Large-scale Photovoltaic Deployment Research Articles

Assessing Australia’s future solar power ramps with climate projections

Increasing levels of photovoltaic (PV) penetration to the electricity grid brings challenges to both design and operation of the grid due to its vulnerability to climate change. A crucial aspect of PV operation is power ramps leading to variability and instability in the grid. With notable large-scale PV deployment planned, including the world’s largest planned solar energy infrastructure in Powell Creek Australia, characterising future ramps is crucial for ensuring stable power generation to support large-scale economic development. Using CORDEX-Australasia projections under RCP8.5 and RCP4.5 emission scenarios, future solar ramps across Australia have been characterised up to 2100. Results predict a reduction in ramp magnitude across Australia, with changes in frequency and period length varying with the location. This work highlights the importance of considering future changes in climate when designing large-scale solar farms to ensure the incorporation of frequency control devices and storage plans for a reliable power supply.

Barriers for Large Integration of PV and Onshore Wind Energy in the Distribution Network on the Selected European Union Electricity Markets

The European Union (EU) has adopted ambitious and wide-ranging binding targets for the reduction of greenhouse gas emissions, energy systems’ transformation, and becoming climate neutral. The transformation of the energy sector towards more sustainable electricity production increases the importance of distributed generation from renewable sources, such as solar photovoltaics (PV) and wind energy. Large integration of PV and onshore wind energy in the EU distribution network is key to success in the energy transition. Despite significant progress in the field of renewable energies’ regulations, in particular, due to the implementation of the RES Directive, several barriers remain, and still, the development of photovoltaics and wind energy are being slowed by various types of market, regulatory barriers, as well as technological and social obstacles. The aim of this article is to investigate the main barriers to the development of distributed generation from renewable sources, such as solar photovoltaics and wind energy, in order to increase their share in the EU electricity market. This paper is focused on the most common regulatory, technological, administrative, financial, social, and environmental barriers, which slow down the large-scale deployment of PV and wind energy into the distribution networks in 5 EU countries: Austria, Greece, France, Poland and Spain and also in Norway.

Comprehensive benefit evaluation of solar PV projects based on multi-criteria decision grey relation projection method: Evidence from 5 counties in China

The photovoltaic poverty alleviation program is an innovation of sustainable development strategy by the Chinese government, which aims to promote the development of renewable energy while reducing rural poverty through the systematic deployment of solar photovoltaic (PV). Nevertheless, the actual benefits of photovoltaic poverty alleviation projects (PV-PAPs) need to be verified. Yet, little research currently explored the regional differences of large-scale PV deployment. This study intends to construct a comprehensive benefit evaluation system in 4 dimensions of finance, energy, environment and society including 15 secondary indicators. The multi-criteria decision making (MCDM) grey relation projection (GRP) method and first-hand field survey data of 5 counties in China were used to evaluate the comprehensive benefit of PV-PAPs. The results show that PV-PAPs have good benefits potential with a solid demonstration effect and robust replicability. However, obvious differences were found between actual performance and theoretical design, benefits vary enormously among regions, modes, and solar resource areas. Management styles, operational and maintenance mechanisms are the main sources of regional differences. Besides, sensitive analysis show that financial benefit of PV-PAPs is relatively vulnerable to risk factors. Finally, corresponding policy recommendations are proposed to facilitate the sustainable development of PV-PAPs.

Sustainability strategies for PV: framework, status and needs

The large-scale deployment of photovoltaics (PV) is a central pillar in decarbonizing energy systems and reaching climate goals. Although PV is inherently associated to environmental awareness, it is not immune to reputational risks nor exempt of a responsibility for transparency and sustainability leadership. So far, advances in the PV industry have mainly been shaped by cost-reduction targets. We identified in previous works 16 topics where the PV sector comes short in addressing the United Nations Sustainable Development Goal 12 (SDG 12) “Ensure sustainable consumption and production patterns”. In this paper, practical approaches to address each of these sustainability gaps are proposed. The best-practices identified cover all aspects of sustainability as defined by SDG 12–from resource use and hazardous substances through corporate reporting and risk assessment to due diligence and waste management. Insights on methodological needs to improve sustainability assessment and accounting in PV are also provided. The compiled list of actions needed, although not intended to be exhaustive, constitutes a starting point for stakeholders to raise their ambitions and achieve more sustainability in PV value chains.

PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model

Photovoltaics (PV), the primary use of solar energy, is growing rapidly. However, the variable output of PV under changing weather conditions may hinder the large-scale deployment of PV. In this study, we propose a two-stage classification-prediction framework to predict contemporaneous PV power output from sky images (a so-called “nowcast”), and compare it with an end-to-end convolution neural network (CNN). The proposed framework first classifies input images into different sky conditions and then the classified images are sent to specific sub-models for PV output prediction. Two types of classifiers are developed and compared: (1) a CNN-based classifier trained on clear sky index (CSI)-labeled sky images and (2) a physics-based non-parametric classifier based on a threshold of fractional cloudiness of sky images. Different numbers of classification categories are also examined. The results suggest that the cloudiness-based classifier is more suitable than the CSI-based classifier for the framework, and the 3-class classification (i.e., sunny, cloudy, overcast) is found to be the optimal choice. We then fine-tune the cloudiness threshold for the non-parametric classifier and tailor the architecture for each sky-condition-specific sub-model. Under the best design, the proposed framework can achieve a root mean squared error (RMSE) of 2.20 kW (relative to a 30 kW rated PV array) on the test set comprising 18 complete days (9 sunny, RMSE = 0.69 kW; 9 cloudy, RMSE = 3.06 kW). Compared with the end-to-end CNN baseline model, the overall prediction performance can be improved by 6% (7% in sunny and 6% in cloudy), with 6% fewer trainable parameters needed in the architecture.

A systematic analysis of meteorological variables for PV output power estimation

While the large-scale deployment of photovoltaics (PV) for generating electricity plays an important role to mitigate global warming, the variability of PV output power poses challenges in grid management. Typically, the PV output power is dependent on various meteorological variables at the PV site. In this paper, we present a systematic approach to perform an analysis on different meteorological variables, namely temperature, dew point temperature, relative humidity, visibility, air pressure, wind speed, cloud cover, wind bearing and precipitation, and assess their impact on PV output power estimation. The study uses three years of input meteorological data and PV output power data from multiple prosumers in two case studies, one in the U.S. and one in the Netherlands. The analysis covers the correlation and interdependence among the meteorological variables. Then, by using machine learning-based regression methods, we identify the primary meteorological variables for PV output power estimation. Finally, the paper concludes that the impact of using a lower-dimensional subspace of meteorological variables per location, as input for the regression methods, results in a similar estimation accuracy in the two case studies.

Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty

The large-scale deployment of photovoltaics (PV) on building rooftops can play a significant role in the transition to a low-carbon energy system. To date, the lack of high-resolution building and environmental data and the large uncertainties related to existing processing methods impede the accurate estimation of large-scale rooftop PV potentials. To address this gap, we developed a methodology that combines Machine Learning algorithms, Geographic Information Systems and physical models to estimate the technical PV potential for individual roof surfaces at hourly temporal resolution. We further estimate the uncertainties related to each step of the potential assessment and combine them to quantify the uncertainty on the final PV potential. The methodology is applied to 9.6 million rooftops in Switzerland and can be transferred to any large region or country with sufficient available data. Our results suggest that 55% of the total Swiss roof surface is available for the installation of PV panels, yielding an annual technical rooftop PV potential of 24±9TWh. This could meet more than 40% of Switzerland’s current annual electricity demand. The presented method for an hourly rooftop PV potential and uncertainty estimation can be applied to the large-scale assessment of future energy systems with decentralised electricity grids. The results can be used to propose effective policies for the integration of rooftop photovoltaics in the built environment.

Economic and regulatory feasibility of solar PV in the Austrian multi-apartment housing sector

Austria established energy policy targets to decarbonize the housing sector with an increasing usage of low carbon electricity. Solar photovoltaic (PV) is one of the technologies being used to reach this target. Currently, the deployment of PV in the multi-apartment building sector is supported by subsidies. Taking into account the available potential and the policy goals for large-scale PV deployment in the residential sector in Austria, this paper investigates the economic feasibility of PV generation in multi-apartment buildings in the absence of subsidies. It also looks at the necessary regulatory conditions for implementation of economically feasible business models for PV generation in multi-apartment buildings. The empirical data for current research came from case studies of three actual projects, which were implemented by stakeholders with practical experience and knowledge in residential PV.

A multi-disciplinary analysis of UK grid mix scenarios with large-scale PV deployment

The increasing contribution of renewable energies to electricity grids in order to address impending environmental challenges implies a reduction in non-renewable resource use and an alignment with a global transition toward a low-carbon electric sector. In this paper, four future UK grid mix scenarios with increased photovoltaic (PV) installed capacity are assessed and compared to a benchmark “Low PV” scenario, from 2016 to 2035. The complexity of the issue requires a multi-disciplinary approach to evaluate the availability of net energy, environmental aspects and technical performance. Hence, the comparison between scenarios includes short-term and long-term energy metrics as well as greenhouse gas (GHG) and technical metrics. Also, the paper considers the viewpoints offered by both an “integrative” and a “dynamic” approach to net energy analysis. Results for all five analysed scenarios indicate that increased PV deployment will not be detrimental to the UK grid performance from the points of view of a wide range of system-level technical (% renewable energy curtailment to ensure grid stability), energy (energy return on investment and non-renewable cumulative energy demand) and environmental (greenhouse gas emissions) metrics.

Observed surface radiation and temperature impacts from the large-scale deployment of photovoltaics in the barren area of Gonghe, China

Abstract As the installation of large-scale photovoltaic (PV) facilities in the barren area of Gonghe, China, would cover a substantial portion of the Earth's surface with PV panels, concerns exist about the extent to which PV plants induce unfavorable surface radiative effects from the change in land use. In contrast to the theoretical calculations and model simulations of previous studies, parallel observations derived from in situ experiments spanning two contrasting land surfaces in Gonghe, consisting of a natural barren surface and one covered with PV panel, provide new insight into the potential impact of large-scale PV plants on the surface radiation and temperature. The novel results presented here are as follows. (1) The specific variations of the surface shortwave radiation components enable the PV panels to serve as energy sinks throughout the whole year, especially in the warm season. (2) The cooling of the land surface associated with the PV plant is related to the physical shading by the PV panels and the solar energy conversion. (3) The enhanced temperature of the surface of the PV panel heats the adjacent ambient air, which may subsequently drive a PV heat-island effect. Moreover, recommendations for an improved experimental design are presented. Our findings provide a solid basis for the parameterization of a PV plant in numerical models, with the view to delivering a more realistic quantitative and practical guidance for the development of the solar industry in the future.

SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions

Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model. The proposed model is also effective and practical in forecasting existing grid-connected PV power generation.

Sputter grown sub-micrometer thick Cu2ZnSnS4 thin film for photovoltaic device application

Structural properties and solar cells performance of sputter grown sub-micrometer thick (~600nm) Cu2ZnSnS4 (CZTS) films are investigated for low cost and large scale photovoltaic deployment. The CZTS films are deposited using single quaternary stoichiometric target at room temperature. X-ray diffraction, micro-Raman spectroscopy, and scanning electron microscopy measurements were carried out to investigate the structural property and secondary phase formation. The intense phonon peak at 334cm−1 in the visible Raman spectra confirms the deposition of single phase CZTS films. CZTS-based thin film solar cells show excellent performance with open circuit voltage ~600mV and over all cell efficiency of ~3.07%, exhibiting commercial potential toward large scale fabrication of photovoltaic devices.

GaSe Formation at the Cu(In,Ga)Se2/Mo Interface–A Novel Approach for Flexible Solar Cells by Easy Mechanical Lift‐Off

Implementing photovoltaic devices based on high efficiency thin‐film technologies on cheap, light‐weight and flexible polymeric substrates is highly appealing to cut down costs in industrial production and to accelerate very large scale deployment of photovoltaics in the upcoming years. Lift‐off processes, which allow separating active layers from primary substrates and subsequent transfer onto an alternative substrate without modifying the upstream production process and without performance losses, are an emerging alternative to direct growth on polymeric substrates. This study concerns the feasibily of direct mechanical lift‐off process for high efficiency Cu(In,Ga)Se2 (CIGS) thin film solar cells grown by coevaporation on glass/molybdenum substrates without performance losses. The study presents an in depth characterization (SEM,AFM,GIXRD,XPS) of samples leading to excellent lift‐off properties. They are explained by a specific gallium rich CIGS graded interface structure according to the interfacial sequence glass/Mo/MoSe2/GaxSey/Ga‐rich‐CIGS. The interfacial layer, attributed to GaSe, has a layered structure and out performs the molybdenum diselenide layered layer which forms spontaneously at the interface Mo/CIGS. It allows a very easy lift‐off process at the interface GaSe/CIGS thanks to Van‐der‐Waals adhesion mechanism in GaSe. Key physical‐chemical parameters are identified and analyzed. After lift‐off, an efficiency of 14.3%, higher than the initial reference CIGS solar cell efficiency (13.8%) is measured.

Band-gap-graded Cu2ZnSn(S1-x,Sex)4 Solar Cells Fabricated by an Ethanol-based, Particulate Precursor Ink Route

Solution processing of earth-abundant Cu2ZnSn(S1-x,Sex)4 (CZTSSe) absorber materials is an attractive research area in the economical and large-scale deployment of photovoltaics. Here, a band-gap-graded CZTSSe thin-film solar cell with 7.1% efficiency was developed using non-toxic solvent-based ink without the involvement of complex particle synthesis, highly toxic solvents, or organic additives. Despite the high series resistance due to the presence of a thick Mo(S,Se)x layer and Zn(S,Se) aggregates, a high short-circuit current density (JSC) was generated. In addition, there was no significant difference in open circuit voltages (VOC) between CZTS (0.517 V) and CZTSSe (0.505–0.479 V) cells, despite a significant band gap change from 1.51 eV to 1.24 eV. The high JSC and less loss of VOC are attributed to the effect of band gap grading induced by Se grading in the CZTSSe absorber layer. Our environmentally benign ink approach will enable the realization of low-cost, large-area, high-efficiency thin-film solar cells.

Empirical Studies on Demand Pull in Photovoltaic Sector of Twenty Countries

High cost impedes large-scale deployment of photovoltaics (PV), and the technical innovation is one of most important factors of cost reduction, so how to accelerate the market pull for innovation has practical significance and theoretical value. This paper aims to analyze PV demand pull mechanism based on empirical evidence. According to the features of PV industry and technical innovation theories, make hypotheses of PV market pull and the relationship between diffusion rates and the market pull. Then propose testing method of the market pull and the coefficients assessing diffusion rates, and use historical data of twenty countries to conduct empirical studies. The testing results show that 70% of PV market in the twenty countries pulled technical innovation; at the initial stage of PV diffusion, the market pull was insignificant when the market kept at small scale and the diffusion rate can be indicated by the innovation coefficient; in the acceleration period, the market pull was insignificant when the market grew steeply and the rate can be indicated by the imitating coefficient. So in order to encourage demand pull, policy making is suggested to lead the market growth stable and sustained.

Socio-Economic and Environmental Impacts of Silicon Based Photovoltaic (PV) Technologies

Solar photovoltaic (PV) system provides significant social and environmental benefits in comparison to the conventional energy sources, thus contributing to sustainable development. The worldwide PV market installations reached a very high growth in 2011 (27.4 GW). These are encouraging news since electricity generation from PV produces no greenhouse gas emissions and as such provides a clean alternative to fossil fuels, contributes to job creation and economic prosperity even in less developed areas. However, manufacturing PV modules can have consequences for workers and on the environment throughout their life cycle (from raw material extraction and procurement, to manufacturing, disposal, and/or recycling). Large scale PV deployment also needs land that may not be available, or in competition with other land uses. These potential problems seem to be strong barriers for a further dissemination of PV technologies. Conventional PV (silicon based) manufacturing processes have roots in the electronics industry, many of the chemicals found in e-waste are also found in solar PV, including lead, brominated flame retardants, cadmium, and chromium. The manufacturing of solar cells involves several toxic, flammable and explosive chemicals. Many of those components suppose a health hazard to workers involved in manufacturing of solar cells. Solar panels are often in competition with agriculture and can cause soil erosion. The disposal of electronic products is becoming an escalating environmental and health problem in many countries. Recycling of PV panel is currently not economically viable because waste volumes generated are too small; significant volumes of end-of-life photovoltaic panels will begin to appear in 2025 or 2030. An overview of social and environmental impacts of PV technologies is presented in this paper along with potential benefits and pitfalls.

The potential for air-temperature impact from large-scale deployment of solar photovoltaic arrays in urban areas

Among the many benefits of solar photovoltaic (PV) systems, the direct effects are those of providing local power and the indirect ones include avoided generation from fossil-fuel power plants. The latter translate into reduced emissions of greenhouse gas (thus reduced radiative forcing) and other pollutants, such as ozone precursors (thus improved air quality). Because large-scale PV deployments can alter the radiative balance at the surface-atmosphere interface, they can exert certain impacts on the temperature and flow fields.In this analysis, meteorological modeling was performed for the Los Angeles region as a case study to evaluate the potential atmospheric effects of solar PV deployment. The simulations show no adverse impacts on air temperature and urban heat islands from large-scale PV deployment. For the range of solar conversion efficiencies currently available or expected to become attainable in the near future, the deployment of solar PV can cool the urban environment. The cooling can reach up to 0.2C in the Los Angeles region. Under hypothetical future-year scenarios of cool cities (urban areas with extensive implementations of highly-reflective roofs and pavements) and high-density deployments of urban solar PV arrays, some adverse impacts (0.1C or less in warming) might occur. However, such extreme high-density deployments of cool surfaces are not expected and thus the warming effects are unlikely to result.

Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.