigation is presented. The proposed control of TCLs not only minimizes VU, and hence increases the dynamic reactive power reserve of the inverter based DGs, but also effectively maintains the customers’ thermal comfort. The proposed VU mitigation scheme is studied with comprehensive simulations in PSCAD/EMTDC, and is verified using a real-time digital simulator, OPAL-RT. Simulations and real-time results of the proposed scheme demonstrated the regulation of voltage unbalance factor (VUF) within the permissible range defined by IEC and CIGRE Working Group 36.07.

Among all renewable energy sources, solar cells are considered the most popular solution for a clean source of energy and have a wide range of applications from few watts to Megawatt industrial and domestic loads. Building a precise mathematical model based on nonlinear equations for solar cells as well as photovoltaic (PV) modules is an essential issue for reasonable performance assessment, control and optimal operation of PV energy systems. In the current study, a novel optimization algorithm, Tree Growth Algorithm (TGA), is applied for accurate and efficient extraction of the unknown solar cell and PV module parameters. TGA is applied for estimating the unidentified parameters of PV models. Single diode model (SDM), double diode model (DDM) and three diode model (TDM) are investigated in the mathematical models of both solar cells and PV modules. The obtained results from the application of TGA to achieve this objective are compared with different algorithms reported in the literature. Moreover, the results demonstrated that the proposed algorithm of TGA superior to other reported methods. The good matching of the I-V characteristic curve of the computed parameters with those of the measured data from the manufacturer’s PV modules/cells datasheet proved that the proposed TGA may function as a competitor to the methods provided in literature for parameters’ identification of PV of solar cells.