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This paper studies the coordinated use of smart inverters with legacy voltage regulating devices to help increase the energy savings from conservation voltage reduction (CVR) in distribution systems with high photovoltaic penetration. Two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control. In each algorithm, smart inverters are coordinately controlled with the operation of legacy voltage regulating devices including load tap changers and capacitor banks. Both algorithms are tested on representative utility distribution system models. Simulation results demonstrate that the proposed algorithms can achieve around 1.8-3.6% energy savings when only using legacy voltage regulating devices and an additional 0.3-0.9% when adding smart inverters.

Cooling enhances the energy conversion efficiency and output of photovoltaic (PV) panels. In this work, the effects of natural convection, forced convection, and evaporative cooling on the performance of polycrystalline PV panels were investigated. The output and efficiency of a cooled PV panel were monitored and compared to those of an uncooled PV panel under the same conditions. The cooling was conducted using an insulated channel installed below a PV panel. Natural convection cooling was investigated for various channel air gaps (H = 30, 60, 90, and 120 mm). Natural convection currents in the cooling channels were capable of cooling the panel with wide air gaps. In forced convection cooling, the air was introduced by fans installed at the bottom opening of the cooling channel with various air velocities (ua = 1, 2, and 3 m/s). Evaporative natural convection cooling was performed by a wetted fabric along the lower surface of the cooling channel, whereas evaporative forced convection cooling by pushing air along the wetted lower surface of the channel. The experimental data showed that the panel efficiency and output increased due to cooling. The experimental results of natural convection cooling revealed that the use of an air gap of 120 mm to cool the solar panel contributed to an increase in the panel daily energy production and efficiency by 1.7% and 1.2%, respectively. For forced convection cooling, using air at a speed of 3 m/s increased the daily energy production by 4.4% and the efficiency by 4%. Natural convection evaporative cooling increased the daily energy production and the efficiency by 3.6% and 2.7%, respectively. Forced convection evaporative cooling contributed, at a speed of 2 m/s, to an increase in the daily energy production by 3.8% and an increase in efficiency of 3.8%.

Solar energy is widely available in nature and electricity can be easily extracted using solar PV cells. A fuel cell being reliable and environment friendly becomes a good choice for the backup so as to compensate for continuously varying solar irradiation. This paper presents simple control schemes for power management of the DC microgrid consisting of PV modules and fuel cell as energy sources and a hydrogen electrolyzer system for storing the excess power generated. The supercapacitor bank is used as a short term energy storage device for providing the energy buffer whenever sudden fluctuations occur in the input power and the load demand. A new power control strategy is developed for a hydrogen storage system. The performance of the system is assessed with and without the supercapacitor bank and the results are compared. A comparative study of the voltage regulation of the microgrid is presented with the controller of the supercapacitor bank, realized using a traditional PI controller and an intelligent fuzzy logic controller.

The present work carries about a unique experimental investigation in the thermal management and cooling realms of the Photovoltaic (PV) panels under the most harsh environmental conditions. The performance of the heat sink and thermal storage heat sink techniques were analysed amongst the three passively cooled PV panels. Moreover, the efficacy of the immersion and coolant thermal properties was evaluated and discussed for the four actively cooled Photovoltaic Thermal (PVT) collectors. Even though, it was found that the heat sink-based PV panel configuration is more performant relatively compared to the PCM heat sink based-PV one since a drop of ten-degree in PV cell temperature was recorded, but still, the heat dissipation rate is not enough to withstand the severe mechanical and electrical degradation challenges under the harsh environmental climate of Saudi Arabia. For the actively cooled PVT collectors, it was found that the immersion technique protects the PV panel from the hot spot apparatuses and lead to better heat dissipation uniformity. The heat pipes based-PVT collector immersed in regular water has recorded the optimal temperature distribution and maximum temperature drop by 53% relatively compared to best passively cooled configuration. With ethylene glycol, engine oil, and active cooling without immersion, the steady-state PV temperature has been reduced relatively compared to a passively cooled PV panel by about 48%, 25%, and 21%, respectively.

Some revised versions of the analog ensemble (AnEn) algorithm to improve its performance when predicting rare events of solar power production are presented. Rare events are considered both extremely cloudy situations and clear sky conditions with very low aerosol concentrations. In these cases, the solar power production can be respectively unusually low or high, making it more difficult to find good analog dates in a historical archive of past forecasts. The lack of suitable quality analogs determines conditional biases from the AnEn when predicting these rare events. Here, an application of the AnEn to predict solar power generated by two photovoltaic plants located in Kuwait is presented. The modified versions of the AnEn are compared to the standard algorithm. We focus the verification of the AnEn’s performance over the rare conditions outlining the improvements achieved with the new AnEn models.

The aim of this paper is to further deepen the use of a very robust mathematical technique in the analysis of data. The Principal Component Analysis (PCA) method is used to analyze the performance of three PV systems and to determine the correlation between performance parameters and meteorological variables. The three systems are based on Silicon monocrystalline (2 kWp), polycrystalline (1.82 kWp) and amorphous (1.55 kWp) PV modules. The performance indicators used are the parameters described in the international standard IEC 61724. The evaluation of parameters such as performance ratio, final yield and capacity factor were found to be 79.08%, 1876.48kWh/kWp/year and 38.46% for polycrystalline, 71.13%, 1688.97kWh/kWp/year and 38.99% for monocrystalline and 78.89%, 1866.42kWh/kWp/year and 33.02% for amorphous, respectively. The projection of the data showed that more than 87% of the information is retained and presented in 2D space. The correlation between temperature and performance of mc-Si, pc-Si, and a-Si systems is −21%, −12.5% and −42.8%, respectively.

Small-scale PV generation has become popular with residential customers in several jurisdictions with high solar radiation, as an alternative to improve their carbon footprint and reduce their electricity bills. However, massive deployment of such distributed generation is creating a particular and undesirable shape in the net demand, which deepens at hours of peak solar PV injections at noon and suddenly rises towards the evening, known as the “duck curve". Hence, this paper investigates the use of pre-cooling strategies in residential households to mitigate the duck-curve effects. To this aim, appropriate thermal models and simulations of houses are first developed and carried out to demonstrate the technical feasibility of pre-cooling in a house with a typical configuration, based on the Smart Residential Load Simulator (SRLS) developed at the University of Waterloo. Then, an aggregation technique is proposed to evaluate the effects on a large grid of different penetration levels of PV, and pre-cooling approaches to manage the duck-curve in California and Texas, concluding that such techniques are capable of substantially flattening the system net demand curve.

The main contribution of this paper is proposing a new approach for retrieving the five parameters of the single diode equivalent model (SDM) of photovoltaic cells/modules including the series and shunt resistances. The least square method is used as an error minimization technique for fitting the non-linear transcendental model equation of the solar panel to the measured I–V characteristics. Newton Raphson method is applied to solve the system of five non-linear equations which represent the error of each parameter. Initial guess values are calculated with an optimised algorithm depending on information extracted from the same measured data. MATLAB programming script was used in all implementation steps. This approach is useful for a wide variety of applications where the five SDM parameters have to be determined from the measured curves, particularly, for self-fabricated cells/modules or in case of no available datasheet. One of the strengths of this method is the higher level of accuracy because of the absence of mathematical simplifications and physical assumptions. The method was validated on different types of PV devices, including a crystalline silicon cell, a polycrystalline module, and an amorphous module by using measurement data obtained at a wide range of solar irradiance conditions and temperatures.

Accurate estimation of residential solar photovoltaic (PV) generation is crucial for the power distribution and demand response program implementation. Currently, most distributed PVs are installed behind-the-meters (BTMs), and are thus invisi-ble to the utilities. The existing methods separate the BTM solar generation from the available net load in a centralized manner as-suming that all data are accessible to utilities. However, this can cause privacy issues, since the data are owned by different utilities and they may be unwilling to share their data. To this end, a novel method is proposed for disaggregating community-level BTM so-lar generation using a federated learning-based Bayesian neural network (FL-BNN), which can preserve the privacy of utilities. Specifically, a Bayesian neural network (BNN) is designed as the probabilistic energy disaggregation model with the ability to cap-ture uncertainties. The BNN training process is extended into a decentralized manner based on the federated learning framework. To enable the model customized for each community, the layers of BNN are categorized into shallow and deep layers, and a layerwise parameter aggregation strategy is proposed to update the model. Both community-specific features and community-invariant fea-tures can be learned. The effectiveness of the proposed method is validated on a publicly available dataset.

Charging the electric vehicles through the use of solar PV systems is a major hurdle in today's era. In the present work, a system is designed for charging Electric bikes at workplaces like schools, colleges, offices, etc. To ensure a reliable charging system, a standalone solar PV system with a battery bank based energy storage unit is employed. It can be seen from the present work that the electric bike at different voltage configurations i.e., 12 V, 24 V, and 36 V, can be charged efficiently. The irradiance level is varied in a way to show a real-life effect that occurs during a summer day. To depict this, irradiance level is assumed to rises from 300 W/m2 to 1000 W/m2 and decline again to 300 W/m2 during the process. The DC bus voltage is successfully maintained constant at a level of 48 V in all three cases. The contribution of the study is the application of a robust solar PV based off-grid design for charging electric bike at the workplaces.

Many cities across the world are committing to deep decarbonisation efforts. While solar photovoltaics (PV) will play a critical role in this pursuit, the role of rooftop and facade-integrated PVs within the urban landscape is yet to be fully understood. This work presents an analysis into the solar energy harvesting potential of PVs integrated as building rooftops, walls, and windows at various spatial resolutions that range from city to building scale within the City of Melbourne, Australia, as a contemporary case study. It further investigates the relationship between calculated electricity production from such PVs with the urban morphology, seasonal variation and the measured electricity consumption by the local distribution network service providers. The results indicate that PV rooftops are responsible for the largest share of the city’s solar energy potential. However, for individual blocks with high densities of high-rise and glazed buildings, it is shown that the PV potential from windows becomes more prominent. The technical workflow presented here will enable different cities to facilitate decision-making on the PV implementation in urban environments.

To study the internal flow characteristics of the photovoltaic pump under the transient change of solar radiation, the simulation algorithm of the photovoltaic pump system was established by MATLAB/Simulink and CFD for the first time and the results were validated by the test. Firstly, the change rule of pump flow rate and rotation speed under transient solar radiation was obtained by Simulink. Then the results of the change rule were transformed into the boundary condition of CFD by CEL function and the transient flow field in the photovoltaic pump was obtained. The internal flow characteristics and pressure pulsation in the pump were analyzed when the solar radiation increases or decreases transiently. The results demonstrate that the numerical calculation can provide accurate prediction for the characteristics of internal flow in the pump. The numerical results are closed to the experimental results, the minimum error of pressure is 0.93% and the maximum error is 1.78%. When the solar radiation increases transiently, the low pressure area at the impeller inlet gets larger obviously and the jet-wake at the impeller outlet becomes more obvious. The pressure pulsation in the impeller gradually increases and becomes stable after 0.6 s. The pressure from the impeller outlet to the guide vane outlet is stable at 123 kPa. When the solar radiation decreases transiently, the pressure in the impeller takes 1.6 s to be stable. Larger pressure pulsation occurs from the impeller outlet to the guide vane inlet and the maximum differential pressure is 10 kPa. Compared with the transient increase of solar radiation, the pressure in the impeller takes over 0.2 s to stabilize when the solar radiation transient decreases. Meanwhile, the results in this paper can provide references for other transient characteristics research. Highlight First, the relation between pump flow rate and rotation speed under transient solar radiation was obtained by Simulink. Then the results from Simulink were transformed to be the boundary condition of CFD by CEL function. The internal flow characteristics and pressure pulsation in the pump were analyzed under the instantaneous increase or decrease of the solar radiation.

The output power of solar photovoltaic (PV) system is intermittent in nature with a non-linear output voltage. This has peak power points in accordance with varying irradiation and temperature. Hence a Maximum Power Point Tracker (MPPT), which is power extraction technique is essential in PV power systems to ensure maximum power delivery for a given point of time. Thenonlinear power voltage curve gets more intense when shading of PV panels takes place, as the panels receive irregular and different solar irradiation, which alters the profile of power –voltage (P-V) curves. Due to the partial shading, the panels exhibit peculiar multiple power peaks instead of one single peak as in the case of uniform irradiation and as a result the conventional MPPT schemes could attain only local maxima and the global one. This research article proposes a new intelligent, bio inspired Meerkat optimization algorithm (MOA) which is capabale finding the global power peak and ensured maximum power delivery. ThisMOA MPPT technique is employed for a 120 W PV system and the versatility of the said scheme is tested by subjecting the PV panel for three different partially shaded conditions. The results reveal that the MOA exhibits fast tracking speed of with improved efficiencyof 99.8% when compared with particle swarm optimization (PSO) and differential evolution algorithm (DE)MPPT schemes.

This paper presents a novel DSO support tool with visualisation capability for forecasting network congestion in distribution systems with a high level of renewables. To incorporate the uncertainties in the distribution systems, the probabilistic power flow framework has been utilised. An advanced photovoltaic production forecast based on sky images and a load forecast using an artificial neural network is used as the input to the tool. In addition, advanced load models and operating modes of photovoltaic inverters have been incorporated into the tool. The tool has been applied in case studies to perform congestion forecasts for two real distribution systems to validate its usability and scalability. The results from case studies demonstrated that the tool performs satisfactorily for both small and large networks and is able to visualise the cumulative probabilities of nodes voltage deviation and network components (branches and transformers) congestion for a variety of forecast horizons as desired by the DSO. The results have also shown that explicit inclusion of load‐voltage dependency models would improve the accuracy of the congestion forecast. For demonstrating the applicability of the tool, it has been integrated into an existing distribution management system via the IoT platform of a DMS vendor, Atos Worldgrid.

Future of generation depends on the renewable energy resources due to lack of fuel and the problems of emissions. Photovoltaic (PV) system is a promising type of renewable energy resources. Non uniformity of irradiance, temperature variation, and partial shading conditions are the main disturbances associated with PV systems. Most of literatures focus on enhancing the PV performance and to achieve Maximum Power Point Tracing (MPPT) based on offline Optimization techniques. These techniques suffer from complicated mathematical burden, long computing time, and should be repeated for any change in the operating conditions/system parameters. This paper proposed a hybrid adaptive controller to improve the system performances under several disturbances, as well as to stabilize the operation of the MPPT. Thus, the proposed controller affords tight and accurate MPPT. The gains of the proposed controller are updated automatically with the variation in the operating conditions. The initialization of the proposed a hybrid adaptive controller is carried out utilizing the Sine-Cosine Algorithm (SCA). Finally, the proposed system is evaluated through comparison with one of the newly presented techniques that achieve accurate MPPT.

For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (GSK) to solve the solar PV model parameter extraction problem. This algorithm simulates the process of knowledge acquisition and sharing in the human life cycle and is with strong competitiveness in solving optimization problems. It includes two significant phases. The first phase is the beginner–intermediate or junior acquisition and sharing stage, and the second phase is the intermediate–expert or senior acquisition and sharing stage. In order to verify the effectiveness of GSK, it is applied to five PV models including the single diode model, double diode model, and three PV modules. The influence of population size on the algorithm performance is empirically investigated. Besides, it is further compared with some other excellent metaheuristic algorithms including basic algorithms and advanced algorithms. Among the five PV models, the root mean square error values between the measured data and the calculated data of GSK are 9.8602E−04 ± 2.18E−17, 9.8280E−04 ± 8.72E−07, 2.4251E−03 ± 1.04E−09, 1.7298E−03 ± 6.25E−18, and 1.6601E−02 ± 1.44E−16, respectively. The results show that GSK has overall better robustness, convergence, and accuracy.

Ensuring power system reliability under high penetrations of variable renewable energy is a critical task for system operators. In this study, we use a loss of load probability model to estimate the capacity credit of solar photovoltaics and energy storage under increasing penetrations of both technologies, in isolation and in tandem, to offer new understanding on their potential synergistic effects. Increasing penetrations of solar PV alter the net load profile on the grid, shifting the peak net load to hours with little or no solar generation and leading to diminishing capacity credits for each additional increment of solar. However, the presence of solar PV decreases the duration of daily peak demands, thereby allowing energy-limited storage capacity to dispatch electricity during peak demand hours. Thus, solar PV and storage exhibit a symbiotic relationship when used in tandem. We find that solar PV and storage used together make a more significant contribution to system reliability: as much as 40% more of the combined capacity can be counted on during peak demand hours compared to scenarios where the two technologies are deployed separately. Our test case demonstrates the important distinction between winter and summer peaking systems, leading to significantly different seasonal capacity values for solar PV. These findings are timely as utilities replace their aging peaking plants and are taking energy storage into consideration as part of a low carbon pathway.

Industrial development, increased demand for energy, limited fossil fuels, and the prevention of environmental damage have led to the use of renewable energy to produce power. The combined system includes renewable wind and solar energy systems or other renewable energy. The hybrid system has been widely used in recent decades to provide energy. In this research, an optimal integrated system has been used to generate electricity. The proposed system includes wind, hydropower, and photovoltaic systems. to estimate the maximum power generation and reduce fluctuations in electricity generation, three proposed algorithms have been used and three common solutions have been applied to solve the optimization problem. The performance of the combined system in different weather conditions has also been assessed. The results showed that among the three proposed algorithms, the model improved the Barnacles Mating Optimizer Algorithm (IBMO) has the best distribution and convergence. Among the three common solutions, a common solution has the most logical solution to the optimization problem. And among the different hydrological conditions, the highest electricity generation is related to the wet season. the most important role in the hybrid system related the hydropower so that despite the reduction of electricity produced in the wind and photovoltaic power plants, the hydropower plant compensates for the drop in production of these systems and causes the production flow to continue.

The performance of photovoltaic (PV) modules gradually declines as the operational time continues due to the impacts of workload and environmental factors. This performance degradation process is dynamic and random. Hence, two stochastic process models, exponential dispersion process with fixed effect and with random effect are proposed to describe this degradation law. With these two models, the procedure for evaluating the reliability of PV modules is illustrated based on a practical case. The proposed model is demonstrated to be rationality and applicability for the PV modules through three common information criteria and Kolmogorov–Smirnov test. Due to the exponential dispersion process includes some common stochastic process, e.g. Wiener process, Inverse Gaussian process and Gamma process, which makes the proposed models have a wide range of applications. The proposed models can provide references for the warranty life, maintenance of PV modules.

In this paper, an in-depth analysis of the photovoltaic parameters of solar cells is carried out to reveal the prospect of fabricating TX-100 surfactant-induced thiol-amine cosolvent-assisted CdS/CdTe thin-film solar cells (TFSCs) via the spin-coating process. The research is carried out through SCAPS-1D simulations using the parameters obtained from thin-film characterizations of the as-deposited and/or pre-annealed and annealed CdS and CdTe thin-films deposited on the SLG substrates at various annealing temperatures (200–350​ ∘ C), time (3–8 min) and circumstances (low-vacuum, glass-protected air, open-air) with and without surfactant, respectively. First, the effects of surfactant and different annealing conditions on the device performance are reproduced through simulations. Thereafter, the temperature-annealed solar cell with added surfactant is optimized by extensive variation in thickness, doping, bulk, and interface defect densities, resistance, and temperature. This article also provides a plausible description of the function and choosing guidelines of the TCO layer and back metal in ameliorating the performance/cost ratio of the device. Results show that the use of surfactant and heat treatment on the deposited thin films of CdS window and CdTe absorber layers can greatly enhance the performance. Under global AM 1.5 irradiance at 300 K, the optimized CdS/CdTe TFSC presents an efficiency of 18.37% with J SC of 25.64 mA/cm2, V OC of 870 mV, FF of 82.31%, and QE of 90.44%, respectively. This study reveals that a novel chemical route employing TX-100 surfactant and thiol-amine cosolvent for synthesizing solution-processed CdS/CdTe solar cells using a low-cost facile spin-coating method could be a cutting edge TFSC technology for industrial scaling in the near future.

The past decade has not only witnessed a great shift in the energy mix, with electricity gaining a more and more prominent role, but has also seen electricity coming increasingly from renewable energy sources, whose proportion is forecasted to reach 50% by 2030. As the integration of photovoltaic energy cannot be deemed successful without the electricity supply being both sustainable and secure, such far-reaching developments prompt legislations and policy makers, including those of the European Union, to make changes to accommodate not only ever-changing technologies, including energy storage solutions, but also new players in the market. Currently, battery energy storage systems are not used for enhancing the precision of photovoltaic power generation schedules, so actors in the market find it difficult to make well-grounded decisions on the viability of utilizing batteries for such a purpose. The innovative novelty of the procedure presented in this paper is that it is suitable for the planning, analysis and evaluation of the schedule accuracy enhancing impacts of sodium-sulfur and lithium-ion-based battery energy storage systems of various capacities. By the examples of two European Union countries, this article studied the deviations of day-ahead and intraday photovoltaic power generation forecasts from the actual electricity generation of 1000 MWp photovoltaic systems. The two main objectives of the project were to investigate the amount of the photovoltaic balancing needs, based on actual data compared to the day-ahead and intraday forecasts, and to establish to what extent sodium-sulfur and lithium-ion-based battery storage systems can decrease those requirements. Furthermore, by using a model for simulation, the potentials of a number of different energy storage capacities (nominal net storage capacities from 10 MWh to 10,000 MWh) for reducing grid balancing needs were assessed. The practical use of the model is that its application makes it possible to create a dynamic design, analysis and assessment system mechanism that can allow one to establish the multilevel schedule accuracy enhancing impacts of sodium-sulfur and lithium-ion energy storage systems linked to solar power plant systems. Real photovoltaic data from Belgium and Hungary were used to find out how the accuracy of PV power generation forecasts influence the level of the annual utilization of energy storage systems.

The viability of the store-on grid (SoG) scheme model across 13 selected Sub-Saharan African (SSA) countries was investigated. The individual country's datasets applicable to the SoG scheme and a typical industrial building as a baseline prosumer were used in the evaluation. Two SoG scheme scenarios (A and B) based on solar photovoltaic (PV) module prices were used in this analysis. The electricity tariffs under the SoG scheme were compared with the time of use rates for the selected SSA countries. The analysis results revealed that the scheme could be viable in only nine and four out of 13 countries under scenario A and scenario B, respectively, with high lending interest rate in some countries being the key hindrance. Under the SoG scheme model, solar PV systems in all the selected countries could feed about 15.65–39.25% of the generated energy to the grid. The government tax in the range of 1.95–3.38 and 2.48–4.31 c$/kWh could be collected from the energy fed to the grid under SoG scheme scenario A and scenario B, respectively. Overall, the SoG scheme exhibits greater potential as an energy business model to foster rooftop solar PV technology adoption in developing countries.

Access to sustainable energy source is crucial for healthcare facilities to deliver their services. Hybrid solar energy systems (HSES) are seen as a strong solution given the proximity of the Philippines to the equator. This study addresses the knowledge gap in the estimated energy consumption profile of Philippine rural health units (RHU) using easily replicable questionnaire and work sampling technique. The estimated energy consumption profile was used to determine the optimal HSES design and benefits. The RHU of Los Banos, Laguna was considered, and simulation of different energy system configurations was performed in the HOMER Pro software. Two scenarios were considered: a grid-connected and an off-grid RHU. The estimated average scaled load of the facility is ∼92 kWh per day. Estimates were validated against actual consumption, with 15–21% error. Results of the simulations show that a solar photovoltaic panel-grid system is optimal for the grid-connected scenario and can reduce cost of electricity by ∼35%. Meanwhile, a solar photovoltaic panel-generator-battery system is optimal for the off-grid scenario and can reduce cost of electricity by ∼61%. In both scenarios, the solar component is estimated to contribute ∼71–85% of the total annual energy production and can help reduce greenhouse gas emissions.

Among the emerging renewable energy technologies, solar photovoltaic (PV) power generation is growing steadily in the mainstream energy supply mix contributing about 2.58% of the global total power generation by 2018 from 2.1% in 2017. The negative high PV module temperature effects continue to pose significant hurdles though being addressed through active and passive cooling methods. Thermoelectric generator (TEG) technology, given its modularity, augments well in cooling PV modules’ and generating additional electricity. However, thermal coupling of the two technologies has remained an impediment to their good performance due to the microscopic roughness of the PV and TEG surfaces. Non-uniform temperature distribution from the PV cells hinders efficient heat transfer thus affecting the performance the two technologies. In this study, PV cell temperature distribution have been evaluated analytically and experimentally under outdoor setup environments. Further, cell temperatures distribution is investigated using three thermal interface materials (TIM) under air- and water-cooled environments with aluminium honeycomb cooling panels as the cooling contact medium. Results show that the three TIMs substantially reduced the temperature mismatch effects with the heat spreader (HS) presenting lower temperature and voltage mismatch compared with the other two TIMs under both air- and water-cooled test conditions exhibiting preference. Based on the best observed conditions, PV module power output increased by 1.8% and 2.5% under the two test conditions while the TEG generated an additional 19.7% and 24.85% of power, respectively. This translated to an improvement of 11.3% and 50.6%, respectively, compared to the bare cell TEG power generation. The use of TIMs hence has the potential to mitigate thermal coupling challenges associated with PV–TEG systems improving their overall power output.

Systems involving more than one energy source, namely hybrid systems, can be arranged in different configurations and layouts depending on the available or used energy source, application and scale of the system. Despite an increasing awareness and penetration of renewable energy sources at different scales in many countries over the world, the adoption of hybrid and novel renewable systems is relatively scarce, especially at a small scale. This scenario is also presented from the point of view of the research of such systems. Therefore, the scope of the paper is to improve the knowledge regarding small-scale hybrid renewable energy systems performance and operation by investigating a novel trigeneration system based on a biomass fired steam cycle, wind turbine, photovoltaic panels and adsorption chiller. In the proposed system, the thermal energy recovered by the steam cycle is used to match the user thermal demand by means of a storage tank supplying directly heat to the user during winter, and driving the adsorption unit during summer. The electrical energy produced by the steam turbine, photovoltaic field and wind turbine is managed by a control system that integrates a bidirectional connection with the electric grid. The grid allows one to virtually store the electrical energy produced in excess and to recover it in part when needed by the user. In order to assess the system performance, a farm and residential buildings are considered as case study. The proposed system is modelled and dynamically simulated by means of Transient System Simulation software. In the paper, the dynamic operation of the system and the energy and economic performance of the system is assessed. The operation of the system is presented in a daily and monthly basis, while the global performance parameters of the system are reported considering a one-year period. The primary energy saving of the system is above 70% when a reference system based on natural gas, electric chiller and grid is considered. For this scenario, the Simple Pay Back of about 10 years is achieved. The results of the analysis show that the system is feasible from the technical and energy point of view, whereas its economic profitability is significantly affected by its cost and the reference system that is adopted by the user.

In grid-connected PV systems, the inverter is one of the important components. Inverter efficiency may vary depending on the input power and voltage of the PV array. This paper analysed three factors affecting inverter efficiency. The first one was the effect of the duration of inverter operations. Analysis of the operation of a PV system that has been operating four years showed an annual average inverter efficiency of 0.90, almost equal to the manufacturer’s specification of 0.91. The study showed that there was no significant degradation in inverter efficiency because a cool temperature (of 25°C) was maintained for the inverter storage room through an air conditioning system. The second analysis investigated the effect of the power input from different types of PV module technology. The study showed that the inverter connected to p-Si PV modules operated the highest efficiency at 0.91. However, detailed analyses showed that PV module technology had less or minimal impact on inverter efficiency. It was the power input from the PV module that has influence on the inverter efficiency. The third analysis involved the study of the effect of irradiance distribution on inverter efficiency. The study shows that the inverter operates at the maximum efficiency of 0.90 at irradiance of above 350 W/m2, at which range solar energy potential is at its highest at around 85% of the total generation. This means that inverter converts almost all the energy supplied from solar PV at this irradiance range. In conducting the third analysis, a location-specific equation, η PHS, for determining inverter efficiency was also developed. The η PHS equation generates the actual inverter efficiency values based on the local irradiance conditions in Phitsanulok province.

Integration of solar photovoltaic systems to low-voltage distribution networks is witnessing an unprecedented growth in many parts of the world. Although solar photovoltaic generation is of significant benefit from a number of angles, exceedance of hosting capacity levels by such installations in low-voltage distribution networks continue to cause significant technical challenges in network operation, especially to the management of network voltage. Modern smart inverters are equipped with Volt-VAr and Volt-Watt control capabilities, which can assist in the management of network voltage levels. This paper provides a detailed analysis of the influence of different connection standards which cover these strategies on solar photovoltaic hosting capacity and their applicability in low-voltage distribution networks. Smart inverters with differing Volt-VAr and Volt-Watt control functions are modelled in the DIgSILENT PowerFactory platform. Influence of different connection standards on solar photovoltaic hosting capacity is analysed to investigate the most beneficial connection approach/es to address the issue of voltage violations. Furthermore, the work presented in this paper provides a greater understanding on the hosting capacity improvement by employing advanced inverter control functions where such improvements are subjected to locational aspects of inverters in low-voltage distribution systems.

Global electricity consumption has raised in the last century due to many reasons such as the increase in human population and technological development. To keep up with this increasing trend, the use of fossil resources has increased. But these resources are not environmentally friendly, and for this reason, many countries and governments are encouraging the use of green sources. Among these sources, PV technology is widely promoted and used due to its improved efficiency and lower prices for photovoltaic panels. Therefore, the importance of forecasting power production for these plants is necessary. In this work, two hybrid models were proposed (CNN-LSTM and ConvLSTM) to effectively predict the power production of a self-consumption PV plant. To confirm the efficiency of the proposed models, the LSTM model was used as a baseline for comparison. The three models were trained on two datasets, a univariate dataset containing only the power output of the previous days, while the multivariate dataset contains more features (weather features) that affect the production of the PV plant. The time frames for the forecast ranged from one day to one week ahead of time. The results show that the proposed methods are more accurate than a normal LSTM model.

This paper presents an intelligent photovoltaic (PV) fault detection system using Adaptive Neuro-Fuzzy Inference System (ANFIS) methodology. To accomplish this objective, it is necessary to train the ANFIS model for an effective PV fault detection and classification system by deploying Grid Partition (GP) and Subtractive Clustering (SC) strategies using some research data. Moreover, the ANFIS SC approach’s performance was better and more accurate than the ANFIS GP approach to predict and classify various PV systems’ faults. Additionally, the trained model with the ANFIS SC approach demonstrated excellent performance when it was compared with the unseen empirical data points, which were not included in the training process. The values obtained from statistical analysis such as coefficient correlation R, root mean squared error (RMSE), and coefficient of determination R2 were 0.9989, 0.0383, and 0.9978. These obtained results show that the ANFIS SC framework with cluster radius 0.6 can remarkably diagnose the PV system faults with high accuracy.

This study presents a methodological framework for auditing, optimization as well as the integration of hybridized solar energy in Ghana’s agro-industrial sector. The Functional Analysis System Technique was introduced for systematically translating plant-wide energy requirements into detailed functional and performance design criteria for energy efficiency, optimization, and solar energy integration. The results of the case study indicated that insulating the fermenter and distiller as well as using energy-efficient rasper, pumps and cables reduced energy consumption by 41.8%. The cost of integrating solar thermal energy was 31.8% higher than solar photovoltaic power due to a lack of subsidies in the sector. A 250 kW solar photovoltaic model was designed with a performance ratio of 75% and a solar hot water system with a thermal fraction of 91% for electricity and process water respectively. The framework, therefore, presents an invaluable tool for optimizing energy efficiency and integrating solar thermal and solar photovoltaic energy sources for the agro-industrial sector.

The European regulatory shift towards energy-efficient buildings and nearly zero energy buildings (NZEB) leads architects and engineers to the need for investigation of new innovative design methodologies. The reduction of the energy demand for cooling, heating and artificial lighting is now mandatory for new buildings and for large renovations of existing buildings, in order to comply with these new regulations. This is a challenge since the majority of the current building stock is consisted by over than 25-year-old existing buildings, which are inefficient in terms of energy needs. The aim of this study is to completely renovate an existing building in the centre of Nicosia, Cyprus, by applying a novel double facade system. This is consisted by a combination of a novel Building Integrated Photovoltaic/Thermal system (BIPV/T) and a Corridor Type Double facade system, which acts autonomously in every floor. The proposed research starts with an analysis of building and site geometry and moves through a literature review, along with the presentation of case studies where active solar energy systems are integrated on double building facades. Digital energy simulations are performed where the proposed double facade’s passive contribution is investigated. At the same time, these simulations aim to analyse whether the system can cover the energy needs of the aforementioned building. The investigation focuses into calculating the facade’s electricity production potential, whilst its passive contribution is recorded through the reduction potential of the building’s energy needs for heating, cooling and artificial lighting. Through a 3D simulations modelling, the existing energy needs are recorded and then compared to the calculated needs of the renovated building. The ultimate aim of this research is to determine whether the use of BIPV/T double facade solutions for the refurbishment of existing buildings consist an environmental viable solution.

The selection of a proper drying method can play a vital role in reducing the energy consumption and the quality of the final product. In this study, three dryers, including conventional refractance window (CRW), solar refractance window (SRW) based on the photovoltaic-thermal system, and hot air (HA) dryers, were used to dry the Aloe vera gel. Experiments were carried out at four levels of temperature (60, 70, 80, and 90 °C) and three levels of thickness (1, 2, and 3 mm) for the puree product. An increase in temperature and a decrease in thickness reduced specific energy consumption, specific heat consumption, specific heat utilization, pollutant emission, and social costs derived from the pollutant. The results showed that the SRW dryer significantly improved the energy and drying parameters compared to the HA method. The pollutant emission and social costs in the SRW dryer were zero, while these values were significant using the CRW and HA dryers. The highest amounts of antioxidant potential, phenol, flavonoids, and glucose were 98.61%, 0.0435%, 0.0103%, and 19.28% (mg/g dry matter), which were obtained at 60 °C and 3 mm thickness using the SRW/CRW dryers. However, these values were 76.69%, 0.0246%, 0.0039%, and 16.31% (mg/g dry matter) using the HA dryer. In general, according to energy consumption and drying parameters, the best experimental results were obtained at temperatures between 70 and 80 °C and a thickness of 2 mm.

The utilization of renewable energy to run desalination plants has enormously expanded in the last two decades. In this study, a grid-connected hybrid solar-wind system is proposed to power a small-scale Reverse Osmosis (RO) desalination unit. In a case study, the system's performance has been analyzed under the weather conditions of the Eastern Province, Saudi Arabia. A numerical model has been developed based on a mixed-integer linear programming (MILP) approach to design and size the proposed system. The developed model is solved on an hourly basis to capture hourly variations of weather conditions with the aim to obtain an efficient design to operate the RO plant and supply freshwater to a small community living in a remote area at minimum cost. The developed model allows finding the optimal number of wind turbines, the number of photovoltaic (PV) modules, and the energy purchased from the national grid. Since the desalination energy consumption depends on the feed water conditions, two energy consumption rates are considered, namely, 2 and 4 kWh/m3. The results show that brackish water can be purified for the two different energy requirements at a cost varying between 1.72 and 1.84 $/m3, respectively.

This paper presents a case study of photovoltaic (PV) module failures after over 10 years of operation in the tropical climate of Singapore. Three types of modules (two samples from each type) were analysed: multi-crystalline silicon (multi-Si), mono-crystalline silicon (mono-Si), and copper indium selenide (CIS). Visual inspection revealed several problems, including encapsulant discoloration (to different extents), backsheet yellowing, and soiling among others. Different degradation behaviour of the samples was observed from current-voltage (IV) and electroluminescence characterization. The maximum power of the multi-Si samples degraded by more than 9% (on average), likely due to corrosion around the cell edges. The mono-Si modules suffered a catastrophic power reduction (>40%) that could be ascribed to a combination of encapsulant discoloration, potential-induced degradation (PID), and corrosion. While one CIS module was primarily affected by encapsulant discoloration and possibly corrosion, the other sample also exhibited signatures of PID and experienced about 45% power drop. Furthermore, external quantum efficiency measurements of the multi-Si modules identified cell mismatch and changes to the additives in the encapsulant. Overall, PID, corrosion and encapsulant degradation are found to be the most detrimental degradation processes for PV modules in the tropical climate of Singapore.

With the large-scale grid connection of wind and photovoltaic power, the contradiction between renewable energy and thermal power is becoming more and more serious. To solve this problem, this paper comprehensively considers the construction and operation costs of power plants, and constructs an optimal generation expansion planning model of a combined thermal–wind–photovoltaic​ power system with the objective of minimizing total cost. The planning is calculated under the boundary conditions of ensuring the safe operation of the power grid and taking into account the share requirement, utilization requirements and construction requirements for renewable energy. The model is applied to the self-use and supporting power plants in Xinjiang. The results show that the proposed generation expansion plan can realize the coordinated development of three types of power sources under multiple boundary conditions, which has good applicability.

The significant global trend towards solar energy has led to the development of studies on the fabrication of high-performance solar cells. Accurate modeling and parameter identification of solar cells is of paramount importance. So far, several models have been proposed for the solar cell, including single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). Each model has a number of unknown parameters and several methods have been presented in the literature to find their optimal values. In this paper, an efficient optimization algorithm, namely Whippy Harris Hawks Optimization (WHHO), is proposed to estimate the model parameters of solar systems. The proposed WHHO is an enhanced version of the HHO algorithm and has the advantages of high convergence speed, global search capability, and high robustness over the original method. To evaluate the efficiency of the proposed WHHO algorithm, it is utilized to identify the parameters of various models of solar cells, and photovoltaic (PV) module. The results are compared with those obtained from a number of other recently presented optimization methods, which shows the superiority of the proposed algorithm. Furthermore, the effectiveness of WHHO algorithm in the practical application has been assessed for the parameter estimation of three commonly-used commercial modules under different irradiance and temperature conditions, which yield variations in the parameters of the PV model. The results obtained from various experimental setups confirm the high performance and robustness of the proposed algorithm.

A hybrid resonant three-level converter comprised of dual transformers is proposed in this paper, which is suitable for the application of distributed photovoltaic power accessing the medium voltage dc distribution network. The proposed converter can be obtained by adding a control circuit into the traditional neutral point clamped (NPC) three-level (TL) circuit, achieving the basic TL circuit operating with a fixed duty cycle. Pulse width modulation (PWM) is adopted for the control circuit to realize zero current switchings for the basic TL circuit, which delivers most of the power, under full load range. As a result, the switching loss of the converter can be significantly reduced. The influences of the turn ratio of the second transformer and resonant capacitance on the switch current, the peak value of resonant voltage, and the value of the resonant inductance value are discussed in detail, and the parameters design principles are put forward. Finally, a prototype is built to verify the performance of the proposed converter.

A techno-economic study has been proposed for an off-grid combined renewable energy system (HRES) by photovoltaic (PV) and fuel cell (FC) systems. The proposed HRES has been studied to provide electricity for a remote area in Jiaju Tibetan Village, Danba, Sichuan Province China The main idea is formulated according to the Total Annual Cost (TAC). The study uses a new improved metaheuristic, called Amended Water Strider Algorithm (AWSA) to optimal estimation of the photovoltaic panels’ number, electrolyzer, fuel cells, and hydrogen storage tanks to get the minimum value of the overall net present value (ONPV). For increasing the system efficiency, the loss of power supply probability (LPSP) has been considered. Sensitivity analysis is used on the changing of the proposed HRES according to Levelized cost of energy (LCOE). Also, a comparison of the algorithm with several techniques from the literature is performed to illustrate its effectiveness. The results showed that the proposed method has further proper results than the other algorithms.

The article presents the influence of changes in the solar radiation spectrum distribution on the properties of various photovoltaic modules, with particular emphasis on the scattered component. We compared the relative efficiency of the photovoltaic modules based on various semiconductor absorbers during bright and sunny, and cloudy summer days. Additionally, we presented the impact of the module tilt angle on the magnitude of the surface incident scattered component and on the efficiency of the module. The solar spectra for various weather conditions were estimated using specialised computer programmes,such as SolarSpectrum or SMARTS2, and we present here the validation results for the former. The obtained results confirm the published experimental data and the conclusions drawn show the applicability of the tested modules with various absorbers for the specific photovoltaic systems. The theoretical analysis of the a-Si structures, both single-, as well as multi-junction shows better performance of these structures at low-irradiance conditions (in comparison with e.g., c-Si, CIS structures), during days characterised by a spectral distribution common for cloudy days with high air water vapour content. This type of device should show unaltered efficiency both at mid-day, as well as during the morning and late afternoon.

This paper presents renewable energy systems based on micro-hydro and solar photovoltaic for rural areas, with a case study in Yogyakarta, Indonesia. The Special Region of Yogyakarta, located on the island of Java, Indonesia, has a high potential for the development of renewable energy resources, especially hydropower and solar power. Many rural areas in Yogyakarta lack a supply of electricity. In this study, data on the potential for hydropower and solar power in rural regions of Yogyakarta are processed to determine the best capacity of hydroelectric and solar power plants. The extended particle swarm optimization (PSO) technique has been used to ensure optimal capacity optimization of this hybrid systems. The final result of this study is the most optimal of hydropower and solar power generation capacity based on the calculation of cost of capital, grid sales, cost of energy, and net present value.

Solar photovoltaic (PV) technologies receive investment, support, and incentives despite their apparent high costs. In terms of renewable energy policies and spatial planning, site selection for solar PV farms is a critical issue. A novel comprehensive framework for assessing the site suitability of solar PV farms is proposed, which considers the preservation of natural, ecological conservation, and cultural areas. The framework combines a Geographical Information System (GIS) with layers of satellite-derived data for energy resources as well as locally collected data, and the Multi Criteria Decision Making (MCDM) method based on the Analytic Hierarchy Process (AHP). In the GIS environment, maps belonging to fourteen sub-criteria of the three main criteria (Geography, Climate and Location) for Kahramanmaras, Turkey were obtained, as well as suitability map derived from their weighted sum. The AHP method generates the appropriate weight for each input criteria using a pairwise comparison matrix. GHI, aspect, distance from power line network, land use/cover, annual average temperature, and others were found as the most necessary sub-criteria, respectively. According to the findings, 9.62% of study area very low, 20.15% is low, 22.51% is moderate, 23.98% is high and 23.74% is very high, and 26% is unsuitable for solar PV farms. Based on the results, the northern districts of the study region were determined to be the most suitable for the construction of solar PV farms. When existing five PV farms' location selection decisions were examined, it was observed that the investment outcomes were consistent with the results of the study. The proposed framework is projected to minimize the cost, time, and resources used on the construction of solar PV farms.

The operation of photovoltaic (PV) array under shade faces great challenges due to the power loss that reduces the extracted power. Reconfiguration process is one of the most promising solutions to reduce the effect of shadow on the array. However, the physical array relocation methods like total cross-tied (TCT) and Su Do Ku have some limitations due to their complicated arrangements. Therefore, this paper presents a recent metaheuristic approach of coyote optimization algorithm (COA) to solve the reconfiguration process of the partially shaded PV array. The main target is maximizing the global maximum power (GMP) extracted from the array. The proposed COA is applied on 9 × 9 PV array operated under four standard shadow patterns which are short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN). The obtained configurations via the proposed COA method are compared to TCT, Su Do Ku, flower pollination algorithm (FPA), marine predators algorithm (MPA), and butterfly optimization algorithm (BOA) based arrangements. The best enhancement of GMP obtained via the proposed COA with respect to TCT configuration occurs in SW shadow pattern of 26.58% while the least on is 7.68% placed in SN pattern. The obtained results confirmed the competence and superiority of the proposed COA in reconfiguring the shaded array optimally.

This article presents methods to increase the efficiency of liquid methanol production and purification installations. Three solutions were proposed: CO2 compression on the side of the CCS power plant, replacement of the throttle valve with an expander, and replacement of the heat exchanger with an ORC module. The calculation methodology was presented, and each concept was thermodynamically analyzed and compared with the reference variant. The installation for the synthesis of liquid methanol was integrated with a wind farm with a nominal power of 8 MW, a photovoltaic farm with a capacity of 2 MW (based on the measurements of the photovoltaic farm installed on the campus of the Silesian University of Technology in Poland), and a water electrolysis installation. This made it possible to increase the efficiency of the entire system to 52.41%, which corresponds to an increase in efficiency relative to the reference case of 3.57 pp. The synergy effect accompanying the use of CO2 from CCS power plants was also noticed, consisting in a simultaneous increase in the efficiency of both the analyzed installation and the power plant.

This article provides an extensive review on design, thermal modelling approaches, and economic, energy and environmental aspects of solar greenhouse dryers developed for drying various agricultural products. Further, the selection and usage of solar photovoltaic panels and thermal energy storage units in the solar greenhouse dryers for achieving continuous and grid-independent drying are discussed in detail. Performances of the various configurations/shapes of the greenhouse dryers in terms of energy requirements are compared. Thermodynamic and thermal modelling studies reported on the performance prediction of solar greenhouse dryers and drying kinetics studies on various agriculture products are summarised. A detailed report on the economic (payback time, cost of the greenhouse dryer, and product drying cost), energy (embodiment energy, specific energy consumption) and environmental (CO2 emission, CO2 mitigation, and carbon credit) aspects of the solar greenhouse dryer based on the dryer type, mode of operation and product dried are presented.

This paper proposes a hybrid algorithm for capacity configuration optimization of a solar PV-battery-based micro-grid. The hybrid algorithm (BAPSO), which is a combination of Particle Swarm Optimization (PSO) and Bat Algorithm (BA), is designed to optimize the solar generation location and capacity for the efficient performance of a micro-grid. The algorithm considers dynamic transmission power loss optimization and integrates PSO and BA algorithms’ respective advantages to form a hybrid algorithm. The proposed algorithm combines PSO’s fast convergence ability with the less computation time ability of BA to better optimal solution by incorporating the BA’s frequency into the PSO velocity equation to control the pace. The design from the proposed algorithm is tested and validated on IEEE 30 bus test system. The transmission power loss before implementing the algorithm was 22 kW and reduced to 20 kW after the algorithm is used. A further reduction of 0.3 kW of losses is observed by placing the solar generation system at bus number 29.

Domestic hot water (DHW) heating is one of the most energy-consuming activities in a typical household. Photovoltaics (PV) connected with a ground source heat pump (GSHP) offers a low-emission method for DHW heating. This paper studies four different control methods for DHW heating in a building with a GSHP and a PV system. The main control method aims to minimize DHW heating costs by utilizing Nord Pool Spot market information together with a PV production output forecast. The results of this control method, implemented with a perfect PV output forecast and assessed over three years of hourly data, indicate that annual cost savings over other methods are achievable. Results with the real-world actual PV output forecast, evaluated between June–September 2020, demonstrate DHW heating cost savings up to 36–53%, even though forecasting errors are present. The heating costs are 9–11% higher compared against the perfect forecast case. The suggested control method thereby effectively reduces the costs when compared with all other methods, and its performance is not significantly affected even when an actual imperfect forecast is implemented. The results also indicate that minimizing energy consumption does not offer the lowest cost.

As fossil fuels dry up and the proportion of renewable energy increases, multi-energy system (MES) becomes an effective way to realize renewable energy accommodation. High penetration of renewable energy brings challenges to the operation of power system, such as frequent curtailment of wind and the uncertainty of renewable energy threaten the security of the system. This paper proposes a two-stage robust scheduling model of MES considering integrated heat-electricity demand response (DR) to derive robust operation decisions. The proposed robust model minimizes the operation costs under base-case scenario, while guaranteeing all operation constraints are met in any scenarios. Power-to-Gas (P2G) equipment which converts surplus wind and photovoltaic generation into natural gas is included to effectively reduce curtailment of renewable generation. Furthermore, integrated heat-electricity DR, fully utilizing the couplings among various systems, is proposed to decrease operation costs and enhance system security against uncertainties. Column-and-Constraint Generation (CCG) method is used to effectively solve the proposed robust model. Numerical results in MATLAB indicate that robust optimization ensures secure operation of the system, and the integrated heat-electricity DR could promote the accommodation of renewable energy as well as enhance economic benefits and robustness of the system.

A local load connected with the grid-interfaced photovoltaic (GIPV) system demands reactive power compensation at the distribution level. The compensation either fulfilled by the PV inverter or grid side arrangements such as capacitor bank, static VAR compensator or tap-changing transformers. Amongst both, the inverter has merit to compensate reactive power without using an additional compensator or oversizing the inverter rating. However, it always depends upon the availability of irradiance and especially when the inverter transfers power with full capacity has no margin to generate the reactive power. Therefore, to make the system flexible according to the demand of the local loads and to create a margin to generate the reactive power at any time instant, a GIPV system with modified perturb & observe (MP&O) maximum power point tracking (MPPT) technique has been proposed. This proposed technique with an intermediate boost converter extracts maximum power more efficiently as compared to the traditional MPPT technique. On the other hand, it curtails the generated active power and provides margin for the PV inverter to generate the reactive power. Further, the PV inverter generates active and reactive power to the local loads as well as transfer power to the grid using inverter control. The inverter control comprises of decoupled instantaneous active and reactive power control. In this control scheme, it maintains the DC-link voltage and power flow between the GIPV system and the grid under all available irradiance conditions. In this respect, a 30 kW GIPV system is simulated and performance of the system is validated using real-time OP4510 hardware-in-loop (HIL) setup.

In recent years, emphasis has been placed on the design and implementation of sustainable energy system solutions to combat the adverse environmental impact of emissions from the power and transportation sectors. This study applies a systems elimination method using numerical simulation to validate and optimise recently-reported results demonstrating the benefits of photovoltaic (PV)–diesel — battery hybrid integrated power systems (IPS) for commercial centres, with Abuja in Nigeria used for the case study. An optimal IPS was identified from 20,200 candidate solutions analysed by assessment against environmental (1st priority) and economic (2nd priority) metrics. Although environmental conditions were prioritised, the optimal system was economically viable. The environmentally optimal system emitted 33% less greenhouse gas (GHG) emissions (CO2 tonnes/yr.) than the economically optimised solution (PV–diesel) over their operational lifetimes ($/20 years), and was 4% costlier than same. The results demonstrate that carbon taxation or outright bans on independent fossil fuel systems (IFFSs) in emerging economies might not be effective policies in mitigating the impact of climate change on our environment. This study contributes to the body of knowledge on energising unserved and underserved communities in sub-Saharan Africa, considering the case study country of Nigeria. It decries the common practice of prioritising economic factors over environmental factors in optimising the operations of grid defected power system solutions as continental and regional electrification efforts are being ramped up. This is particularly of importance (an environmental responsibility), as immediate economic gains could have far-reaching environmental and social implications that elicits the limitations of economically prioritised power development projects in the offing.