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Accurately simulating photovoltaic (PV) modules requires precise parameter extraction, a complex task due to the nonlinear nature of these systems. This study introduces the Mother Tree Optimization with Climate Change (MTO-CL) algorithm to address this challenge by enhancing parameter estimation for a solar PV three-diode model. MTO-CL improves optimization performance by incorporating climate change-inspired adaptations, which affect two key phases: elimination (refreshing 20% of suboptimal solutions) and distortion (slight adjustments to 80% of remaining solutions). This balance between exploration and exploitation allows the algorithm to dynamically and effectively identify optimal parameters. Compared to seven alternative methods, MTO-CL shows superior performance in parameter estimation for various solar modules, including ST40 and SM55, across different irradiances and temperatures. It achieves exceptionally low Root Mean Square Error (RMSE) values from 0.0025A to 0.0165A and Mean Squared Error (MSE) values between 6.2 × 10^−6 and 2.7 × 10^−4, while also significantly minimizing power errors, ranging from 22.86 mW to 239.40 mW. These results demonstrate MTO-CL’s effectiveness in improving the accuracy and reliability of PV system modeling, offering a robust tool for enhanced solar energy applications.

The principal aim of this study is to devise a combined market operator and a distribution network operator structure for multiple home-microgrids (MH-MGs) connected to an upstream grid. Here, there are three distinct types of players with opposite intentions that can participate as a consumer and/or prosumer (as a buyer or seller) in the market. All players that are price makers can compete with each other to obtain much more possible profitability while consumers aim to minimize the market-clearing price. For modeling the interactions among partakers and implementing this comprehensive structure, a multi-objective function problem is solved by using a static, non-cooperative game theory. The propounded structure is a hierarchical bi-level controller, and its accomplishment in the optimal control of MH-MGs with distributed energy resources has been evaluated. The outcome of this algorithm provides the best and most suitable power allocation among different players in the market while satisfying each player’s goals. Furthermore, the amount of profit gained by each player is ascertained. Simulation results demonstrate 169% increase in the total payoff compared to the imperialist competition algorithm. This percentage proves the effectiveness, extensibility and flexibility of the presented approach in encouraging participants to join the market and boost their profits.

According to the European Union Emissions Trading Scheme, energy system planners are encouraged to consider the effects of greenhouse gases such as CO 2 in their short-term and long-term planning. A decrease in the carbon emissions produced by the power plant will result in a tax decrease. In view of this, the Dynamic carbon-constrained Equilibrium programming equilibrium constraints (DCC-EPEC) Framework is suggested to explore the effects of distinct market models on generation development planning (GEP) on electricity markets over a multi-period horizon. The investment incentives included in our model are the firm contract and capacity payment. The investment issue, which is regarded as a set of dominant producers in the oligopolistic market, is developed as an EPEC optimization problem to reduce carbon emissions. In the suggested DCC-EPEC model, the sum of the carbon emission tax and true social welfare are assumed as the objective function. Investment decisions and the strategic behavior of producers are included at the first level so as to maximize the overall profit of the investor over the scheduling period. The second-level issue is market-clearing, which is resolved by an independent system operator (ISO) to maximize social welfare. A real power network, as a case study, is provided to assess the suggested carbon-constrained EPEC framework. Simulations indicate that firm contracts and capacity payments can initiate the capacity expansion of different technologies to improve the long-term stability of the electricity market.

With the negative climate impact of fossil fuel power generation and the requirement of global policy to shift towards a green mix of energy production, the investment in renewable energy is an opportunity in developing countries. However, poor economy associated with limited income, funds availability, and regulations governing project funding and development are key factors that challenge investors in the energy sector. Given the various power generation resources, including renewables, it is necessary to evaluate the possible power generation investment options from an economic perspective. To realize this objective, solar PV, wind and diesel power generations are economically compared, considering the incremental rate of return and incremental benefit to cost ratio techniques. The alternative investment options of distributed generation technologies are evaluated for Maharashtra, India under different depreciation methods, and the effect of the latter on selecting the best investment candidate is investigated. The paper also conducts sensitivity analysis to examine the impact of capital cost, operation and maintenance cost, and fuel cost variations on the selection decision considering a comparison of the different general projects’ cash flow structures discussed in the literature. The economic aspects of selecting a project among possible alternatives for an investment in the power sector are analyzed, and the presented review provides comprehensive comparisons with respect to the literature approaches. The results reveal that, in the benchmark case study, the PV project is rejected and disregarded from further comparisons with other candidate projects since its equity internal rate of return (10.25%) is less than the minimum accepted rate of return, leaving the selection between wind and diesel energy projects. The study reveals that the incremental rates of return under such a comparison are 37.88%, 45.94% and 37.50% when MACRS, declining balance and straight line depreciations techniques are applied, respectively. Thus, the wind energy project is the favored option in this case. For the economic assessment of other case studies, the application of both sensitivity analysis on the capital cost and operation and maintenance cost and literature approaches to structure the projects reveal that wind energy for Maharashtra, India is a more attractive and feasible option compared to other distribution generation projects, while diesel is only considered to be a good option when its fuel cost is reduced by 5%. Finally, the paper highlights policy implications that can influence the decision to move towards investment in distributed generation technologies as a future research direction.

The United Arab Emirates is moving towards the use of renewable energy for many reasons, including the country’s high energy consumption, unstable oil prices, and increasing carbon dioxide emissions. The usage of electric vehicles can improve public health and reduce emissions that contribute to climate change. Thus, the usage of renewable energy resources to meet the demands of electric vehicles is the major challenge influencing the development of an optimal smart system that can satisfy energy requirements, enhance sustainability and reduce negative environmental impacts. The objective of this study was to examine different configurations of hybrid renewable energy systems for electric vehicle charging in Abu Dhabi city, UAE. A comprehensive study was conducted to investigate previous electric vehicle charging approaches and formulate the problem accordingly. Subsequently, methods for acquiring data with respect to the energy input and load profiles were determined, and a techno-economic analysis was performed using Hybrid Optimization of Multiple Energy Resources (HOMER) software. The results demonstrated that the optimal electric vehicle charging model comprising solar photovoltaics, wind turbines, batteries and a distribution grid was superior to the other studied configurations from the technical, economic and environmental perspectives. An optimal model could produce excess electricity of 22,006 kWh/year with an energy cost of 0.06743 USD/kWh. Furthermore, the proposed battery–grid–solar photovoltaics–wind turbine system had the highest renewable penetration and thus reduced carbon dioxide emissions by 384 tons/year. The results also indicated that the carbon credits associated with this system could result in savings of 8786.8 USD/year. This study provides new guidelines and identifies the best indicators for electric vehicle charging systems that will positively influence the trend in carbon dioxide emissions and achieve sustainable electricity generation. This study also provides a valid financial assessment for investors looking to encourage the use of renewable energy.

Modern electrical power systems now require the spread of microgrids (MG), where they would be operating in either islanded mode or grid-connected mode. An inherent mismatch between loads and sources is introduced by changeable high renewable share in an islanded MG system with stochastic load demands. The system frequency is directly impacted by this mismatch, which can be alleviated by incorporating cutting-edge energy storage technologies and FACTS tools. The investigated islanded MG system components are wind farm, solar PV, Electric vehicles (EVs), loads, DSTATCOM, and diesel power generator. An aggregated EVs model is connected to the MG during uncertain periods of the generation of renewable energy (PV and wind) to support the performance of MGs. The ability to support ancillary services from the EVs is checked. DSTATCOM is used to provide voltage stability for the MG during congestion situations. The MG is studied in three scenarios: the first scenario MG without EVs and DSTATCOM, the second scenario MG without DSTATCOM, and the third scenario MG with all components. These scenarios are addressed to show the role of EVs and DSTATCOM, and the results in the third scenario are the best. The system voltage and frequency profile is the best in the last scenario and is entirely satisfactory and under the range of the IEEE standard. The obtained results show that both EVs and DSTATCOM are important units for improving the stability of modern power grids. The Matlab/Simulink program is considered for checking and validating the dynamic performance of the proposed configuration.

With the increasing demand for wind energy in the electric power generation industry, optimizing robust and efficient control strategies is essential for a wind energy conversion system (WECS). In this regard, this study proposes a novel hybrid control strategy for wind power systems directly coupled to a permanent-magnet synchronous generator (PMSG). The contribution of this work is to propose a control strategy design based on a combination of the nonlinear Backstepping approach for system stabilization according to Lyapunov theory and the application of artificial neural network to maximize energy harvesting regardless of wind speed fluctuations. The hybrid control strategy, which is highly efficient in reducing current/torque ripples, as well as the THD ratio of PMSG currents, was applied to achieve good system performance. The overall system is implemented in MATLAB/Simulink to verify the effectiveness of the proposed control technique under varying wind conditions. Analysis of the simulation results for the proposed control versus field-oriented control (FOC) shows that the proposed control strategy exhibits less ripples in the electromagnetic torque, with the ripple ratio decreasing significantly from 32.95% to 19.43%. In addition, the THDs of the stator current decreased from 20.87% to 14.88%, proving the reliability and efficiency of the proposed control strategy compared to FOC. Meanwhile, these results are valuable for the application of the proposed control strategy in a real WECS system.

Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort.

The proportional integral derivative (PID) regulator is the most often utilized controller in the industry due to its benefits. It permits linear systems to operate well, but it causes non-linear behavior when the system is subjected to physical variable circumstances, such as temperature and saturation. A PID controller is insufficient in this case. The proportional integral (PI) controller inside the direct torque control (DTC) regulates the speed of the doubly fed induction motor (DFIM). However, the system consisting of DTC and a DFIM is non-linear due to its multivariable parameters, resulting in undesirable overshoots and torque ripples. As a result, several approaches are used to improve the DTC’s robustness. The integration of optimization methods was discovered. These algorithms are used to provide gains that are near-optimal, bringing the system closer to its ideal state in order to accomplish effective torque and speed control. This article focuses on a comparative study of the different objective functions, in order to have very effective DFIM behaviors, by using a genetic algorithm. Agenetic algorithm (GA) is presented in this study for adjusting the optimal PID parameters in DTC to control the DFIM, utilizing objective functions such as integral square error (ISE), integral time absolute error (ITAE), and integral absolute error (IAE), employed independently and in a weighted combination. This article offers a comparison of several objective functions inside the DTC and DFIM, which will be utilized in future research into another optimization technique for this control type. Matlab/Simulink was used to construct the novel hybrid structure based on the GA-DTC intelligent control. The simulation results demonstrated the efficiency of the GA-DTC intelligent control with a weighted combination, providing acceptable performance with respect to rapidity, precision, and stability, as well as an improvement of 14.53% in the rejection time reduction, fewer torque ripples and flux ripples on the stator and rotor by 27.88%, 15.13%, and 4.375%, respectively, and respective increases of 32.45% and 71% in the THDs of the stator and rotor currents, which are acceptable.

The present work consists of developing a new hybrid FL-INC optimization algorithm for the solar water pumping system (SWPS) through a SEPIC converter whose objective is to improve these performances. This technique is based on the combination of the fuzzy logic of artificial intelligence and the incremental conductance (INC) technique. Indeed, the introduction of fuzzy logic to the INC algorithm allows the extraction of a maximum amount of power and an improvement in the efficiency of the SWPS. The performance of the system through the SEPIC converter is compared with those of the direct coupling to show the interest of the indirect coupling, which requires an adaptation stage driven by an optimal control algorithm. In addition, a comparative analysis between the proposed hybrid algorithm and the conventional optimization techniques, namely, P&O and INC Modified (M-INC), was carried out to confirm improvements related to the SWPS in terms of efficiency, tracking speed, power quality, tracking of the maximum power point under different weather changes, and pumped water flow.