Solar Photovoltaic Generation Research Articles

Comparative Numerical Energy and Performance Analysis of Solar Photovoltaic and Solar Thermal Power Generation

This study analyses fixed and tracking solar photovoltaics and, solar thermal power. Performance in different locations was analysed for each configuration and technology using simulations based on a Typical Meteorological Year data. The study analysed a 25MWp solar photovoltaic and solar thermal power plants in each of the eleven selected locations in Zimbabwe. The performance of the solar photovoltaic and solar thermal power plants under different meteorological variables were assessed for all the selected locations. It was shown that different configurations together with different technologies have different conversion efficiencies. A high solar thermal conversion efficiency was found to be 18.718% in Gweru while it was 15.502% for solar photovoltaics in Mutare. The study also showed that highest insolation and clearness index values were found in Gweru. The average energy generated by the fixed photovoltaic collectors, tracking photovoltaic collectors and the Concentrating Solar Power plant were respectively 47.38GWh, 68.18GWh and 192.86GWh. There was a maximum percentage difference in the LCoE generated of 32.03% between the fixed PV collectors and the Concentrating Solar Power plant and a difference of 4.74% was realised between the tracking photovoltaics and Concentration Solar Power.

Enhancing Probabilistic Solar PV Forecasting: Integrating the NB-DST Method with Deterministic Models

Accurate quantification of uncertainty in solar photovoltaic (PV) generation forecasts is imperative for the efficient and reliable operation of the power grid. In this paper, a data-driven non-parametric probabilistic method based on the Naïve Bayes (NB) classification algorithm and Dempster–Shafer theory (DST) of evidence is proposed for day-ahead probabilistic PV power forecasting. This NB-DST method extends traditional deterministic solar PV forecasting methods by quantifying the uncertainty of their forecasts by estimating the cumulative distribution functions (CDFs) of their forecast errors and forecast variables. The statistical performance of this method is compared with the analog ensemble method and the persistence ensemble method under three different weather conditions using real-world data. The study results reveal that the proposed NB-DST method coupled with an artificial neural network model outperforms the other methods in that its estimated CDFs have lower spread, higher reliability, and sharper probabilistic forecasts with better accuracy.

A novel predictive optimal control strategy for renewable penetrated interconnected power system

AbstractThe stable and efficient regulation of voltage and frequency is a critical issue associated with the functioning of modern power system network having renewable integration. This manuscript focuses on the concurrent regulation of voltage and frequency in a power network comprising multiple generating sources. Herein, along with conventional thermal and diesel plants, renewable generations from different sources have been considered such as wind and solar photovoltaic (PV) resources. Further, in a fresh attempt, the authors have modeled wind and solar PV generation using two different stochastic modeling methods concerning combined voltage and frequency loops in the regulated power system model. Furthermore, the novel Leader Harris Hawks Optimized Model Predictive Controller (MPC‐LHHO) has been applied to concurrently minimize the voltage and frequency deviations in the given power network. To address the intermittent nature of load and renewable generations, various auxiliary frequency controllers, such as the unified power flow controller (UPFC) and electric vehicle (EV) integration with the grid, have been implemented. The robustness of the proposed control method has been successfully validated through diverse scenarios of random loadings.

Optimal sizing of standalone hybrid renewable energy system based on reliability indicator: A case study

The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction.Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels.

Analysis of the Problems and Reasons for the Development of Suzhou Hydrogen Energy Industry and Industrial Policies

In recent years, although there have been some policy plans related to hydrogen energy development in China, a complete fiscal policy system has not yet been established. In policies related to hydrogen energy, we cannot keep up with the times. Only some policies are specifically targeted at solar photovoltaic and wind power generation, and some policies are even temporary. These fiscal policies are severely lagging and lack uniformity, which not only weakens the overall effect of the money supply, but also to some extent hinders the healthy and rapid development of the hydrogen energy industry. In addition, the national development plan, including national and local plans, cannot be well connected, resulting in some policies not forming strong support for hydrogen energy. In these industrial plans, production capacity targets account for the majority, but there is a lack of refinement of utilization targets and little mention of the development balance of the industrial chain.

Farm electricity system simulator (FESS): A platform for simulating electricity utilisation on dairy farms

The objective of this paper was to define, validate and demonstrate a model capable of accurately simulating dairy farm electricity consumption across varying herd and parlour sizes, to facilitate research investigating renewable energy systems (RES) and demand side management (DSM). The Farm Electricity System Simulator (FESS) was developed using grey-box modelling techniques utilizing empirical data for parameter tuning. Empirical data were gathered from nine spring calving, pasture based dairy farms located in the Republic of Ireland. A k-means clustering analysis was conducted, separating the farms into three, near homogenous groups, from which representative farms were selected. FESS was trained using 12 months of data from three representative farms using the repeat hold out method for data partitioning with 75 % of data used for training and 25 % used for validation. An optimisation algorithm was used to minimize the error during model training. Through cross-validation, FESS achieved a root mean squared error (RMSE) of 7.65 kWh, mean absolute percentage error (MAPE) of 7.10 %, mean percentage error (MPE) of −0.86 % and a relative prediction error (RPE) of 7.56 % for total daily electricity consumption. Across the three farms, the simulated outputs of FESS achieved an average R2 value of 0.72, demonstrating good agreement with observed data. FESS’s utility was demonstrated by analysing the effects of different electricity pricing structures and on-site solar photovoltaic electricity generation on total farm energy costs. We concluded that FESS simulated on-farm electricity consumption with sufficient accuracy for the intended application. FESS accurately simulated dairy farm electricity consumption across three dairy farms of different herd and parlour sizes while evaluating the effects of demand side management and renewable generation on farm electricity consumption and costs.

Design and assessment of energy infrastructure in new decarbonized urban districts: A Spanish case study

New urban districts have great potential to reduce greenhouse gas emissions by electrifying energy demand in various sectors, such as transportation and buildings. This paper proposes a novel approach that combines different energy models to plan the infrastructure for supplying the energy demand of new, decarbonized, and highly electrified urban districts on a real-world scale. First, a model for energy management in buildings is used to plan and operate equipment for heating, cooling, and solar photovoltaic distributed generation. Second, a model that plans electric vehicle charging infrastructure determines the number and types of charging stations. Then, a large-scale distribution network planning model designs a cost-efficient electricity distribution grid to supply the district. A case study is presented for a new urban district in Madrid, Spain, to demonstrate how this approach can be applied to energy infrastructure planning in a real-world context. This case study presents different scenarios of energy efficiency performances in buildings and penetration levels of solar installations, electric vehicles, and heating and cooling systems. The results show that even in a high electrification scenario, improving energy efficiency in buildings through a district heating network based on heat pumps can lead to a lower peak electricity demand. This peak load reduction allows for integrating more electric vehicles, avoiding further investments in the electricity distribution network. In addition, the results confirm that a highly electrified scenario, which combines energy-efficient buildings with high integration of solar and electric vehicles, significantly reduces non-renewable energy consumption and greenhouse gas emissions.

Enhancement in efficiency of solar photovoltaic power generation with the assistance of PVC/TiO2 reflective composite applied for double-sided power generation

Solar photovoltaic power generation is a productive and environmentally friendly technique. The results of objective evaluations show that double-sided power generation is more efficient than single-sided power generation, with a possible increase of 5 %–30 %. Hence, it is necessary to identify a composite that reflects the exact sunlight waveband (300–1100 nm) onto the backside of photovoltaic panels used for double-sided power generation. Herein, two different crystalline types of titanium dioxide (TiO2), rutile (R–TiO2) and anatase (A-TiO2), were combined with plasticized polyvinyl chloride (PVC) to create composites. A comparative analysis was conducted with other commonly utilized reflective fillers, such as barium sulfate (BaSO4) and magnesium hydroxide (Mg(OH)2). As a result, the R–TiO2 was identified as the most appropriate substance to be blended with PVC for the production of PVC/R–TiO2 composite. As the quantity of R–TiO2 increased from 0 to 100 phr, reflectance in the distinctive waveband of 300–1100 nm of the PVC/R–TiO2 rose from 5.30 % to 82.97 %. Moreover, an improvement in the thermal conductivity of PVC/R–TiO2 sample was observed, which increased by 59.2 % from 0.179 W m−1 K−1 to 0.285 W m−1 K−1, indicating a positive effect on heat transfer. The rheological measurement showed that the amount of reflective filler was inversely proportional to the compatibility of the reflective filler with plasticized PVC in the overall composite system, whereas the thermogravimetric analysis indicated that the thermal stability of the composites improved as the number of reflective fillers increased. Substantially, PVC/R–TiO2 composites exhibit better performance in enhancing the power generation efficiency of solar photovoltaic panels.

Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems

Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems

Geospatial optimisation of wind and solar energy generation: A framework for optimised resource allocation

Wind and solar power generation profiles exhibit a high degree of power output variability and uncertainty compared to conventional fossil fuel generation plants. However, it is generally accepted that this variability and uncertainty can be mitigated by geographic siting and dispersion of the renewable energy plants, particularly with the view to leverage aggregation effects. In this context, this paper proposes a framework for the geospatial optimisation of wind and solar PV generation for scenarios with a high penetration of wind and solar renewable energy. The proposed optimisation framework represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, the wind and solar resources are categorised in terms of the statistical properties of the associated spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time of Use periods. It is thereby recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time of Use periods are more important than others. The proposed framework is implemented for a number of baseline case studies and optimisation case studies, using wind and solar resource data sets covering South Africa. Overall, it is concluded that this approach can leverage geospatial site optimisation to substantially reduce the risks associated with high residual load events. Furthermore, the proposed framework is highly flexible in that the formulation of the minimum and maximum allocation constraints allows application for real-world scenarios whereby stringent capacity allocation constraints can be applied.

A comparative study on the combination of life cycle assessment and ecological footprints: solar photovoltaic power generation vs. coal power generation in Ningxia

In China, where energy activities, predominantly driven by fossil fuel combustion, account for nearly 90% of the country’s greenhouse gas (GHG) emissions and coal power alone contributes over 40%, the shift towards carbon neutrality is a critical national ambition. This study conducts a comprehensive comparison of the environmental impacts of solar photovoltaic power generation (SPPG) and coal power, employing both life cycle assessment and ecological footprint analysis. We meticulously analyze the complete lifecycle of SPPG, pinpointing key stages of GHG emissions, and offer nuanced, localized policy recommendations. Our findings indicate that a 1 kWp SPPG module emits 1,601.18 kg of GHGs over its lifespan, equating to 1.35 kg/kW·h per unit of electricity produced—substantially lower than the 4.81 kg/kW·h emitted by coal power, thus highlighting the latter’s heightened environmental detriment. Additionally, this study assesses the ecological footprint of both energy sources in Ningxia. SPPG emerges with an ecological surplus, showcasing a per capita footprint of 0.0342 hm2, compatible with Ningxia’s ecological capacity. In stark contrast, coal power exhibits a sustained ecological deficit over the past 5 years, with a growing per capita footprint of 0.6529 hm2, underscoring its unsustainability. This research provides a detailed comparative analysis of the environmental impacts of SPPG and coal power in Ningxia, offering valuable insights for energy policymakers and industry stakeholders. It underscores the urgent need for industrial restructuring towards more sustainable and renewable energy sources, aligning with China’s broader objectives of environmental preservation and achieving carbon neutrality.

Exploring the Feasibility of Solar Photovoltaic Generation for Residential Applications in Hubballi, Karnataka: A Simulation and Analysis Approach

This research paper explores the potential of solar photovoltaic (PV) generation in Hubballi, Karnataka, India, focusing on a small residential home premises. The objective is to assess the solar PV potential at this location using PVsyst simulation software and analyze the results. The paper begins with an introduction to solar PV technology and its significance in meeting energy demands. A literature review examines previous studies on solar PV potential in Karnataka, highlighting the factors influencing solar PV generation efficiency. The methodology section describes the study area, data collection methods, and the PVsyst simulation setup. The data analysis section presents the collected solar irradiation and temperature data and discusses the simulation results for solar PV generation at the selected premises. The performance evaluation of the PV system is based on the simulation outcomes. The discussion section compares the simulated results with expected solar PV potential, identifies factors affecting solar PV generation efficiency in Hubballi, and discusses the implications of the findings for residential installations. The conclusion summarizes the research findings, emphasizes the solar PV potential in Hubballi, and provides recommendations for future research and practical applications. This research contributes to the understanding of solar PV generation at the specific location of Hubballi and offers valuable insights for promoting renewable energy in residential settings

Is there a stronger willingness to pay for photovoltaic power generation with high education in China?

Adoption of clean electric energy depends not only on administrative regulations, but also on public support, in particular, the public is willing to pay for environmental improvements. However, the increase of solar photovoltaic power generation willingness to pay (WTP) associated with higher education attainment and the identification of their causality has been missing. Present paper used the enactment of the Compulsory Schooling Law as an instrumental variable to solve the causal relationship between education and willingness to pay for photovoltaic power generation. The results are as follows:Heckman two-stage model and instrumental variable both confirmed that higher education has a positive impact on WTP for solar photovoltaic power generation. For each level of public education in the east, the WTP of photovoltaic power generation will increase by 7.540 CNY, 8.343 CNY and 8.343 CNY respectively, the central public will increase by 9.637 CNY, 10.775 CNY and 11.758 CNY, and the western public will increase by 12.723 CNY, 15.740 CNY and 17.993 CNY respectively. The positive influence of education level is smaller among the people who know the ladder price better, but it is bigger among the people who are male, older than 45 years old, healthier, higher income and stronger awareness of safe electricity use. The total socio-economic value of photovoltaic power generation is significantly different in eastern, central and western region China.

A Novel Deep Learning-Based Data Analysis Model for Solar Photovoltaic Power Generation and Electrical Consumption Forecasting in the Smart Power Grid

With the installation of solar panels around the world and the permanent fluctuation of climatic factors, it is, therefore, important to provide the necessary energy in the electrical network in order to satisfy the electrical demand at all times for smart grid applications. This study first presents a comprehensive and comparative review of existing deep learning methods used for smart grid applications such as solar photovoltaic (PV) generation forecasting and power consumption forecasting. In this work, electrical consumption forecasting is long term and will consider smart meter data and socioeconomic and demographic data. Photovoltaic power generation forecasting is short term by considering climatic data such as solar irradiance, temperature, and humidity. Moreover, we have proposed a novel hybrid deep learning method based on multilayer perceptron (MLP), long short-term memory (LSTM), and genetic algorithm (GA). We then simulated all the deep learning methods on a climate and electricity consumption dataset for the city of Douala. Electrical consumption data are collected from smart meters installed at consumers in Douala. Climate data are collected at the climate management center in the city of Douala. The results obtained show the outperformance of the proposed optimized method based on deep learning in the both electrical consumption and PV power generation forecasting and its superiority compared to basic methods of deep learning such as support vector machine (SVM), MLP, recurrent neural network (RNN), and random forest algorithm (RFA).

Method for Spatiotemporal Solar Power Profile Estimation for a Proposed U.S.–Caribbean–South America Super Grid under Hurricanes

Solar photovoltaic (PV) generation technology stands out as a scalable and cost-effective solution to enable the transition toward decarbonization. However, PV solar output, beyond the daily solar irradiance variability and unavailability during nights, is very sensitive to weather events like hurricanes. Hurricanes nucleate massive amounts of clouds around their centers, shading hundreds of kilometers in their path, reducing PV power output. This research proposes a spatiotemporal method, implemented in MATLAB R2023b coding, to estimate the shading effect of hurricanes over a wide distribution of PV solar plants connected to a high-voltage power infrastructure called the U.S.–Caribbean–South America super grid. The complete interconnection of the U.S., the Caribbean, and South America results in the lowest power valley levels, i.e., an overall percentual reduction in PV power output caused by hurricane shading. The simulations assess the impact of hurricanes in 10 synthetic trajectories spanning from Texas to Florida. The Caribbean would also experience lower power valleys with expanded interconnectivity schemes. The U.S.–Caribbean–South America super grid reduces Caribbean variability from 37.8% to 8.9% in the case study. The proposed spatiotemporal method for PV power profile estimation is a valuable tool for future solar power generation expansion, transmission planning, and system design considering the impact of hurricanes.

A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model

Prediction of solar irradiance is crucial for minimizing energy costs and ensuring high power quality in electrical power grids that incorporate distributed solar photovoltaic generation. Traditional methods have focused mainly on time series prediction of irradiance or studies of the relationship between irradiance and environmental factors at a given moment. This paper presents a novel global horizontal irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model. In the proposed method, cloud fraction, cloud albedo and aerosol optical thickness are selected as critical characteristic factors in the sunlight-atmosphere interaction. Long short-term memory (LSTM) is utilized to extract temporal features of the critical characteristic factors in various physical processes of atmospheric optics and predict their values. Subsequently, back propagation (BP) neural network is constructed to investigate the relationship between these characteristic factors and global horizontal irradiance under a given point in time. The global horizontal irradiance is predicted by combining the predicted values of critical characteristic factors and BP neural network for modelling irradiance decay process. To demonstrate the superior performance of the proposed model, the results obtained by the proposed method are compared with those of the LSTM network model for time series forecasting, in which six error evaluation indicators are used in the comparison. The proposed method for solar irradiance forecasting is verified in different time scales and under various weather conditions and shows better accuracy comparing with time series forecasting methods.

Multiparameter Bifurcation Analysis of Power Systems Integrating Large-Scale Solar Photovoltaic and Wind Farms Power Plants

In this paper, we propose a novel codimension-three-parameter bifurcation analysis of equilibria and limit cycles when integrating Renewable Energy Sources (RESs) power plants with an exponential static load model. The study investigates the effect of solar photovoltaic generation margin, wind power generation margin, and loading factor on the local bifurcation of the modified IEEE nine-bus system. The proposed technique considers the real case of the West System Coordination Council (WSCC), the western states of the USA, by using specific models of RES power plants and static loads. The proposed technique helps to create a set of linearly varying parameters for critical operating points of nonlinear systems. The study explores detailed voltage stability analysis through the examination of bifurcation diagrams. The Hopf, limit-induced, and saddle-node bifurcation branches are identified, defining the parameter space’s stable and unstable operational regions. Additionally, the stability regions surrounding the equilibrium points are diligently explored, clarifying the consequences that various bifurcations may exert on these regions. The study offered in this proposed work aids in determining the best ways to monitor and improve these margins while considering system variables and load design.

Soil bacterial community in a photovoltaic system adopted different survival strategies to cope with small-scale light stress under different vegetation restoration modes.

Solar photovoltaic (PV) power generation is a major carbon reduction technology that is rapidly developing worldwide. However, the impact of PV plant construction on subsurface microecosystems is currently understudied. We conducted a systematic investigation into the effects of small-scale light stress caused by shading of PV panels and sampling depth on the composition, diversity, survival strategy, and key driving factors of soil bacterial communities (SBCs) under two vegetation restoration modes, i.e., Euryops pectinatus (EP) and Loropetalum chinense var. rubrum (LC). The study revealed that light stress had a greater impact on rare species with relative abundances below 0.01% than on high-abundance species, regardless of the vegetation restoration pattern. Additionally, PV shadowing increased SBCs' biomass by 20-30% but had varying negative effects on the numbers of Operational Taxonomic Unit (OTU), Shannon diversity, abundance-based coverage estimator (ACE), and Chao1 richness index. Co-occurrence and correlation network analysis revealed that symbiotic relationships dominated the key SBCs in the LC sample plots, with Chloroflexi and Actinobacteriota being the most ecologically important. In contrast, competitive relationships were significantly increased in the EP sample plots, with Actinobacteriota having the most ecological importance. In the EP sample plot, SBCs were found to be more tightly linked and had more stable ecological networks. This suggests that EP is more conducive to the stability and health of underground ecosystems in vulnerable areas when compared with LC. These findings offer new insights into the effects of small-scale light stress on subsurface microorganisms under different vegetation restoration patterns. Moreover, they may provide a reference for optimizing ecological restoration patterns in fragile areas.

Predictive Energy Management of a Building-Integrated Microgrid: A Case Study

The efficient integration of distributed energy resources (DERs) in buildings is a challenge that can be addressed through the deployment of multienergy microgrids (MGs). In this context, the Interreg SUDOE project IMPROVEMENT was launched at the end of the year 2019 with the aim of developing efficient solutions allowing public buildings with critical loads to be turned into net-zero-energy buildings (nZEBs). The work presented in this paper deals with the development of a predictive energy management system (PEMS) for the management of thermal resources and users’ thermal comfort in public buildings. Optimization-based/optimization-free model predictive control (MPC) algorithms are presented and validated in simulations using data collected in a public building equipped with a multienergy MG. Models of the thermal MG components were developed. The strategy currently used in the building relies on proportional–integral–derivative (PID) and rule-based (RB) controllers. The interconnection between the thermal part and the electrical part of the building-integrated MG is managed by taking advantage of the solar photovoltaic (PV) power generation surplus. The optimization-based MPC EMS has the best performance but is rather computationally expensive. The optimization-free MPC EMS is slightly less efficient but has a significantly reduced computational cost, making it the best solution for in situ implementation.

Fast Frequency Response for Future Low-Carbon Indian Power System: Challenges and Solutions

India has transitioned from being an energy-deficient nation to an energy-surplus nation. As of July 2023, India’s power system has seen a rapid transformation, with renewable energy, including large hydro, accounting for 40% of the total installed capacity at 177 GW. The ongoing energy transition has not only changed the country’s energy mix but also brought new challenges for system operators. The primary challenge is frequency instability due to the reduction of system inertia and governor-based primary frequency response (PFR) availability by large-scale integration of uncertain renewable energy resources (RES) such as wind and solar photovoltaic generators. Furthermore, the absence of frequency control ancillary services market, inadequate penetration of electric vehicles (EVs) and uncertainty of RES generation enhance the complexity of the frequency response services in the Indian power system. In this context, this paper presents a comprehensive review of the frequency response challenges of the future low-carbon Indian power system and underlines the new potential fast frequency response (FFR) services from advanced energy storage and renewable generation technologies. Such FFR services can be swiftly deployed in low-inertia grids through the integration of energy storage systems and inverter-based resources in a shorter span. Modeling of FFR services in security constraint unit commitment (SCUC) can enhance grid resiliency and facilitate high penetration of RES in the Indian power system without loss of frequency stability.