• Energy Research

SummaryMicrogrids (MGs) are known as suitable options to accommodatethe high penetration of renewable energies, like solar and wind. MGs have provided the requirements of controlling and adjusting these sources. In addition, batteries are becoming indispensable components of MGs because of their capabilities in addressing the renewable energies' power output intermittency. In MGs, the problem of smart energymanagement along with battery sizing has been introduced as the necessity to ensure the efficient use of renewable sources and decrease traditional fossil‐fuel‐based generation technology penetration level in power systems. Accordingly, a novel method is presented in this paper to effectively address the above‐mentioned requirements, utilizing the modified shuffled frog leaping algorithm (MSFLA), applied to different case studies. The results, obtained from the numerical simulation, are compared to several well‐established optimization approaches to verify the performance of MSFLA. In terms of computational efficiency and quality of the obtained solution, the MSFLA has demonstrated promising outcomes, together with superior performance in comparison with other algorithms. The results show that the presented framework, including the battery sizing, would be very beneficial to minimize the operating cost of MGs.

An excess non-linear and unbalanced load application increases the power quality (PQ) problem by injecting harmonic current. The avoidance of neutral current control creates additional PQ problems due to excess circulating current in modern 3φ4W applications. Therefore, this manuscript suggests improved αβ transform-based voltage and current control approaches for both 3φ3W and 3φ4W shunt active filter (SAF) applications. In the proposed approach, a novel combined voltage and current (NCVC) control approach is presented for modern 3φ3W systems by using voltage and closed-loop current controllers. However, due to the absence of a neutral current, the NCVC is not sufficient for 3φ4W system application. Therefore, by considering the current reference parameter generated from the NCVC, a novel harmonic compensation technique (NHCT) is proposed with proper mathematical expressions for the 3φ4W application. To show the importance of NHCT over traditional P-Q-R control, the developed MATLAB/Simulink model is tested by using different 1φ and 3φ nonlinear/unbalanced load conditions. The comparative results indicate that, by using NHCT, the 3φ4W system contains a lesser total harmonic distortion (THD) and harmonic mitigation ratio (HMR), less ripple frequency, an improved power factor, a lesser neutral current, and a balanced active/reactive power condition. From the above comparative analysis results, it is found that the overall improvement percentage is 66.78%. The above findings justify the significance of the NCVC and NHCT approach during both unbalanced and non-linear load-based 3φ4W applications.

Recent developments have increased the availability and prevalence of renewable energy sources (RESs) in grid-connected microgrids (MGs). As a result, the operation of an MG with numerous RESs has received considerable attention during the past few years. However, the variability and unpredictability of RESs have a substantial adverse effect on the accuracy of MG energy management. In order to obtain accurate outcomes, the analysis of the MG operation must consider the uncertainty parameters of RESs, market pricing, and electrical loads. As a result, our study has focused on load demand variations, intermittent RESs, and market price volatility. In this regard, energy storage is the most crucial facility to strengthen the MG’s reliability, especially in light of the rising generation of RESs. This work provides a two-stage optimization method for creating grid-connected MG operations. The optimal size and location of the energy storage are first provided to support the hosting capacity (HC) and the self-consumption rate (SCR) of the RESs. Second, an optimal constrained operating strategy for the grid-connected MG is proposed to minimize the MG operating cost while taking into account the optimal size and location of the energy storage that was formerly determined. The charge–discharge balance is the primary criterion in determining the most effective operating plan, which also considers the RES and MG limitations on operation. The well-known Harris hawks optimizer (HHO) is used to solve the optimization problem. The results showed that the proper positioning of the battery energy storage enhances the MG’s performance, supports the RESs’ SCR (reached 100% throughout the day), and increases the HC of RESs (rising from 8.863 MW to 10.213 MW). Additionally, when a battery energy storage system is connected to the MG, the operating costs are significantly reduced, with a savings percentage rate of 23.8%.

Battery lifetime represents a significant concern for the techno-economical operation of several applications based on energy storage. Moreover, the charging method is considered as one of the main critical elements in defining and influencing the operating lifetime of batteries. Several charging techniques have been addressed in the literature, however almost all of them are suffering from lack of temperature feedback in order to maintain battery lifetime. This paper presents a new high-reliable charging method for battery energy storage systems (ESSs). The proposed temperature compensated multi-step constant current (TC-MSCC) method is developed based upon the modified (MSCC) charging method. It enhances the operating lifetime of batteries by employing a feedback from the battery temperature to control the duration and starting time of each charging current step. Compared with the traditional charging methods addressed in the literature, the proposed TC-MSCC method achieves faster charging than the conventional constant current (CC) and the constant-current constant-voltage (CC-CV) methods. Moreover, the proposed TC-MSCC method possesses longer operating battery lifetime with reduced thermal stresses compared to the traditional MSCC methods. The proposed charging method is verified by simulation and experimental results using 9 Ah lead-acid battery. However, the new proposed TC-MSCC method is generalized and can be applied to various types of batteries. The detailed performance comparisons and results show the superiority of the proposed methods over the most widespread charging methods in the literature.

In the third millennium, developing nations face the most pressing challenges involving climate change, fossil fuel scarcity, ozone depletion, global warming, and the production of greenhouse gases. To mitigate these challenges, renewable energy-driven power generation systems are of paramount importance. Present paper aims at proposal of two novel configurations for clean electricity generation with high-efficiency. The first system (standard system) is composed of an OFWH-assisted steam Rankine cycle and a gas turbine cycle which is driven solely by biomass energy. On the other hand, the second system (hybrid system) uses biomass and geothermal energy to drive the system, where the geothermal energy heats the water entering the OFWH and augments the power generation. Detailed analyses and comparisons between two systems are conducted using thermodynamics and exergoenvironmental methods. After completing the parametric analysis, a Pareto front is obtained for highest efficiency and lowest exergoenvironmental index (EEI). A notable finding of the parametric study is that the hybrid system is less energy efficient than the standard system, however it has higher exergetic efficiency and lower negative impacts on environment. Furthermore, increasing the pressure ratio of the air compressor leads to a consistently increasing EEI, while output electricity and exergy and energy efficiencies show an increasing-decreasing trend. Based on the bi-objective optimization, the gasifier with 3569 kW is the most exergy destructive component of the system in the base case and optimal case. Finally, the optimum values are obtained as 0.4734 and 42.63% for the EEI and exergy efficiency, respectively.