Solar Potential Research Articles

Assessment of clear-sky irradiance from 6S affected by local climatology of India

Accurate estimation of solar power generation is crucial for the effective planning and operation of solar energy sector. Solar potential can be estimated at a location from long-term historical irradiance data processed from atmospheric reanalysis datasets. The current study employs a clear-sky radiative transfer model (6S) to simulate the net surface irradiance at four distinct locations in India, comparing our model's output with mean monthly ground-based measurements of surface irradiance. Our findings reveal that the 6S model marginally underestimates solar irradiance, with potential deviations varying depending on the accuracy of input data. This research evaluates the influence of particulate matter concentration and relative humidity on the scattering and absorption of solar radiation. We also state the variability in surface irradiance across the country with the rainfall trends that enhance assessment accuracy. The study reports an annual deviation of 10–15 % in surface irradiance across the country, where rural settlements show lower deviations in simulations. The radiative transfer calculations mentioned in the study are simplistic yet beneficial for a priori evaluation of solar potential. Transmittance estimated from clear-sky models is further implemented in all-sky (with clouds), and thus, model accuracy is an important parameter. Additionally, we explore the trends in aerosol concentrations and the impact of local climatological factors on surface irradiance, providing insights critical for optimizing solar energy utilization.

The Advancement of Artificial Intelligence's Application in Hybrid Solar and Wind Power Plant Optimization: A Study of the Literature

The harnessing of solar, wind, and hydroelectric energy sources has rendered them easily accessible renewable resources, owing to their abundant availability. There is a growing body of research evincing interest in the deployment of hybrid renewable energy systems. Over recent decades, adopting hybrid technologies has engendered a positive trend, marked by broader considerations of configurations and applications within these systems. This study analytically examines the potential of hybrid solar and wind energy harvesting devices. As such, the project aims to extensively evaluate relevant literature and statistical analysis of data extracted from journal papers published between 2004 and 2023. A specific objective is to develop a complete database matrix surrounding multiple categories, including component configurations, methodological approaches, and supporting software infrastructures. Moreover, an assessment of the socio-economic, environmental, and ecological impacts of these systems is undertaken to ascertain their salience. Furthermore, this inquiry delves into the optimization strategies of these systems leveraging artificial intelligence methodologies. Critical lacunae identified during this review pertain to more emphasis on optimization metrics for PV-wind hybrid energy systems, impeding a holistic understanding of their implications on energy, economics, environment, and society. Our findings underscore prevalent methodologies such as computational modelling utilizing software suites like MATLAB/Simulink, HOMER, and others to derive empirical data. Additionally, parametric analyses emerge as the predominant approach, characterized by the application of algorithms such as Particle Swarm Optimization (PSO), Fuzzy Logic Control (FLC), and Genetic Algorithms (GA), among others. PV-wind hybrid energy systems are classified into autonomous and grid-interconnected configurations, with primary components comprising PV-wind generators. The anticipated trajectory suggests a burgeoning development of these hybrid energy harvesting systems, underpinned by their potential as clean, sustainable, and eco-friendly energy sources.

Offshore solar photovoltaic potential in the seas around China

China has embarked on the promotion of offshore solar photovoltaic (PV) development along its coastal regions in pursuit of carbon neutrality. An evaluation of the inherent features and exploitative potential of offshore solar PV resource stands as a pivotal measure to the development and utilization of China's offshore solar PV resource. To this end, the tripartite objectives of this study unfold as follows: i) investigating offshore solar energy features, which include spatial distribution of PV resource and its monthly and hourly variations, along with an examination of climate change impacts; ii) analyzing ocean conditions that exert an influence on offshore solar PV farms; and iii) proposing criteria for assessing the suitability of offshore solar PV development, considering both resource characteristics and ocean conditions, thereby identifying the most promising areas for forthcoming offshore PV projects. The findings reveal that the South China Sea has the richest offshore solar PV resource and the least intra-annual fluctuation, despite challenging ocean conditions. Over the past 60 years, there has been a slight decrease in the solar resources in the majority of the South China Sea, in contrast to a marginal upswing observed in the Yellow Sea and Bohai Sea. Recommendations for future offshore solar PV development suggest considering the southwest waters of Hainan Island, where the proportion of annual PV power generation to power consumption of the island in 2022 is nearly 225%.

Photovoltaic penetration potential in the Greek island of Ikaria

The operation of Distribution Networks (DNs) has been affected by the ongoing energy transition, gradually incorporating more Distributed Energy Resources (DERs), mainly Renewable Energy Sources (RES), as well as Energy Storage Systems (ESS) sustainably enhancing DN’s flexibility. In the case of the non-interconnected island of Ikaria, Greece, with high solar and wind potential, the DN includes conventional generators, Photovoltaic systems (PVs), wind farms and a hydro-pumped ESS. Scope of this study is to assess the possible impact of the PVs expansion considering either: i) fixed, ii) single-axis or iii) dual-axis tracking panels. For this purpose, CERTH’s in-house INTEMA.grid platform is used. Tracking mechanism’s effectiveness is studied considering that the expansion doubles or triples the rated power of the existing, fixed 0.4 MW PVs, following the directions of the Distribution System Operator (DSO). Additionally, a monthly analysis is presented, because Ikaria is an island with extremely higher load during summer months due to tourism. According to the results, if the current PV capacity is doubled or tripled, a dual-axis expansion yields 16.0% or 21.3% yearly production increase compared to fixed panels, respectively, with the single-axis effect though being much higher (14% or 18.7%, respectively) than the incremental effect of the second axis (further comparative 1.8% or 2.3%, respectively). The effectiveness of tracking mechanisms is highlighted during summer months and particularly early in the morning or late in the afternoon. Finally, environmental and economic indicators for the proposed installations are assessed.

From Urban Design to Energy Sustainability: How Urban Morphology Influences Photovoltaic System Performance

In response to the pressing need for sustainable urban development amidst global population growth and increased energy demands, this study explores the impact of an urban block morphology on the efficiency of building photovoltaic (PV) systems amidst the pressing global need for sustainable urban development. Specifically, the research quantitatively evaluates how building distribution and orientation influence building energy consumption and photovoltaic power generation through a comprehensive simulation model approach, employing tools, such as LightGBM, for the enhanced predictability and optimization of urban forms. Our simulations reveal that certain urban forms significantly enhance solar energy utilization and reduce cooling energy requirements. Notably, an optimal facade orientation and building density are critical for maximizing solar potential and overall energy efficiency. This study introduces novel findings on the potential of machine learning techniques to predict and refine urban morphological impacts on solar energy efficacy, offering robust tools for urban planners and architects. We discuss how strategic urban and architectural planning can significantly contribute to sustainable energy practices, emphasizing the application of our results in diverse climatic contexts. Future research should focus on refining these simulation models for broader climatic variability and integrating more granular urban morphology data to enhance precision in energy predictions.

LCZ-based city-wide solar radiation potential analysis by coupling physical modeling, machine learning, and 3D buildings

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of 3.28∗1011kwh. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO2 emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.

Hybrid NenG/ZSM‐5 Towards Highly Selective and Efficient Photocatalysis for Generation of Leucodopaminechrome and Gluconic Acid Under Solar Light

AbstractGlucose, an abundant and renewable form of biomass, has garnered significant attention in research for its conversion into high‐value chemicals such as gluconic acid. Traditional methods for biomass transformation typically involve high energy input, elevated temperature, pressure, and costly systems. In contrast, photocatalysis emerges as a promising approach to produce organic molecules under mild conditions, harnessing energy from natural sunlight or lamps. This study presents the nitrogen‐enriched graphene with zeolite second Mobil–5 (NenG/ZSM‐5) photocatalyst, evaluated for generating high‐value chemicals (gluconic acid and leucodopaminechrome) under solar light irradiation. The NenG/ZSM‐5 photocatalyst, synthesized from nitrogen‐doped graphene and ZSM‐5, successfully converted dopamine into leucodopaminechrome (71.54 %) — a critical step in dopamine regeneration and transformed glucose into gluconic acid (85 %). The addition of ZSM‐5 to NenG provided stability and enhanced product selectivity. The outstanding performance of the NenG/ZSM‐5 photocatalyst can be attributed to its heightened solar light harvesting potential, appropriate energy band gap, and uniformly arranged π‐electron channels. This research focuses on solar conversion of glucose to gluconic acid and dopamine regeneration, with potential for further exploration in the research domain.

Analysing the prospects of grid-connected green hydrogen production in predominantly fossil-based countries – A case study of South Africa

Importing substantial amount of green hydrogen from countries like South Africa, which have abundant solar and wind potentials to replace fossil fuels, has attracted interest in developed regions. This study analyses South African strategies for improving and decarbonizing the power sector while also producing hydrogen for export. These strategies include the Integrated Resource Plan, the Transmission Development Plan, Just Energy Transition and Hydrogen Society Roadmap for grid connected hydrogen production in 2030. Results based on an hourly resolution optimisation in Plexos indicate that annual grid-connected hydrogen production of 500 kt can lead to a 20–25% increase in the cost of electricity in scenarios with lower renewable energy penetration due to South African emission constraints by 2030. While the price of electricity is still in acceptable range, and the price of hydrogen can be competitive on the international market (2–3 USD/kgH2 for production), the emission factor of this hydrogen is higher than the one of grey hydrogen, ranging from 13 to 24 kgCO2/kgh2. When attempting to reach emission factors based on EU directives, the three policy roadmaps become unfeasible and free capacity expansion results in significant sixteen-fold increase of wind and seven-fold increase in solar installations compared to 2023 levels by 2030 in South Africa.

Forecasting the solar modulation potential: Tests of time series models

This study analyzes the predictability of the solar modulation potential using time series models. Recently, new data sets for the modulation potential have become available, at daily, monthly, and annual resolutions. At lower frequencies, the data show the well-known 11-22-year cycle. Both the periodicity and amplitude vary over time. At higher resolutions, the probability distribution has heavy tails, while the data show the intermittent outliers characteristic of multifractal processes. Forecasting experiments are run using regressions in levels and differences, frequency domain methods, models with sinusoidal terms and neural networks. For the daily data, all the models achieve high degrees of accuracy at proximate horizons. As the horizon extends, accuracy falls away rapidly. At 27 days, corresponding to one solar rotation, a transfer function in differences achieves a more accurate forecast than either regressions or neural nets, since it is able to replicate the range of the data. At the annual resolution, both the regression and neural net predict well at horizons of 1 year. Again, forecast accuracy deteriorates sharply as the forecast horizon extends. At the monthly resolution, forecasting is problematic. The resolution is not low enough to bring out the low frequency cycles, but there is so much short-term dependence that the data are completely dominated by serial correlation. Any model incorporating proximate lags will generate inertial forecasts. Any model using lower frequency cyclical terms will be unable to pick up on near-term patterns. The forecasting skill of time series models appears limited to short horizons. The recommendation for forecasting over longer intervals is some combination of physics and statistical models.

From roofs to renewables: Deep learning and geographic information systems insights into a comprehensive urban solar photovoltaic assessment for Stonehaven

As urban solar photovoltaic (PV) construction emerges as a leading renewable energy technology, there is a growing focus on its implementation. However, the challenges of scarce, low-resolution, and inaccurate PV-related data sources hinder accurate assessments of urban PV potentials and are not conducive to efficient and rational smart city planning. This study tackles these challenges by introducing a mature, detailed, and accurate assessment process, taking Stonehaven as an example, aimed at leveraging limited data to mine more data and geographic information useful for guiding urban PV planning. Initially, utilise existing Digital Surface Model (DSM) and optical image data, combined with deep learning techniques and a PV potential assessment model, to comprehensively assess the PV power generation potential of the Stonehaven area. Our results demonstrate that integrating DSM significantly enhances the accuracy of roof segmentation. Furthermore, compared with DeeplabV3, U-Net performs better in roof segmentation. Additionally, the solar radiation potential (SRP) map generated by DSM highlights the superior solar radiation receiving capacity of south-facing and flat roofs. We provide a detailed PV power generation potential (PPGP) map of individual building roofs, revealing the substantial potential of this area for generating up to 1.12 × 10^7 kWh of electricity per year. Detailed and fine-grained PPGP can also help to optimise PV siting and electricity resource allocation. Furthermore, our return-on-investment period (ROIP) analysis indicates that most Stonehaven roofs have ROIPs between 8.1 and 11.3 years. The detailed ROIP distribution map can help people make informed PV investment decisions. Future research directions include enhancing data quality, refining segmentation algorithms, and exploring assisted urban energy planning analysis for smarter urban planning.

Solar Self-Consumption and Urban Energy Vulnerability: Case Study in Lisbon

This paper investigates the potential of rooftop photovoltaic (PV) systems in mitigating energy vulnerability in the urban context. Based on a geospatial data-driven approach, it combines georeferenced assessment of solar potential and high-resolution demand data with energy vulnerability indicators for both heating and cooling needs, to identify priority areas for supporting PV deployment. Results show more than 50% saving potential in the energy bill for the selected priority areas. The mismatch between PV supply and demand supports the development of demand-aggregating collective self-consumption approaches such as solar energy communities, whose challenges and opportunities are discussed.

Economic Potential of Solar Projects in India: Assessing Levelized Cost of Electricity andPayback Period withVariations in PV Module Efficiency and Solar Irradiation

India is endowed with vast solar resources, with about 5,000 trillion kWh per year of energy incident over India’s land area, with most parts receiving 4-7 kWh per sqm per day. The National Institute of Solar Energy (NISE) has assessed the country’s solar potential to be about 748 GW. The government is bullish on realizing this solar potential, setting out ambitious targets to install 500 GW of non-fossil fuel capacity by 2030, with Solar projects contributing a major chunk of it. Reports suggest that investments up to $294bn would be required to meet India’s solar and wind targets. However, this capital expenditure can be reduced or controlled by deploying our solar projects prudentially. This paper intends to quantify the effect of variations in Solar PV module efficiency due to varying technology and changes in solar irradiation according to differing geographies on the cost-effectiveness of Solar projects through the calculation of the Payback period and Levelized Cost of Electricity for Solar PV projects in different parts of India. Comparison of these techno-economic factors will aid policymakers and project developers in decision-making and deploying their resources to achieve maximum financial output. The results reveal that higher module efficiency and greater solar irradiation significantly bring down the cost of Solar projects, bringing it even below the global average LCOE of solar PV of 49$/MWh for higher efficiencies and reducing the payback period to as low as shorter than 5 years. Therefore, this paper aims to provide valuable insights to the policymakers and investors working in the solar energy sector in India to help build a clean, secure, and resilient energy future for India

Daily sunshine grids for Austria since 1961 – combining station and satellite observations for a multi-decadal climate-monitoring dataset

Grid datasets of sunshine duration at high spatial resolution and extending over many decades are required for quantitative applications in regional climatology and environmental change (e.g., modelling of droughts and snow/ice covers, evaluation of clouds in numerical models, mapping of solar energy potentials). We present a new gridded dataset of relative (and derived absolute) sunshine duration for Austria at a grid spacing of 1 km, extending back until 1961 at daily time resolution. Challenges in the dataset construction were consistency issues in the available station data, the scarcity of long time series, and the high variation of cloudiness in the study region. The challenges were addressed by special efforts to correct evident breaks in the station series and by adopting an analysis method, which combines station data with satellite data. The methodology merges the data sources non-contemporaneously, using statistical patterns distilled over a short period, which allowed involving satellite data even for the early part of the study period. The resulting fields contain plausible mesoscale structures, which could not be resolved by the station network alone. On average, the analyses explain 47% of the spatial variance in daily sunshine duration at the stations. Evaluation revealed a slight systematic underestimation (− 1.5%) and a mean absolute error of 9.2%. The average error is larger during winter, at high altitudes, and around the 1990s. The dataset exhibits a conditional bias, which can lead to considerable systematic errors (up to 15%) when calculating sunshine-related climate indices.

Multi-criteria decision-making approach in assessing the key barriers to the adoption and use of SWH in West Africa–Combination of modified Delphi and Fuzzy AHP

Africa faces a significant energy crisis, with over 600 million people lacking access to affordable and reliable power to meet their energy demands, despite the continent's vast solar energy potential. Several studies have indicated the high potential of solar water heaters (SWH) in various African countries in Africa due to the enormous solar energy resource. However, the diffusion and use of the same in Africa, especially in the West African sub-region, have been very slow compared to other sub-regions. This study thus assesses the barriers that affect the adoption and use of SWH systems using the Delphi method and the Fuzzy Analytical Hierarchy Process to prioritize and rank them for decision-making. The study identified five main barriers and prioritized them in the order of financing, gender, and market-related barriers (51 %), political, policy, and regulatory barriers (19 %), technical barriers (14 %), socio-cultural and public awareness barriers (11 %), as well as lack of standardization and quality assurance (6 %). The high cost of finance as well as difficulties in accessing finance prevent private sector investment, which ultimately affects the large-scale use of the SWH system in the sub-region. Also, the uneducated and a section of the educated do not fully appreciate the merits of the SWH system. This paper is expected to inform academics, practitioners, and regulators on future efforts to adopt for the SWH technologies in Africa, and to improve policies and strategies for their adoption across the continent.

Analisis dan Desain Floating PV System di Beberapa Waduk Batam untuk Optimalisasi Bauran Energi Terbarukan

The climate crisis caused by the use of fossil fuels is prompting countries around the world to shift to renewable energy. Indonesia has substantial solar energy potential, but this must be balanced with land conservation efforts. PT PLN Batam, a subsidiary of PT PLN (Persero), shares the responsibility of meeting the national renewable energy mix target of 23% by 2025. Adding new power plants requires a large area, which is a significant challenge in the development of both fossil and renewable energy plants in Batam. The floating PV system is an alternative solution to this problem. This study aims to design a floating solar power generation system at Tembesi and Mukakuning Reservoirs, focusing on solar energy potential, energy production, and system design. The results indicate that solar irradiation at both locations reaches 1,650 kWh/m² with an average horizontal PSH of 4.52 hours/day and net PSH Modules of 4.32 hours/day. Energy production in Year-1 is 55.83 GWh with a performance ratio of 81.65%, resulting in annual energy production of 1,345 kWh/kWp. The floating PV system design at Tembesi and Mukakuning Reservoirs consists of 10 islands, each with 19 array boxes and 20 strings per array box, comprising 28 PV modules per string. A total of 106,400 PV modules with a capacity of 390 Wp each is utilized. The system employs 3,125 kVA PV inverters, 7,500 kVA MV transformers, and 35 MVA HV transformers to ensure high efficiency and reliability. The system design also considers environmental durability, including resistance to corrosion and water level fluctuations. Recommendations include optimizing the design based on seasonal variations and implementing effective monitoring and maintenance systems.

Time-variable diffuse γ-ray foreground

ABSTRACT While the data analysis of γ-ray telescopes has now become more robust, some signals may be misinterpretations of a time-variable foreground emission from the Solar system, induced by low-energy cosmic-ray interactions with asteroids. Our goal is to provide emission templates for this time-variable diffuse γ-ray foreground by considering the populations of Main Belt Asteroids, Jovian and Neptunian Trojans, Kuiper Belt Objects, and the Oort Cloud. We model the spatial distribution of all known asteroids by performing 3D-fits to determine their density profiles and calculate their appearances by line-of-sight integrations. Because Earth and the asteroids are moving with respect to each other, we obtain diffuse emission templates varying on time-scales of days to decades. We find that the temporal variability can lead to flux enhancements that may mimic emission features unless properly taken into account. This variation is further enhanced by the Solar cycle as the cosmic-ray spectrum is attenuated by the Solar modulation potential, leading to a relative flux increase of the outer asteroids. The cumulative effect of the time-dependent emission is illustrated for the case of the ‘511 keV OSSE fountain’, and for emission features near the Galactic Centre, both being possible misinterpretations of the Solar system albedo. We recommend that γ-ray data analyses should always take into account the possibility of a time-variable foreground. Due to the ecliptic overlap with the Galactic plane, the Galactic emission is expected to be weaker by 0.1–20 per cent, depending on time (relative planetary motion), energy, and Solar cycle, which has consequences for the interpretation of dark matter annihilation cross sections, cosmic-ray spectra and amplitudes, as well as nucleosynthesis yields and related parameters.

Evaluation Analysis of Small Capacity Rooftop Solar Electricity (PLTS) Development Plans at PT Pertamina Gas

Solar Power Plant (PLTS) is one type of new and renewable energy (EBT) generated from the Sun. Indonesia is a country that has considerable solar energy development potential of 4.8 Kw h/m2, but currently its utilization is still very low at only 0.01%. This study aims to evaluate the development plan of small-capacity rooftop solar power plants at oil and gas facilities owned by PT Pertamina Gas (Pertagas). This study uses PVSyst and Helioscope software to analyze the potential and efficiency of rooftop solar power plants. The results show that the planned installation of small-capacity rooftop solar power plants (±0.15 kW) in several Pertagas operational areas can support electricity supply for oil and gas distribution in Indonesia. The utilization of solar energy, which is one of the new renewable energy (EBT), is expected to increase the national energy mix in accordance with the government's target. This research provides evaluation and recommendations for the development of rooftop solar power plants at oil and gas facilities to support national energy independence and security.

Development of a cross-latitude applicable predictive model for solar radiation acquisition potential of urban residential blocks

Existing research has revealed that predictive models can be developed based on urban morphology indicators to assess the potential of solar radiation acquisition (SRA) on building surfaces and provide much faster results compared to simulation software. However, such existing models can only be utilized in specific cities or regions, which restricts their popularity. To fill this scientific gap, this study develops a cross-latitude predictive model for assessing the SRA of buildings. Building solar radiation acquisition capacity, selected key morphology indicators, and cosine of latitude (cosφ) are employed as dependent and independent variables in the model to discuss the interaction between the urban form and the SRA based on the analysis of sixty sample urban residential blocks across nine cities in China with varying latitudes. The obtained results are indicative of relatively high prediction accuracy for the solar radiation acquisition capacity of buildings (with an adjusted R2 of 0.931 for roofs, and 0.962 for exterior walls). As a result, the proposed model has the potential to serve as a valuable tool for incorporating solar radiation considerations into sustainable urban design practices.

Hybrid wind-solar energy potential modeling using ERA5 and solar irradiation data in google Earth Engine

Renewable energies play a significant role in mitigating the climate change impacts. Wind and solar energy are projected to supply larger amount to the total renewable energy capacity of many nations. Long-term wind and solar data availability and analysis is vital for sustainable renewable energy farming. In this research, we analyze wind speed u- and v-components at 10 m height of the ERA5 daily reanalysis aggregates at a mesh of 31 Km during a long period (2010–2020) over the Red Sea and the Gulf of Suez coasts of Egypt. Google Earth Engine (GEE) code is developed and used for the analysis. The prime objective is to derive estimates on the spatio-temporal variability of speed, direction, and the wind energy estimates in the area. Further, the solar energy potential has been evaluated using reference data of the PVOUT – Photovoltaic power potential (kWh/m2/day) data, at nearly 1 km resolution, which is normalized at the evaluated sites, and then used to rank sites with their solar energy potential. Evaluation of estimates is carried out at selected thirteen sites distributed along the coasts to better understand the hybrid wind-solar energy potential. Frequency analysis is carried out to the wind speed spatial estimates to demarcate highest frequency wind events potential for wind farming. Reference in-situ wind speed observations available at daily and monthly time scales for Hurghada weather station is used for investigating the reanalysis data performance. The Hurghada site had the highest average wind speed (6.7 m s−1), frequency of events (75 %), and mean daily (60.4 kWh) and annual (22.02 MWh) wind energy. The windiest time intervals were the September month, 2017 year, and the summer season. The north had wind energy directions of 130°, while the south had 110°. Trend analysis showed declining trends for northern locations and rising yearly wind speed estimates for Berenice (0.005) and Halayeb (0.002). El-Tor location had the highest solar energy output, with a daily mean of 5.52 kWh/m2 and an annual mean of almost 2015 kWh/m2. The Hurghada, Jemsa, G. Zeit, and El-Tor sites achieved the highest rankings in terms of hybrid wind-solar energy potential. The results can be very valuable for optimal location of hybrid solar-wind energy facilities and provide viable and promising solutions for sustainably meeting Egypt's growing clean energy demands.

Optimization study of an energy storage system supplied solar and wind energy sources for green campus

This comprehensive study aimed to develop a hybrid system capable of fulfilling the yearly energy demand of the Erciyes University Campus, which amounts to 30.4 GWh. The hybrid system consists of photovoltaic (PV) panels, wind turbines, converters and batteries. An optimization process was carried out to find the most suitable system configuration considering various criteria, including net present cost (NPC), levelized cost of energy (COE), operating cost, initial cost, and cost associated with the battery to be used in the system. The study was also conducted to determine the most suitable energy storage solution for a hybrid system that uses both wind and solar energy sources. This study presented an innovative approach to optimizing the selection of the most suitable energy storage battery for such systems. The wind and solar potential of the specified area were taken from the NASA database using HOMER Pro. The optimized system generated a power of 52.7 MWh with a COE of $0.352, it covered the annual electricity needs of Erciyes University, and the produced excess energy of 18.6 MWh/yr was utilized with the energy storage system.