|Study on air pollution implications for solar photovoltaic power generation
|Yang, Hongxing (BEEE)
Cao, Sunliang (BEEE)
|Solar energy -- China
Photovoltaic power generation -- China
Air -- Pollution -- China
Hong Kong Polytechnic University -- Dissertations
|Department of Building Environment and Energy Engineering
|xxxi, 212 pages : color illustrations
|Driven by the increase in technical efficiency and decrease in capital costs, the global solar photovoltaic (PV) capacity has experienced exponential growth over the past decade. China, as the leading country in the global PV market, accounts for more than one-third of worldwide installed capacity. Given the rapid expansion of the solar PV sector in China, it is imperative to gain a deeper understanding of solar energy resources in order to effectively implement solar energy projects. On the other hand, China faces the challenge of severe environmental issues arising from extensive anthropogenic aerosol emissions, leading to heavy air pollution. This challenge is primarily attributed to China’s energy-intensive and fossil-fueled economic growth pattern. Despite a gradual decrease in the proportion of fossil fuel consumption during the past decade, the coal-based structure of the energy mix still dominates energy consumption in China. Therefore, in this energy and environmental context, it is increasingly important to identify the role of air pollution in altering solar PV resources and the consequent impacts on PV power potential.
This thesis presents a comprehensive and systematic study of solar PV power generation and its relationship with air pollution. The primary objectives are to generate a long-term solar radiation dataset for the assessment and understanding of the geographically specific solar energy resources and solar PV power potential (Chapters 3 and 4), quantify the air pollution impacts on solar PV power capacity factors (CFs) and the potential benefits for the future PV sector from improved air quality in China (Chapter 5), and investigate the attenuation effects of airborne fine particulate matters (PM2.5) on solar spectrum and different PV technologies under clear sky conditions through field experiments (Chapter 6).
Firstly, a two-phase framework for solar radiation modeling is developed, which offers highly accurate output and fully transparent model interpretation. In the first phase, an improved machine learning model called PSO-XGBoost based on extreme gradient boosting optimized by the particle swarm optimization algorithm (PSO-XGBoost) is developed to estimate global solar irradiance at locations without solar radiation measurements. The PSO-XGBoost model showed the most superior accuracy and stability with overall R2, RMSE, MAE, and MAPE of 0.953, 1.597 MJ·m-2·day-1, 1.138 MJ·m-2·day-1, and 10.500%, respectively. In the second phase, the state-of-the-art game theory-based Shapley additive explanations (SHAP) technique is combined with the PSO-XGBoost model to provide complete transparency on the model output in terms of both global and local feature importance together with revealing pairwise interaction effects between model features. This is the first comprehensive attempt to explain machine learning-based models against the background of solar radiation modeling. This study contributes an accurate, reliable, and transparent machine learning model and an enlightening framework for estimating solar radiation at sites without observations.
Secondly, a high-quality long-term solar radiation dataset with national coverage is generated using the developed PSO-XGBoost model, addressing the issue of sparse solar radiation monitoring networks in China. More importantly, high-resolution maps of solar radiation resources and PV power potential are developed based on the generated dataset and GIS-based approaches, improving the understanding of the long-term spatial and temporal evolutions of solar energy in China. The national annual long-term mean global solar radiation is estimated at 174.36 W·m-2, with a decreasing trend of -0.83 W·m-2·decade-1. The provinces in and around the Beijing-Tianjin-Hebei region show the highest decreasing trend between -2.89 and -3.98 W·m-2·decade-1. Furthermore, the long-term average yearly potential for solar power generation in China is approximately 285.00 kWh·m-2. The yearly PV power potential decreased by 1.69 kWh·m-2·decade-1 from 1961 to 2016, with an attenuation of above 5 kWh·m-2·decade-1 in heavily polluted regions. During the 2010s, 30 out of the 31 provinces experienced a reduction in PV power potential between 0.25% and 10.27%, with an average national reduction of 2.88%, compared to the 1960s scenario. Moreover, the PV sector in China faces a regional mismatch between PV power potential and installed PV capacity. Targeted development strategies adapted to local environmental, economic and resource characteristics are crucial to put China’s solar PV industry on a sustainable development track. Also, government policy guidance and support for regional grid interconnection and PV power trading between regional grids are crucial to developing an efficient solar PV power market.
Thirdly, the impact of anthropogenic air pollution on PV CFs in China is quantified using a PV power potential evaluation model with the reconstructed long-term surface solar radiation dataset. Moreover, the geographically specific potential benefits of improved air quality for the future PV sector are investigated. The all-sky and clear-sky PV CFs show consistent annual anomalies, indicating that air pollution due to aerosol emissions rather than clouds is the primary driving factor for CF temporal evolutions and trends in China. Between 1961 and 2016, the national average clear-sky CFs for fixed modules with optimal tilt angle (FIX) demonstrated a significant decreasing trend of –0.0025 per decade. Following early significant downward trends, a reversal of the decreasing trend emerged in the 1980s. Starting around 2000, the clear-sky CFs for FIX experienced significant increasing trends of more than 0.005 per decade, benefiting from air pollution control laws and regulations. Compared with fixed modules, tracking systems suffer greater productivity losses due to air pollution. In comparison to 1961-1965 averages, the provincial CFs for 2012-2016 decreased by 0.48-13.54%, 1.15-11.40%, 1.06-14.83%, and 1.31-16.51% for FIX, horizontal fixed, one-axis horizontal tracking, and two-axis tracking modules, respectively, with decreases of 7-17% in the central and southeast. Furthermore, according to the provincial PV installation targets projected in China’s 14th Five-Year Plan, national PV power generation is expected to increase by 81.333 TWh, representing an 8.2% growth compared to the scenario where PV CFs remain at the 2012-2016 averages. In a scenario of keeping grid parity, additional power generation would offer an economic benefit of approximately 30.102 billion CNY.
Finally, long-term and short-term field experiments are carried out to quantify the impact of airborne PM2.5 on the solar spectrum and the energy performance of different solar PV technologies. The findings indicate that airborne PM2.5 leads to a red shift of the solar spectrum. Consequently, PV cells that rely more on spectral irradiance at shorter wavelengths are more sensitive to this alteration induced by PM2.5. In general, the performance ratio of mono-Si, poly-Si, and CIGS PV systems, which have a wide spectral response range, demonstrates a positive trend with increasing PM2.5 concentrations. In contrast, a negative trend is observed in the performance ratio of a-Si and CdTe PV systems, which have a narrower spectral response range. Furthermore, the measured energy outputs of the experimental PV systems indicate that PM2.5 affects the energy performance of thin-film solar cells with a larger band gap more significantly than that of crystalline silicon solar cells. Specifically, the measured average maximum relative reduction in net alternating current (AC) energy output and final yield for crystalline silicon PV modules is approximately 7.00%, while the value reaches up to 9.73% for thin-film PV modules. Additionally, poly-Si systems experience minor losses with a relative reduction of around 6% under heavy air pollution conditions, with PM2.5 concentrations ranging from 35.5 to 55.4 µg·m−3. However, CdTe and a-Si PV systems show an average decrease in energy output of approximately 10% under such conditions.
In summary, this thesis provides an interpretable framework for solar radiation modeling that overcomes the non-transparent issue of complex machine learning models, develops a nationwide solar radiation dataset, advances the understanding of the spatiotemporal patterns of long-term solar energy in China, and highlights the air pollution impacts on PV CFs, and the effects of airborne PM2.5 on the system performance of different PV technologies.
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