Virtual Net Metering (VNM) is a pioneering regulatory mechanism introduced by the Maharashtra Electricity Regulatory Commission (MERC) to accelerate the adoption of renewable energy, particularly solar power, by enabling multiple electricity consumers to share the benefits of a single offsite solar power installation. This study critically examined the operational framework, policy design, stakeholder impact, and challenges of implementing VNM in Maharashtra. This research employs a mixed-methods approach, incorporating policy analysis, stakeholder interviews, and evaluation of pilot project data from housing societies and public institutions. The results indicate that the VNM enhances energy accessibility, reduces consumer electricity bills, and promotes distributed energy generation. However, issues such as regulatory delays, a lack of consumer awareness, and infrastructural limitations hinder their widespread adoption. This study proposes policy reforms, capacity-building measures, and incentive structures to strengthen the deployment of VNM. Findings position VNM as a transformative tool in achieving the state's clean energy targets, democratizing energy access, and fostering public-private collaboration in the green energy transition. The emergence of Virtual Net Metering (VNM) marks a significant shift in how we approach urban sustainability, particularly within the regulatory landscape of Maharashtra. Traditionally, solar adoption was a privilege reserved for those with ample private roof space. However, through the forward-thinking initiatives of the Maharashtra Electricity Regulatory Commission (MERC), this barrier is being dismantled. VNM functions as a collaborative bridge, allowing diverse groups—from apartment dwellers in high-rises to sprawling public institutions—to collectively invest in and reap the rewards of a single, offsite solar array. It effectively decouples the physical location of energy production from its consumption, making renewable energy a shared community asset rather than a solitary luxury. Our deep dive into the state’s VNM framework reveals a dual narrative of immense potential and practical friction. On one hand, the socioeconomic benefits are undeniable. By analyzing pilot data from housing societies, it is clear that VNM acts as a powerful equalizer, democratizing access to clean energy and providing tangible relief on monthly utility expenditures. It transforms "passive consumers" into "active stakeholders" in the green transition.
The energy industry faces a variety of challenges as a result of the growing demand for electricity. The emphasis is shifting to optimizing energy use in residential settings so as to achieve sustainable alternatives. The escalating demand for sustainable energy practices in residential environments gives rise to innovative approaches to home energy management. In order to significantly reduce home energy use and contribute to a more sustainable future, this paper proposes an optimization model for home energy management that combines Model Predictive Control (MPC) with Demand Response (DR) strategy to reduce energy consumption. The study used several types of data, such as the hourly load demand of a house and solar irradiance data. Load demand profile, derived from historical electricity usage records, provided hourly energy consumption over a 24-hour period, serving as essential input for predicting future energy needs using the MPC algorithm. Solar irradiance data and PV system specifications were utilized to model the power generated by PV panels, while information about the battery energy storage system, including its capacity, efficiency, and state of charge (SOC) limits, was essential for modelling the behavior of the battery in storing and discharging energy. The model encompasses mathematical models and optimization tools for the efficient usage of photovoltaic (PV) panels, battery energy storage systems (BESS), and grid power. With the aid of MATLAB/Simulink simulations, the study demonstrated that MPC effectively predicts energy demand and allocates power sources effectively, achieving a 41% reduction in energy costs compared to grid-only scenarios. Considering the results obtained, this paper suggests areas of further research work, such as integrating dynamic pricing models in countries like Nigeria and exploring hybrid renewable energy systems. This will build on the findings obtained in this work and further improve household energy efficiency and sustainability.
Post-harvest losses of perishable agricultural products remain a significant challenge in Central Africa, particularly in regions such as N'Djamena, where high ambient temperatures and limited access to preservation technologies accelerate food spoilage. Okra (Abelmoschus esculentus), widely consumed in the region, is especially susceptible to deterioration due to its high moisture content. Solar drying offers a sustainable and energy-efficient solution to extend shelf life while maintaining product quality. This study presents a comprehensive thermal and airflow modeling of an indirect natural convection solar dryer for okra drying under typcal Central African climatic conditions. The proposed model integrates the fundamental mechanisms of buoyancy-driven airflow, convective heat transfer, and moisture diffusion within a coupled framework based on the conservation of mass, momentum, and energy. Airflow within the system is induced by the stack effect resulting from temperature differences between the inlet and outlet, leading to continuous natural circulation of air through the dryer. The thermal performance of the system is analyzed through energy balance equations applied to both the solar collector and the drying chamber. The drying process is further characterized using a thin-layer drying approach, allowing the prediction of moisture removal over time. Climatic conditions representative of the region, including high solar radiation and low relative humidity, are incorporated into the model. The results indicate that increased solar radiation enhances air temperature and airflow rate, thereby improving drying efficiency. The drying process occurs predominantly in the falling-rate period, suggesting that internal moisture diffusion governs the kinetics. Overall, the study demonstrates that natural convection solar dryers are well adapted to semi-arid environments and provide an effective, low-cost solution for reducing post-harvest losses and improving food preservation.
This study develops a Pyomo-based Mixed Integer Linear Programming (MILP) model to optimize electricity tariffs in Senegal, aiming to design a framework that is economically efficient, socially equitable, and environmentally sustainable. The model integrates generation, storage, and dynamic pricing mechanisms into a unified optimization structure covering the period 2022–2050. Five tariff scenarios are simulated - Reference, Progressive, Feed-in Tariff, Static Hybrid, and Dynamic Hybrid -allowing a comparative assessment of their technical and financial performance. Results demonstrate that the Dynamic Hybrid scenario achieves the most favorable outcomes. By 2050, renewable energy reaches 80% of the total generation mix, while the average cost of electricity decreases by 18% (from 83.8 to 68.9 FCFA/kWh). Public subsidies fall dramatically, from 27.5%to 6.8% of sector revenues. Dynamic hourly pricing reduces peak demand by 12–15%, limits reliance on thermal generation, and improves system flexibility through expanded energy storage (10% of the mix by 2050). Moreover, the social lifeline tariff (65 FCFA/kWh for the first 50 kWh/month) remains fiscally sustainable, ensuring protection for low-income households. Overall, the study highlights that dynamic tariff optimization, enabled by open-source algorithmic tools such as Pyomo, can serve as a strategic instrument for predictive regulation and sustainable energy governance. Policy recommendations are proposed for institutional strengthening, data-driven tariff setting, and regional integration within ECOWAS, positioning Senegal as a potential model for resilient energy transition in West Africa.
Dynamic stall occurring the operation of vertical axis wind turbine (VAWT) have attracted great attention in the field of wind power due to their detrimental effects on aerodynamic performance. Extensive flow control strategies have been conducted to alleviate the adverse effects of dynamic stall on aerodynamic performance. This study proposed the active control of adaptive flaps and innovatively introduced a trailing-edge splitter plate as a control method to improve the aerodynamic performance of VAWT. A computational fluid dynamics (CFD) simulation is conducted to investigate the influence of two flow control methods on the aerodynamic performance of a National Advisory Committee for Aeronautics (NACA) 0015 airfoil and a VAWT. The findings indicate that the adaptive flap should be positioned at the trailing edge. An optimal deployment angle exists across various angles of attack (AOA), with the flap length of 0.15c determined as most effective for flow separation control at moderate Reynolds numbers. In pre-stall, the splitter plate achieves a maximum lift-drag ratio improvement of approximately 61.6% (l = 0.2c). Regarding the application effects of both flow control methods on the VAWT, the flaps can significantly reduce the vortex size near the blades and promote vortex shedding near the blades. At λ = 1.2-2.0, the flaps can improve the power coefficient factor of the VAWT by up to 42.5% (λ = 1.6).The splitter plate can increase the power coefficient of the VAWT by a maximum of about 29.4% (λ = 0.8) at low TSRs (λ = 0.4 to 1.2). At high TSRs (λ = 2.0 to 2.4), the splitter plate can increase the power coefficient by a maximum of about 25.8% (λ = 2.8).
This work proposes a combined integration approach for a photovoltaic (PV) system and a Unified Power Quality Conditioner (UPQC) in order to simultaneously improve the power quality and energy efficiency of distribution networks. The study aims to determine the optimal positioning and sizing of these two devices in the standard IEEE 69-bus radial distribution network, using multi-objective optimization. The goal is to minimize active and reactive power losses while improving the voltage profile of the network. The optimization problem is solved using Genetic Algorithms, combined with the Backward/Forward Sweep (BFS) power flow calculation method, which is particularly suited to radial networks. The results show that combined integration (PV + UPQC) offers significantly better performance than individual integrations. Active losses decrease from 224.93 kW to 48.45 kW (a reduction of 78.46%), while reactive losses decrease from 102.14 kVAr to 25.95 kVAr (a reduction of 74.60%). In addition, the minimum grid voltage is improved from 0.90919 p.u. to 0.96791 p.u. These results validate the effectiveness of the multi-objective approach using genetic algorithms for the optimal dimensioning and stabilization of distribution networks integrating renewable sources and active compensation devices.
The connection between a photovoltaic array and a load is still an interest subject of research. The impedance matching between a PV array and a load is a technological problem that basically means the maximum power transfer from a PV panel to a load. Although there are many works devoted to the problem of the maximum power point tracking (MPPT) in a PV array, only few of them deal with the nature of the power interface while most of them focus on different types of tracking algorithms. The problem is addressed in this study by a systemic approach from a power interface point of view in order to obtain high levels of efficiency, reliability and flexibility. Moreover, the simulations and experimental results prove that the proposed system gives a fast response and is suitable for rapidly changing weather conditions. In steady state, the tracking of the maximum power point with the proposed system is very effective or efficient and the quality of the signals is substantially improved. In addition, the output power fluctuation is also reduced, which is a major problem in all MPPT maximum power point trackers and especially the traditional ones, and reducing the output power fluctuation is a goal. adapting it to the load.
Solar thermal energy is available in abundance in a country like Senegal where direct solar radiation is on average 1950kWh/m2 per year. Solar thermal treatment is one of the methods to preserve food. Thermal treatment of agricultural products using solar thermal energy utilizes collectors to capture solar irradiation and convert its energy into heat, which is then used for drying, heating, cooking, or cooling the products. This study focuses on thermal treatment using a solar cooker. The work involves performing a numerical simulation of a solar cooker using COMSOL Multiphysics software to analyze the temporal and spatial distribution of physical parameters such as temperature, air velocity, and absolute pressure within the cooker. A theoretical model is made in order to establish the heat balance at the level of the cooker components. A model of the cooker was developed within the software after establishing assumptions and defining boundary conditions. The simulation results show that in the solar cooker, the absorber temperature can reach 123°C, allowing the cooking of many types of food. The isothermal profile reveals a dome-shaped structure evolving from the absorber, where the temperature is highest, towards the glass cover. The pressure is also uniform within the cooker. The pressure is approximately equal to 1.11 104Pa. Similarly, the air velocity inside the cooker is low.
Most faults in power lines are caused by short circuits resulting from phenomena such as lightning, severe weather, or power surges linked to circuit breaker operations. These short circuits, whether temporary or permanent, require accurate detection and location to enable rapid repair and restoration of power supply. To protect the system against short-circuit currents, which can cause irreversible damage to key equipment, it is essential to quickly disconnect the faulty part of the network. In order to correctly size this equipment, it is essential to estimate the magnitude of the currents likely to flow during a short circuit. This study involved calculating single-phase short-circuit currents in the event of a fault on the Cable, Soluxe, Airoport, Talladje, and Gawaye feeders at the Niamey3 electrical substation. The method used to calculate short-circuit currents in HTB and HTA networks is based on the principle of symmetrical components. This method was chosen for its accuracy and analytical nature. The results obtained show that the Soluxe feeder has the highest short-circuit current, with a value of 1.95 kA, compared to those of the Cable, Airoport, Talladje, and Gawaye feeders, which are 1.86 kA, 0.67 kA, 0.64 kA, and 0.56 kA, respectively. This is explained by the fact that the calculated impedances (direct, inverse, and zero-sequence) of this feeder are lower than those of the other four feeders.
The idea is to create a magnetic field configuration that can be repeatedly "stressed" and then triggered to reconnect at specific locations. This "directed multiple magnetic reconnection" (DMMR) would act like a series of precisely controlled explosions, dumping immense energy into the fuel ions and bringing them to fusion temperatures. This process would naturally operate in a duty cycle. Energy would be injected to "wind up" the magnetic field, which is then released in a powerful pulse through reconnections. This cycle of charging and discharging would be repeated, leading to a pulsed fusion energy output, much like an internal combustion engine. This contrasts with the continuous operation sought by most mainstream designs like tokamaks and stellarators. The foundation of this idea lies in the intricate interplay between three key concepts: turbulent pumping, stochastic resonance, directed multiple magnetic reconnections, and fusion, which are considered in this work.
Electricity is the most cost-effective and efficient energy source for pumping water, but farmers with small, scattered plots might not have access to it. To raise water for irrigation, farmers rely on diesel or gasoline pumps, which is expensive and non-sustainable. For better management of water and economic benefit, considering another option for irrigation such as the solar pumped irrigation system could be important. Solar power enhances efficiency, productivity, and sustainability in agricultural operations in addition to offering a clean alternative to fossil fuels. In agriculture, it is increasingly being integrated through several innovative applications that are transforming traditional farming practices. The future of solar energy in agriculture is promising, driven by technological advancements, supportive policies, and increasing awareness of sustainable practices. The objective of the study is to identify the practical applicability of solar pump in other countries and the challenges and opportunities for its applicability in Ethiopia in irrigated agriculture. Existing scholarly research that has been published as journal articles serves as the study's methodology. The resources (Scopus and Google customized search), eligibility and exclusion criteria, review process phases, data abstraction, and analysis are all part of the methods used. The study shows that the solar radiation is the primary source of energy for solar pump and it depends on the climatic condition and geographical location of the area. Most African countries are practicing the solar pump and it was highly practiced in sub-Saharan African countries such as Kenya, Ethiopia, Sudan and also other equatorial and sub-equatorial countries. Additionally, since the North and South hemisphere are linked with permanent cloud cover and only intermittent bright sunshine, the future installation of solar pump will also be practiced in these areas such as the Congo, Gabon, Rwanda, and Senegal. It is also highly practiced in Mali for irrigation, livestock production and for domestic use. There is a growing demand for solar pump irrigation in Ethiopia. Accordingly, one of the government’s strategy is to transit existing motor pump users to solar, while also introducing new solar pump irrigation to those not currently irrigating. The primary challenges of utilizing solar pumps in Ethiopia was high initial costs, while the country's abundant solar radiation and potential for increased agricultural productivity were the best opportunities for its implementation. However, this technology has to be supported through evidence by conducting research and creating awareness for the end users and other policy makers.
The purpose of this paper is to propose Madagascar Interprovincial Network using Graph Theory with Power Flow. This is accomplished by simulating the electrical network's topology using algorithms programmed with the Python language. The initial phase of the proposed algorithms consists in establishing connections between 17 source nodes and 94 load nodes, using the shortest path and maximizing the number of load nodes. The next phase consists in removing triangular links deemed superfluous. The third phase involves giving priority to load nodes located close to an electrical source node. The final phase involves removing any remaining superfluous links and applying the (n-k) rule. After the calculations, eight topologies of the Madagascar Interprovincial Network were established. Topology number 8 is the optimal one, comprising 126 links with optimal total distances of 7324 km. Based on this last topology, we carried out simulations of the transit and flow of energy in the static regime across the different busbars of the major mining projects and the different provinces of Madagascar by the PowerFactory software using the Newton-Raphson method. In the transmission line, we used the THTB 220 kV voltage. The simulation revealed, firstly, the location of reactive energy compensation devices, secondly, the removal and installation of new links, and thirdly, the placing on hold of 120 MW of electrical power out of the 300 MW of the slack bus Sahofika hydroelectric plant. For 2030 - 2040, with hydroelectric power plants generating a total of 1,454 MW and loads with a total capacity of 1,344.5 MW, the simulation results showed voltage drop levels with a ∆U value of ± 5% in all busbars and losses of 4.5% in relation to total production. In perspective, further studies in dynamic regime, interactions between emerging technologies and the power system across all voltage levels are to be carried out on the development of the Madagascar Interprovincial Network.
The paper considers the justification of a magnetic reconnection converter (MRC) based on a single-volume plasma (spheromak) with a variable β in the turbulent pumping (charging)/discharging phases of the thermodynamic duty cycle “α-dynamo – magnetic reconnection”. To obtain helpful energy, the proposed MRC uses a cyclic combination of two physical processes: 1) α-dynamo, generated by controlled turbulence, increases the global helicity H through the processes of twisting, writhing and bending of magnetic field (MF) flux tubes to the level of a local maximum (optimally global), which is determined by the plasma parameters, boundary conditions, tension of magnetic field lines, etc., and corresponding the MF strength and stochasticity in a limited plasma volume. At this stage of MF turbulent pumping, which corresponds to the α-dynamo physical process, β of the plasma will decrease to the minimum possible value with a corresponding increase in the accumulated "topological" energy of the MF; 2) when reaching the local (if possible global) maximum of the MF strength and stochasticity, turbulent magnetic reconnection (TMR) occurs in many places of the plasma, which lowers the state of the local (if possible global) maximum of the MF strength and stochasticity and increases the kinetic stochasticity of the plasma particles, accelerating and heating them, which is used in direct energy converters (DECs), and receiving coils of electrical energy. At this stage of turbulent discharge, which corresponds to multiple TMR, β of the plasma will increase to the maximum possible value with a corresponding increase in its kinetic and thermal energy. When the kinetic stochasticity of plasma particles decreases and reaches a minimum, the control system repeats the MF's turbulent pumping, generating multiple α-dynamo processes in the plasma, and the cycle repeats.
The energy created by the force of water can provide a more sustainable, non-polluting alternative to fossil fuels, with other renewable energy sources including wind, solar, tidal, geothermal, and bioenergy. Micro hydropower, which is hydro energy on a ‘small’ scale, provides hydro-mechanical and hydroelectricity to small communities. The purpose of this study is to conduct technical assessments of the micro-hydropower potential of generating hydroelectric and hydro mechanical power from existing irrigation schemes in Southwestern Oromia. From three zones, 14 schemes from Jimma Zone, 14 schemes from Buno Bedele Zone, and 3 schemes from Ilubabor Zone were selected; all of these schemes had the potential for irrigation and were functional, out of 13 woreda, 31 irrigation schemes were assessed. Among the analyzed schemes, the maximum Hydraulic power potential for micro-hydropower generation at 80% efficiency was 5.14kW at the Gura scheme in the Gechi woreda in the Buno Bedele zone. The maximum discharge and head recorded were 1.027m3/s and 1.4m at the Gura scheme in the Gechi woreda in the Bedele zone, and the Hursa scheme in the Gomma woreda in the Jimma zone. Some of the assessed schemes are not sufficient for micro-hydro power generation, except the Gura scheme in the Gechi woreda in the Buno Bedele zone. However, some of them are possible with technical advances for Pico-hydropower.
This study investigates the effects of doping concentration and absorber layer thickness on the performance of Cu(In,Ga)Se2 (CIGS) thin-film solar cells using detailed numerical simulations. The work focuses on identifying optimal design parameters to maximize power conversion efficiency by analyzing their influence on key device characteristics, including short-circuit current density, open-circuit voltage, and fill factor. The results indicate that the doping concentration critically impacts carrier transport and recombination dynamics. An optimal doping level of 6×1016 cm-3 enhances charge carrier collection, leading to simultaneous improvements in short-circuit current density, open-circuit voltage, and fill factor. Doping beyond this value increases series and shunt resistances, which reduces the efficiency gains, emphasizing the importance of precise doping control. The absorber layer thickness also plays a significant role in device performance. Increasing the thickness from 0.1 µm to 1 µm substantially improves photon absorption and carrier generation, resulting in a marked enhancement in efficiency. However, further increasing the thickness above 1 µm yields only marginal efficiency gains, as photon absorption reaches saturation and the recombination rate increases, highlighting the trade-off between absorption depth and minority carrier lifetime. Overall, the study demonstrates that careful optimization of both doping and absorber thickness is essential to achieving high-efficiency CIGS solar cells. Specifically, a doping concentration of 6×1016 cm-3 combined with an absorber thickness in the range of 0.1-1 µm provides the most favorable conditions for device performance. These findings offer practical guidelines for experimental fabrication and numerical optimization, contributing to the design of more efficient thin-film photovoltaic devices. The insights provided by this work can guide future research in enhancing the performance of CIGS solar cells and other related thin-film technologies.
A new distributed voltage control strategy for PV power systems that does not need support from centralized SVCs is proposed. The methodology uses smart inverters, agent-based coordination, and machine learning-based forecasting to offer a scalable and economical solution for decoupling voltage variations in the context of high penetration of PV. Each inverter acts as an autonomous agent that regulates its reactive power output using local voltage measurements and short-term irradiance predictions derived from a Long Short-Term Memory (LSTM) model. The agents cooperate with their neighbors, utilizing a consensus algorithm for coordinated voltage control throughout the network. This decentralized strategy enables fast, adaptive, and cost-effective voltage stabilization without relying on hardware-intensive centralized devices. The effectiveness and reliability of the proposed control strategy are verified through a simulation study using a five-bus radial distributed generation (DG) system with high PV penetration. Simulation results on a five-bus radial distribution feeder show better voltage stability, fault recovery, and reactive power utilization as compared with conventional and existing distributed control strategies. The findings confirm the feasibility of software-defined, inverter-based voltage regulation as a practical alternative for future smart grids. In addition, the proposed framework offers extensibility to hybrid renewable energy systems, such as wind and storage, supporting the transition toward resilient, low-carbon, and data-driven energy infrastructures.
The growing demand for clean, reliable energy in off-grid and rural areas has made Solar Photovoltaic (PV) systems a viable alternative to conventional power sources; however, maximising their efficiency while balancing cost and reliability remains a significant challenge, especially in developing regions. This study presents a novel, locally engineered Pulse Width Modulation (PWM) Solar charge controller (SCC) designed to enhance energy conversion efficiency in stand-alone PV systems while maintaining affordability and ease of maintenance. Unlike existing studies that rely on imported or commercially available controllers, this research integrates indigenous design optimisation, locally sourced components, and context-specific testing under Nigerian climatic conditions. The locally constructed PWM charge controller was experimentally compared with a foreign PWM and a Maximum Power Point Tracking (MPPT) controller. Results showed that the MPPT controller achieved the highest efficiency (45-77.6%), while the PWM SCC recorded 43-66%. The inverter efficiency reached 89.7%, and the overall system efficiency was 24.4% for MPPT and 17.4% for the local PWM design. Despite its lower efficiency, the locally built PWM controller demonstrated significant potential as a cost-effective and reliable solution for rural electrification, particularly where access to advanced components is limited. The novelty of this study lies in the development and validation of a locally fabricated PWM SCC tailored to regional energy demands and environmental conditions, bridging the gap between performance optimisation and economic feasibility. It also offers a platform for standardising the overall efficiency of stand-alone Solar PV systems while providing practical insights for advancing contextualised renewable energy technologies that promote sustainable, community-driven electrification in Nigeria and similar developing regions.
Predictive maintenance (PdM), supported by artificial intelligence (AI) and digital twin methods, is gaining attention as a practical and cost-efficient way to manage power generation assets. In the renewable energy sector, where performance, stability, and cost control are central concerns, PdM enables operators to anticipate equipment faults, schedule interventions more effectively, and reduce unplanned downtime. This paper reviews how such approaches are being applied in four different national contexts: China, Germany, Norway, and the Netherlands, and considers their contribution to cleaner and more reliable energy systems. The discussion highlights several patterns that emerge across these countries. In China, the rapid expansion of wind and solar capacity has driven the use of PdM to improve fault detection and optimize turbine and panel performance. Germany demonstrates how PdM can be integrated into broader energy transition policies, using digital twins and AI to balance fluctuating renewable output with grid demands. Norway shows the value of predictive tools in extending the life and efficiency of hydropower equipment, while the Netherlands illustrates the benefits of PdM in offshore wind projects, where remote monitoring and early fault recognition are critical. Evidence from these cases points to three consistent outcomes: improved uptime of renewable assets, measurable reductions in maintenance costs, and smoother integration of intermittent power sources through more advanced grid management. Taken together, these findings suggest that PdM is not only a set of technical tools but also a strategic component in building sustainable, resilient, and economically viable energy systems. Its wider adoption may help accelerate the transition toward low-carbon power on a global scale.
This article presents a method for power factor correction and power compensation taking into account the injection of distributed generators in the distribution networks. Distribution networks are most often exposed to problems of harmonic disturbance. This work proposes a method that combines active filters and perturb and observe algorithms to reduce the rate of harmonic distortion in a photovoltaic system that is to be fed into power grids. A THD of 2.14% is achieved in compliance with the IEEE 519-2014 standard. Voltage and current profiles have good waveforms. The voltage level is regulated by the PI regulator. The perturb and observe algorithms associated with the filter developed in this work have shown their superiority in terms of voltage stability and power demand management for a grid-connected photovoltaic system.
A gap in Zimbabwe’s energy supply and demand can be filled by extensive incorporation of solar energy in the country’s current energy mix. The amount of solar energy to be harvested at any site varies in quantity with time and location following variations in the received solar radiation. This research was conducted to develop an automated system which uses solar radiation equations, geospatial techniques and python programming to estimate received solar radiation in Zimbabwe. To validate the system performance a comparison between system results and ground measured radiation was conducted using statistical metrics such as Pearson correlation (R), Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Normalised Mean Absolute Error (NMAE). Suitable sites for solar harvesting were determined using Multi-criteria Decision Making (MCDM) and weighted overlay analysis. The developed system determined temporal evolution in ground solar radiation from sunrise to sunset, and hours before 08:21am had radiation values below 0.9Mj. From 9:21am to 14:21pm radiation values were above 1.5Megajoules (Mj) with peak radiation of 2.13Mj at 12:21pm. The computed statistical metrics showed that there was a good agreement and better performance as most months had a Person correlation above 0.57, RMSE less than 2.7 and NMAE less than 1.7. The months of May, June and July were the peak of winter season evidenced by less radiation intensities between 14Mj and 18.5Mj whilst September to March had higher radiation ranging 20Mj to 26Mj. From the conducted site suitability analysis, 0.77% was highly suitable, 30.67% was suitable, and 5.1% moderately suitable and 63.45% falls under restricted areas. By consideration of only 1% of the highly suitable areas while using a solar system with 10% efficiency, 197.41 Gigajoules (GJ) can be harvested in Zimbabwe. Therefore, this sustainable energy can be used to supply Zimbabwe and bridge the current energy gap.
