Non-linear loads are a major contributor to harmonics in the power system. Harmonics harm electrical network components by destroying the sinusoidal waveform of current and voltage. The goal of this article is to provide and control active and reactive power, as well as to reduce network harmonic currents caused by non-linear loads. As a result, the proposed method decreases grid current harmonics by employing a Battery Energy Storage system and providing a control mechanism for it, in addition to managing and supplying the amount of active and reactive power of the nonlinear load individually. Power control and harmonic reduction are the two parts of the proposed control system. Power control is done with classic PI controllers, whose coefficients are obtained with Ziegler-Nichols method, while harmonic reduction is done with PI-Fuzzy controllers. The current harmonic signal is sent into the PI-Fuzzy controller, while the main component (50 Hz) is isolated from it. The simulation results in three distinct scenarios demonstrate that the proposed approach can regulate the active and reactive powers of the nonlinear load under different charging and discharging situations, as well as reduce current harmonics up to the 31st harmonic.

This study aims to assess the potential of coupling solar PV power plants with Battery Energy Storage System ((BESS)) to curtail load-shedding and provide a stable and reliable baseload power generation in Zimbabwe. Data from geographical surveys, power plant proposals, and investment information from related sources were reviewed and applied accordingly. Areas considered to be of good potential to employ the use of (BESS) were identified considering such factors as feasibility of PV plants, proximity to transmission lines, the size of a town or neighborhood, and Energy demands for (BESS) Return On Investment (ROI) calculations. Previous studies have proven that 10% of the suitable land for PV systems has the capability to generate thirty times the current power demand of the nation operating even with the least efficiency. In recent years, coupling renewable Energy sources with a suitable Energy Storage system yielded improved performances, giving consumers a reliable, stable, and predictable grid. (BESS) technologies on the utility scale have improved in recent years, giving more options with improved safety, and decreasing the purchase costs, too.

In this paper, the optimal allocation of Battery Energy Storage in the distribution network is performed for peak shaving and maximizing profitability. To this end, indicator shave been introduced using hourly load information, feeder upgrade cost and electricity sales price to various tariffs. Then, using the Analytic Hierarchy Process (AHP), the indicators are weighted and a suitable feeder is indicated for installing Energy Storage. Then, to achieve the maximum possible peak shaving and maximize profit, an economic objective function is defined to determine the optimal sizing and charge-discharge of the Energy Storage. The objective function includes the investment and operating cost of Battery Energy Storage and the profits of Energy arbitrage, deferring facility investment, environmental issues, and reducing the upstream access cost. Appropriate constraints are considered according to the peak shaving, range of Battery power and Energy capacity as well as balance in the amount of charge and discharge. Due to the nonlinearity of the objective function, the components involved in the nonlinearity of the objective function are determined according to the heuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Tabu Search (TS)) and then the objective function is solved by the Interior-point linear programming. The results provide the most suitable Battery type and optimization method among the introduced batteries and methods while fulfilling the objectives.

This study presents modeling and simulation of a stand-alone hybrid Energy system for a base transceiverstation (BTS). The system is consisted of a wind and turbine photovoltaic (PV) panels as renewableresources, and also batteries to store excess Energy in order to boost the system reliability. Two differenttypes of batteries are considered for Storage purposes; lead-acid and vanadium redox-flow batteries (VRB)batteries. Most stand-alone Energy systems for various applications take advantage of at least a singleStorage technology, generally lead-acid batteries. However, with recent advances in different Batterytechnologies, vanadium redox-flow batteries could be taken into account as reliable candidate. Thevanadium redox-flow Battery has a desirable prospect due to its extended life span and also the potentialfor separating and scaling up involved nominal power and nominal Energy. The system is modelled andsimulated hourly (quasi-dynamically) in Matlab for an operational year. The model utilizes insolation,wind speed and air temperature data. The system performance has been assessed with a mobile telephoneBase Transceiver Stations (BTS) as the case study. Simulations results have shown that the suggestedmodel can be used to study the effect of the altering weather conditions on each charge/discharge cyclesand batteries voltage. Finally the proposed model yields the optimal Battery network design for a varietyof applications.

Vanadium redox flow batteries (VRFBs) with large Energy Storage capability are one of the most important electrochemical technologies in the world. The specific design of these batteries has made it possible to increase the Storage capacity to very high values. Hence, this technology is referred to as the electricity supplier of cities in the not too distant future. In this article, fabrication, development, and the simulation of VRFBs in order to take an important step towards achieving technical knowledge of this promising Storage technology, have been considered. In this regard, first, the designed cell structure was introduced and the method of material selection and initial preparation for the Battery startup were described. In addition, the governing equations of the system were presented. Then, experimental and numerical investigation of the system performance was followed by simulation validation. Evaluation and analysis of cyclic performance, system efficiency, effect of current density, electrolyte concentration, pressure distribution and electrolyte velocity distribution were among other items of interest in the present work. The model predictions were validated with an average error of 4.92% against experimental data of charge and discharge cycles at current densities of 40 and 60 mA/cm2. The results obtained from cyclic performance of the Battery clearly indicate that the average coulombic efficiencies of the system at current densities of 40, 50, and 60 mA/cm2 are 90.25%, 92.45%, and 94.35%, respectively. Moreover, the Storage capacity of 133 mA. h with a pressure drop of 2400 Pa were obtained for the electrolyte concentration of 1 mol/L and the volumetric flow rate of 40 mL/min, which indicates the proper hydrodynamic and kinetic performance of the designed system.

Using Energy Storage technologies with proper control can improve the flexibility of power systems and cost-effective performance. However, none of the existing types of Storage systems can optimally respond under any conditions. An independent Storage solution is mostly limited by Energy and power density, response speed, lifetime, and cost. On the contrary, hybrid Energy Storage systems are composed of two or more Storage systems that usually have complementary features to achieve much better performance in different operating conditions. Superconducting magnetic Energy Storage systems (SMESs) with high power density can be used in high Energy density Storage systems such as lithium batteries (LBs) to create hybrid Energy Storage systems (HESSs) in electric vehicles. This paper proposes an optimal robust super-twisting sliding mode control (ORST-SMC) to increase the controllability of this Energy Storage system. By fast estimating the perturbations through the high-gain perturbation observer, the proposed controller can compensate for the uncertainties and nonlinearities of the modeling of the hybrid Energy Storage systems as an additional input. Considering the multi-objective nature of the nonlinear electric vehicle control problem, a multi-objective stochastic fractal search algorithm (MOSFSA) is used to extract the optimal parameters of the proposed controller. The proposed system has been modelled using MATLAB-Simulink software. The simulation results confirm that the proposed ORST-SMC controller has significantly improved the system transient responses, as well as provided accurate and fast-tracking performance.

Among various Storage technologies used for the Energy Storage systems, the supercapacitors, the Pb-acid-Batteries (PABs) and the lithium-Batteries (LBs) are widely used for microgrid applications. The supercapacitors with high-power density are suitable for fast power regulations; conversely, the PABs have high-Energy density, which makes them suitable for long-term Energy management. Since the PABs and the supercapacitor can complement each other deficiencies, their combination as a hybrid Energy Storage system is used. However, the LB has both high-Energy and high-power densities. Therefore, an LB Energy Storage System (L(BESS)) can similarly function like a Pb-acid Battery-supercapacitor-hybrid-ESS (PSHESS). This paper tends to determine which one is technically and economically more suitable for applications in islanded microgrids. For this purpose, a frequency control and Energy management scheme is proposed. It maintains the balance between demand and supply, and also keeps the microgrid frequency within safe operational limits using the least needed sizes for the Energy Storage systems. Simulation results reveal the costs of LI(BESS) and PSHESS would be $140325.93 and $209408.37, respectively, which shows that the cost of the PSHESS is $69082.44 or almost 49.2 % more than the cost of the LI(BESS). This indicates that the L(BESS) is more cost-effective than the PSHESS.

This research paper comprehensively analyzes the advantages of integrating Energy Storage resources into an Energy management system, highlighting how it can significantly improve profitability and overall Energy quality. One of the key benefits is the ability to regulate charging and discharging cycles, which helps prolong the service life of Storage devices. In addition, the study delves into the influence of consumer behavior and the Internet of Things (IoT) on Energy Storage charge management, identifying important insights for enhancing efficiency. The optimization process for this investigation utilizes the YALMIP and MOSEK toolboxes, ensuring rigorous and accurate results. The experimentation is conducted on an IEEE standard 33-bus network, offering a robust foundation for real-world applications. The research outcomes demonstrate remarkable enhancements in both technical and economic parameters, including Energy Storage resources. By harnessing the potential of Energy Storage, businesses and industries can achieve greater cost savings and operational efficiencies. Furthermore, the paper considers the longevity of Storage resources, comprehensively comparing results. This factor is crucial in determining such systems' long-term benefits and sustainability. In conclusion, the study underscores the immense advantages that IoT technology brings to Energy management and its positive impact on consumers. By leveraging IoT capabilities and integrating Energy Storage resources intelligently, optimal consumption management can be achieved, leading to a more sustainable and efficient Energy ecosystem.