Installed Photovoltaic Capacity Research Articles

Agrivoltaic Systems: Potential Opportunities for South Africa

South Africa has seen a drastic uptake of solar photovoltaic (PV) systems with an increasing number of solar farms over the last decade. From an available land perspective, there is also much potential to significantly expand the generation capacity, when compared to the rest of the world. More than 80% of the land area has a solar resource greater than 1600 kWh/m2/yr. Over 79% of the land is used for agricultural purposes, yet only 22.5% of the installed PV capacity is in the agricultural sector. This highlights the potential of dual land usage with agrivoltaic systems. This paper investigates the opportunities to develop such systems by considering agricultural production in South Africa that may be suitable – to pave the way forward for the implementation of appropriate agrivoltaic systems in the country. A Geographic Information System (GIS) analysis was undertaken, considering the solar resource and land with a slope of less than 2 degree – to minimise construction costs. Current large-scale solar projects in South Africa indicate that at least 0.6 GWh of electricity can be generated annually per hectare. The current total capacity (of all sources) generated around 237 TWh of electricity in 2022. To generate an equal amount of electricity with agrivoltaic systems would then (roughly) require less than 400 thousand hectares of agricultural land; or less than 2% of the available land suitable for agrivoltaic systems (depending on the designed panel density). Further site-specific techno-economic analyses are underway to provide greater insight into the potential opportunities for South Africa.

A comparative study of LSTM-ED architectures in forecasting day-ahead solar photovoltaic energy using Weather Data

Solar photovoltaic (PV) energy, with its clean, local, and renewable features, is an effective complement to traditional energy sources today. However, the photovoltaic power system is highly weather-dependent and therefore has unstable and intermittent characteristics. Despite the negative impact of these features on solar sources, the increase in worldwide installed PV capacity has made solar energy prediction an important research topic. This study compares three encoder-decoder (ED) networks for day-ahead solar PV energy prediction: Long Short-Term Memory ED (LSTM-ED), Convolutional LSTM ED (Conv-LSTM-ED), and Convolutional Neural Network and LSTM ED (CNN-LSTM-ED). The models are tested using 1741-day-long datasets from 26 PV panels in Istanbul, Turkey, considering both power and energy output of the panels and meteorological features. The results show that the Conv-LSTM-ED with 50 iterations is the most successful model, achieving an average prediction score of up to 0.88 over R-square (R2). Evaluation of the iteration counts’ effect reveals that the Conv-LSTM-ED with 50 iterations also yields the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values, confirming its success. In addition, the fitness and effectiveness of the models are evaluated, with the Conv-LSTM-ED achieving the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values for each iteration. The findings of this work can help researchers build the best data-driven methods for forecasting PV solar energy based on PV features and meteorological features.

The spatiotemporal distribution prediction method for distributed photovoltaic installed capacity based on power supply measurement data

Abstract With the anticipated expansion of distributed power grid integration in the foreseeable future, the consideration of distributed power’s impact on power balance becomes paramount in distribution network planning. In this research, we presented a novel approach for predicting the spatial and temporal distribution of distribution network planning areas, with a specific focus on estimating the installed capacity of distributed photovoltaic (PV) systems. Our method leveraged the saturated capacity of distributed PV, requiring minimal data inputs. By establishing a quantitative model that elucidated the relationship between installed distributed PV capacity and land area, we generated PV installed capacity evolution curves for various types of land. Subsequently, we derived the development coefficient of distributed PV installed capacity. By combining this coefficient with the current status of installed distributed PV capacity in the target area’s land parcels, we forecasted the spatial and temporal distribution of future distributed PV capacity within the region. The proposed prediction model held significant implications for the planning of new distribution networks. Additionally, this study predicted the installed distributed PV capacity for distinct land use types, including residential, commercial, and industrial land, using a regional power supply unit as a representative example. We employed the installed PV capacity unit to forecast the electricity loss rate and energy saving rate within the planning area. By validating the model and method through exemplary test results, we demonstrated the model’s feasibility and accuracy. Furthermore, our model effectively predicted the impact of distributed PV integration on overall load forecasting, thereby offering the power grid more precise load forecasting capabilities.

Comparison And Analysis of The Photovoltaic Industry Between China and the US

Nowadays, the world is increasingly recognizing the limitations of non-renewable energy resources such as oil and fossil fuels. The continued reliance on these finite resources poses a significant risk of energy shortage in the future. As a result, there is a growing emphasis on promoting renewable energy sources like wind energy, solar energy, and other non-polluting alternatives. In this article, the focus is on the development of the photovoltaic (PV) industry in China and the United States, with a specific emphasis on photovoltaic energy. The aim of this study is to analyze and compare the current state of the PV industry in both countries, taking into account various factors such as PV power generation, installed PV capacity, and policy support. By conducting a comprehensive assessment of these aspects, the research findings reveal that the PV industry in China has experienced remarkable growth and has surpassed the United States in terms of competitiveness. China has emerged as a global leader in the PV industry, with significant advancements in technology and manufacturing capabilities. The country's PV power generation has witnessed substantial expansion, contributing significantly to its renewable energy targets. Moreover, China's installed PV capacity has experienced rapid growth, supported by favorable government policies and incentives that have stimulated investment and innovation in the sector. In contrast, the United States, while still a prominent player in the PV industry, has faced certain challenges in keeping pace with China's progress.

Exploring the merits of geographical diversification of solar PV power plants for a resilient PV-dominated electricity grid in India

Abstract India is highly dependent on solar photovoltaics (PV) to harness its vast solar resource potential and combat climate change. However, $\sim$90% of the installed PV capacity in India is concentrated in the top nine states, with the remaining states lagging behind. The research reveals that during monsoons, heavy cloud cover and rain lead to high solar resource variability, intermittency and the risk of very low PV generation, which can result in reliability issues in future PV-dominated electricity grids. Although energy storage can help in overcoming high intermittency, there are multiple challenges associated with it. The novelty of this study lies in demonstrating the benefits of combining multiple PV sites in various regions to mitigate the risks of low PV generation and high variability. The variability of individual sites was found to be up to $\sim$3.5 times higher than the variability of combined generation. During noon, prominent solar park sites like Bhadla and NP Kunta experience a decrease in power generation to values as low as $\sim$10% of the rated PV capacity. However, the minimum generation of the large-scale dispersed PV generation is $>$30%. Furthermore, the research identifies other benefits of dispersing PV generation across the country, viz., reduction of seasonal variability by adding PV capacity in the southern region, widening of the PV generation span, more room for PV capacity addition, reduction in storage and ramping needs, utilization of hydroelectric potential of the north-east and PV potential of Ladakh, and creating opportunities for sustainable development in rural agrarian regions through agrivoltaics.

Congestion control strategies for increased renewable penetration of photovoltaic in LV distribution networks

Domestic photovoltaic (PV) installations are increasing their spread due to decreased cost, environmental friendliness, and relatively easy installation. However, the massive domestic PV deployment can bring the LV network (LV) distribution network to its sustained operating limits, as overvoltage and congestion can arise. The overvoltage problems can emerge due to reverse power flows and congestion can be caused if the installed PV capacity is higher than the distribution line loading capacity. The limitations of the LV networks arise especially during the seasons when domestic power demand is minimal, and PVs are generating at their peak. The research presented here incorporates a real suburban LV network with fifteen residential users. The measured load and PV generation data for one year are used to carry out power flow simulations. Several congestion leveraging strategies such as battery energy storage system (BESS) incorporation, reactive power control (RPC), and curtailment of peaks are discussed.

An Overview on Recycling of Photovoltaic Modules at the End of Life

Abstract: “Solar energy is clean, renewable, sustainable, and cost-effective, but it also requires time to develop by individual”. Due to the current situation of fossil fuels eradication and over-exploitation across the world. The energy demands of the world are to be fulfilled by renewable energy sources. Among this Solar energy has the maximum potential to be exploited to the maximum level to full fill the energy demands of the world. Renewable energies contribute to the environmental, social, and economic development of countries [1]. There are various of clean and renewable energy sources such as Solar, biomass, wind, hydrogen, fuel cell, nanocomposite, and supercapacitor. Each of the energy sources are suitable for specific geographical locations and can suits from region to region [6]. Since ages Sun has been praised as a vitalizer of life. The total Solar energy absorbed by Earth's atmosphere, oceans and land masses is approximately 122 PW·year = 3,850,000 exajoules (EJ) per year. Most of the world's population live in areas with insolation levels of 150–300 watts/m2, or 3.5–7.0 kWh/m2 per day [2 google- Solar Wikipedia]. Solar energy is the most secure and ubiquitous source of energy to meet the power needs after coal exploitation. In present time installation of photovoltaic (PV) Solar modules are growing extremely fast. As result of the increase, the volume of modules that reach the end of their life will grow at the same rate in the near future. Global installed PV capacity reached around 773 GW at the end of 2020 [3] and is expected to rise further to 2840 GW by 2030 and 4500 GW by 2050 [4, 5-Abstract ].

Abundant Material Consumption Based on a Learning Curve for Photovoltaic toward Net‐Zero Emissions by 2050

The global cumulative photovoltaic (PV) installed capacity is now over 1 TW. While this is an impressive amount of PV growth, it contributes less than 3% of total electricity generation and, therefore, requires significantly more PV to decarbonize the electricity sector completely. In order to achieve this decarbonization sustainably, all factors must be considered, including the extraction and purification of abundant materials. Based on conservative and ambitious future PV production scenarios and learning rate (LR) for material consumption reduction, the material demands for the future are projected. The concept of LR is applied to estimate the reduced material consumption based on the “maturity” of PV technology or the cumulative installed PV capacity. Herein, it is suggested that abundant materials like copper, concrete, and aluminum may face shortages if PV production follows the broad electrification scenario. Steel, in comparison, likely does not encounter any material shortages. Nevertheless, the work here demonstrates that the demand for even abundant materials should be minimized to decarbonize energy usage and mitigate climate change sustainably.

An Overview on Recycling of Photovoltaic Modules at the End of Life (EoL)

Due to the current situation of fossil fuels eradication and over-exploitation across the world. The energy demands of the world are to be fulfilled by renewable energy sources. Among these Solar energy has the maximum potential to be exploited to the maximum level to full fill the energy demands of the world. Renewable energies contribute to the environmental, social, and economic development of countries [1]. There are various of clean and renewable energy sources such as Solar, biomass, wind, hydrogen, fuel cell, nanocomposite, and supercapacitor. Each of the energy sources are suitable for specific geographical locations and can suits from region to region [6]. Since ages Sun has been praised as a vitalizer of life. The total Solar energy absorbed by Earth's atmosphere, oceans and land masses is approximately 122 PW·year = 3,850,000 exajoules (EJ) per year. Most of the world's population live in areas with insolation levels of 150– 300 watts/m2, or 3.5–7.0 kWh/m2 per day [2 google- Solar Wikipedia]. Solar energy is the most secure and ubiquitous source of energy to meet the power needs after coal exploitation. In present time installation of photovoltaic (PV) Solar modules are growing extremely fast. As result of the increase, the volume of modules that reach the end of their life will grow at the same rate in the near future. Global installed PV capacity reached around 773 GW at the end of 2020 [3] and is expected to rise further to 2840 GW by 2030 and 4500 GW by 2050 [4, 5-Abstract ]. Considering an average module lifetime of 25 years, the worldwide Solar PV waste is anticipated to reach between 4%-14% of total generation capacity by 2030 and rise to over 80% (around 78 million tonnes) by 2050 [5-Abstract]. Therefore, the disposal of PV modules will become a pertinent environmental issue in the next decades. Eventually, there is plenty of room for progress in this area. This review present core reality of Photovoltaic Life Cycle, Photovoltaic Technology, Photovoltaic Recycling Process as well as global Waste Management Regulation in different countries and the Environmental & Economic Aspects of recycling. At present, PV recycling management in many countries envisages to extend the duties of the manufacturers of PV materials to encompass their eventual disposal or reuse. However, further improvements in the economic viability, practicality, high recovery rate and environmental performance of the PV industry with respect to recycling its products are indispensable. There is potential to develop new pathway for PV waste management industry development and offer employment and prospects for both public and private sector investors. Keywords: Recycling, End of life (EoL), Photovoltaic, Waste, Regulatory, c-Si, Thin film, Life cycle

Investigation of stochastic unit commitment to enable advanced flexibility measures for high shares of solar PV

As the share of solar photovoltaics (PV) in the power system increases, there is a growing need for flexibility from multiple, possibly interdependent sources to adjust to PV’s variability, uncertainty, and diurnal dependence. This paper investigates how stochastic unit commitment leveraging probabilistic solar forecasts can support other flexibility measures under high solar shares. We consider two flexibility measures relevant to day-ahead scheduling: battery energy time-shifting and solar ancillary service provision. Unit commitment and economic dispatch simulations are conducted on a realistic test system based on Texas using day-ahead solar trajectories. The benefits of the two flexibility measures are pronounced when the instantaneous solar share is high, offering cost savings of 10%–20% in the spring. For a Texas-sized system, this translates to hundreds of millions of dollars in cost savings once the installed PV capacity enables instantaneous solar shares regularly exceeding 40%. Using probabilistic forecasts also greatly increases the reliability of upward reserve provision from solar PV, reducing unserved reserves by 50%–100%. Both day-ahead forecast resolution and errors can impact system reliability at high solar shares, but the stochastic formulation has significant value, mitigating reliability impacts on over-forecast days.

Fault tree analysis of fires on rooftops with photovoltaic systems

A fault tree analysis of fires related to photovoltaic (PV) systems was made with a focus of understanding the failure rate of the electric components. The failure rate of different components of these systems was calculated from data obtained from reports, research studies, and fire incident statistics of four countries. The results explain the significant causes of fire on the component level and various failure patterns resulting in PV-related fires. The qualitative analysis identified seven major events that led to incidents caused by a PV-related ignition source, with electrical arcing being the main cause of fires. This finding is highly related to the imprudent installation practices due to negligence and low awareness of the fire risk associated with PV systems by installers. The quantitative results show that 33% of the PV fire incidents are due to unknown or unrelated ignition sources, indicating that great focus should be given to mitigate the consequences caused by PV-related fires. The PV module, isolator, inverter, and connector are the major PV system components that are highly responsible for the ignition of PV-related fires, with the connector being the prime contributor in 17% of the PV-related fires. Finally, the quantitative analysis established an annual fire incident frequency of 0.0289 fires per MW. The results enable estimation of the number of fire incidents linked to the installed PV capacity, and the fault tree analyses highlight where improvements are most critical. Based on the results of the analyses, two questions are suggested for implementation in the post-incident reports of the national fire and rescue services.

Power Limit Control Strategy for Household Photovoltaic and Energy Storage Inverter

The increased installation capacity of grid-connected household photovoltaic (PV) systems has been witnessed worldwide, and the power grid is facing the challenges of overvoltage during peak power generation and limited frequency regulation performance. With the dual purpose of enhancing the power grid safety and improving the PV utilization rate, the maximum feed-in active power can be regulated by modifying the maximum power point tracking (MPPT) algorithm and battery energy storage (BES) accessibility as control instructions. However, the existing methods not only waste installed PV capacity, but it becomes no longer accessible when the state of charge (SOC) of the BES approaches its upper limit. In response to the above problem, this paper proposes a power limit control strategy to coordinate the MPPT algorithm and the BES accessibility. The proposed strategy directly controls the inverter output current according to the power limit instructions from the electric operation control centers, leading to a bus voltage difference. The difference serves as a control signal for BES and PV. Under a power-limiting scenario, priority is given to power regulation through energy storage to absorb the limited active power. When the SOC of the BES reaches the upper limit of charging, modification of the PV MPPT algorithm facilitates the inverter output power to meet the power limit requirements. To further verify the effectiveness of the proposed power limit control strategy, both simulation and experimental studies are conducted, which consistently indicated a synchronized inverter current with grid voltage and a rapid power response of the power-limiting instruction within 0.2 s. The power limit control strategy not only improves the PV energy utilization but also supports the safe and reliable operation of the power gird in the context of soaring renewable energy penetration.

Cost-Effective Increase of Photovoltaic Electricity Feed-In on Congested Transmission Lines: A Case Study of The Netherlands

In many areas in the world, the high voltage (HV) electricity grid is saturated, which makes it difficult to accommodate additional solar photovoltaic (PV) systems connection requests. In this paper, different scenarios to increase the installed PV capacity in a saturated grid are assessed on the basis of the net present value (NPV). The developed scenarios compare an increase of grid capacity, PV system azimuth variation, curtailment, and battery storage. For each scenario the net present value (NPV) is assessed using an optimization model as a function of the overbuild capacity factor, which is defined as the relative amount of PV capacity added beyond the available capacity. The scenarios are applied on a case study of the Netherlands, and the analysis shows that, by optimising curtailment, a PV system’s capacity can be increased to 120% overbuild capacity. For larger overbuild capacity investments in the electricity-grid are preferred when these costs are taken into account. However, the optimum NPV lies at 40% overbuild, thus the societal and NPV optimum are not always aligned. Furthermore, the use of a battery system as an alternative to an infrastructure upgrade was not found to be a cost-effective solution. Thus, applying curtailment could be cost-efficient to a certain extent to allow for additional PV capacity to be connected to a saturated grid. Furthermore, the inverter size compared to the installed PV capacity should be significantly reduced. For a connection request that exceeds 120% overbuild increasing network capacity should be considered.

Sources of uncertainty in long-term global scenarios of solar photovoltaic technology

The deployment of solar photovoltaic (PV) technology has consistently outpaced expectations over the past decade. However, long-term prospects for PV remain deeply uncertain, as recent global scenarios span two orders of magnitude in installed PV capacity by 2050. Here we systematically compile an ensemble of 1,550 scenarios from peer-reviewed and influential grey literature, including IPCC and non-IPCC scenarios, and apply a statistical learning framework to link scenario characteristics with foreseen PV outcomes. We show that a large portion of the uncertainty in the global scenarios is associated with general features such as the type of organization, energy model and policy assumptions, without referring to specific techno-economic assumptions. IPCC scenarios consistently project lower PV adoption pathways and higher capital costs than non-IPCC scenarios. We thus recommend increasing the diversity of models and scenario methods included in IPCC assessments to represent the multiple perspectives present in the PV scenario literature. Analysis of 1,550 future energy scenarios finds that uncertainty in solar photovoltaic (PV) uptake is mainly driven by institutional differences in designing and modelling these scenarios, rather than PV cost assumptions. This suggests more organizational diversity is needed in IPCC scenario design.

Reduction of Power Imbalances Using Battery Energy Storage System in a Bulk Power System with Extremely Large Photovoltaics Interactions

Power imbalances such as power shortfalls and photovoltaic (PV) curtailments have become a major problem in conventional power systems due to the introduction of renewable energy sources. There can be large power shortfalls and PV curtailments because of PV forecasting errors. These imbalances might increase when installed PV capacity increases. This study proposes a new scheduling method to reduce power shortfalls and PV curtailments in a PV integrated large power system with a battery energy storage system (BESS). The model of the Kanto area, which is about 30% of Japan’s power usage with 60 GW grid capacity, is used in simulations. The effect of large PV power integration of 50 GW and 100 GW together with large BESS capacity of 100 GWh and 200 GWh has been studied. Mixed integer linear programming technique is used to calculate generator unit commitment and BESS charging and discharging schedules. The simulation results are shown for two months with high and low solar irradiance, which include days with large PV over forecast and under forecast errors. The results reveal that the proposed method eliminates power shortfalls by 100% with the BESS and reduce the PV curtailments by 69.5% and 95.2% for the months with high and low solar irradiance, respectively, when 200 GWh BESS and 100 GW PV power generation are installed.

Global analysis of optimal cleaning cycle and profit of soiling affected solar panels

The photovoltaics (PV) industry is poised to capture most of the energy sector within the next few decades. As the installed PV capacity increases, even the smallest improvements on the system and operations of the solar farms can accumulate to a significant gain in revenue. One such scope is efficient mitigation of dust accumulation on panels or soiling. While installed capacities are two of the highest in Asia and around US deserts, these locations are also dust prone. The Middle East and North Africa (MENA) regions having good insolation are also highly potential candidates for PV farms with the added complexity of soiling losses. Therefore, while soiling may not be an issue in many locations over the globe, it is in fact relevant to the majority of the PV installation sites. The analysis of the effects of soiling losses on energy yield and economics are of great importance for these locations. In this work, we have extended the empirical soiling model found in the literature to include the effects of temporal variation on soiling and insolation. Our study on variation in revenue with unoptimized cleaning intervals estimates the soiling loss, which can particularly interest PV farms with accessibility issues such as agrophotovoltaic systems. We analyze the optimal cleaning cycle and corresponding normalized revenue (cash inflow normalized to the rated clean farm revenue). A numerical model is used to explain the effects under seasonal and sudden (e.g., sand storm or rain) variations in soiling and insolation. Our closed-form analytical expressions can predict the cleaning cycle and normalized revenue within 0.1% of the numerical results by using the soiling and insolation data averaged over the seasons. Finally, we discuss these results in a global scenario using our estimated world-map for soiling rates. This predicts the worldwide revenue loss under location-specific optimal cleaning cycles, assuming one of the lowest cleaning costs seen globally. In Asia and MENA regions, for example, median revenue loss due to soiling is 2.5% even after optimal cleaning every 5–6 days. This loss is 1.5% in the US for 12 days of cleaning interval. Considering region-specific cleaning costs, the revenue loss of optimally cleaned PV farm is 2%–5% for Asia, MENA, North America, and Europe.

“My Electricity” Program Effectiveness Supporting the Development of PV Installation in Poland

There are a lot of studies that show the legitimacy of subsidizing renewable energy; however, some mechanisms are defective, and there are problems with the appropriate allocation of funds. Therefore, this paper aims to look at the situation of allocating funds to photovoltaics (PV) micro-installations in Poland through the “My Electricity” program. The article presents the results of analyses aimed at identifying inequalities between provinces in the use of funds available under the “My Electricity” program and verifying whether these inequalities are getting worse and whether the intensity of support should not be territorially conditioned in terms of maximization an electricity production. As part of two editions of the “My Electricity” program (until 1 August 2020), over 64,000 PV micro-installations were created with an average power of approximately 5.7 kWp. The total installed PV capacity was 367.1 MWp (1st edition: 159.3 MWp, 2nd edition: 207.8 MWp). Financial resources (as a whole), in the second edition of “My Electricity” program, were distributed better than in the first edition. In the first edition, as much as 7.60% of funds were allocated inefficiently; in the second edition, it was only 3.88%. Allocation surpluses occur in provinces where the average disposable income is low and where there are a small number of households. There is a potential to introduce a territorial project selection criteria. The analysis shows that the criteria should promote provinces with higher disposable income and a larger number of households.

A Probabilistic Estimation of PV Capacity in Distribution Networks From Aggregated Net-Load Data

Globally, solar photovoltaic (PV) installations on distribution LV feeders have increased significantly. The increased penetration leads to several technical problems on existing networks and impacts utilities’ business models as energy sales drop. For proactive management of these challenges, utilities need to continually monitor the capacity of installed PV. To this end, some utilities typically require PV installations to be registered and sometimes use GIS mapping to approximate the installed PV capacity. However, these GIS PV capacity estimations methods are unreliable. Therefore, to obtain reliable PV capacity estimates at a distribution level, comprehensive modeling is required to accurately represent the generation output and the distribution load. This paper proposes a novel probabilistic PV estimation method that uses time-series historical sets of load and irradiance data to estimate the embedded PV capacity while considering the uncertainty in solar irradiance and the measured net load. Uncertainty characterization is implemented using empirical probability density functions, and simulation is performed stochastically using Monte-Carlo methods. A novel quantile analysis approach is developed and used in the computation of the final PV estimates. The proposed methodology is tested using measured load data from Ausgrid customers, Australia, and achieves reasonable accuracy (between 86% and 90% for the tested cases) and a 10% mean absolute percentage error. This approach is robust to the effects of input uncertainty and can be used by distribution utilities to estimate and monitor PV capacity installed on the distribution networks without incurring extra advanced metering investment costs.

The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images

Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time.

Rushing for subsidies: The impact of feed-in tariffs on solar photovoltaic capacity development in China

Governments across countries often offer dynamic subsidies to clean technologies that decrease over time. However, the impact of government subsidies on technology deployment is difficult to gauge due to many confounding factors and the selection bias problem caused by the phenomenon of rushing for subsidies. This study takes China’s solar photovoltaic (PV) as an example, and uses a difference-in-difference framework that leverages China’s zonal feed-in tariff (FIT) policy design and its multiple changes over time. The parallel rushing for subsidies by two neighboring FIT zones provides a unique opportunity to identify the causal effect of FIT policy on newly installed PV capacity. Results show that an increase of 0.1 yuan/kWh (~$0.014/kWh) in PV subsidies adds about 18 GW/year of installed capacity to the national PV market, right in the middle of previous estimates in the literature. From a different perspective, if China did not have any PV subsidies, the PV deployment market would virtually disappear. The cost of carbon mitigation through PV feed-in tariffs is estimated at around 120 yuan (~$17) per ton of CO2. Our estimate of the impact of FIT on PV capacity is useful for the government to design policies that help the PV industry transit to a subsidy-free era.