Frequency regulation in a hybrid renewable power grid: an effective strategy utilizing load frequency control and redox flow batteries.

Renewable energy sources (RESs) have become integral components of power grids, yet their integration presents challenges such as system inertia losses and mismatches between load demand and generation capacity. These issues jeopardize grid stability. To address this, an effective approach is proposed, combining enhanced load frequency control (LFC) (i.e., fuzzy PID- T I λ D µ ) with controlled energy storage systems, specifically controlled redox flow batteries (CRFBs), to mitigate uncertainties arising from RES integration. The optimization of this strategy's parameters is achieved using the crayfish optimization algorithm (COA), known for its global optimization capabilities and balance between exploration and exploitation. Performance evaluation against conventional controllers (PID, FO-PID, FO-(PD-PI)) confirms the superiority of the proposed approach in LFC. Extensive testing under various load disturbances, high renewables penetration, and communication delays ensures its effectiveness in minimizing disruptions. Validation using a standardized IEEE 39-bus system further demonstrates its efficiency in power networks grappling with significant renewables penetration. In summary, this integrated strategy presents a robust solution for modern power systems adapting to increasing renewable energy utilization.

Enhanced gorilla troops optimizer powered by marine predator algorithm: global optimization and engineering design.

This study presents an advanced metaheuristic approach termed the Enhanced Gorilla Troops Optimizer (EGTO), which builds upon the Marine Predators Algorithm (MPA) to enhance the search capabilities of the Gorilla Troops Optimizer (GTO). Like numerous other metaheuristic algorithms, the GTO encounters difficulties in preserving convergence accuracy and stability, notably when tackling intricate and adaptable optimization problems, especially when compared to more advanced optimization techniques. Addressing these challenges and aiming for improved performance, this paper proposes the EGTO, integrating high and low-velocity ratios inspired by the MPA. The EGTO technique effectively balances exploration and exploitation phases, achieving impressive results by utilizing fewer parameters and operations. Evaluation on a diverse array of benchmark functions, comprising 23 established functions and ten complex ones from the CEC2019 benchmark, highlights its performance. Comparative analysis against established optimization techniques reveals EGTO's superiority, consistently outperforming its counterparts such as tuna swarm optimization, grey wolf optimizer, gradient based optimizer, artificial rabbits optimization algorithm, pelican optimization algorithm, Runge Kutta optimization algorithm (RUN), and original GTO algorithms across various test functions. Furthermore, EGTO's efficacy extends to addressing seven challenging engineering design problems, encompassing three-bar truss design, compression spring design, pressure vessel design, cantilever beam design, welded beam design, speed reducer design, and gear train design. The results showcase EGTO's robust convergence rate, its adeptness in locating local/global optima, and its supremacy over alternative methodologies explored.

Review of the marine energy environment-a combination of traditional, bibliometric and PESTEL analysis.

Marine renewable energy is regarded as a nascent renewable energy resource that is less utilized due to a number of challenges in the sector. This paper focused on using both traditional and bibliometric analysis approaches to review the marine energy industry. It also assessed the various opportunities and challenges in the sector beyond technological challenges using PESTEL analysis. The results from the study identified the availability of renewable energy targets, international and national greenhouse gas (GHG) emissions reduction targets, job creation, skill transfer from offshore industries, renewable support, and low GHG emissions as the major opportunities for the sector. The challenges in the sector include the lack of commonality in device designs, high initial capital costs, lack of appropriate legal and regulatory frameworks, lack of funding, fragmentations in regulatory institutions, bad macro-economic indicators in some countries, environmental challenges, the survivability of the various technologies in the harsh oceanic environment, and strong competition from other renewable energy sources. The outcome of the bibliometric analysis spanning from 2013 to 2023 shows that tidal power is the focus of research in the field, and most studies are either focused on ways to improve its efficiency in terms of technology or on the identification of resource potentials for the siting of the various marine renewable power systems. Recommendations such as strong cooperation between the government and private sector, increased public education, collaboration with existing players in the marine sector, and increased research and development, among others, were proposed for the development of the sector.

OptiCoord: Advancing directional overcurrent and distance relay coordination with an enhanced equilibrium optimizer.

In this article, an improved optimization technique is used to get a solution to the problem of coordination between directional overcurrent relays (DOCR) and distance relays. An enhanced version of an equilibrium optimization algorithm (EO), referred to as EEO is proposed to solve this problem. The suggested approach optimises the parameter that regulates the balance between exploration and exploitation to identify the potential optimum solution while enhancing the EO algorithm's exploration properties. The main task for the EEO is to get the best settings. Also, the proposed algorithm shall maintain operation in sequence between the main and backup relays. The capability of the suggested EEO algorithm is assessed in 8-bus, IEEE thirty-bus, and IEEE 39-bus systems. The obtained results prove the effectiveness of the EEO technique in solving the coordination problem of the combined directional overcurrent relays and distance relays. Also, the results show the ability of the suggested algorithm to overcome the drawbacks of the traditional EO algorithm and achieve faster protection (the reduction ratio reaches about 12 % compared to the traditional EO.

A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources.

This paper presents a novel approach to solve the optimal power flow (OPF) problem by utilizing a modified white shark optimization (MWSO) algorithm. The MWSO algorithm incorporates the Gaussian barebones (GB) and quasi-oppositional-based learning (QOBL) strategies to improve the convergence rate and accuracy of the original WSO algorithm. To address the uncertainty associated with renewable energy sources, the IEEE 30 bus system, which consists of 30 buses, 6 thermal generators, and 41 branches, is modified by replacing three thermal generators with two wind generators and one solar PV generator. And the IEEE 57-bus system, which consists of 57 buses, 7 thermal generators, and 80 branches, is also modified by the same concept. The variability of wind and solar generation is described using the Weibull and lognormal distributions, and its impact on the OPF problem is considered by incorporating reserve and penalty costs for overestimation and underestimation of power output. The paper also takes into account the unpredictability of power consumption (load demand) by analyzing its influence using standard probability density functions (PDF). Furthermore, practical conditions related to the thermal generators, such as ramp rate limits are examined. The MWSO algorithm is evaluated and analyzed using 23 standard benchmark functions, and a comparative study is conducted against six well-known techniques using various statistical parameters. The results and statistical analysis demonstrate the superiority and effectiveness of the MWSO algorithm compared to the original WSO algorithm for addressing the OPF problem in the presence of generation and demand uncertainties.

Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization.

The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to its reliance on the alpha wolf for guiding the position updates of search agents, the risk of being trapped in a local optimal solution is notable. Furthermore, during stagnation, the convergence of other search wolves towards this alpha wolf results in a lack of diversity within the population. Hence, this research introduces an enhanced version of the GWO algorithm designed to tackle numerical optimization challenges. The enhanced GWO incorporates innovative approaches such as Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), and it's called CMWGWO. MRS, in particular, empowers certain wolves to extend their exploration range, thus enhancing the global search capability. By employing COL, diversification is intensified, leading to reduced solution stagnation, improved search precision, and an overall boost in accuracy. The integration of WID fosters more effective information exchange between the least and most successful wolves, facilitating a successful exit from local optima and significantly enhancing exploration potential. To validate the superiority of CMWGWO, a comprehensive evaluation is conducted. A wide array of 23 benchmark functions, spanning dimensions from 30 to 500, ten CEC19 functions, and three engineering problems are used for experimentation. The empirical findings vividly demonstrate that CMWGWO surpasses the original GWO in terms of convergence accuracy and robust optimization capabilities.

Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains.

As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua's dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua's solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses.

Techno-economic optimization for isolated hybrid PV/wind/battery/diesel generator microgrid using improved salp swarm algorithm.

The main objective of this study is to develop a new method for solving the techno-economic optimization problem of an isolated microgrid powered by renewable energy sources like solar panels, wind turbines, batteries, and diesel generators while minimizing greenhouse gas emissions. An Improved Salp Swarm Algorithm (ISSA) with a position adaptation mechanism for the salp leader that involves a leader salp that moves about depending on both food availability and its previous position has been proposed to overcome the convergence problem. In the original SSA, as the approach converges, it can no longer find optimal solutions and becomes trapped in a local minimum. Three Microgrid System (MS) configurations are discussed: PV/WT/BESU/DG, PV/BESU/DG, and WT/BESU/DG. The proposed method seeks to find a middle ground between technical criteria and environmental concerns when deciding on PV, WT, BESU, and DG sizes. The findings indicate that the proposed ISSA approach gives superior results compared to other well-known algorithms like the original SSA, the Ant Lion Optimizer (ALO), the Dragonfly Approach (DA), and the Moth-Flame Optimization Algorithm (MFO), which, after significant investigation, has been proven to help determine the appropriate microgrid size. With PV sizes of 10, 9 WT, 24 BESU, and 3 DG, the PV/WT/BESU/DG configuration offers the highest level of cost-effectiveness with Cost of Energy (COE) of 0.2109 $/kWh, Net Present Cost (NPC) of 376,063.8 $, Loss of Power Supply Probability (LPSP) of 4%, Renewable Energy Fraction (REF) of 96%, and CO2 emission of 12.4457 tons/year. ISSA is brought up as a possible solution to both the problem of rising energy prices and the difficulties inherent in microgrid design.

DGS-SCSO: Enhancing Sand Cat Swarm Optimization with Dynamic Pinhole Imaging and Golden Sine Algorithm for improved numerical optimization performance.

This paper introduces DGS-SCSO, a novel optimizer derived from Sand Cat Swarm Optimization (SCSO), aiming to overcome inherent limitations in the original SCSO algorithm. The proposed optimizer integrates Dynamic Pinhole Imaging and Golden Sine Algorithm to mitigate issues like local optima entrapment, premature convergence, and delayed convergence. By leveraging the Dynamic Pinhole Imaging technique, DGS-SCSO enhances the optimizer's global exploration capability, while the Golden Sine Algorithm strategy improves exploitation, facilitating convergence towards optimal solutions. The algorithm's performance is systematically assessed across 20 standard benchmark functions, CEC2019 test functions, and two practical engineering problems. The outcome proves DGS-SCSO's superiority over the original SCSO algorithm, achieving an overall efficiency of 59.66% in 30 dimensions and 76.92% in 50 and 100 dimensions for optimization functions. It also demonstrated competitive results on engineering problems. Statistical analysis, including the Wilcoxon Rank Sum Test and Friedman Test, validate DGS-SCSO efficiency and significant improvement to the compared algorithms.

S-shaped grey wolf optimizer-based FOX algorithm for feature selection.

The FOX algorithm is a recently developed metaheuristic approach inspired by the behavior of foxes in their natural habitat. While the FOX algorithm exhibits commendable performance, its basic version, in complex problem scenarios, may become trapped in local optima, failing to identify the optimal solution due to its weak exploitation capabilities. This research addresses a high-dimensional feature selection problem. In feature selection, the most informative features are retained while discarding irrelevant ones. An enhanced version of the FOX algorithm is proposed, aiming to mitigate its drawbacks in feature selection. The improved approach referred to as S-shaped Grey Wolf Optimizer-based FOX (FOX-GWO), which focuses on augmenting the local search capabilities of the FOX algorithm via the integration of GWO. Additionally, the introduction of an S-shaped transfer function enables the population to explore both binary options throughout the search process. Through a series of experiments on 18 datasets with varying dimensions, FOX-GWO outperforms in 83.33 % of datasets for average accuracy, 61.11 % for reduced feature dimensionality, and 72.22 % for average fitness value across the 18 datasets. Meaning it efficiently explores high-dimensional spaces. These findings highlight its practical value and potential to advance feature selection in complex data analysis, enhancing model prediction accuracy.

Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization.

This paper proposes a plan to manage energy consumption in residential areas using the demand response method, which allows electricity users to contribute to the reliability of the power system by controlling their usage. Due to the growing population, the residential sector consumes a significant amount of energy, and the objectives of this study are to lower electricity costs and the peak to average ratio, as well as reduce the amount of imported electricity from the grid. The study aims to maximize profit by properly utilizing renewable energy sources and addressing energy trading. The manta ray foraging optimization (MRFO) and long term memory MRFO (LMMRFO) algorithms are used to solve this problem. Firstly, the validation of the proposed LMMRFO technique is confirmed by seven benchmark functions and compared its results with the results of the well-known optimization algorithms including hunter prey optimization, gorilla troops optimizer, beluga whale optimization, and the original MRFO algorithm. Then, the performance of the LMMRFO is checked on the optimization of smart home energy management. In the suggested approach, a smart home decides whether to purchase or sell electricity from the commercial grid based on the cost, demand, and production of electricity from its own microgrid, which consists of a wind turbine and solar panels. Energy storage systems support the stable and dependable functioning of the power system since the solar panel and wind turbine only occasionally produce electricity. Through various case studies, the proposed plan is tested and found to be effective in reducing electricity costs and the peak to average ratio while maximizing profit. Furthermore, a comparative study is conducted to demonstrate the legality and effectiveness of LMMRFO and MRFO.

Single-tuned passive filter (STPF) for mitigating harmonics in a 3-phase power system.

Numerous integrals of the fundamental frequency are known as harmonics and can be found in power systems or electrical circuitry systems. Non-linear loads occasionally drain current or contains a varying impedance with each period of the AC voltage are often responsible for power system harmonics. This can result in system overheating, system losses, and equipment or system damage. In order to achieve the IEEE 519 power quality standard, filters are routinely employed to lower harmonic levels. In this work, we designed a single tuned passive filter (STPF) to minimize harmonics of sequence 5th, 7th, 11th, 13th, 17th, and 19th in a three (3) phase power system. The measurements were taken at the point of common coupling. To test the filter performance, the system and STPF were designed in MATLAB/Simulink, and the simulated results produced without and with STPF were compared. The [Formula: see text] was reduced from 14.93% down to 4.87% when STPF was connected which is within the IEEE 519-2022 standard; proving that the STPF was effective in decreasing the harmonics to the desired level.

Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos.

This work proposed a new method to optimize the antenna S-parameter using a Golden Sine mechanism-based Honey Badger Algorithm that employs Tent chaos (GST-HBA). The Honey Badger Algorithm (HBA) is a promising optimization method that similar to other metaheuristic algorithms, is prone to premature convergence and lacks diversity in the population. The Honey Badger Algorithm is inspired by the behavior of honey badgers who use their sense of smell and honeyguide birds to move toward the honeycomb. Our proposed approach aims to improve the performance of HBA and enhance the accuracy of the optimization process for antenna S-parameter optimization. The approach we propose in this study leverages the strengths of both tent chaos and the golden sine mechanism to achieve fast convergence, population diversity, and a good tradeoff between exploitation and exploration. We begin by testing our approach on 20 standard benchmark functions, and then we apply it to a test suite of 8 S-parameter functions. We perform tests comparing the outcomes to those of other optimization algorithms, the result shows that the suggested algorithm is superior.

A robust control approach integrating with optimal fuel cells to strengthen the frequency stability of a diverse-sources power system including renewables.

In the current power landscape, renewable energy sources (RESs) have assumed a crucial role in satisfying consumer demand. However, as the deployment of renewables increases, certain challenges arise, including issues with system frequency stability, inertia, and damping reduction. To address these concerns, an innovative approach is suggested in this study. The proposed strategy aims to maintain frequency stability in a diverse-source power system that encompasses two interconnected regions incorporating RESs. The proposed strategy comprises a new multi-degree of freedom FOTID controller known as the MDOF-TIλDµN controller in the secondary control loop (SCL) and optimally controlled fuel cells (OFCL) to enhance the system's stability under the effect of renewable energy (RESs) fluctuations. In this context, the gains of the considered strategy (optimal MDOF-TIλDµN in addition to OFCL) have been picked out by using an innovative optimization approach known as the Capuchin search algorithm (CapSA). The statistical tests are used to examine the efficacy of the considered CapSA compared to those of other optimization strategies utilized in previous studies. Furthermore, the performance of the proposed controller in the SCL is verified by contrasting its performance with that of another suggested controller known as MDOF-PIDN as well as other controllers such as PD-IT, PDµN-IλT, 2DOF-TIλDµN, 3DOF-PIDN, 3DOF-TIDN, and 3DOF-PIλDµN. Additionally, grid nonlinearities, including Boiler Dynamics, Generation Rate Constraint, Governor Dead Band, and random communication time delay (CTD), are considered. Moreover, the proposed strategy's performance is verified in the face of system constraints and nonlinearities. Different scenarios are implemented, and the simulation outcomes emphasize the superior performance of the suggested strategy. Therefore, the suggested strategy provides consistent power system adoption wherever it is implemented.

Seasonal correlation of aerosols with soil moisture, evapotranspiration, and vegetation over Pakistan using remote sensing.

Aerosols have a severe impact on the Earth's climate, human health, and ecosystem. To understand the impacts of aerosols on climate, human health, and the ecosystem we must need to understand the variability of aerosols and their optical properties. Therefore, we used Aqua-MODIS retrieved aerosol optical depth (AOD) (550 nm) and Angstrom exponent (AE) (440/870) data to analyze the Spatio-temporal seasonal variability of aerosols and their relationship with different meteorological parameters over Pakistan from 2002 to 2021. High (>0.5) AOD values were observed during the summer season and low (<0.8) in the spring season. AE values were observed to be high (>1) in the northern regions of Pakistan indicating the dominance of fine mode particles during the winter season. Moreover, AOD showed a positive correlation with Relative Humidity (RH), Evapotranspiration, Wind speed (WS), and Temperature. On the other hand, it showed a negative correlation with Soil moisture (SM), Normalized difference vegetation index (NDVI), and precipitation over Pakistan. Therefore, considering the outcomes of this study will help policymakers to understand the spatiotemporal variability of aerosols and their seasonal correlation with different meteorological parameters.

A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy generations.

The supply-demand-based optimization (SDO) is among the recent stochastic approaches that have proven its capability in solving challenging engineering tasks. Owing to the non-linearity and complexity of the real-world IEEE optimal power flow (OPF) in modern power system issues and like the existing algorithms, the SDO optimizer necessitates some enhancement to satisfy the required OPF characteristics integrating hybrid wind and solar powers. Thus, a SDO variant namely leader supply-demand-based optimization (LSDO) is proposed in this research. The LSDO is suggested to improve the exploration based on the simultaneous crossover and mutation mechanisms and thereby reduce the probability of trapping in local optima. The LSDO effectiveness has been first tested on 23 benchmark functions and has been assessed through a comparison with well-regarded state-of-the-art competitors. Afterward, Three well-known constrained IEEE 30, 57, and 118-bus test systems incorporating both wind and solar power sources were investigated in order to authenticate the performance of the LSDO considering a constraint handling technique called superiority of feasible solutions (SF). The statistical outcomes reveal that the LSDO offers promising competitive results not only for its first version but also for the other competitors.

What is the role of remittance and education for environmental pollution? - Analyzing in the presence of financial inclusion and natural resource extraction.

This study assessed the impact of gross domestic product (GDP), education, natural resources, remittances, and financial inclusion on carbon emissions in G-11 countries from 1990 to 2021. Based on the negative impact of pollution and the need for sustainable development, this study examined factors affecting CO2 emissions in G-11 countries using non-linear panel ARDL model. The study found that a positive GDP shock increases CO2 emissions in the short and long term, while a negative shock decreases emissions in the short term and increases emissions in the long term. Education was found to increase CO2 emissions in the long term but decrease them in the short term, emphasizing the need for education on combating emissions. Natural resources were also found to increase emissions in the long term, highlighting the need for government-defined institutions to minimize extraction effects and enforce transparency and accountability. Positive changes in personal remittances and financial inclusion were found to increase emissions in both the short and long term, suggesting the need for policies that encourage renewable energy sources and energy efficiency improvement. The study concludes that policymakers should prioritize efficient resource allocation, promote renewable energy usage, and enhance environmental awareness to achieve sustainable development goals in G-11 countries. The possible applications of this study include the use of the models to investigate the asymmetric effects on CO2 emissions. This model can be applied in future studies to examine the relationship between GDP, education, natural resources, personal remittances, financial inclusion, and CO2 emissions in other countries.

A robust optimization model for microgrid considering hybrid renewable energy sources under uncertainties.

Hybrid renewable energy sources and microgrids will determine future electricity generation and supply. Therefore, evaluating the uncertain intermittent output power is essential to building long-term sustainable and reliable microgrid operations to fulfill the growing energy demands. To address this, we proposed a robust mixed-integer linear programming model for the microgrid to minimize the day-ahead cost. To validate the proposed model piecewise linear curve is to deal with uncertainties of wind turbine, photovoltaic, and electrical load. The proposed solution is demonstrated through a case study compared under a robust worst-case scenario, deterministic model, and max-min robust optimization that aim to find optimal robustness. So, a piecewise linear curve is considered to obtain uncertain parameters in order to deal with uncertainties and predict the day-ahead cost. This study illustrates how the Uncertainty Budget Set selection used to integrate renewable energy sources into a microgrid, which manages the energy system. Therefore, the model complexity was slightly modified by adjusting the Uncertainty Budget Set to obtain the optimal decision and control the load demand and uncertainty of renewable energy sources. The comparative results demonstrate that the proposed robust optimization can achieve high solutions under microgrid's availability and is intended to confirm that the proposed method is more cost-effective than alternative optimization techniques. Additionally, the effectiveness and advantage of the proposed methodology in the IEEE 33-node system are validated in this case study by comparing it to the existing optimization. The comparison results show that the proposed robust optimization methods illustrate the model's efficiency, concluding remarks, and managerial insights of the research.

Developing a strategy based on weighted mean of vectors (INFO) optimizer for optimal power flow considering uncertainty of renewable energy generation.

In recent years, more efforts have been exerted to increase the level of renewable energy sources (RESs) in the energy mix in many countries to mitigate the dangerous effects of greenhouse gases emissions. However, because of their stochastic nature, most RESs pose some operational and planning challenges to power systems. One of these challenges is the complexity of solving the optimal power flow (OPF) problem in existing RESs. This study proposes an OPF model that has three different sources of renewable energy: wind, solar, and combined solar and small-hydro sources in addition to the conventional thermal power. Three probability density functions (PDF), namely lognormal, Weibull, and Gumbel, are employed to determine available solar, wind, and small-hydro output powers, respectively. Many meta-heuristic optimization algorithms have been applied for solving OPF problem in the presence of RESs. In this work, a new meta-heuristic algorithm, weighted mean of vectors (INFO), is employed for solving the OPF problem in two adjusted standard IEEE power systems (30 and 57 buses). It is simulated by MATLAB software in different theoretical and practical cases to test its validity in solving the OPF problem of the adjusted power systems. The results of the applied simulation cases in this work show that INFO has better performance results in minimizing total generation cost and reducing convergence time among other algorithms.

Technical modelling of solar photovoltaic water pumping system and evaluation of system performance and their socio-economic impact.

Water is a precious resource for agriculture and most of the land is irrigated by tube wells. Diesel engines and electricity-operated pumps are widely used to fulfill irrigation water requirements; such conventional systems are inefficient and costly. With rising concerns about global warming, it is important to choose renewable energy source. In this study, SPVWPS has been optimally designed considering the water requirement, solar resources, tilt angle and orientation, losses in both systems and performance ratio. A PVSyst and SoSiT simulation tools were used to perform simulation analysis of the designed solar photovoltaic WPS. After designing and performance analysis, farmers were interviewed during fieldwork to assess socioeconomic impacts. In the result section, performance of PV system is analyzed at various tilt angles and it is established that system installed at a 15° tilt angle is more efficient. The annual PV array virtual energy at MPP of designed photovoltaic system is 33342 kWh and the annual energy available to operate the WPS is 23502 kWh. Module array mismatch and ohmic wiring losses are 374.16 kWh and 298.83 kWh, respectively. The total annual water demand of the selected site is 80769 m³ and designed SPWPS pumped 75054 m³ of water, supplying 92.93% of the irrigation demand. The normalized values of the effective energy, system losses, collection losses and unused energy in the SPVWP system are 2.6 kW/kWp/day, 0.69 kW/kWp/day, 0.72 kW/kWp/day and 0.48 kW/kWp/day, respectively. The annual average performance ratio of the proposed system is 74.62%. The results of the interviews showed that 70% of farmers are extremely satisfied with the performance of SPVWPS and 84% of farmers indicated that they did not incur any operating costs. The unit cost of the SPWPS is 0.17 €/kWh, which is 56.41% and 19.04% less expensive than the cost of diesel and grid electricity.