Solar Photovoltaic Energy Systems Research Articles

Design and optimisation of hybrid solar PV energy system for rural areas in the Narmada Valley, India

This study aims to design and select an optimal hybrid photovoltaic (PV) energy system for rural areas in the Narmada Valley (21.68°N, 74.92°E), Madhya Pradesh (MP) state, India. Two case studies in Narmada Valley (farmhouse and healthcare) were analysed for technical, economic, environmental, and reliability parameters, using MATLAB/Simulink and HOMER Pro software. The result indicates that the PV/BB/DG farmhouse energy system has capital cost of ₹2643200 (CC), an operating cost (OC) of ₹955763, fuel cost (FC) of ₹7240725, net present cost (NPC) of ₹3496135, an annualized cost (AC) of ₹257246 and cost of energy (COE) of ₹12.05. The PV/BB/DG healthcare energy system is CC ₹528640, OC ₹221191, FC ₹1979132, NPC ₹802055, AC ₹53213 and COE ₹12.34. The failure rate, expected energy not supplied (EENS) and loss of load probability (LOLP) of the farmhouse hybrid PV/BB/DG system are 0.0501/yrs, 0.0927 hrs/yrs, 0.4356 kWh/hrs and 1.05822E-05, respectively. The healthcare hybrid PV/BB/DG system failure rate (0.0117199), interruption duration (0.09009), system unavailability (2.16 hours.), EENS (0.234234 kWh/yrs), and LOLP (1.02842E–05) are all much lower than those of other configurations. Hybrid solar PV/BB/DG energy systems provide reliable, cost-effective, reduced environmental impact, sustainability, and job creation in rural areas.

Development of A Man-Machine Interface for Managing Photovoltaic Energy: Applied Study

Objective: Develop and evaluate a human-machine interface (HMI) that integrates advanced monitoring, forecasting, and management functionalities for photovoltaic solar energy systems, aiming to optimize energy production and operational efficiency. Theoretical Framework: This study is based on concepts of modeling and simulation, solar energy management, and problem-solving methodologies such as Soft System Methodology (SSM). Method: An applied approach was adopted using modeling, simulation, and statistical analysis techniques. The research included a bibliographic review in scientific databases, a case study, and SSM to organize and solve complex problems. 121 digital solar energy platforms in Brazil were analyzed to define the interface requirements. The interface was developed with React JS, Axios, Bootstrap v5, Apache Echarts, HTML, CSS, JavaScript, and Python libraries for forecasting models. Results and Discussion: The interface, named "Solar Smart Manager," enables efficient monitoring and management of energy production using critical data such as temperature, time of day, and solar irradiation. Tests in a real operational environment demonstrated improvements in energy management, incident response, and preventive maintenance. The functionality of validating solar radiation incidence data represents a significant contribution to the energy sector, promoting sustainability and innovation. Research Implications: The practical and theoretical implications of this research provide insights into the efficient and optimized management of photovoltaic solar energy systems, contributing to a better understanding and optimization of available solar resources. Originality/Value: This study contributes to the literature by developing an innovative interface that improves operational efficiency and solar energy management. The relevance and value of this research are evidenced by its positive impact on the energy sector, promoting sustainability and innovation.

Experimental development of a method of short and medium-term photovoltaic generation forecasting using multivariate statistics and mathematical modeling

Uncertainties in photovoltaic solar energy production can make it challenging to dispatch energy into the electricity grid. Although photovoltaic generation storage solves this problem, a forecasting of the photovoltaic solar energy produced is necessary to control the energy injected into the grid. This article aims to develop the probabilistic methodology Reduced-Rank Regression (RRR) for forecasting photovoltaic generation in the short and medium terms. The RRR methodology forecasting uses the generation data of a grid-connected photovoltaic system. The proposed RRR model is simple, easy to access and apply, and does not use irradiance data. The model developed uses the multivariate statistical analysis technique. A advantage is that with a correlation with the performance indices of photovoltaic solar energy systems, the proposed method can be applied in any geographical location on the planet and with different photovoltaic solar energy systems. The application of the RRR methodology requires two searches/inputs. The first input is weather forecast data obtained from a weather forecasting platform, and the second is actual historical data on photovoltaic generation at the site where the method was developed. The proposed method was compared with the persistence method. Using a horizon of 1–10 h, the average monthly root mean square error for the RRR ranged from 7.3 % to 50.1 %. For the persistence method, the average monthly root mean square error ranged from 15.1 % to 65.0 %. Therefore, with the horizon of 24 h, the average monthly root mean square error for the RRR ranged from 4.5 % to 43.2 %. For the persistence method, the average monthly root mean square error ranged from 11.5 % to 75.0 %. We show experimentally that our method is competitive with the state-of-the-art in terms of obtaining photovoltaic generation forecasting without using solar radiation data.

Real options analysis of seawater desalination with solar photovoltaic energy systems: A least-squares stochastic perspective

Real Options Analysis (ROA) offers a modern theoretical framework to evaluate the impact of managerial flexibilities and uncertainties on the intrinsic investment value of renewable-powered desalination projects. This study analyzes Photovoltaic-powered Reverse Osmosis (PV-RO) desalination plants using a novel Least Square Monte-Carlo Real Options Analysis (LSMC-ROA) to assess the deferral of investment decisions. Three scenarios for powering RO are explored: reliance on the national grid with a subsidized electricity tariff for of $0.085/kWh, which applicable only to governmental sections in Saudi Arabia, full PV power with grid balancing ($0.083/kWh), and PV with a battery storage system (average electricity cost of $0.138/kWh). The presented modeling results reveal that considering the flexibility to defer and wait for new information, the total investment values would be $43.9 million, $59.6 million, and $30.5 million, respectively. Therefore, the optimal investment timing might be to defer investment to the second year. Additionally, the analysis reveals an unexpected outcome: a threshold salinity level for feedwater exists that can affect the economic viability of renewable-powered desalination. The proposed LSMC-ROA framework emerges as a robust tool for addressing uncertainties and assessing economic feasibility in the context of PV-RO desalination projects.

Investigating the impact of solar PV and wind energy systems on the strength of a longitudinal power grid

The integration of inverter-based generation (IBG), such as solar photovoltaic (PV) and wind systems, presents unique challenges for power grid strength, especially in regions with limited conventional generation capacity. This study investigates the impact of such integration on the strength of Nigeria's power grid, focusing on sparsely connected northern region with abundant renewable resources. Using the network response short-circuit ratio (NRSCR) metric, which accounts for interactions between IBGs, we assess grid strength in three case studies. Simulations were conducted with the Power System Simulation for Engineering (PSS®E) software. Static analysis reveals inherent weakness in the northern region, characterized by low short-circuit capacity. Integrating IBGs at nine strategic locations shows varying responses: strong buses (NRSCR > 10) adapt well, while weak buses (NRSCR < 10), particularly in Kano and Katsina, constrain the operational capacity of the IBGs. Furthermore, close interaction among IBGs at these locations significantly impacts the system's dynamic response, although planned addition of conventional generators offers marginal improvement. This work highlights the potential of renewable resources to address energy shortages but also underscores the crucial need for detailed design studies at each point of interconnection (PoI), considering the longitudinal grid layout in the northern region and potential limitations imposed by IBG interactions. The findings also offer a structured approach that can be applied to power grids with comparable IBG integration levels, similar to the energy landscape of Nigeria.

Comparison of externalities of biogas and photovoltaic solar energy for energy planning

The Brazilian electricity matrix is around 80% renewable, and it has registered continuous growth in the expansion of biogas and photovoltaic (PV) systems. The main current Brazilian incentive program for the distributed generation of renewable energy, through a single net metering tariff, that is, a generation compensation system in credits to the consumer who owns the generating unit, does not seem to be effective to promote the expected growth of the biogas. Thus, the objective of this paper was to evaluate quantitatively and qualitatively a set of significant externalities for both sources to contribute to a more realistic energy planning. The externalities quantitatively evaluated for these two renewable sources were: avoided emissions, employment and income, biofertilizers for biogas and use as a constructive element for PV systems. A significant result was that the economic value of the externalities of biogas can exceed those of PV systems for the rural producer by more than 11 times, for society by 1.7 times. It is concluded that, due to the value of biogas externalities being much higher economically than those of PV systems, Brazilian public policies for energy planning should prioritize biogas over solar photovoltaic energy systems.

Immersive Learning in Photovoltaic Energy Education: A Comprehensive Review of Virtual Reality Applications

This paper presents a comprehensive and systematic review of virtual reality (VR) as an innovative educational tool specifically for solar photovoltaic energy systems. VR technology, with its immersive and interactive capabilities, offers a unique platform for in-depth learning and practical training in the field of solar energy. The use of VR in this context not only enhances the understanding of solar photovoltaic (PV) systems but also provides a hands-on experience that is crucial for developing the necessary skills in this rapidly evolving field. Among the 6814 articles initially identified, this systematic review specifically examined 15 articles that focused on the application of VR in PV education. These selected articles demonstrate VR’s ability to accurately simulate real-world environments and scenarios related to solar energy, providing an in-depth exploration of its practical applications in this field. By offering a realistic and detailed exploration of PV systems, VR enables learners to gain a deeper understanding of harnessing, managing and using such a vast energy resource. The paper further discusses the implications of employing VR in educational settings, highlighting its potential to change the way solar energy professionals are trained, thereby contributing significantly to the acceleration of photovoltaic technology adoption and its integration into sustainable energy solutions.

Investigation and Optimization of Integrated Electricity Generation from Wind, Wave, and Solar Energy Sources

This study investigates the potential for renewable energy-based electricity generation using existing wave, wind, and solar energies in Türkiye. A significant part of Türkiye’s energy needs is still met using fossil fuels. Considering the country’s resources, renewable energy sources appear as an alternative source to meet these needs. The objective of this study is to find an effective, efficient, economical, environmentally friendly, and sustainable way to produce electricity to reach net-zero targets and transition towards low-carbon and carbon-free energy systems. To be able to make a deep investigation about the relevant issue, six provinces from different regions of Türkiye (Antalya, Çanakkale, İstanbul, İzmir, Kırklareli, and Muğla) are assessed in terms of wave, wind, and solar energy potential, including wave data, wind speeds, sunshine duration, and global radiation values. Wind, wave, and solar energy data of the selected regions were taken from the ERA5 database, which is the weather forecast model of the European Center for Medium-Term Weather Forecasts (ECMWF), and the Ministry of Energy and Natural Resources of the Republic of Türkiye and the General Directorate of Meteorology. Calculations were made using monthly data for the last 5 years. Considering the coastal lengths in the determined regions, the annual total electrical power produced from wave, solar, and wind energies was calculated. In these calculations, the coastal length parameter was assumed to be uniform across all cities, and the electrical power potential from these energy sources was analyzed. Within the framework of these analyses, the number of houses in the selected regions whose electricity needs can be met was calculated. As a result, the potential electrical power and the amount of affordable housing units in the selected regions were compared. As an important result of the studies, it was determined that the characteristic features of the selected regions, such as wavelength, wave height, and wind speed, were directly related to the applicable coast length. The power obtained from wave energy was higher than that from other renewable energy sources, considering the determined coast lengths. Wave energy was followed by parabolic solar collector, wind, and photovoltaic solar energy systems. According to the model, the power obtained from renewable energy systems was at the highest level in the Kırklareli/Demirköy province compared to other locations. Kırklareli was followed by İstanbul, Antalya, İzmir, Muğla, and Çanakkale. It was also found that the electricity needs of 763,578 houses were met in the Kırklareli/Demirköy region, and the electricity needs of 470,590 houses were met in the Çanakkale/Ayvacık region. The statistically optimized factors using the Response Surface Methodology (RSM) for wind, photovoltaic, parabolic solar collector, and wave power were reported as 995.278, 4529.743, 2264.546, and 276,495.09, respectively. The optimal factors aim to achieve a total electricity generation rate of 2.491 × 109 (kWh/year), a total number of houses of 682,590.55 (number/year), and a total cost of USD 813,940,876. In line with the results obtained, the Kırklareli/Demirköy region becomes favorable when considering wave and wave-integrated wind and solar energies. The proposed system has the potential to meet the entire electricity demand of the Kırklareli province based on data from the Republic of Türkiye Energy Market Regulatory Authority (EMRA).

Potentials and opportunities of solar PV and wind energy sources in Saudi Arabia: Land suitability, techno-socio-economic feasibility, and future variability

Solar and wind energy sources hold significant potential to meet the escalating energy demand in Saudi Arabia sustainably. This research aims to assess the feasibility and prospects of deploying solar photovoltaic (PV) and wind energy systems in Saudi Arabia (SA). The study adopts a comprehensive approach, encompassing spatial analysis of land suitability, techno-socio-economic feasibility studies, and prediction of renewable energy (RE) resource variability and trends using machine learning techniques. Geographical Information System (GIS) spatial analysis is employed to identify suitable areas for establishing solar PV and wind farms based on multi-criteria evaluation methods. Techno-socio-economic feasibility analysis of solar PV and wind energy systems, under various configurations and scenarios, is conducted using the System Advisor Model (SAM) tool. Furthermore, a Support Vector Machine (SVM) machine learning algorithm is developed and deployed to forecast Global Horizontal Irradiation (GHI), wind speed, and influential weather parameters at different locations in SA. The projections of these variables derived from global climate models are utilized to analyze prospects and variabilities. The land suitability analysis identified four optimal locations for large-scale solar PV fields in Tabuk, Al Madinah, Makkah, and Riyadh provinces. Additionally, four promising wind farms were found in Al Madinah, Makkah, Riyadh, and Eastern provinces. The analysis also identified one location in Al Jouf province suitable for hybrid systems combining solar PV and wind energy. The techno-economic assessment revealed that wind farms performed best overall, achieving a capacity factor of 42.6 % in Al Madinah province. Although current tariffs render projects economically unviable, solar PV, wind energy, and hybrid solar PV-wind technologies are economically feasible in SA at Power Purchase Agreement (PPA) rates above $32.8/MWh, $26.1/MWh, and $50.6/MWh, respectively. The social development analysis estimated potential job creation from solar PV and wind energy deployment under different scenarios from 2020 to 2060, indicating that more ambitious climate targets could translate to millions of renewable energy jobs. The SVM model predicted solar irradiance with an R-squared value of 0.893. The CMIP6 model was then used to project the GHI for SA in 2049, suggesting an increase of approximately 19 %. Wind speed is also expected to rise roughly 5 % over the same period. The integration of GIS spatial analysis, techno-socio-economic modeling, and machine learning-based forecasting provides comprehensive insights into harnessing solar and wind energy in SA. This study facilitates evidence-based planning and risk assessment, crucial for a sustainable energy transition. The insights and roadmap derived from this research can inform policy frameworks, support the United Nations Sustainable Development Goals (UNSDGs), and attract private investments for RE development in SA. Consequently, this study establishes solar and wind energy as viable and promising solutions for meeting SA's growing energy demands sustainably, while minimizing the associated environmental impact.

Sustainable Energy Management using Machine Learning

The global energy sector is encountering escalating difficulties, including rising demands for efficiency, shifts in supply and demand patterns, and a lack of optimal management analysis. Utilizing machine learning (ML) to process energy sector data can gradually address these issues. ML algorithms have the capability to analyze equipment data, construct predictive models, and address sustainability-related problems. In smart cities, the integration of machine learning algorithms enables automatic responses to fluctuations in electricity prices, facilitating effective control of energy consumption. Systems employing machine learning can assist energy suppliers in adapting to variable renewable energy supplies. Worldwide, there is a growing emphasis on low-emission energy sources, leading to increased installed capacities of solar photovoltaic, wind farms, and marine energy systems. Consequently, artificial intelligence and machine learning are poised to play a vital role in effectively managing the challenges of the energy sector. The implementation of micro-grids presents significant challenges that necessitate advanced control techniques like model predictive control (MPC). This paper focuses on employing MPC for energy management in micro-grids and aims to provide an up-to-date overview of the development of MPC methods for sustainable energy management.

Mathematical Modelling and Statistical Analysis of Improved Grey Wolf Optimized Maximum Tracking for Solar Photovoltaic Energy System Under Non Linear Operational Conditions

IAs the utilization of solar photovoltaic (PV) energy systems continues to expand, the efficient extraction of energy under non-linear operational conditions becomes paramount. This research focuses on the development and enhancement of a Maximum Power Point Tracking (MPPT) algorithm, specifically tailored for solar PV systems, through the integration of Improved Grey Wolf Optimization (IGWO) techniques. The study utilizes mathematical modeling and statistical analysis to evaluate the performance of the proposed IGWO-based MPPT algorithm. This research, we first establish a comprehensive mathematical model of a solar PV energy system that accurately represents its non-linear operational characteristics, taking into account factors such as temperature variations, shading effects, and changing environmental conditions. Subsequently, we introduce the Improved Grey Wolf Optimization algorithm to optimize the MPPT process, aiming to enhance energy extraction efficiency by dynamically adapting to varying conditions. The statistical analysis includes the comparison of the IGWO-based MPPT algorithm with conventional MPPT methods, such as Perturb and Observe (P&amp;O) and Incremental Conductance (IncCond), under various non-linear operational scenarios. Key performance metrics, including energy conversion efficiency, response time, and tracking accuracy, are thoroughly evaluated to assess the algorithm's effectiveness in real-world conditions. The results of this study demonstrate the superior performance of the IGWO-based MPPT algorithm in enhancing the energy harvesting capabilities of solar PV systems under non-linear operational conditions. The proposed approach not only improves the overall energy conversion efficiency but also reduces the adverse effects of environmental variables on the system's performance. In conclusion, the integration of Improved Grey Wolf Optimization into the MPPT process represents a promising advancement in the field of solar photovoltaic energy systems. The mathematical modeling and statistical analysis conducted in this research provide valuable insights into the practical benefits of this approach, paving the way for more efficient and reliable solar energy utilization in the future.

Mathematical Modelling Non Linearity Characteristic Analysis and Efficiency Enhancement Strategies for Hybrid Solar Photovoltaic Energy System

As the world continues to grapple with the challenges posed by climate change and the depletion of conventional energy sources, renewable energy systems such as solar photovoltaic (PV) technology have gained significant prominence. Hybrid solar PV systems, which combine multiple energy sources and storage solutions, offer a promising avenue to improve the reliability and efficiency of renewable energy generation. This research focuses on the mathematical modeling, non-linearity characteristic analysis, and the development of efficiency enhancement strategies for hybrid solar PV energy systems. The study begins with the development of comprehensive mathematical models that accurately represent the complex interactions within hybrid solar PV systems. These models consider various factors, including solar irradiance, temperature variations, load profiles, and the dynamic behavior of energy storage components. The incorporation of non-linear characteristics, often overlooked in conventional models, allows for a more realistic representation of system performance. To enhance the efficiency of hybrid solar PV systems, a range of strategies are proposed and evaluated. These strategies encompass advanced control algorithms, optimized sizing and placement of energy storage elements, and the integration of emerging technologies such as artificial intelligence and machine learning. The goal is to mitigate non-linear effects, maximize energy utilization, and improve system response to dynamic operating conditions.&#x0D; The findings from this research provide valuable insights into the design, operation, and performance optimization of hybrid solar PV energy systems. By addressing non-linearity characteristics and developing efficient strategies, this study contributes to the advancement of renewable energy technologies and fosters the transition towards a more sustainable and resilient energy future.

Power quality investigation of a grid tied hybrid energy system using a D-STATCOM control and grasshopper optimization technique

This research delves into the control strategy and modeling of a utility-grid tied hybrid system that conjoins a Solar Photovoltaic (SPV) and wind energy system. To maximize real power output under varying environmental conditions, both SPV-wind energy systems are controlled by Perturb & Observe (P&O) type Maximum Power Point Tracking (MPPT) algorithm. A sine varying modulating signal is generated through Proportional Integral (PI) based voltage and current controllers. Additionally, the target of reactive power compensation is achieved by connecting Distributed Static Synchronous Compensator (D-STATCOM) at coupling point. Its application helps in maintaining a stable utility-grid connection, and efficient power distribution. Simulation studies reveal that harmonics are reduced at various coupling points and are less than 5 %, as per IEEE-519 standard. It ensures that the proposed system operates at its peak efficiency, taking into account fluctuations in SPV radiations and wind speed. In addition, the steady-state response of the developed system is obtained with PI controller and analyzed. Results obtained are compared by implementing Grasshopper Optimization (GO) technique for validation and accuracy. It is found that rise time, settling time and maximum overshoot are 0.246 s, 0.38 s and 6 %, respectively with GO technique. Therefore, it is envisaged that the operation of D-STATCOM with GO technique is successfully integrated and is capable to address the key PQ issues, like voltage and current profile amongst others. Overall, the results demonstrate that the proposed system is highly efficient, making it an ideal choice for rural communities seeking to adopt renewable energy solutions.

Maximum Power Point Tracking Improvement Using Model Reference Adaptive Control Scheme

This paper presents maximum power point tracking improvement using model reference adaptive control (MRAC) scheme. Solar photovoltaic (PV) energy system remains one of the renewable energy applications which operate by tracking energy from the sun and converting it into useful electrical energy. However, harnessing the generated energy has been the major concern of the engineers hence the adoption of various control strategies for efficient control of the generated power. The idea is to transfer the optimal generated power to the output considering the fact that PV energy produced may have been affected by irradiation and temperature conditions. To avoid the unstable output power, there is need to deploy an efficient control scheme. Most of the conventional control techniques are not optimal to resolve power inaccuracies in the system and they have shortcomings. This work is therefore aimed at analyzing solar photovoltaic energy system using MRAC technique to investigate the control performance. The results obtained by simulation on MATLAB/Simulink software, show an average tracking efficiency of 73.41%, the rise time of 0.7093s and a duty cycle of 0.7. Moreover, MRAC operates at small gain values and the range at which MRAC can operate was found to be 0.1 &lt; 𝛤 &lt; 2.

A New Low-Cost Internet of Things-Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation

The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these systems significant areas of research. The objective here is to identify maintenance requirements early and predict potential problems within the system. In this study, a cost-effective Internet of Things-based remote monitoring system for solar photovoltaic energy systems is presented, along with a machine learning-based photovoltaic power estimator. An Internet of Things-compatible data logger developed for this system gathers critical data from the photovoltaic system and transmits them to a server. Real-time visualization of these data is facilitated through web and mobile monitoring interfaces. The measured data encompass current, voltage, and temperature information originating from the photovoltaic generator and battery, alongside environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, these acquired data are employed for photovoltaic power estimation using machine learning techniques. This enables the estimation of potential issues within the photovoltaic system. In the event of a problem occurring within the photovoltaic system, users are alerted through a mobile application. Early detection and intervention assist in preventing power loss and damage to system components. When evaluating the results according to performance assessment criteria, it was observed that the random forests algorithm yielded the best results with an accuracy rate of 87% among the machine learning methods such as linear regression, support vector machine, decision trees, random forests, and k-nearest neighbor. When prediction models using other algorithms were ranked in terms of success, decision trees exhibited an accuracy rate of 81%, k-nearest neighbor achieved 79%, support vector machine reached 67%, and linear regression achieved 64% accuracy. In conclusion, the developed monitoring and estimation system, when integrated with web and mobile interfaces, has been demonstrated to be suitable for large-scale photovoltaic energy systems.

Comparison Study in Terms of The Results of Photovoltaic Solar Energy Systems Designed with PVsyst and MATLAB Simulink Software

With the advancement of technology and the increase in the global population, the need for energy is growing daily. Today, the majority of energy needs are met from fossil fuels. The limited reserves of fossil fuels have increased the tendency towards alternative energy sources due to reasons such as harming the environment and causing global warming. Some renewable energy sources are sun, wave, wind, biomass, and others. Among them are the most preferred solar energy systems. Before implementing any photovoltaic (PV) project, technological and economic feasibility is required to optimize electricity generation, reliability, and costs. As of today, various simulation tools have been created to predict and optimize a PV system. This study examined the differences between the results obtained from photovoltaic solar energy systems designed using PVsyst and MATLAB/Simulink software and how these differences affect the energy production and performance of the systems. Regression analysis was performed by comparing the output data with each other, and it was determined which direction was advantageous or disadvantageous. 75 kW PV system was designed in Ankara using PVsyst and MATLAB/Simulink software, and the simulation outputs obtained from the two software were compared. Three different PV systems were designed and numbered: Design-1: PVsyst System, Design-2: MATLAB/Simulink with MPPT Algorithm, and Design-3: MATLAB/Simulink without MPPT Algorithm. The monthly differences between the two software and the three design outcomes are highly variable. These differences are between 0.36% and 10.72% in Design-1 with Design-2. It is between 14.21% and 43.71% in the Design-1 with Design-3. It is between 17.65% and 49.32% Design-2 with Design-3. According to analysis findings, it has been determined that the sensitivity of the MATLAB/Simulink software to temperature change is higher than the PVsyst software. In addition, reasons such as the variable MPPT algorithms and the automatic or manual entry of the data are also effective in the difference.

Towards Efficient Energy Usage at Ain Shams University Campus

In the light of global energy transition to renewable resources and energy efficiency usage, Ain Shams University (ASU) developed an ambitious plan to transform its campus into Green Campus. From an energy perspective, energy consumption data were continuously collected and audited to calculate the university campus carbon footprint. An energy usage strategy was established to tackle various pillars such as electrifying the campuses’ transportation system, improving energy efficiency usage, generating Renewable Energy (RE) for self-consumption, etc. Extensive research has been initiated on electric vehicles, wind and solar Photovoltaic (PV) energy generation with students’ activities/competitions. Thus, electric cars and buses were manufactured at the Faculty of Engineering (FoE) for elderly people and staff movement in ASU campus. Solar PV lighting poles with batteries were installed in the main campus. A small-scale Wind Turbine (WT) is manufactured and installed at the FoE and a pilot solar PV system is installed as well. Currently, an energy efficiency project is under implementation in various buildings/faculties and a parking lot that targets energy efficiency and solar PV energy generation. An energy efficiency measure is under implementation through replacing lamps with LED lamps, installing motion sensors, setting up a control center for monitoring and operation that is supported by Artificial Intelligence decision making algorithms. Rooftop solar PV energy systems are under design with smart meters. The project is targeting energy saving and bill reduction by at least 30% and as a result a reduction of carbon footprint will be achieved following the COP27 recommendations.

Fuzzy Controlled Power Management and Power Quality Enhancement for Grid Integrated Renewable Energy sources with EV Fed UPQC

Recently, the merging of Renewable Energy Sources (RES) and Electric Vehicles (EVs) has brought about significant changes in distribution systems. The main goal of this research is to effectively tackle power quality (PQ) concerns such as voltage fluctuations(sags and swells) and total harmonic distortion(THD) in a system connected to the grid .This system combines Solar Photovoltaic(SPV) and Wind Energy Systems(WES) and integrates a Battery Energy Storage System(BESS) and Electrical vehicles(EVs). To accomplish this, a versatile Unified Power Quality Conditioner (UPQC), referred to as U-SWEBEV, is employed. The control of power flow, encompassing the transfer from energy generation sources to consumer loads and among various sources, is efficiently managed through the application of a Fuzzy Interface System (FIS) regulation. This FIS system plays a key role in regulating power distribution. Additionally, a fuzzy Controller is implemented to enhance Maximum power Point Tracking (MPPT) from the SPV system. This combined approach , utilizing both Fuzzy Logic and the Unified power Quality Conditioner (UPQC), significantly contributes to the efficient and effective. utilization of power resources while successfully addressing power quality (PQ) concerns with different load combinations . The proposed method reduces the total harmonic distortion of load current and source voltage at linear nonlinear, nonlinear, Nonlinear and unbalanced linear loads with % THD 1.64 ,0.04,1.20,0.04 and 2.84,2.32 which are lower than the existing methods that are available in literature.

Critical Minerals in the Context of Canada: Concepts, Challenges and Contradictions

Increased use of renewable energy, coupled with electrification of the economy, is considered important in efforts to limit future climate change. This energy transition is predicted to increase demands for some commodities, many of which are now labelled as critical mineral. The quest for such commodities is now a persistent theme for the resource industry and emerging government policies. This review for non-specialists explains several key concepts but also explores some challenges and apparent contradictions in the context of Canada. Canada now has a list of 31 critical minerals, but this includes some major commodities for which domestic production is significant and supply risk is low. The differences between our list and those of other jurisdictions reflect our more specific definitions. Most other commodities on Canada’s list are also identified by other countries and some are specifically linked to the energy transition. These include cobalt, lithium, manganese, nickel, graphite and vanadium (used in electric vehicle batteries and static energy storage), rare earth elements (REE; used for magnets in EV motors and wind turbines) and some rarer elements (e.g. germanium, gallium, indium and tellurium) used in photovoltaic (solar) energy systems. Some of these are potential primary products (e.g. lithium, graphite and REE) but many others (e.g. cobalt, platinum group elements and the photovoltaic elements) are byproducts from the production of major commodities, notably nickel, copper and zinc. The REE represent coproducts that are closely associated in nature and very hard to separate from each other; they are produced as a group. There are some specific challenges in exploring for and developing critical mineral resources. The end-use technology driving demand evolves on a timescale of years, but mineral exploration and development now typically take multiple decades. Material substitutions and unpredictable developments in technology complicate the exact prediction of future demands. The forecasts of overall relative demand growth are impressive, but for some key commodities global production will remain small in absolute terms, which may limit the potential for new discoveries. Simple measures of grade and tonnage are not always guarantees of viability, because deposits of some commodities (e.g. the REE) are mineralogically complex. Byproduct commodities cannot be produced in isolation, and many of these are only extracted in smelting and refining. Domestic production of these commodities is effectively lost if concentrates are exported for processing. The emissions and environmental impacts associated with production of critical mineral resources will also become important if such activity is to be linked to wider climate goals. This may present challenges in northern Canada, where renewable or low-carbon energy options are limited. Most draft Land Use Plans in the north presently emphasize large-scale land conservation, which could limit future exploration access before resource potential is fully assessed. Given the strong divisions of opinion about resource development, especially in the north, controversy and polarized debate will not easily be avoided. There are no simple answers to challenges that are political or jurisdictional rather than technical, but there is definitely a need for more public geoscientific information. This will help to identify areas of greatest potential, evaluate known deposits and contribute to future sustainable development. For many of the commodities on our critical mineral resources list, data for Canada remains incomplete, especially in more remote regions that are generally considered to have the highest potential.

A review of advanced cooling methodologies for solar photovoltaic and thermoelectric hybrid energy systems

A review of advanced cooling methodologies for solar photovoltaic and thermoelectric hybrid energy systems