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In response to the increasing demand for water in irrigated areas, the use of groundwater pumping is emerging as a compelling alternative, particularly in arid and semi-arid regions. However, the costs associated with fossil fuel based pumping systems, as well as their negative environmental impact through the emission of harmful gases, have prompted a shift towards renewable energy. The main objective of this study is to analyze and optimize the feasibility of a groundwater pumping system using different energy sources by HOMER Energy. A farmer in the Nabeul region (Chiba) was selected for an in-depth analysis of water demand and pumping station sizing. The evaluations showed that a 3 kW photovoltaic panel installation per hectare could sufficiently meet the irrigation water needs of this farmer. After optimizing the proposed pumping systems, including diesel generator (DG), photovoltaic (PV) and hybrid (DG + PV) panels, it was determined that the most economical and environmentally friendly solution was the hybrid system. This system produces a power output of 3.3 kW, with CO2 emissions in the order of 5.117 kg per year. HOMER software is a powerful tool for optimizing energy-efficient irrigation practices and assessing the best mix of renewable and conventional energy resources to meet specific needs. [ABSTRACT FROM AUTHOR]

This study explores the techno-economic feasibility of, both off-grid and on-grid, hybrid renewable energy systems for remote rural electrification in Thala City, located in the highest region of Tunisia, using wind and biomass resources. Employing Hybrid Optimization of Multiple Energy Resources based on different scenarios includes grid-connected and stand-alone configurations with pumped storage hydropower and lead acid battery storage while minimizing the levelized cost of energy, the net present cost, and greenhouse gas emissions. The optimal configuration wind/biomass/pumped-hydro storage/Converter grid-connected, minimizes the levelized cost of energy (LCOE) and net present cost (NPC), resulting in a cost of 501,540 US$ and LCOE of 0.042 US$/kWh. Notably, 7% of electricity is generated from olive mill waste, 69% from wind turbines, and 24% is purchased from the grid. This hybrid system emits 342 tons/year of CO2, 76% less than a grid-alone system, contributing to an annual CO2 reduction of 1000 tons. [ABSTRACT FROM AUTHOR]

This paper presents the development of an Artificial Intelligence (AI)-based integrated dynamic hybrid PV-H2 energy system model together with a reflective comparative analysis of its performance versus that of the commercially available HOMER software. In this paper, a novel Particle Swarm Optimization (PSO) dynamic system model is developed by integrating a PSO algorithm with a precise dynamic hybrid PV-H2 energy system model that is developed to accurately simulate the hybrid system by considering the dynamic behaviour of its individual system components. The developed novel model allows consideration of the dynamic behaviour of the hybrid PV-H2 energy system while optimizing its sizing within grid-connected buildings to minimize the levelized cost of energy and maintain energy management across the hybrid system components and the grid in feeding the building load demands. The developed model was applied on a case-study grid-connected building to allow benchmarking of its results versus those from HOMER. Benchmarking showed that the developed model's optimal sizing results as well as the corresponding levelized cost of energy closely match those from HOMER. In terms of energy management, the benchmarking results showed that the strategy implemented within the developed model allows maximization of the green energy supply to the building, thus aligning with the net-zero energy transition target, while the one implemented in HOMER is based on minimizing the levelized cost of energy regardless of the green energy supply to the building. Another privilege revealed by benchmarking is that the developed model allows a more realistic quantification of the hydrogen output from the electrolyser because it considers the dynamic behaviour of the electrolyser in response to the varying PV input, and also allows a more realistic quantification of the electricity output from the fuel cell because it considers the dynamic behaviour of the fuel cell in response to the varying hydrogen levels stored in the tank. [ABSTRACT FROM AUTHOR]

This study presents a techno-economic and environmental analysis of hybrid renewable energy systems to identify the optimal configuration for supplying the planned 850 MW renewable energy plant in Yanbu city, Saudi Arabia. Ten grid-connected system designs combining photovoltaic (PV), wind turbine (WT), and battery storage were simulated and optimized using the HOMER Grid software (1.10.2 pro edition). A site suitability analysis was conducted to evaluate potential locations based on climatic, topographic, and infrastructure-related factors. A sensitivity analysis considered variations in solar irradiation, wind speed, temperature, load demand, and economic parameters. The results showed that the PV-only system with an 850 MW capacity achieved the lowest net present cost (NPC) of USD 201 million and levelized cost of energy (LCOE) of 0.0344 USD/kWh, making it the most economically feasible option. However, a hybrid WT–PV configuration of 212.5 MW WT and 637.5 MW PV was also proposed to support local manufacturing. All proposed systems provided over a 91% renewable energy contribution while reducing CO2 emissions by 53% compared to grid supply only. Up to 1152 jobs are estimated to be created through renewable energy deployment in Yanbu city. [ABSTRACT FROM AUTHOR]

This article explores the status of dreams in Homer’s Iliad and Odyssey.Dreams serve many different purposes in the epic; they also continuously elude all attempts to define or categorize them in a single, conclusive way. Stubbornly, they resist any model that would force them to conform to a single interpretation. I argue that there is a “subver­sive” side of dreams under(mining) the epic conventionality, a side that seems to question the epic itself but, at the same time, to originate it and I suggest that the poetics of the Homeric epics may be based precisely on a subterranean merging between the “awake” story of the epic and its dreams. The dreams of the Homeric poems seem to yield both epiphanies and the epic song; they are situated as an intermediary realm both between men and gods, and between men and their stories.

Within the previous decade, the burgeoning re-imagination of the Greco-Roman canon in an African American context, presently referred to as “Black classicism,” has traversed artistic mediums not only as a cultural transliteration but an active subversion, rejection, and reclamation of the sociological underpinnings of a historically eurocentric area of study. Though modern reception studies are fraught with racial prejudice accusing Black authors of passively imitating—or merely deriving influence from—antiquity, I argue that the neoclassical inventions of Phillis Wheatley, W.E.B. Du Bois, Rita Dove, and Robert Hayden have transposed myth’s most defining elements onto two prominent social narratives: the public reception of African American scholarship and the collective female consciousness. Of these elements, imagery invoking the movement across transitory ‘liminal’ states has been enfranchised to actively subvert the traditionally-eurocentric classical canon while transferring intellectual ownership to African American authorship. Visualizations of the cyclicality of the rhapsodic oral tradition have further been harnessed to defend the female canon, translating myth’s cyclicality onto contemporaneous social dilemmas.

The global demand for energy has increased exponentially over the years. To reduce the dominance of fossil fuels in energy production, there has been a shift towards energy production models based on renewable sources. In the design of hybrid energy systems, it is essential to keep investment costs low while ensuring the security of the energy supply by meeting the consumer’s energy demands without interruption. The success of a good energy production model can be directly associated with the results of load estimation. The primary objective of this research is to predict the electricity demand for the Göksun district until 2028, utilising a data set that encompasses electricity usage from 2019 through the first four months of 2024 for the Göksun district in Kahramanmaraş. This endeavour includes the application of various machine learning (ML) paradigms (long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN)-LSTM, support vector regression (SVR)) to produce load forecasting outcomes and to engineer an optimally performing hybrid system. On evaluation of the performance metrics derived from the experimental data, it has been established that the LSTM model outperforms other methodologies, yielding more favourable results. The simulation studies of the designed hybrid system were conducted using the hybrid optimisation model for electric renewables software (HOMER Pro), demonstrating improvements in both economic and environmental parameters. Our study is unique in that it is the first to utilise a data set specific to the Göksun region and to model predictions obtained from this data set using HOMER software.

Abstract This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, “PV” specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model’s efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model.

This paper examines the tension between Cyrus and Cyaxares in Xenophon's Cyropaedia, and the ways in which Cyrus succeeds in mollifying the Median king (and his maternal uncle), who is angry because Cyrus has usurped his authority. It argues that, although Cyrus has been charged with deceit in his effort to conciliate Cyaxares, in fact such dissembling is ineliminable where there is a genuine offense to another's honor. It suggests further that the episode is a deliberate rejoinder to the conflict between Achilles and Agamemnon, as described in the Iliad, and is intended to show how properly to resolve a clash between a king and his more warlike subordinate. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality in 2050, Thailand has focused on reducing CO2 emissions in the energy sector. Small modular reactors (SMRs) and renewable energy such as wind and solar represent an interesting alternative for the decarbonization of the energy sector. This study aims to investigate the possibility of establishing a grid-connected hybrid energy system (Grid/Solar PV/Wind Turbine/BESS/SMRs) to fulfill the energy demand of Phuket Island in Thailand and to minimize net present cost (NPC), levelized cost of energy (LCOE), and greenhouse gas (CO2) emissions. A grid-connected hybrid renewable generation system was simulated using HOMER. Four combinations of grid-connected and renewable energy sources were developed based on the electricity demand and renewable resources available at the site. The simulation results indicate that the most optimal scenario is the Grid/PV/WT/SMR system, which offers a 28% reduction in NPC and LCOE compared to the grid-only system and reduces CO2 emissions by over 58% compared to the total emissions from the utility grid. The simulation results demonstrate that the grid-connected and hybrid energy system is the most viable option to meet electricity demand and reduce greenhouse gas emissions on Phuket Island. [ABSTRACT FROM AUTHOR]

This work utilizes the particle swarm optimization (PSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two-step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained are then compared with results from the Non-dominant Sorting Genetic Algorithm II (NGSA-II), hybrid genetic algorithm–particle swarm optimization (GA-PSO) and the proprietary derivative-free optimization algorithm. An energy management system monitors the energy balance and ensures that the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consists of solar panels (151), wind turbine (3), inverter (122) and batteries (31). This results in a minimized TEC, TAC and LCOE of USD 469,200, USD 297,100 and 0.007/kWh, respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient conditions. [ABSTRACT FROM AUTHOR]

The current study focuses on the issue of anger in ancient and biblical texts. The author initially analyzes various angry gods and considers the 'logic' of God's anger. It pays special attention to the understanding of anger in Homer's Iliad and shows that anger aimed at social justice is understood as good, while self-centred anger is always an issue. The author also analyzes Bible verses attributing anger to God, but also those in which Scripture speaks about our need to manage our anger. Anger is depicted here as a moral but also an amoral emotion, depending on whether we leave place for God or if we take God's place and become an absolute moral authority. [ABSTRACT FROM AUTHOR]

This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, "PV" specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model's efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model. Highlights: Introduction of a novel approach: This study presents a pioneering methodology focusing exclusively on supervised machine learning techniques for short-term uncertainty management within 100% PV microgrids, aimed at optimizing energy management efficiency. Comprehensive comparative analysis: Through meticulous comparative investigation, various regression techniques for predicting solar radiation within PV microgrids are scrutinized, providing valuable insights into their efficacy across diverse environmental conditions. Validation across diverse locations: The validation of findings across four distinct geographical locations, encompassing both PV and concentrated PV (CPV) systems, substantially enhances the generalizability of the results, advancing the understanding of solar radiation prediction dynamics for renewable energy integration strategies. [ABSTRACT FROM AUTHOR]

In this study, a metaheuristic Particle Swarm Optimization based optimization tool has been designed to be used in the optimization of Hybrid Renewable Energy Systems, which eliminates the negative aspects of the most frequently used HOMER software in this field. In the comparison made in terms of processing speed, it was seen that the designed system reached the result faster by obtaining the result in 17 seconds, which the HOMER software obtained in 936 seconds in optimizing the same system. In the economic comparison, as a result of the optimization made by two different tools; There is a difference of 1.737% in Energy cost, 0.85% in Total Net Present Cost and 1.895% in Initial Capital, and there is no significant difference between the results. In the comparison of the electrical results, there is a difference of 0.031% for the fed loads and 1.071% for the unmet loads, and the reliability of the results in electrical terms has been demonstrated. The capacity shortage parameter was used for the first time in the optimization of renewable energy sources with the metaheuristic algorithm. Energy Cost is used as the objective function. In short, a unique and reliable optimization tool has been designed as an alternative to the HOMER program that can achieve much faster results and eliminates the sensitivity, cumbersomeness and difficult search space generation processes in the HOMER program. This tool will also facilitate the generation of a large amount of data by obtaining the necessary optimization outputs very quickly to train surrogate models, machine learning or deep learning based optimization systems. [ABSTRACT FROM AUTHOR]

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Renewable energy technologies offer the promise of clean and abundant energy harvested from natural resources, self-renewable sources such as sun, wind, water, earth, and plants. In this work, we optimize the hybrid system using Homer power program, where the hybrid system is composed by solar panels, wind turbines with batteries to supply 20 homes that are not equipped with electricity in Ouzera area (Medea, Algeria), and by taking the results presented by the Homer program for Ouzera region, we obtained the cost of each day, of each season and the cost of energy ($/kWh), as well as the optimal number and characteristics for each solar panels and wind turbines with storage batteries. The homer software allows us to obtain real results in taking into account the constraints cost and variations in off-grid weather data. The most important criterion of this technique for optimizing renewable energy systems was the cost, seeking to minimize the expenses, while ensuring optimum quality and continuity of electricity supply. [ABSTRACT FROM AUTHOR]

Access to energy and electricity drives the economy, including industrial activities in isolated areas. Currently, the shrimp industry on Bawean (an isolated area) is facing the problem of many unserved power needs. Thus, this study aims to design hybrid renewable energy for the productive sector. There are two configurations, namely configuration 1 (PV SYSTEM, WIND TURBINE SYSTEM, GRID, BESS) and configuration 2 (PV SYSTEM, GRID, BESS), which find the potential for solar and wind radiation very suitable for renewable energy sources. Thus, based on the HOMER simulation, the best alternative solution is configuration 1 which utilizes PV SYSTEM (8.9 MW), 2 Wind Turbine System (1.6 MW), and 3 BESS, resulting in the lowest LCOE value of 0.137 $/kWh. The results of configuration 1 can meet the electricity needs of Bawean Island and shrimp ponds with the lowest LCOE value, so it can potentially increase productive sectors such as shrimp ponds. [ABSTRACT FROM AUTHOR]

The finest microgrid design has been proposed in this research to ensure a smooth energy supply for the University of Management and Technology (UMT) Lahore, Sialkot Iqbal campus, Pakistan at the cheapest price. The design methodology considered resource availability, environmental feasibility, economic feasibility, and renewable integration. The university can utilize the energy from the Sun, fossil fuel, and national grid. Integrating the available energy resources, data collection, and forecasting multiple parameters were simulated to design the optimal microgrid. The optimal design is selected based on the net present cost (NPC), renewable fraction (RF), operating cost (OC), repair and maintenance cost (O and MC), etc. Out of the 48 viable simulated designs, the optimal design has a PV system, generators, and the grid to fulfill energy demand at a net present cost of $116,987, with a renewable portion of 95%. In contrast, the worst simulated design depends on generators and battery backup, resulting in a $2.93 million NPC with a renewable segment of 24.3%. [ABSTRACT FROM AUTHOR]

The global demand for energy has increased exponentially over the years. To reduce the dominance of fossil fuels in energy production, there has been a shift towards energy production models based on renewable sources. In the design of hybrid energy systems, it is essential to keep investment costs low while ensuring the security of the energy supply by meeting the consumer's energy demands without interruption. The success of a good energy production model can be directly associated with the results of load estimation. The primary objective of this research is to predict the electricity demand for the Göksun district until 2028, utilising a data set that encompasses electricity usage from 2019 through the first four months of 2024 for the Göksun district in Kahramanmaraş. This endeavour includes the application of various machine learning (ML) paradigms (long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN)- LSTM, support vector regression (SVR)) to produce load forecasting outcomes and to engineer an optimally performing hybrid system. On evaluation of the performance metrics derived from the experimental data, it has been established that the LSTM model outperforms other methodologies, yielding more favourable results. The simulation studies of the designed hybrid system were conducted using the hybrid optimisation model for electric renewables software (HOMER Pro), demonstrating improvements in both economic and environmental parameters. Our study is unique in that it is the first to utilise a data set specific to the Göksun region and to model predictions obtained from this data set using HOMER software. [ABSTRACT FROM AUTHOR]

Morocco contains one of the greatest phosphate deposits and is the second-largest international phosphate fertilizer producer. However, it heavily relies on imported grey ammonia. To reduce this dependency, a paradigm shift is required toward local green ammonia production to strengthen the fertilizer industry. The purpose of the study is to identify the most promising locations in Morocco for hosting a green ammonia unit through a land suitability analysis. This was carried out using multi-criteria decision-making (MCDM) and geographical information systems (GIS). Eight relevant criteria were considered, based on carefully studying the relevant literature and consultation with renewable energy experts and professionals. The land suitability analysis revealed high suitability locations and five sites were selected from the regions of Dakhla, Laayoune, Boujdour, and Tarfaya. These locations were introduced to Hybrid Optimization of Multiple Electric Renewables (HOMER) software 3.16.2 for simulation. The simulation findings showed that the levelized cost of hydrogen (LCOH) ranges from 1.67 USD/kg to 1.82 USD/kg, with the lowest LCOH at Dakhla. The corresponding levelized cost of ammonia (LCOA) ranges from 646 USD/t to 687 USD/t. Dakhla was identified as the location with the lowest LCOA, accounting for 646 USD/t. The outcomes showed a similar trend compared to other studies (Saudi Arabia, Jordan, Iran). Considering improvements in the electrolyzer's efficiency and cost, a technical and financial sensitivity analysis was conducted, identifying highly promising LCOA in Morocco, reaching 548 USD/t. [ABSTRACT FROM AUTHOR]

As the world's second-largest palm oil producer, Malaysia heavily depends on its extensive oil palm cultivation, which accounts for nearly 90% of the country's lignocellulosic biomass waste. Approximately 20–22 tonnes of empty fruit bunches (EFBs) can be derived from an initial yield of 100 tonnes of fresh fruit bunches (FFBs) from oil palm trees. The average annual amount of EFBs produced in Johor is 3233 tonnes per day. Recognising that urban areas contribute significantly to anthropogenic greenhouse gas emissions, and to support Malaysia's transition from fossil fuel-based energy to a low-carbon energy system, this research employed HOMER Pro software 3.18.3 to develop an optimal hybrid renewable energy system integrating solar and biomass (EFB) energy sources in Johor, Malaysia. The most cost-effective system (solar–biomass) consists of 4075 kW solar photovoltaics, a 2100 kW biomass gasifier, 9363 battery units and 1939 kW converters. This configuration results in a total net present cost (NPC) of USD 44,596,990 and a levelised cost of energy (LCOE) of USD 0.2364/kWh. This system satisfies the residential load demand via 6,020,427 kWh (64.7%) of solar-based and 3,286,257 kWh (35.3%) of biomass-based electricity production, with an annual surplus of 2,613,329 kWh (28.1%). The minimal percentages of unmet electric load and capacity shortage, both <0.1%, indicate that all systems can meet the power demand. In conclusion, this research provides valuable insights into the economic viability and technical feasibility of powering the Kulai district with a solar–biomass system. [ABSTRACT FROM AUTHOR]

Homer's treatment of the Matter of Troy illuminates the foundational impossibility of representation in ways that sponsor examination of the semiotic choices, the costs and benefits and tradeoffs, of different practices. Homer's ekphrastic description of the shield of Achilles highlights a variety of available semiotic systems focused on different media of representation. This article explores Homeric ekphrasis in relation to later theories of media intersection, interaction, and transformation. Not only word, image, music, and dance, but also human perception as such, are subject to mediation. [ABSTRACT FROM AUTHOR]

The most memorable protagonists in any given piece of Classical Literature are often the characters who face and overcome the greatest psychological trials and obstacles. This article studies the choices of two such characters who both experience extreme and unimaginable sorrow – Homer’s Andromache and Shakespeare’s Juliet. While one character is content with her role in society, the other tries to break convention in order to be with her love. A deeper psychoanalytical study of their actions and choices, with particular emphasis on the effects of ‘sorrow’ through the lens of Freud’s concepts of bereavement and melancholia, are analyzed in this article. From love and contentment, both women face trials and tribulations which lead to loss and death. However, both these characters’ reactions toward their sorrows are what set them apart from one another; while one woman is idolized as the quintessential pinnacle of womanhood for all generations, the other is immortalized as the rebellious protagonist of a Shakespearean Romantic tragedy.

The study aims to optimize wind farm efficiency in low wind speed regions using the HOMER Pro tool to examine the impact of wind turbine ratings on overall efficiency of wind farms. boosting wind farm efficiency is essential for improving economic viability and grid integration. We propose the establishment of three wind farms, each possessing equal capacities but different in individual turbine capacities 1.5 kW, 3.4 kW and 5.1 kW, then optimize their performance in simulation environment. Through employing HOMER Pro optimization algorithm, we assess all wind farms over the period of one year, taking into account wind speed, temperature and geospatial coordinates. Although all wind farms have equal total capacities, simulation results revealed disparities in their generation abilities, reaching up to 22%, favouring the farm with smaller turbines. Furthermore, the results demonstrated that as wind speed decreases, the disparity in power generation between wind farms increases, reaching 51.9% in November, the month with the lowest wind speeds. These findings provide a comprehensive understanding of wind farm behaviour, particularly regarding turbine sizes, and contribute to the research community's efforts to enhance wind farm power production in low wind speed regions. They also help find solutions to enable the embrace of wind energy and decrease fossil fuel consumption in such regions, fulfilling their international sustainability commitments.

To foster resilient energy transition systems, the deployment of renewable energy sources is potentially helpful in the acceleration towards sustainable, reliable, and diversified solutions. In this feasibility study, aims to pursuit the best optimal solution for renewable energy resources, integrated into the local university's energy mix, to meet the electrical load demand of 8500 kWhr/day and a peak load of 815 kW. A model of a hybrid grid-connected renewable energy (PV/Biomass/Hydel/Diesel generator/Grid) system was implemented in HOMER software, to obtain the most efficient and cost-effective hybrid energy generation. The outcomes of the simulation seem more viable based on sensitivity analysis, as the cost of energy (COE) for an energy system with a hybrid combination is 0.13 $/kWh, meanwhile energy price with the grid system is 0.18 $/kWh. According to the HOMER analysis, a hybrid grid-connected PV/biomass/hydro system demonstrates both technological feasibility and economic viability, meanwhile it will also save 1575 tons of CO2 per year. Therefore, this proposed hybrid energy system, a step towards sustainable development, can be applied at institutional levels, especially in developing countries.