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Grid-connected PV inverters require sophisticated control procedures for smooth integration with the modern electrical grid. The ability of FCS-MPC to manage the discrete character of power electronic devices is highly acknowledged, since it enables direct manipulation of switching states without requiring modulation techniques. This review discusses the latest approaches in FCS-MPC methods for PV-based grid-connected inverter systems. It also classifies these methods according to control objectives, such as active and reactive power control, harmonic suppression, and voltage regulation. The application of FCS-MPC particularly emphasizing its benefits, including quick response times, resistance to changes in parameters, and the capacity to manage restrictions and nonlinearities in the system without the requirement for modulators, has been investigated in this review. Recent developments in robust and adaptive MPC strategies, which enhance system performance despite distorted grid settings and parametric uncertainties, are emphasized. This analysis classifies FCS-MPC techniques based on their control goals, optimal parameters and cost function, this paper also identifies drawbacks in these existing control methods and provide recommendation for future research in FCS-MPC for grid-connected PV-inverter systems.

In recent years, extreme events have frequently occurred, and the extreme uncertainty of the source-demand side of high-ratio renewable energy systems poses a great challenge to the safe operation of power systems. Accurately generating extreme scenarios related to the source-demand side under a high percentage of new power systems is vital for the safe operation of power systems and the assessment of their reliability. However, at this stage, methods for extreme scenario generation that fully consider the correlation between wind power, solar power, and load are lacking. To address these problems, this paper proposes a method for extreme scenario generation based on information-maximizing generative adversarial networks (InfoGANs) for high-proportion renewable power systems. The example analysis shows that the method for extreme scenario generation proposed in this paper can fully explore the correlation between historical wind–solar–load data, greatly improve the accuracy with which extreme scenarios are generated, and provide effective theories and methodologies for the safe operation of a new type of power system.

Reducing emissions from energy production and enhancing the capacity of land use systems to store carbon are both important pathways towards greenhouse gas neutrality. Expanding photovoltaics (PV) contributes to the former, while the rewetting of drained peatlands preserves the peat soil as long-term carbon store, thus contributing to the latter. However, both options are usually considered separately. This study analyses Peatland PV, defined as the combination of open-space PV with the rewetting of peatlands on the same site, and has an explorative and field-defining character. Due to a lack of empirical data, we used expert interviews to identify the strengths and weaknesses, opportunities, and threats of Peatland PV in the sparsely populated and peatland-rich state of Mecklenburg-Western Pomerania in North-East Germany. The material was analysed using a qualitative content analysis and compiled into SWOT and TOWS matrices. Besides the ecological and technological dimensions, this study focuses on the economic and legal framework in Germany. We found that Peatland PV may mitigate land use conflicts by contributing to climate and restoration targets, energy self-sufficiency, and security. Continued value creation can incentivize landowners to agree to peatland rewetting. Technical feasibility has, however, a significant influence on the profitability and thus the prospects of Peatland PV. Although Peatland PV has recently been included in the Renewable Energy Sources Act (EEG), several specialised legal regulations still need to be adapted to ensure legal certainty for all stakeholders. Pilot implementation projects are required to study effects on vegetation cover, soil, peatland ecosystem services, biodiversity, hydrology, and water management, as well as to analyse the feasibility and profitability of Peatland PV.

This abstract depicts a novel kind of solar-powered grass mower that is capable of being controlled remotely and can avoid obstacles thanks to the Internet of Things technology. This design offers an eco-friendly and effective solution that aims at redefining lawn care. By lowering reliance on fossil fuels, green energy usage promotes sustainability. During operation, an integrated collector accumulates grass clippings, streamlining trash management. This discovery unlocks the door for intelligent, user-friendly robotic grass cutters that can navigate obstacles by utilizing cutting-edge technologies, ushering in a new era of automated and sustainable lawn care.

Generating hydrogen by electrolysis in an alkaline system with a green power source consisting of wind turbines (WTs) and photovoltaic (PV) power is a promising and sustainable way to produce clean hydrogen to reduce greenhouse gas emissions. This study utilized TRNSYS 16 software to perform a dynamic simulation of a hydrogen system. TRNSYS, which stands for Transient System Simulation Program, is a software package designed for simulating the dynamic behaviour of thermal and electrical energy systems. It is widely used to analyze and optimize the performance of various energy systems. This system incorporated a PV power source and a WT for electricity generation, along with an electrolyzer for hydrogen production. The analysis was carried out to evaluate variable weather conditions, specifically wind speed, solar radiation, and temperature. These factors have a direct impact on the system’s performance, influencing the available power as a consequential outcome. The results reveal that, given the specific climate conditions in the Markham zone, Toronto, the integrated renewable system is capable of consistently providing electricity and meeting the load demand throughout the entire year. However, it is noteworthy that on cold days when solar radiation is limited, the WT emerges as the most effective and efficient power source. The analysis also indicates that the system reliably supplies enough energy to meet the laboratory’s load demand. Moreover, the system’s performance is particularly impressive with the WT as the power source, as it can generate a maximum of 9.03 kg of hydrogen per month. In contrast, the PV power source yields only 0.58 kg H2. Additionally, the cost per kilogram of hydrogen (kg H2) is considerably lower when the WT is used, at USD 0.55/kg H2, while it rises to USD 1.5/kg H2 when PV is the power source. These findings underscore the significance of using the most suitable power source, such as a WT, in specific climatic conditions and regions in terms of both performance and cost-effectiveness.

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer’s manual labor.

This paper presents a stochastic approach to assessing the hosting capacity for solar PV. The method is part of the optimal techniques for the integration of renewables. There are two types of uncertainties, namely aleatory and epistemic uncertainties. The epistemic and aleatory uncertainties influence distribution networks’ hosting capacity differently. The combination of the two uncertainties influences the planning of distribution networks. The study introduces and considers the epistemic probability distribution function (PDF). DSO does take levels of risk for a parameter violation when planning. Epistemic PDF is a range of values of the planning risk margin for quantifying the hosting capacity. The planning risk acknowledges that overvoltages may occur at weaker conceivable locations in a distribution network. In the paper, it has been shown that the number of customers who will be able to connect solar PV in future is influenced by the DSO’s planning risk margin. The DSO can be stricter or less strict in planning risk margin. It has been concluded that fewer customers can connect solar PV to a distribution network when a DSO takes a stricter planning risk. Alternatively, more customers can connect solar PV units for a less strict planning risk. How stricter or less strict the DSO is with the planning risk margin determines the investment needed for mitigation measures. The mitigation measures in the future will lead to not exceeding the overvoltage limit when solar PV is connected to the weaker conceivable points of the distribution network.

Under net-zero objectives, the development of electric vehicle (EV) charging infrastructure on a densely populated island can be achieved by repurposing existing facilities, such as rooftops of wholesale stores and parking areas, into charging stations to accelerate transport electrification. For facility owners, this transformation could enable the showcasing of carbon reduction efforts through the self-use of renewable energy while simultaneously gaining charging revenue. In this paper, we propose a dynamic energy management system (EMS) for a solar-and-energy storage-integrated charging station, taking into consideration EV charging demand, solar power generation, status of energy storage system (ESS), contract capacity, and the electricity price of EV charging in real-time to optimize economic efficiency, based on a real-world situation in Taiwan. This study confirms the benefits of ESS in contracted capacity management, peak shaving, valley filling, and price arbitrage. The result shows that the incorporation of dynamic EMS with solar-and-energy storage-integrated charging stations effectively reduces electricity costs and the required electricity contract capacity. Moreover, it leads to an augmentation in the overall operational profitability of the charging station. This increase contains not only the revenue generated from electricity sales at the charging station but also the additional income from surplus solar energy sales. From a comprehensive cost–benefit perspective, introducing this solar-and-energy storage-integrated EMS can increase facility owners’ net income by 1.25 times compared to merely installing charging infrastructure.

Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa’s ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kWpk/taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country’s dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%.

Due to the surge in oil prices, alternative energy sources, like solar power, are being explored to meet energy demands. Solar power is utilized in various industries, including agriculture. In Iraq and other developed countries, solar power is actively being developed due to the abundance of solar radiation. In agriculture, solar standalone pump systems with variable-speed drives are used. Electric motors operate at different speeds depending on the availability of sunlight. Inverters convert this solar energy from direct current to alternating current, enabling the powering of motors with a fixed voltage per frequency ratio and regulating motor current consumption. The variation in motor speed affects hydraulic pump efficiency and water productivity, making it crucial to optimize solar energy utilization in agriculture. The angle of inclination greatly affects the effectiveness of solar panels. The optimal tilt angle depends on location, latitude, season, and time of day. Adjusting this angle based on these factors maximizes power output. Innovative installation methods are being employed to enhance the benefits of solar power, reduce costs, and optimize sunlight capture. These methods drive sustainable development in various industries, including agriculture.

The characteristics, distributions, and trends of the aerosol optical depth (AOD) and dust aerosol optical depth (DAOD) of three major concentrating solar power (CSP)-plant project areas (Hami, Turpan, and Ruoqiang) in Xinjiang, China were investigated and analyzed during 1980–2022 using the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis products. The monthly variation, seasonal variation, inter-annual variation, distributions of AOD and DAOD, and proportions of dust in the aerosols in these three CSP-plant project areas were computed and analyzed. Overall, the annual mean AOD at 550 nm in the Turpan project area was the highest (0.20–0.36), while Ruoqiang had the lowest annual mean AOD at 550 nm (0.13–0.30), and the annual mean AOD at 550 nm in Hami was distributed between 0.17 and 0.33. After 2010, the change in the rate of the annual mean AOD showed an overall downward trend in Hami and Ruoqiang, indicating that the atmospheric environmental changes in both areas were more favorable for the operation of CSP plants. In the project areas of Hami, Turpan, and Ruoqiang, more than 90% of the AOD values were mainly in ranges 0.10–0.30, 0.10–0.35, and 0.05–0.30, respectively. As expected, the AOD values in spring and summer were significantly higher than those in autumn and winter in the three study areas. In spring, the dust contents (i.e., ratios of DAOD to AOD) were the highest, accounting for 64% (Hami), 67% (Turpan), and 69% (Ruoqiang) of the total aerosol contents. In all three areas, the proportions of dust in aerosols in spring have shown an increasing trend since 2000, suggesting that the negative impact of the dust on the power generation efficiency in these areas has gradually been increasing. Therefore, it is recommended that the CSP plants in Hami, Turpan, and Ruoqiang develop a strategy for cleaning heliostats, especially in spring, to reduce the impact of dust adhesion on the efficiency of the CSP plants.

The exponential growth of population and industries has brought about an increase in energy consumption, causing severe climatic and environmental problems. Therefore, the move towards green renewable energy is being ever more intensified. This study aims at estimating the rooftop solar power production for Tehran, the capital city of Iran, using a Geospatial Information System (GIS) to assess the big data of city building parcels. Tehran is faced with severe air pollution due to its excessive fossil fuel usage, and its electricity demand is increasing. As a result, this paper attempts to provide the quantified solar power potential of city roof tops for policymakers and authorities in order to facilitate decision-making in relation to integrating renewable energies into the power production infrastructure. The results shows that approximately 3000 GWh (more than 14% of the total electric energy consumption) of solar power can be produced by the rooftop PV installations in Tehran. The potential nominal power of rooftop PV installations is estimated to be more than 2000 MW, which is four times the current installed PV capacity of the whole country. The findings of the study suggest that there is great potential hidden on the rooftops of the city, which can be utilized to assist the power systems of the city in the longer run for a more sustainable future.

The use of wind power has grown strongly in recent years and is expected to continue to increase in the coming decades. Solar power is also expected to increase significantly. In a power system, a continuous balance is maintained between total production and demand. This balancing is currently mainly managed with conventional power plants, but with larger amounts of wind and solar power, other sources will also be needed. Interesting possibilities include continuous control of wind and solar power, battery storage, electric vehicles, hydrogen production, and other demand resources with flexibility potential. The aim of this article is to describe and compare the different challenges and future possibilities in six systems concerning how to keep a continuous balance in the future with significantly larger amounts of variable renewable power production. A realistic understanding of how these systems plan to handle continuous balancing is central to effectively develop a carbon-dioxide-free electricity system of the future. The systems included in the overview are the Nordic synchronous area, the island of Ireland, the Iberian Peninsula, Texas (ERCOT), the central European system, and Great Britain.

The energy demand generated by fossil fuels is increasing day by day, and it has drastically increased after the COVID-19 pandemic as industries and household utilities rejuvenate. Renewable sources are thus becoming more essential as easily available, alternative methods of low-cost energy generation. Among these renewables, solar energy, i.e., solar power, is a promising energy source and can be used for solar-based H2 evolution because H2 technology is a leading source of eco-friendly electricity generation, and most of the worldwide efforts to develop this method involve heterogeneous catalysis for H2 evolution via water splitting and its storage, i.e., using a fuel cell. In the current scenario, there is a need to develop a stable, recyclable, and reusable heterogeneous catalyst system, which is a great challenge. In the current study, we have focused on novel ferrite magnetic nanomaterials for recyclable and reusable robust photocatalysis. Moreover, discussions of the factors contributing to the photocatalytic hydrogen evolution, low-cost synthesis techniques, and prospects for making them ideal photocatalysts are uncommon in the literature. The study will impart possible approaches for the design and development of novel ferrite nanomaterials and their nanocomposites for H2 generation in the forthcoming years.

Water scarcity is currently one of the world’s major issues. Water treatment technologies are a solution to the water crisis problems. In this study, the outcomes of pre-pilot scale testing of solar-powered Ion-exchange resin technology is presented. The tests were carried out using industrial wastewater at the Kungrad Soda Plant located in Kungrad, Uzbekistan. From the plant, about 1500 m3/day of waste water containing total dissolved solids (TDS) is discharged into the environment. In order to reduce the negative impact on the environment and to reuse waste water, the factory proposed conducting water purification tests from unwanted ions (Ca2+, Mg2+, Cl−, SO42−, dissolved CO2). During the test of the technology, water with a TDS of about 2000 ppm was passed through the ion-exchange resin and clean water of around 30 ppm was obtained (purify up to 98–99%). However, according to the requirement of the plant, a certain amount of daily water is purified and added to the total water, and no more than 1600 ppm water is produced and sent for reuse. Experiments have been successfully carried out on a pre-pilot scale using this technology.

Among developing countries in Asia, Indonesia has realized the importance of transitioning from fossil fuels to renewable energy sources such as solar power. Careful consideration must be given to the strategic placement of solar power installations to fully leverage the benefits of solar energy. This study proposes a methodology to optimize the site selection of solar power plants in Indonesia by integrating Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (F-AHP), and Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (F-MARCOS) models. The proposed methodology considers quantitative and qualitative criteria to evaluate potential locations for solar power plants. In the first stage, DEA is used to identify the most efficient locations based on quantitative measures such as solar radiation, land availability, and grid connectivity. In the second stage, qualitative factors such as technological, economic, environmental, and socio-political aspects are evaluated using F-AHP to prioritize the most important criteria for site selection. Finally, F-MARCOS ranks potential locations based on the selected criteria. The methodology was tested using data from Indonesia as a case study. The results show that the proposed hybrid model optimizes Indonesia’s solar power plant site selection. The optimal locations can contribute to a cost-effective long-term renewable energy supply nationwide. The findings from this study are relevant to policymakers, industry stakeholders, and researchers interested in renewable energy development and site selection. However, to promote sustainable solar energy development, governments and local authorities must also enact supportive policies and mechanisms that encourage the adoption and growth of renewable energy technologies in Indonesia.

Buildings are significant contributors to climate change through their energy consumption, particularly in countries with hot and dry climates where cooling services require substantial amounts of energy. Climate change is expected to increase cooling demand, further exacerbating the problem. This study investigates the impact of climate change on cooling demands in different building types in Qatar and evaluates related environmental impacts. Using a high-resolution regional climate model, future climate data were obtained for 2071 and 2100 under the Representative Concentration Pathway (RCP) 4.5. The energy consumption of eight prototype buildings was simulated under baseline and future climates, and the environmental impacts were assessed using Life Cycle Assessment (LCA) for natural gas and solar power as energy sources. The study found that the cooling demand could increase by 13–53% and 19–67% in 2071 and 2100, respectively, with buildings having higher thermal insulation and lower window-to-wall ratio being less affected by climate change. The LCA results showed that replacing natural gas with solar power can reduce cooling energy-associated CO2 emissions by 92%. However, challenges such as human toxicity and metal depletion need to be addressed. The study highlights the importance of considering potential climate change scenarios to develop more resilient buildings and encourages implementing efficient recycling and waste management strategies before implementing PV panels.

In the step-by-step roadmap for limiting and eliminating power sources that use fossil fuel, especially coal-fired power, as well as for setting the Vietnamese government targets for developing renewable energy for replacement, solar farms and rooftop solar power, followed by floating photovoltaic (PV) power, are considered the best candidates for meeting the goals of land space, PV system operation efficiency, and meeting environmental goals. In addition to the rapid development of PV cell technology are the government’s incentives and financial support for PV plants on the water surface area, which is still very large. Floating PV plants will strongly attract investors in the near future. This study could help stakeholders in the market understand the economic–technical aspects from analyzing economic–financial indicators of floating PV plants with a capacity of 47.5 MW connected to the national utility grid 110 kV at Da Mi hydropower reservoir in Binh Thuan province, Vietnam, in May 2019. This is the first floating PV plant on the reservoir in Vietnam, and is the property of a third party.

The use of renewable resources has been on the rise in recent years. The use of renewable resources is also related to state support, either in the form of a fixed price for the purchase of energy or in the form of direct investment aid. The present article analyzed the use of RES in the Czech Republic and the number of installations depending on state support, which represents a significant factor affecting the end user. The article presented the development of the legislative framework that formed the basis of RES support during the years since independence and the country’s entry into the EU. The breaking points of support, when support was increased, and the impact of this support on the number of new installations were indicated. The development of energy production from RES was analyzed on the basis of historical data. A numerical model using a neural network was developed to illustrate the impact of government measures. The time interval for training the network was chosen on the basis of legislative changes, while the possible course was monitored if the change did not occur, or if the support remained the same. The results of the numerical simulation point to the fact that without adequate legislation and state support, there would probably not be an expansion of the use of RES. On the other hand, if the initial high support were to remain, there would probably be a significant distortion of the energy market. Finally, a prediction for the near future was presented, while a stabilization of the situation was assumed.

In this study, a cogeneration cycle in a time-transient state is investigated and optimized. A quasi-equilibrium state is assumed because of the small time increments. Air temperature and solar power are calculated hourly. The cycle is considered in terms of energy, exergy, and economic and environmental analyses. Increasing the net present value (the difference between the present value of the cash inflows and outflows over a period of time) and reducing exergy destruction are selected as two optimization objective functions. The net present value is calculated for the period of 20 years of operation according to the operation parameters. The optimization variables are selected in such a way that one important variable is selected from each system. To optimize the cycle, the particle swarm optimization method is used. The number of particles used in this method is calculated using the trial-and-error method. This cycle is optimized using 13 particles and 42 iterations. After optimization, the energy efficiency increased by 0.5%, the exergy efficiency increased by 0.25%, and the exergy destruction decreased by 1% compared to the cycle with existing parameters.

One of the main challenges that face the reliable use of photovoltaic solar systems in hot arid regions is the prevailing high temperatures during the day. To overcome this issue, Phase Change Materials (PCM) are used for passive cooling providing different options to attain sufficient thermal management solutions for different applications. Passive cooling can be achieved by adjusting a heat sink to the solar PV module. This can be realized by attaching a PCM layer or sensible heat storage to the backside of PV panels. Few studies have reported on simplified modeling and numerical procedures using the apparent heat capacity formulation and volume averaging technique as a robust approach to solving such sophisticated problems with minimal computational efforts, high accuracy, and in a short period of time. However, there is still a need to bridge the large-scale gap between the macroscale within the PCM layer, with a moving melting front, and the length scale of PV modules. Hence, this work focuses on modeling and simulating PCM-Matrix Absorbers (PCM-MA) that consist of fibrous aluminum cellular structure filled with PCM for passive thermal management of PV panels using apparent heat capacity formulation and homogenization based on volume averaging technique. COMSOL Multiphysics FEM software was used for the numerical simulation of the phase change problem by using a Smoothed Heaviside step function to overcome the singularity of PCM challenge that arises with sharp melting temperatures. To validate the proposed model, it has been compared with a benchmark analytical solution for an ice melting problem, i.e., the Stefan problem, in a semi-finite slab, i.e., Neumann’s solution under the same assumptions and boundary conditions. The specific characteristics of phase change and evolution of melting front with time, heat capacity change with the temperature at different times, and with locations along the slab height are presented. As the phase change is modeled to take place over a mushy region, i.e., a narrow temperature interval rather than a sharp melting point, the results show a good coincidence of the heat capacity profile and its peak at different times and locations. The validated model can be used for the optimization of PCM-MA for any specific geographical location and other applications such as the passive cooling of buildings with PCM integrated with the outer envelope. To this end, the results of the simulation in this work are shown to be in agreement with those obtained from the analytical solution.

The Galapagos Islands have been declared a World Heritage site due to their unique biodiversity, which makes them a living museum and a natural laboratory for humankind. However, to fulfill the energy needs of its habitants and foreign visitors, the islands have depended on fossil fuel energies that have produced levels of lead and chemical agents that are affecting the islands’ air quality, flora, and fauna. Therefore, zero-carbon initiatives have been created to protect the islands, wherein solar and wind power plants have been studied as reliable alternatives. In this way, Geographical Information Systems based on Multicriteria Decision Methods constitute a methodology that minimizes the destruction and disturbance of nature in order to assess the best location for the implementation of these alternative energy sources. Therefore, by exploring the geographical information along with the Analytical Hierarchical Processes and the Ordered Weighted Average methods, it was possible to identify the potential for solar power plants of 10 MW on each island; likewise, for wind power plants, it was found that the islands possess implementation potential that has been analyzed in the field, showing that the best location is on Baltra Island, but is not limited to it.

The importance of renewable energies and energy storage system forming a micro-grid and integrating it to the electrical grid is widely spread. A supervisory system plays a crucial role in controlling, managing, and planning the micro-grid. This paper demonstrates the development of a new custom supervisory system based on Internet of Things (IoT), creating an information sharing environment. The proposed supervisory system is based on open-source tools for a micro-grid, composed of a photovoltaic power plant and a storage system, employing smart devices and making non-smart devices compatible with IoT systems. The new supervisory improves the available system by incorporating new features and devices and increasing the data polling rate when necessary. A comparison between the current supervisory system and the proposed one is performed, showing that the new system is more flexible, easily modified, cost-effective, and more fault-resilient.

Imaging concentrators like the parabolic trough solar concentrators have been widely employed for energy production in solar power plants. The conventional imaging solar concentrators form a non-uniform Gaussian distribution on receiving absorbers yielding the highest temperatures. The traditional CSP system normally truncated a peripheral region of heat flux to better use the central part. CPV/T systems using the waste heat recovery method can largely improve the total efficiency. However, for the CPV module, the coolant temperature was usually below 80 °C, which limited the applications of the thermal cycle such as the ORC system. In this article, a novel trough-type free-form secondary solar concentrator (TFSC) for PV/Thermal hybrid application has been proposed. Different from other CPV/T concepts using a combined PV panel and cooling tunnel/tube, the current concept separates the receiver in two parts. The secondary free-form reflector is generated by the geometric construction method, resulting in uniform heat flux in the edge region and high concentration in the central region. Through the ray tracing method, the optical properties have been verified. Sensitivity analysis of the concentrating structure is also conducted. The results provide supports for the design and applications of novel CPV/T systems.

As a developing country, South Africa relies on electricity as the most critical amenity needed for development. KwaZulu-Natal (KZN) is one of the nine provinces in South Africa that faces an energy supply challenge. KZN is also the warmest province among them all due to its location. As one of the warmest provinces, solar power could be utilised to assist in dealing with the energy crisis. This paper focuses on the cost uncertainties attributed to the implementation of solar power which will assist in meeting the demand of energy in the region. The unsustainability of energy has led to a near collapse of the region’s economy. This study also contributes to improving the scientific implementation of solar power in the region to deal with the unsustainability criteria observed. The employment of Cost Benefit Analysis (CBA) revealed solar power as one of the most reliable renewable energies for planned implementation bases. With the development of the Economical, Methodological and Environmental (EME) strategic framework which addresses all social cohesion of solar power, the early turn around has been seen through the use of cost-benefit analysis (CBA) in the region of KZN.

Although the Weather Research and Forecasting model with solar extensions (WRF-Solar) is tailed for solar energy applications, its official version lacks the consideration of the online aerosol-radiation process. To overcome this limitation, we have coupled the aerosol module online with the radiation module, then assimilated the high-resolution aerosol optical depth (AOD) from the Himawari-8 next-generation geostationary satellite using a three-dimensional variational (3DVAR) AOD data assimilation system to optimize the irradiance predictions with the better aerosol–radiation interaction. The results show that data assimilation can significantly eliminate the AOD underestimations and reasonably reproduce the AOD temporal distributions, improving 51.63% for biases and 61.29% for correlation coefficients. Compared with the original WRF-Solar version, coupled online with an advanced aerosol module minifies the bias value of global horizontal irradiance (GHI) up to 44.52%, and AOD data assimilation contributes to a further reduction of 17.43%.

The mass introduction of variable renewable energies, including wind and solar photovoltaic, leads to additional costs caused by the intermittency. Many recent studies have addressed these “integration costs,” and proposed novel metrics that replace the traditional metric known as the levelized cost of electricity (LCOE). However, the policy relevance of those metrics remains unclear. In this study, the author investigates and re-defines the concept of system LCOE, referring to prior studies, and proposes concrete methods to estimate them. Average system LCOE allocates the integration cost to each power source, dividing that by the adjusted power output. Marginal system LCOE revises the concept of system LCOE and value-adjusted LCOE proposed by prior studies, to be clearer and more policy-relevant. These metrics are also applied to Japan’s power sector in 2050, suggesting the necessity of aiming for a “well-balanced energy mix” in future power systems with decarbonised power sources.

People are increasingly using clean energy sources, contributing to environmental protection according to the general trend of the world. In the form of renewable energy, solar energy has contributed to solving current pressing problems, such as environmental pollution and air pollution, improving people’s quality of life. The design of solar power projects in Vietnam is mainly based on meteorological data sources from Meteonorm and NASA. However, the accuracy assessment of two data sources compared to the actual solar power data in Vietnam is not available, so there is no basis to determine better meteorological data source quality to serve the design of rooftop solar power projects. The content of this paper analyzes the simulation results of a typical rooftop solar power station at the Electric Power University, Hanoi city based on meteorological data sources from Meteonorm and NASA. After that, the simulation results will be compared with the actual operating data of a rooftop solar power station near the Electric Power University and other real PV systems in the world. The study results showed that the amount of electricity production using the Meteonorm meteorological data was closer to the actual data than the NASA data source. Therefore, solar power projects in Vietnam should use Meteonorm data source for the design process to determine the best economic and technical efficiency for investors.

Curtailment losses for large-scale hybrid wind–solar photovoltaic (PV) plants with a single grid connection point are often calculated in 1 h time resolution, underestimating the actual curtailment losses due to the flattening of power peaks occurring in shorter time frames. This paper analyses the curtailment losses in hybrid wind–PV plants by utilising different time resolutions of wind and PV production while varying the grid cut-off power, wind/solar PV farm sizes, and shares of wind/PV capacity. Highly resolved 1 s measurements from the operational wind farm and pyranometer are used as an input to specialized wind and PV farm power production models that consider the smoothing effect. The results show that 15 min resolution is preferred over 1 h resolution for large-scale hybrid wind–PV plants if more accurate assessment of curtailment losses is required. Although 1 min resolution additionally increases the estimation accuracy over 15 min resolution, the improvement is not significant for wind and PV plants with capacity above approx. 10 MW/10 MWp. The resolutions shorter than 1 min do not additionally increase the estimation accuracy for large-scale wind and PV plants. More attention is required when estimating curtailment losses in wind/PV plants with capacity below approx. 10 MW/10 MWp, where higher underestimation can be expected if lower time resolutions are used.

In the context of the energy crisis and global climate deterioration, the sustainable development of clean energy will become a new direction for future energy development. Based on the development process of clean energy in China in the past ten years, this paper expounds on China’s clean energy policy and development plan. The development of hydropower, wind power, and solar power in China in recent years is analyzed. On this basis, the Grey Forecasting Model is used to forecast the development and structure of China’s clean energy in the next 10 years, point out the direction and market opportunities of China’s clean energy development in the future, and put forward the implementation methods for the sustainable development of China’s clean energy. It provides a reference for the policy decision-making of China’s clean energy development.

PV (photovoltaic) capacity is steadily increasing every year, and the rate of increase is also increasing. A desert area with a large equipment installation area and abundant solar radiation is a good candidate. PV power plants installed in the desert have advantages in themselves, but when combined with desert aquacultures, additional benefits can be obtained while compensating for the shortcomings of the aquaculture industry. The importance of the aquaculture industry is increasing, with aquaculture products approaching half of the total supply of marine products due to sea environmental pollution and reduced resources. Moreover, in deserts, where marine products are difficult to obtain, aquaculture is a good way to save marine products. However, one of the many problems that complicate the introduction of aquaculture in the desert is that it is difficult to supply and demand electricity because the site is not near a viable electric grid. However, combination with PV can solve this problem. This paper investigates the solar power and aquaculture industry in the desert and explains the limitations and challenges of the solar power and aquaculture industry in the desert. Based on this, we hope to increase interest in the solar power and aquaculture industry in the desert and help with future research.

The University of Alabama at Birmingham (UAB) was one of 16 collegiate teams from around the world that participated in the U.S. Department of Energy Solar Decathlon 2017 competition. An interdisciplinary team of students from across the university was engaged in a 2-year long process to design and build a house that is powered completely by solar power. The house was equipped to run all the typical appliances of an average modern house at similar levels on a conventional power grid. The net-zero house was built and tested on the UAB campus. Considering Birmingham’s weather, a safe room was built to ensure the safety of occupants during events of extreme weather, such as a tornado. A ductless HVAC unit consisting of an inverter-type 3-speed outdoor unit supplied refrigerant to four high-wall indoor units providing the primary source of space conditioning. To achieve a model of efficiency and cost effectiveness, the house incorporated a heavily insulated envelope and precise glazing protection. The roof, floor framing and walls had R-30 batt and foam insulation. With a design informed by southern vernacular language, the building is oriented to maximize solar access and to use roof planes for shading the majority of the year. Peak power generation of the panels was recorded at 9.6 kW. The home has a centralized energy management system that can provide access to energy consumption data and allow control of lighting, appliances, and plug loads remotely. Energy modeling showed that the annual electricity consumption for heating and cooling with variation in wall types were 8470 to 11,661 kWh. For the month of October, it was calculated varying from 683 to 763 kWh, with varying air changes per hour from 0 to 1.5.

The receiver serves as a critical component in tower-type concentrated solar power plants. Responsible for light-heat conversion, the efficiency of the receiver significantly affects the overall performance of the power plant. In the current study, the thermal performance of external receivers was investigated. An experiment was set up similarly using the solar simulator to experimentally investigate the heat losses of a billboard receiver. A billboard-type external receiver was designed, fabricated, and experimented with. A solar simulator having seven xenon lamps characteristics similar to the sunlight spectrum was used to obtain heat flux at the surface of the receiver. Convection losses in the head-on wind direction were evaluated, along with the radiation losses. The thermal efficiency of the billboard receiver calculated experimentally was around 83.9%. Numerical simulations were also carried out to compare the results against the experimental data. A variation of ±5% observed between both results validate the model proposed in the current study.

Azerbaijan has a well-developed hydrocarbon industry backed with abundant domestic resources. Oil and gas have played a crucial role in the economic revival of the country since independence was regained back in 1991. The legal foundation of the transition to carbon-zero energy generation was laid in the 1990s with a number of acts mentioning the importance of the shift. The government has an ambitious plan to improve the situation, though an action plan with targeted renewables share in production and consumption is still to be prepared. This study, based on systematic review methodology for qualitative research, analyzes the potential of renewables in Azerbaijan with a focus on solar and wind power, discusses the deficiencies hindering the development of the renewables industry, and develops recommendations on applicable actions to improve the situation in this regard. It also includes legislative acts of the Republic concerning renewable energy. The main objective of the study is to explore renewable energy potentials and assess the readiness of the country to make a shift towards green energy. The findings of the article demonstrate enough potential to increase the share of renewables. The potential, however, is obscured with a relatively less solid legal framework and a lack of expertise in the industry.

This paper discusses the resource, technical, and economic potential of using solar photovoltaic (PV) systems in Belarus and Tatarstan. The considered countries are characterized by poor actinometric conditions and relatively low tariffs for traditional energy resources. At the same time, Belarus is experienced with solar power due to different incentive mechanisms that have been used over the past decade. Moreover, the cost of building solar power plants in Belarus in 2013–2017 was lower than the world average. The cost of electricity production is analyzed depending on the geographical location of sites and the type of owners of solar power plants (i.e., households, businesses and industrial enterprises, electricity producers). Using the data on the cost of photovoltaic systems as presented by IRENA and considering actinometric data for Belarus and Tatarstan, a long-term forecast of PV electricity cost is made. The moments of the break-even points and payback periods are defined for Belarus and Tatarstan.

Renewable energy (RE) power plants are deployed globally because the renewable energy sources (RESs) are sustainable, clean, and environmentally friendly. However, the demand for power increases on a daily basis due to population growth, technology, marketing, and the number of installed industries. This challenge has raised a critical issue of how to intelligently match the power generation with the consumption for efficient energy management. To handle this issue, we propose a novel architecture called ‘AB-Net’: a one-step forecast of RE generation for short-term horizons by incorporating an autoencoder (AE) with bidirectional long short-term memory (BiLSTM). Firstly, the data acquisition step is applied, where the data are acquired from various RESs such as wind and solar. The second step performs deep preprocessing of the acquired data via several de-noising and cleansing filters to clean the data and normalize them prior to actual processing. Thirdly, an AE is employed to extract the discriminative features from the cleaned data sequence through its encoder part. BiLSTM is used to learn these features to provide a final forecast of power generation. The proposed AB-Net was evaluated using two publicly available benchmark datasets where the proposed method obtains state-of-the-art results in terms of the error metrics.

Hybrid microgrids constitute a promising solution for filling the electricity access gap that currently exists in rural areas; however, there is still relatively little information about their reliability and costs based on measured data in real working conditions. This article analyzes data obtained from the operation of a 9 kW hybrid microgrid in the fishermen’s cove of Laguna Grande, Paracas, in the Ica region of Perú, which has been running for 5 years. This microgrid has been equipped with data acquisition systems that measure and register wind speed, solar radiation, temperatures, and all the relevant electric parameters. Battery dynamics considerations are used to determine the depth of discharge in a real-time operative situation. The collected data are used to optimize the design using the specialized software HOMER, incorporating state-of-the-art technology and costs as a possible system upgrade. This work aims to contribute to better understanding the behavior of hybrid rural microgrids using data collected under field conditions, analyzing their reliability, costs, and corresponding sensitivity to battery size as well as solar and wind installed power, as a complement to a majority of studies based on simulations.

Photovoltaic (PV) technology is becoming more popular due to climate change because it allows for replacing fossil-fuel power generation to reduce greenhouse gas emissions. Consequently, many countries have been attempting to generate electricity through PV power plants over the last decade. Monitoring PV power plants through satellite imagery, machine learning models, and cloud-based computing systems that may ensure rapid and precise locating with current status on a regional basis are crucial for environmental impact assessment and policy formulation. The effect of fusion of the spectral, textural with different neighbor sizes, and topographic features that may improve machine learning accuracy has not been evaluated yet in PV power plants’ mapping. This study mapped PV power plants using a random forest (RF) model on the Google Earth Engine (GEE) platform. We combined textural features calculated from the Grey Level Co-occurrence Matrix (GLCM), reflectance, thermal spectral features, and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) from Landsat-8 imagery and elevation, slope, and aspect from Shuttle Radar Topography Mission (SRTM) as input variables. We found that the textural features from GLCM prominent enhance the accuracy of the random forest model in identifying PV power plants where a neighbor size of 30 pixels showed the best model performance. The addition of texture features can improve model accuracy from a Kappa statistic of 0.904 ± 0.05 to 0.938 ± 0.04 and overall accuracy of 97.45 ± 0.14% to 98.32 ± 0.11%. The topographic and thermal features contribute a slight improvement in modeling. This study extends the knowledge of the effect of various variables in identifying PV power plants from remote sensing data. The texture characteristics of PV power plants at different spatial resolutions deserve attention. The findings of our study have great significance for collecting the geographic information of PV power plants and evaluating their environmental impact.

This paper discusses the conversion of solar irradiance energy into electricity. Double-sided (bifacial) panels are gaining increasing popularity in commercial applications due to the increased energy yield with a constant occupied mounting surface. However, the value of the additional energy yield produced by the back of the panel depends on several important factors. This paper presents the influence of working conditions on electricity generation in bifacial modules. This paper also investigates the influence of weather conditions, the module inclination angle, and the substrate beneath the panel surface on electricity generation. Fill factor and efficiency were calculated for each case included in the study scope. Based on the current voltage, power characteristics, and calculations, the module operation for different conditions was compared. It was observed that the optimal inclination angle to the surface is higher for the bifacial modules compared to the unilateral modules. The type of surface under the module has also been indicated to impact the amount of electricity generated. The additional energy yield associated with the panels’ rear side accounts for 2% to more than 35% of the total power generated by a photovoltaic (PV) module. The unit cost of electricity generation in the analyzed cases was also determined.

The aim of this paper is to review and compare present European and Indian grid code requirements imposed to hybrid power plants (HPPs) combining wind, solar and storage technologies. Since there are no grid codes specifically for HPPs, the paper will review grid codes for the power plant based on individual renewable technology in the HPP. European grid codes specifies ranges for parameters inside which each national transmission system operators (TSO) has to specify the set of national parameters (Danish specifications in this paper). The comparisons are performed with respect to fault-ride-through capability, frequency and voltage operation ranges, active power control/frequency support as well as reactive power control/voltage support.

This paper presents a computational tool for estimating energy generated by low-power photovoltaic systems based on the specific conditions of the study region since the characteristic energy equation can be obtained considering the main climatological factors affecting these systems in terms of the symmetry or skewness of the random distribution of the generated energy. Furthermore, this paper is aimed at determining any correlation that exists between meteorological variables with respect to the energy generated by 5-kW solar systems in the specific climatic conditions of the Republic of Cuba. The paper also presents the results of the influence of each climate factor on the distribution symmetry of the generated energy of the solar system. Studying symmetry in statistical models is important because they allow us to establish the degree of symmetry (or skewness), which is the probability distribution of a random variable, without having to make a graphical representation of it. Statistical skewness reports the degree to which observations are distributed evenly and proportionally above and below the center (highest) point of the distribution. In the case when the mentioned distribution is balanced, it is called symmetric.

Probabilistic solar power forecasting has been critical in Southern Africa because of major shortages of power due to climatic changes and other factors over the past decade. This paper discusses Gaussian process regression (GPR) coupled with core vector regression for short-term hourly global horizontal irradiance (GHI) forecasting. GPR is a powerful Bayesian non-parametric regression method that works well for small data sets and quantifies the uncertainty in the predictions. The choice of a kernel that characterises the covariance function is a crucial issue in Gaussian process regression. In this study, we adopt the minimum enclosing ball (MEB) technique. The MEB improves the forecasting power of GPR because the smaller the ball is, the shorter the training time, hence performance is robust. Forecasting of real-time data was done on two South African radiometric stations, Stellenbosch University (SUN) in a coastal area of the Western Cape Province, and the University of Venda (UNV) station in the Limpopo Province. Variables were selected using the least absolute shrinkage and selection operator via hierarchical interactions. The Bayesian approach using informative priors was used for parameter estimation. Based on the root mean square error, mean absolute error and percentage bias the results showed that the GPR model gives the most accurate predictions compared to those from gradient boosting and support vector regression models, making this study a useful tool for decision-makers and system operators in power utility companies. The main contribution of this paper is in the use of a GPR model coupled with the core vector methodology which is used in forecasting GHI using South African data. This is the first application of GPR coupled with core vector regression in which the minimum enclosing ball is applied on GHI data, to the best of our knowledge.

The integration of distributed power plants that rely on renewable energy sources (RESs) is a major challenge for system operators (SOs) due to the variable nature of the input energy (e.g., wind and solar irradiation) to these power sources. A key solution to such a challenge is to coordinate and combine the power generation of these sources such that their behavior is closer to a conventional and dispatchable power station, taking into account the limitations imposed by the battery storage system (BESS), so it is seen as a hybrid power plant (HPP) from the SOs’ viewpoint. This paper develops a model of HPP that encompasses two generation technologies, wind and photovoltaic farms, which are assisted by a BESS. The paper proposes a comprehensive control method that can smooth the HPP output with minimized energy rejection whilst enabling the HPP to provide synthetic inertia and primary frequency response, which are grid-code compliant. The proposed control method is validated through various scenarios, which are implemented on a detailed electromechanical test system modeled in MATLAB/Simulink. The results show and quantify the achieved improvement on stabilizing the HPP capacity factor under variable wind speed. The HPP also enhances the system response to frequency events.

A three-phase multi-level multi-input power converter topology is presented for grid-connected applications. It encompasses a three-phase transformer that is operated on the primary side in an open-end winding configuration. Thus, the primary winding is supplied on one side by a three-phase N-level neutral point clamped inverter and, on the other side, by an auxiliary two-level inverter. A key feature of the proposed approach is that the N-level inverter is able to perform independent management of N − 1 input power sources, thus avoiding the need for additional dc/dc power converters in hybrid multi-source systems. Moreover, it can manage an energy storage system connected to the dc-bus of the two-level inverter. The N-level inverter operates at a low switching frequency and can be equipped with very low on-state voltage drop Insulated-Gate Bipolar Transistor (IGBT) devices, while the auxiliary inverter is instead operated at low voltage according to a conventional high-frequency two-level Pulse Width Modulation (PWM) technique and can be equipped with very low on-state resistance Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) devices. Simulations and experimental results confirm the effectiveness of the proposed approach and its good performance in terms of grid current harmonic content and overall efficiency.

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

Primary frequency reserves in Northern Europe have traditionally been provided with hydro plants and fossil fuel-burning spinning reserves. Recently, smart distributed energy resources have been equipped with functionality needed to participate on frequency reserves. Key categories of such resources include photovoltaic systems, batteries, and smart loads. Most of these resources are small and cannot provide the minimum controllable power required to participate on frequency reserves. Thus, virtual power plants have been used to aggregate the resources and trade them on the frequency reserves markets. The information technology aspects of virtual power plants are proprietary and many of the details have not been made public. The first contribution of this article is to propose a generic data model and application programming interface for a virtual power plant with the above-mentioned capabilities. The second contribution is to use the application programming interface to cope with the unpredictability of the frequency reserve capacity that the photovoltaic systems and other distributed energy resources are able to provide to the frequency reserves markets in the upcoming bidding period. The contributions are demonstrated with an operational virtual power plant installation at a Northern European shopping center, aggregating photovoltaic Primary Frequency Reserves resources.

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modification factor (CMF), for all-sky and clear sky conditions, for more generalized results. The forecast accuracy was evaluated against the real COT and CMF images under different cloud movement patterns, and the correlation was found to range from 0.9 to 0.5 for 15 min and 3 h ahead, respectively. The CMV forecast variability revealed an overall DSSI uncertainty in the range 18–34% under consecutive alternations of cloud presence, highlighting the ability of the proposed system to follow the cloud movement in opposition to the baseline persistent forecasting, which considers the effects of topocentric sun path on DSSI but keeps the clouds in “fixed” positions, and which presented an overall uncertainty of 30–43%. The proposed system aims to support the distributed solar plant energy production management, as well as electricity handling entities and smart grid operations.

The Internet has been going through significant transformations and changing the world around us. We can also see the Internet to be used in many areas, for innumerable purposes, and, currently, it is even used by objects. This evolution leads to the Internet of Things (IoT) paradigm. This new concept can be defined as a system composed of storage resources, sensor devices, controllers, applications, and network infrastructure, in order to provide specific services to its users. Since IoT comprises heterogeneous components, the creation of these systems, the communication, and maintenance of their components became a complex task. In this paper, we present a dependability model to evaluate an IoT system. Amid different systems, we chose to assess availability in a smart building. The proposed models allow us to calculate estimations of other measures besides steady-state availability, such as reliability. Thus, it was possible to notice that there was no considerable gain of availability in the system when applying grid-tie solar power or off-grid solar power. The grid-tie solar power system is cheaper than the off-grid solar power system, even though it produces more energy. However, in our research, we were able to observe that the off-grid solar power system recovers the applied financial investment in smaller interval of time.

Combined heat and power systems (CHP) produce heat and electricity simultaneously. Their resulting high efficiency makes them more attractive from the energy managers’ perspective than other conventional thermal systems. Although heat is a by-product of the electricity generation process, system operators usually operate CHP systems to satisfy heat demand. Electricity generation from CHP is thus driven by the heat demand, which follows the variability of seasonal temperature, and thus is not always correlated with the fluctuation of electricity demand. Consequently, from the perspective of the electricity grid operator, CHP systems can be seen as a non-controllable energy source similar to other renewable energy sources such as solar, wind or hydro. In this study, we investigate how ‘non-controllable’ electricity generation from CHP systems combines with ‘non-controllable’ electricity generation from solar photovoltaic panels (PV) and run-of-the river (RoR) hydropower at a district level. Only these three energy sources are considered within a 100% renewable mix scenario. Energy mixes with different shares of CHP, solar and RoR are evaluated regarding their contribution to total energy supply and their capacity to reduce generation variability. This analysis is carried out over an ensemble of seventeen catchments in North Eastern Italy located along a climate transect ranging from high elevation and snow dominated head-water catchments to rain-fed and wet basins at lower elevations. Results show that at a district scale, integration of CHP systems with solar photovoltaic and RoR hydropower leads to higher demand satisfaction and lower variability of the electricity balance. Results also show that including CHP in the energy mix modifies the optimal relative share between solar and RoR power generation. Results are consistent across the climate transect. For some districts, using the electricity from CHP might also be a better solution than building energy storage for solar PV.