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Regional planning of solar photovoltaic technology based on LCA and multi-objective optimization
Abstract
Solar power is a crucial force in renewable energy. The production of solar photovoltaic (PV) has nonnegligible effects on the environment, and the economic properties of various PV technologies range. Life Cycle Assessment (LCA) and Multi-objective Optimization (MOO) methodologies were utilized in this research to establish an optimization model of PV technology regional planning that took into account combined environmental impacts and Electricity Supply Cost (ESC). Through NSGA-II genetic algorithm, the Pareto optimal solution with the minimum environmental pollution and the lowest economic cost in the whole life cycle of solar PV technology is obtained. The results demonstrated that toxic environmental impacts are the primary categories of crystalline silicon PV panels' potential environmental impacts, and monocrystalline silicon PV technology is the more advantageous choice when considering environmental impact and ESC. Results of sensitivity analysis indicated that uncertain factors would affect the scheme selection. This research offers a planning methodology for regional sustainable development of solar PV technology considering both environmental and economic objectives.
... In the effort to optimize photovoltaic (PV) systems, various research studies contribute to a range of methodologies. For example, Liu et al. (2023) employ the slime mold algorithm (SMA), Yuan et al. (2023) utilize multi-objective optimization (MOO) techniques, and Dezhdar et al. (2023) apply response surface methodology (RSM). Together, these efforts contribute to advancing more efficient and versatile approaches in solar energy systems. ...
... It has been observed that the performance assessment is combined with energy optimization (Kiehbadroudinezhad et al. 2022;Rigogiannis et al. 2022;Bedair et al. 2023;Dezhdar et al. 2023). The economic analysis is mostly enhanced by environmental impact (Daniela-Abigail et al. 2022;Cetina-Quiñones et al. 2023;Yuan et al. 2023). Notably, energy optimization emerges as the most extensively addressed area among researchers. ...
... Liu et al. (2023) applied the slime mold algorithm (SMA) with nanofluid-aided geothermal PV hybrids, exploring shrinkage simulation intricacies and providing standardized environmental impact values (2.10/m 2 for monocrystalline and 3.31/m 2 for polycrystalline PV panels) in the production phase. Yuan et al. (2023) utilized multi-objective optimization (MOO) to analyze "monocrystalline silicon PV," emphasizing the need to minimize environmental pollution and reduce economic costs throughout its life cycle. Scheme III is optimal for an irradiance of 160 W/m 2 but not for 120 W/m 2 . ...
Purpose
Solar energy, especially through photovoltaic systems, is a widespread and eco-friendly renewable source. Integrating life cycle cost analysis (LCCA) optimizes economic, environmental, and performance aspects for a sustainable approach. Despite growing interest, literature lacks a comprehensive review on LCCA implementation in photovoltaic systems. The purpose of this review is to identify key factors influencing LCCA in photovoltaic systems and to propose a general framework for its sustainable implementation such as energy output, initial investment, maintenance costs, environmental impact, and financing schemes.
Methods
Methodology involves meticulous data extraction from databases, screening titles, abstracts, and full texts, along with keyword analysis, leading to a detailed review of 67 articles.
Results
This review explores LCCA’s significance in global photovoltaic system evaluation, encompassing performance, energy optimization, environmental impacts, and economic dimensions. Key findings show that LCCA is essential for improving economic viability and environmental sustainability. Additionally, the proposed framework incorporates performance assessment, cost–benefit analysis, energy optimization, and environmental sustainability.
Conclusions
This review highlights the critical role of LCCA in optimizing photovoltaic systems by addressing key economic, environmental, energy, and performance factors. By proposing a comprehensive framework, it offers practical insights for both researchers and practitioners to enhance the decision-making process, leading to more sustainable and cost-effective photovoltaic implementations. Future research should focus on refining LCCA models and integrating emerging technologies to further improve the accuracy and applicability of the analysis.
... OLAR energy is a mainstream renewable energy source, and solar power generation is considered an important means to address future climate change and achieve carbon neutrality goals [1], [2], [3]. In recent years, the scale and number of solar power plants around the world have been developing rapidly, and China is very rich in solar energy resources, accounting for approximately 17% of the world's installed capacity in the past decade [4], [5], [6]. ...
... . METHODS This study used a pixel-based random forest (RF) algorithm [35] to map the SPs of China from 2000 to 2022 in the GEE platform [36] and analyzed their spatial distribution pattern and change. The workflow of the SP extraction could be divided into three parts ( Fig. 3): (1) obtaining sample data, (2) constructing the random forest classifiers and extracting the SPs, and (3) postprocessing the extraction results. The specific process will be explained in detail below. ...
... Finally, a total of 21,700 SP sample points and 667,700 non-SP sample points were obtained across the country (Fig. 4). Given the considerable discrepancy between SP and non-SP sample points, this study allocated distinct weights to the various sample types, with the number of different types of samples in the process of model construction serving as the determining factor (Eqs. (1)(2)). To balance out the discrepancy in the number of SP and non-SP sample points, different weights will be assigned to them. ...
Solar power generation is an effective way to reduce carbon emissions and has a wide range of applications worldwide. China's newly installed photovoltaic capacity has ranked first in the world in recent years. Timely and accurate monitoring of the spatiotemporal distribution characteristics of solar power plants is essential to optimize China's renewable energy power distribution and achieve carbon reduction targets. However, long-term solar panel (SP) datasets are still lacking. Based on the Google Earth Engine platform, this study proposed a fine extraction method framework of SPs in large and complex geographical environments by integrating stratified sampling and zonal modeling and obtained the first comprehensive dataset of SP distribution in China covering 2000 to 2022 to fill the gap in relevant research and practice. It has been verified that the F1-score of the SP datasets was higher than 0.87, which could meet the research needs. On this basis, this study revealed the spatiotemporal variation and development trends of SPs in China over the past 20 years. The proposed method framework can serve as a scientific reference for relevant research, while the interpreted SP datasets and analysis results can provide a basis for regional decision-making and related research in the context of future climate change.
... Some researchers in China have also studied PV systems. Past studies have relied on life cycle assessments (LCAs) to identify environmental pollutants and key impact points throughout the life cycle of PV systems, encompassing production, operation, retirement, and recycling stages Diao & Shi, 2011;Fu et al., 2015;Hong et al., 2016;Huang et al., 2017;Xie et al., 2018;Yuan et al., 2023;Yang et al., 2015;Yu et al., 2017). For instance, Chen et al. (2016) found that impacts such as human toxicity, marine ecotoxicity, and metal depletion dominate the overall environmental burden of monocrystalline silicon PV production in China due to the consumption of silver paste, electricity, and glass. ...
... For instance, Chen et al. (2016) found that impacts such as human toxicity, marine ecotoxicity, and metal depletion dominate the overall environmental burden of monocrystalline silicon PV production in China due to the consumption of silver paste, electricity, and glass. Yuan et al. (2023) contended that "toxic" environmental impacts (industrial pollutants, heavy metals, and carcinogens emitted during production and installation) are a significant portion of PV production's environmental footprint. Xie et al. (2018) evaluated that the environmental impact of PV systems is equivalent to 4.5% of the impact of China's existing coal-fired power systems, and that most pollutants generated can be paid back within the expected lifetime of a PV system. ...
This article studies how to enhance the deployment efficiency of photovoltaics (PVs) and reduce the environmental pollution process of end‐of‐life products through recycling. We consider realistic constraints such as recycling opportunities, resource and mineral supplies, waste treatment capabilities, and climate goals for PV development. To do this, we model the entire life cycle of PV modules and take into account the waste and greenhouse gases generated during their production. We used the established model to estimate China's future photovoltaic deployment strategy and determine the optimal trajectory for related resource utilization. Our analysis results show that given the long‐term PV development target of 2000 GW toward 2070, it is predicted that the total number of scrapped photovoltaics will reach 900 GW in 2060–2070. By then, the copper, zinc, and aluminum metals that can be recycled can each reach about 2 million tonnes. In addition, we also found that recycling can greatly reduce the environmental pollution caused by end‐of‐life photovoltaics, and analyzed the negative impact of different recycling strategies on the environment. Finally, we put forward several suggestions for reducing environmental pollution through the resource recycling process.
... China accounts for 28% of the global carbon emissions [7], and PV industry development was initiated in the early 21st century, with the aim of achieving carbon neutrality [10,11]. By 2021, China ranked first in terms of the installed PV capacity [12]. ...
Solar energy plays a crucial role in mitigating climate change and transitioning toward green energy. In China (particularly Northwest China), photovoltaic (PV) development is recognized as a co-benefit and nature-based solution for concurrently combating land degradation and producing clean energy. However, the existing literature on the subject is limited to the local effects of PV power station construction and ignores the spillover environmental effects in distant regions. Thus, a hotspot of PV development in Northwest China was selected as a case to quantify the spill-over impacts of PV development in Qinghai Province on cross-regional economy and the environment using an environmentally extended multi-regional input–output approach and related socioeconomic and environmental statistical data. A cross-regional carbon footprint analysis revealed that the eastern region of Qinghai Province had the highest carbon footprint, followed by the southwestern, central, southern, northwestern, northern, and northeastern regions; the production and supply sectors of electricity and heat were the primary sources of carbon emissions, followed by metal smelting and rolling processing products, non-metallic mineral products, and the transportation, warehousing, and postal sectors. In addition, the PV development in Qinghai Province strongly supports the electricity demand in the central and eastern coastal areas, while substantially reducing the carbon emissions in the eastern, southwestern, and central regions (through the distant supply of PV products). We quantified the spillover effects of PV development in Qinghai Province and address the challenges of PV development in the carbon emission reduction strategies implemented at the regional and cross-regional scales; our findings will support policymakers in developing plans that ensure sustainable energy supply and help China to achieve its carbon neutrality goals.
... There have been studies covering the life cycle assessment of photovoltaic panels, e.g., [9][10][11]. However, achieving carbon neutrality in the implementation of PV should not only focus on eliminating the negative impact on the natural environment. ...
The sustainability of products remains a challenge, mainly due to the lack of consistent approaches for simultaneously taking into account the key criteria of the concept in the process. This research aims to develop an eco-innovative QLCA method to create new product solutions that integrate quality (customer satisfaction) and environmental impact assessment throughout the product life cycle. The QLCA method includes: (i) product prototyping according to quality and environmental criteria; (ii) prospective assessment of the quality of prototypes, taking into account customer requirements; (iii) prospective life cycle assessment of product prototypes using a cradle-to-grave approach in accordance with ISO 14040; and (iv) setting the direction of product development while taking into account the fulfilment of customer expectations and the need to care for the environment throughout the product life cycle. Owing to the lack of previous research in this area, as well as the popularity of photovoltaic (PV) panels in reducing greenhouse gases, an illustration was obtained and test of the method was carried out on the example of silicon photovoltaic panel modules (Crystalline Si PV Module). In accordance with the adopted assumptions, the results of the QLCA method test showed that the modelled PV prototypes will, in most cases, be satisfactory for customers, but they still require improvement actions to reduce carbon dioxide (CO2) emissions throughout their life cycle. These activities should be consistent so as to achieve quality that satisfies customers. The QLCA method can be used by designers, managers, and decision-makers at the early stages of design, but also during the product maturity phase for its sustainable development.
... Through the NSGA-II genetic algorithm, Yuan et al. [36] obtained the Pareto optimal solution with the minimum environmental pollution and the lowest economic cost in the whole life cycle of solar PV technology. The results of their research demonstrated that toxic environmental impacts are the primary categories of crystalline silicon PV panels' potential environmental impacts, and monocrystalline silicon PV technology is the more advantageous choice when considering environmental impact and electricity supply cost. ...
This study aims to develop a model for the closed-loop supply chain of photovoltaic (PV) systems. The primary objective addresses strategic and tactical decision-making using a two-stage approach. To pinpoint suitable locations for solar power plants, the PROMETHEE II method is utilized, which is a component of multi-attribute decision making (MADM) approaches. Next, a multi-objective modeling of the closed-loop PV supply chain is conducted. This model aims to minimize total supply chain costs, reduce environmental impacts, mitigate adverse social effects, maximize the on-time delivery (OTD) of manufactured products, and maximize market share. Additionally, a robust fuzzy mathematical model is introduced to examine the model’s sustainability under various uncertainties. An evaluation of the effectiveness and utility of this model is conducted in Tehran city. Furthermore, a comprehensive analysis of various supply chain costs indicates that production centers have the highest costs, while separation centers have the lowest costs.
... Luz et al. [26] presented a MOO model for power expansion planning with a high share of renewable energy, especially promoting the use of PV, while maximizing the share of non-hydro renewable energy and generation at the peak load were established along with the cost objective in their model. Yuan et al. [28] established a MOO model to determine the regional planning of PV technology, taking into account the objectives of minimizing the total environmental impact besides minimizing the electricity supply cost. ...
Increasing photovoltaic (PV) installed capacity is considered a primary way to achieve carbon neutrality targets. However, the rapid development has led to the PV market saturation in some areas, and solar curtailment has even occurred due to the oversupply. With more demand for PV installation in the future, the question of where to efficiently deploy PV systems to both meet the increasing PV electricity demand and alleviate the mismatch between PV supply and local demand becomes relevant. To explore these issues, this paper proposes an integrated framework for planning the PV installed capacity allocation. The framework can be decomposed into two stages: at the optimization stage, a multi-objective optimization (MOO) model is proposed, integrating the minimization of mismatch between PV supply and local demand as an additional objective along with the traditional economic and environmental profits maximization objective; and at the evaluation stage, an entropyweighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is employed to make the decision of the PV allocation scheme. By applying this integrated model to the Chinese PV deployment, the results reveal that the consideration of the match between PV supply and local demand has a direct influence on the PV installation layout. More than 50% of the PV systems are expected to be in areas with higher demand and higher profits, rather than the areas with high solar irradiance but lower demand. Distributed PV installations are expected to account for 60% of the total installed capacity. The implementation of this PV installation planning is estimated to result in a reduction of 1036 Mt CO2 emission, a 14.2% PV penetration rate, and 566 billion CNY total profits, making it an attractive and feasible instrument for countries striving towards carbon neutrality goals.
... Luz et al. [26] presented a MOO model for power expansion planning with a high share of renewable energy, especially promoting the use of PV, while maximizing the share of non-hydro renewable energy and generation at the peak load were established along with the cost objective in their model. Yuan et al. [28] established a MOO model to determine the regional planning of PV technology, taking into account the objectives of minimizing the total environmental impact besides minimizing the electricity supply cost. ...
The sunny, sparsely populated sand, gravel, and other desert regions known as the Gobi and desert regions (GDRs) have significant advantages and enormous potential in the development of solar resources. Based on the Google Earth Engine (GEE) platform and multi-source datasets, this study used the random forest algorithm to identify the current distribution of GDRs in China, analyzed the carbon mitigation potential (CMP), and further explored the layout optimization strategies for the future development of China's GDRs photovoltaic (PV) industry. Research has found that only 2.25 % of the land in GDRs of the five provincial-level administrative units involved in GDRs was used for PV development, and there was still a large amount of land suitable for PV development; Regional solar energy resources were crucial for PV development, but policy support and terrain characteristics could not be underestimated; When the installed area of GDRs, which is most suitable for installing PV facilities, reaches 50 %, it can meet the electricity consumption of the entire Chinese society in 2022. At this time, the CMP was about 5.39 × 1012 kg CO2. The research results can provide decision-making basis for China's PV industry, and the research framework is also applicable to other similar regions.
All-perovskite tandem solar cells hold the promise of surpassing the efficiency limits of single-junction solar cells1–3; however, until now, the best-performing all-perovskite tandems have exhibited lower certified efficiency than have single-junction perovskite solar cells4,5. A thick mixed Pb-Sn narrow-bandgap subcell is needed to achieve high photocurrent density in tandems⁶; yet this is challenging owing to the short carrier diffusion length within Pb-Sn perovskites. Here we develop ammonium-cation-passivated Pb-Sn perovskites with long diffusion lengths, enabling subcells having an absorber thickness of ~1.2 μm. Molecular dynamics simulations suggest that widely-used phenethylammonium (PEA) cations are only partially adsorbed on the surface defective sites at perovskite crystallization temperatures. The passivator adsorption is predicted to be enhanced using 4-trifluoromethyl-phenylammonium (CF3-PA), which exhibits a stronger perovskite surface-passivator interaction than does PEA. By adding a small amount of CF3-PA into precursor solution, we increase the carrier diffusion length within Pb-Sn perovskites by 2x, to over 5 μm, and increase the efficiency of Pb-Sn perovskite solar cells to over 22%. We report a certified efficiency of 26.4% in all-perovskite tandem solar cells, exceeding that of the best-performing single-junction perovskite solar cells. Encapsulated tandem devices retain >90% of initial performance following 600 hours of operation at the maximum power point under one-sun illumination in ambient conditions.
Effective recycling of spent perovskite solar modules will further reduce the energy requirements and environmental consequences of their production and deployment, thus facilitating their sustainable development. Here, through ‘cradle-to-grave’ life cycle assessments of a variety of perovskite solar cell architectures, we report that substrates with conducting oxides and energy-intensive heating processes are the largest contributors to primary energy consumption, global warming potential and other types of impact. We therefore focus on these materials and processes when expanding to ‘cradle-to-cradle’ analyses with recycling as the end-of-life scenario. Our results reveal that recycling strategies can lead to a decrease of up to 72.6% in energy payback time and a reduction of 71.2% in greenhouse gas emission factor. The best recycled module architecture can exhibit an extremely small energy payback time of 0.09 years and a greenhouse gas emission factor as low as 13.4 g CO2 equivalent per kWh; it therefore outcompetes all other rivals, including the market-leading silicon at 1.3–2.4 years and 22.1–38.1 g CO2 equivalent per kWh. Finally, we use sensitivity analyses to highlight the importance of prolonging device lifetime and to quantify the effects of uncertainty induced by the still immature manufacturing processes, changing operating conditions and individual differences for each module. Effective recycling of worn-out perovskite photovoltaic modules could improve their energy and environmental sustainability. The authors perform holistic life cycle assessments of selected solar cell architectures and provide guidelines for their future design.
Two electrolysis technologies fed with renewable energy sources are promising for the production of CO2-free hydrogen and enabling the transition to a hydrogen society: Alkaline Electrolyte (AE) and Polymer Electrolyte Membrane (PEM). However, limited information exists on the potential environmental impacts of these promising sustainable innovations when operating at large-scale. To fill this gap, the performance of AE and PEM systems is compared, using ex-ante Life Cycle Assessment (LCA), technology analysis and exploratory scenarios for which a refined methodology has been developed to study the effects of implementing large-scale sustainable hydrogen production systems. Ex-ante LCA allows modelling the environmental impacts of hydrogen production, exploratory scenario analysis allows modelling possible upscaling effects at potential future states of hydrogen production and use in vehicles in the Netherlands in 2050. A bridging tool for mapping the technological field has been created enabling to combine quantitative LCAs with qualitative scenarios. This tool also enables diversity for exploring multiple sets of visions. The main results from the paper show, with an exception for the “ozone depletion” impact category, (1) that large-scale AE and PEM systems have similar environmental impacts with variations lower than 7% in all impact categories, (2) that the contribution of the electrolyser is limited to 10% of all impact categories results, and (3) that the origin of the electricity is the largest contributor to the environmental impact contributing to more than 90% in all impact categories, even when renewable energy sources are used. It is concluded that the methodology was applied successfully and provides a solid basis for an ex-ante assessment framework that can be applied to emerging technological systems.
The Zero Emission Neighbourhood (ZEN) concept gives a unique chance to assess the nexus of buildings, mobility, and energy systems to limit global warming and mitigate climate change. ZENs rely on the use of passive-house technologies in combination with local renewable-energy production such as by photo-voltaic (PV) systems to meet the internal energy demand and for export of surplus energy to the external power grid.
We developed a modular LCA model that includes five physical elements: buildings, mobility, infrastructure, networks, and on-site energy. The model is applied on Ydalir, an ambitious ZEN in the early planning phases in Norway. Several scenarios were created to explore alternative mobility patterns and an upscaling of electricity production.
The results show mobility to be the major source of greenhouse-gas emissions, with 62% of the total emissions in a baseline scenario. To reduce the travel distance of the inhabitants, measures such as car-sharing or greater use of public transports are highlighted as the best options to improve the climate performance of Ydalir. An upscaling of the electricity production from PV panels would allow for significantly reduced system-wide emissions if more surplus electricity is exported and can substitute power generated from fossil fuels or replace fossil fuels used in mobility.
Urbanization and population growth have contributed to a tripling of building material consumption from 2000 to 2017. Building materials have a range of environmental impacts throughout their life cycle, from extraction, processing, and transport of raw materials to building construction, use, and eventual demolition and waste. Mitigation measures that target specific materials or value chain stages may therefore have incremental or even adverse net environmental effects. In this perspective, we develop a framework for applying life cycle thinking to identify key impacts and corresponding mitigation approaches, inform building design and material selection, and ensure effective treatment and recycling of construction and demolition wastes. Life cycle evaluation can also be used to assess and avoid environmental trade-offs among life cycle stages. Challenges for implementing these life cycle principles include collecting and integrating inventory data for products, managing multiple stakeholders within the construction industry, and monitoring end-of-life impacts; measures for overcoming such challenges are discussed.
Assessing system costs for power generation is essential for evaluating the economical aspect of energy resources. This paper examines traditional and renewable energy resources under uncertainty and variability of input variables. The levelized cost of electricity (LCOE) of each technology is computed using a global sensitivity analysis. A Monte Carlo approach is utilized to study the thermoeconomics of a variety of power generation methods in the United States: fossil fuel-based, nuclear, developed renewable, and emerging renewable energy resources. The results of this study demonstrate how uncertainties in input data can significantly influence the LCOE values. Power generation from well-developed energy technologies exhibit less variability in LCOE due to established capital costs, operating and maintenance costs, and power generation. On the contrary, emerging renewable energy technologies are subject to high uncertainties in both technical and economic performance, as expected for technologies in early stages of development. A scenario with carbon pricing in power generation is also carried out in the paper. The presence of carbon pricing significantly increases the LCOEs of fossil fuel technologies, and LCOEs of other technologies also experience significant changes when life-cycle carbon assessments are considered. Several cost reduction opportunities are discussed to guide the development of future energy conversion, especially from emerging renewable energy resources.
It is important to have insights into the potential sustainability impacts as early as possible in the development of technology. Solar photovoltaic (PV) technologies provide significant environmental, economic, and social benefits in comparison to the conventional energy sources. Because most previous studies of multi-crystalline silicon (Multi-Si) PV modules discuss the environmental impacts, this study quantitatively assesses the economic and social impacts of China's multi-crystalline silicon (mc-Si) PV modules production stages. The economic analysis is uses life cycle cost analysis, and the social impact analysis is carried out by applying the social index evaluation method. The economic analysis results demonstrate that the main cost of mc-Si PV modules production in China lies in raw materials and labor and the production of Multi-Si PV cells have the highest cost among the five manufacturing processes involved in Multi-Si PV. The result of the social impact analysis reveal that the employment contribution index, S11, is 0.72, indicating that Multi-Si PV modules production in China has a prominent contribution to employment in comparison with other industries; the labor civilization degree, S12 (i.e., the proportion of mental labor involved in a given job), and labor income contribution index, S13, are both approximately 0.6, indicating that Multi-Si PV modules production has a less-significant labor level and income contribution in comparison with other industries; the production capacity contribution index, S14, is merely 0.183, indicating that production of Multi-Si PV modules does not contribute significantly to the gross domestic product (GDP). Based on the results of these evaluations, some recommendations to improve the economic and social impact of Multi-Si PV modules production in China are presented, including support for research on polycrystalline silicon production for the purpose of reducing the raw material cost, as well as upgrading manufacturing facilities and implementing the corresponding production training in order to promote the labor civilization degree.
This paper reviews the economics of solar power as a source of grid-connected electricity generation. It is widely acknowledged that costs of solar power have declined, but there is disagreement how its economic value should be calculated. Grid parity', comparing generation costs to the retail price, is an often used yet flawed metric for economic assessment, as it ignores grid fees, levies, and taxes. It also fails to account for the fact that electricity is more valuable at some points in time and at some locations than that at others. A better yardstick than the retail price is solar power's market value'. This paper explains why, and provides empirical estimates of the solar market value from a literature review, German spot market analysis, and the numerical electricity market model EMMA. At low penetration rates (<2-5%) solar power's market value turns out to be higher than the average wholesale electricity price - mainly, because the sun tends to shine when electricity demand is high. With increasing penetration, the market value declines - the solar premium turns into a solar penalty. In Germany, the value of solar power has fallen from 133% of the average electricity price to 98% as solar penetration increased from zero to 4.7%. This value drop is steeper than wind power's value drop, because solar generation is more concentrated in time. As a consequence, large-scale solar deployment without subsidies will be more difficult to accomplish than many observers have anticipated.
Global energy and environmental problems are increasing in severity. Countries worldwide are more concerned about and are paying greater attention to strengthening their energy-saving and emissions-reduction efforts, protecting the environment and promoting the development of new energy. Solar energy is becoming the mainstream of the global power industry because of its significant resources and low cost. This paper provides an overview of the current state-of-the-art of photovoltaic electricity technology (‘photovoltaic’ or PV) in China and addresses its potential for future cost reductions. This paper analyses the relationship between current renewable energy costs and cumulative production, development and demonstration expenditures, and other institutional influences. The theoretical framework of a learning curve offers a complete methodology for examining the underlying capital cost trajectory when developing electricity cost estimates used in energy policy planning models. The cumulative production needed to achieve ‘break-even’ (the point at which PV is competitive with conventional alternatives) is estimated for a range of learning curve parameter values. The social cost (pollution costs and value-added tax considered) of PV is calculated, and the question of whether and how the ‘cost cap’ can be bridged is posed, the latter being the difference between what this cumulative production will cost and what it would cost if it could be produced at a currently competitive level. We also estimate how much PV could gain if the external costs (attributable to environmental and health damage) of energy were internalised, such as through an energy tax. We use the simulated results to provide suggestions for relevant PV industrial policymaking.
Life cycle assessment on monocrystalline silicon (mono-Si) solar photovoltaic (PV) cell production in China is performed in the present study, aiming to evaluate the environmental burden, identify key factors, and explore approaches for potential environmental improvement. Results show that the impact generated from the categories of human toxicity, marine ecotoxicity, and metal depletion contribute dominantly to the overall environmental burden because of silver (Ag) paste, electricity, and glass consumption. The energy payback time and greenhouse gas emission range from 0.42 to 0.91 years and 5.60e12.07 g CO 2 eq/kWh respectively, both of which are lower than the previously reported results in studies in Europe, the United States, and Asia. However, compared with coal-based electricity generation that uses ultra-supercritical technology, the environmental payback time in human toxicity, marine ecotoxicity, and metal depletion categories is quite high because of the direct air emissions of lead, arsenic, mercury, copper, and nickel, as well as the use of Ag. Additionally, utilization of PV systems in regions with high solar radiation values has a high potential environmental benefit from PV systems.
Moving from fossil fuels toward renewable resources of energy has a worldwide consensus. Solar energy alone can satisfy all our energy requirements since the earth receives 725 ZJ of energy from the sun each year while total human energy consumption in 2019 was 0.584 ZJ. The 2010s are highlighted as a transitional decade when the photovoltaic conversion industry transformed from a subsidized to a profitable energy sector. While photovoltaic energy conversion is a clean process, technologies for producing photovoltaic materials and solar panels affect the environment. The utilization of photovoltaic materials with low impact on the environment during the entire life cycle will mark the beginning of the sustainable transition toward 100% clean renewable energy sources in a sustainable manner. Thus far, only perovskite compounds have the potential to satisfy these requirements because of their theoretical conversion efficiencies, ease of synthesis, production scalability, adaptability, and comparability to existing photovoltaic systems. In this article, the rise of the photovoltaic industry in the last decade is shown and requirements in further transition from renewable to clean sources of renewable energy are foreseen.
The evaluation of energy efficiency is a key principle in agroecosystem management. In this article, the optimal cropping pattern of irrigated and rainfed lands in the eastern Lorestan province is proposed using multi-objective nonlinear programming (MOP) to maximize net profit, energy efficiency, and net energy and minimize non-renewable energy and GWP. The results showed that in the MOP pattern, the indirect energy levels of irrigated and rainfed lands decreased by 1.90E+13 and 4.80E+13 J in the study area, respectively; non-renewable energy levels decreased by 3.00E+12 and 3.20E+13 J, respectively; and the GWP levels decreased by 1.00E+06 and 9.00E+05 kg CO2-eq in the region compared to the existing pattern. In the MOP pattern, the energy efficiency of irrigated and rainfed lands increased by 0.20 and 0.19, respectively, compared to the existing state. By implementing the optimal cropping pattern, energy inputs can be reduced by 1.20E+14 J and net profit increased by 968,483 USD. By using the proposed pattern, in addition to selecting the appropriate model and optimizing the use of water and land resources, effective steps can be taken for enhancing the profit and declining the energy consumption and environmental impacts.
Demand for electricity is expected to increase significantly in the coming decades, one reason being the ongoing decarbonization process of the power and the transportation sectors, in an effort to reduce greenhouse gas emissions and thus alleviate climate change. Photovoltaics that harvest solar energy, coupled with energy storage systems are addressing these challenges effectively. In the current study, the simulated energy winnings from typical photovoltaic-battery (PV-BAT) configurations were economically evaluated, under equal technical and site-specific meteorological conditions. Furthermore, their capital, replacement, operation, and maintenance costs were inquired and the average unit cost of electricity per kWh was estimated based on specific energy cost estimation methods and a Monte-Carlo analysis addressing uncertain meteorological risk factors. The cost of electrical energy production in Greece was examined for six scenarios with varying battery technologies and module topologies. Calculated costs ranged from 0.17 to 0.24 €/kWh indicating a significant downward trend in the unit cost of electricity generated by PV-BAT systems. These findings indicate the need for further investigation into how the integration and utilization of such systems can be optimized. The proposed methodology is developed in line with the circular economy action plan which requires increased system efficiency, storage and renewable energy use.
As a consequence of the photovoltaic (PV) market expansion in the last 20 years, the cumulative global PV waste is expected to exponentially grow. A proper disposal of decommissioned PV panels is crucial for avoiding environmental risks and for recovering value-added materials. In this study, a Life Cycle Assessment (LCA) was performed in order to assess the environmental performance of a new recycling process for crystalline silicon (c-Si) PV panels, at the End of Life (EoL). The process was developed in the framework of the ReSiELP (Recovery of Silicon and other materials from the End-of-Life Photovoltaic Panels) project, aiming at recovering valuable resources from EoL PV c-Si modules and making the recovered materials readily available for different supply chains, in line with the principles of Circular Economy. A “gate to gate” approach was used to investigate two lines of activities: (i) the Recovery line, dedicated to the recovery of secondary raw materials from EoL c-Si PV panels, namely aluminium, copper, glass, silver and silicon, and (ii) the Glass reuse line, for the employment of the recovered glass in prefabricated building components (predalles slabs). The results highlight energy consumption, chemicals and transportation as the main hotspots of the ReSiELP process. For a comprehensive evaluation, the generated loads were compared with the potential environmental benefits gained thanks to the recovery of aluminium, at the largest extent. Overall, the LCA analysis showed that the investigated process is environmentally favorable, also when compared to other EoL PV panels recycling scenarios reported in literature.
With growing maintenance demands whereas limited budgets, an integrated methodology unifying life-cycle assessment (LCA), life cycle cost assessment (LCCA) and multi-objective optimization is proposed to establish maintenance decision-making system, considering performance, economic as well as environmental dimensions. Firstly, system scope and unit process of the methodology are redefined considering pavement industry in China. Next, assessment models and parameters are determined to establish the inventory. Then, LCCA is adopted to convert environmental footprint into an economically measurable unit - environmental damage cost (EDC). Lastly, a programming optimization is executed to obtain the optimal maintenance schedule over the whole pavement life cycle. To verify, a case study based on an existing highway in southeast China is conducted, considering ECA-10 thin overly (ECA-10), milling and resurfacing the upper layer (MRU) and hot-in-place rehabilitation (HR) strategies. Results indicate that user cost is the dominant factor whereas environmental impact is relatively negligible. Pavement design life and service life of maintenance alternatives have significant influence on the optimal schedule, and longer pavement design life should be considered when conducting LCA-based programming optimization. With the extension of design life, MRU shows the best long-term benefit. Materials and construction are the main stages potentially improved. Few attempts have been made to introduce LCA into the pavement industry of China, but this model successfully contributes to sustainability goal and scientific decision-making.
With the rapid development of renewable energy, the impact on environment and resource caused by waste photovoltaic modules has been realized gradually. To solve the problem, recycling becomes an effectual way. Therefore, the aim of this paper was to assess the economic feasibility of the photovoltaic modules recycling project in China by using cost-benefit analysis. It was found that, under the estimated treatment quantity will generate in China in 2020-2034, the recovery cost per kilowatt (kW) of photovoltaic modules will be 25.11 USD, the unit benefit is 25.68 USD/kW, and the unit net benefit is 0.57 USD/kW. The net present value (NPV) and benefit-cost ratio (BCR) are 21.14 million USD and 1.023. The sensitivity analysis indicated that the sale benefits of recycled materials and tax were the most sensitive factors affecting the project's economy. Finally, we proposed ways to improve the economy for the government and related enterprises.
Multi-objective optimization (MOO) has emerged as the preferable approach to tackle sustainability problems. The solution of MOO models is generally expressed as a set of Pareto optima, representing optimal trade-offs between given criteria. Identifying the final Pareto solution from this set for practical implementation is a challenging task, particularly when several criteria and decision-makers are involved in the analysis. In this work, we propose the rigorous method based on bilevel optimization and MOO to support decision-making in the post-optimal analysis of Pareto fronts. Our approach is capable of selecting non-dominated solutions which are particularly appealing for decision-makers, quantifying the distance between any suboptimal point of a MOO model and its Pareto front, and establishing improvement targets for suboptimal solutions that if attained would make them optimal. Overall, our method allows analyzing Pareto fronts and identifying a final Pareto point to be implemented in practice without the need to define explicit weights. We illustrate the capabilities of our approach through its application to the optimization of the UK electricity mix according to several sustainability indicators.
A Life Cycle Assessment (LCA), using the end-point damage model (CEDM) of impact assessment, was conducted, to analyse the environmental impacts and pollutant payback times of photovoltaic production, including solar-grade silicon, silicon wafers, silicon solar cells and photovoltaic panels, in China. The inputs and outputs were obtained using site investigation, questionnaires, and field monitoring, and a method of aggregating the data into an industry-level database was established. The production yield of the studied samples accounted for an average 66% of the national yield in 2013. The results showed that the respiratory-inorganics and fossil-fuel categories contributed the most impact, because of the large electricity consumption required. Recent technological advances in raw material reduction and energy savings are the primary pathways to decreasing the environmental impacts. The environmental impact of a photovoltaic system is equivalent to 4.5% of that of the impact of the current coal-based electrical power system in China. The pollutant payback times of chemical oxygen demand, chloride, fluoride, ammonia gas, nitric oxide, sulfur dioxide, hydrogen chloride, hydrogen fluoride and carbon dioxide are 5.11, 1.02, 25.1, 8.01, 0.831, 0.784, 0.716, 1.12 and 0.884 years, respectively, indicating that most of the pollutants could be paid back within the expected lifetime of a photovoltaic system. Therefore, installing photovoltaic systems could reduce not only the consumption of non-renewable energy, but also the emitted pollutants, decreasing the environmental impacts of electricity generation.
Increasing needs for decision support and advances in scientific knowledge within life cycle assessment (LCA) led to substantial efforts to provide global guidance on environmental life cycle impact assessment (LCIA) indicators under the auspices of the UNEP-SETAC Life Cycle Initiative. As part of these efforts, a dedicated task force focused on addressing several LCIA cross-cutting issues as aspects spanning several impact categories, including spatiotemporal aspects, reference states, normalization and weighting, and uncertainty assessment. Here, findings of the cross-cutting issues task force are presented along with an update of the existing UNEP-SETAC LCIA emission-to-damage framework. Specific recommendations are provided with respect to metrics for human health (Disability Adjusted Life Years, DALY) and ecosystem quality (Potentially Disappeared Fraction of species, PDF). Additionally, we stress the importance of transparent reporting of characterization models, reference states, and assumptions, in order to facilitate cross-comparison between chosen methods and indicators. We recommend developing spatially regionalized characterization models, whenever the nature of impacts shows spatial variability and related spatial data are available. Standard formats should be used for reporting spatially differentiated models, and choices regarding spatiotemporal scales should be clearly communicated. For normalization, we recommend using external normalization references. Over the next two years, the task force will continue its effort with a focus on providing guidance for LCA practitioners on how to use the UNEP-SETAC LCIA framework as well as for method developers on how to consistently extend and further improve this framework.
This chapter describes how to optimise energy systems considering their economic and environmental performance simultaneously. To this end, we follow an approach that combines life cycle assessment with multi-objective optimisation. We illustrate how to apply such a framework to the strategic design and planning of biofuel supply chains. The problem is formulated in mathematical terms as a multi-objective mixed-integer linear programme. The aim of the design/planning task is to maximise the net present value while the environmental impact is minimised simultaneously. Eco-indicator 99 is the life cycle assessment methodology incorporated in the model to quantify the environmental damage. The implementation of the algorithm in a case study based on the Argentine industry reveals the conflictive trade-off between economic and environmental objectives. The proposed framework provides valuable insight into the incidence of key operational features in the optimal biofuel supply chain network.
Assessments of the environmental impacts of energy generation and storage technologies are essential in evaluating their sustainability. Life-cycle assessment is a well-accepted tool for investigating the environmental profile of energy technologies describing, in detail, material and energy flows and emissions into the environment that may occur at each life stage (e.g., mining of materials, processing and their purification, manufacturing, installation, operation, decommissioning, and disposal or recycling). In this chapter, we describe the environmental footprint of three commercial photovoltaic technologies, that is, monocrystalline silicon, multicrystalline silicon, and cadmium telluride, mounted on flat, fixed ground-mounted systems. In addition to environmental metrics, we quantify life-cycle risk indicators (i.e., fatalities, injuries, and maximum consequences) in a comparative context with other electricity generation pathways.
In this paper we present a life cycle assessment (LCA) based biofuel supply chain model with multi-conversion pathways. This model was formulated as a mixed integer linear programming (MILP) problem which took economic, energy, and environmental criteria (3E) into consideration. The economic objective was measured by the total annual profit. The energy objective was measured by using the average fossil energy input per megajoule (MJ) of biofuel. The environmental objective was measured by greenhouse gas (GHG) emissions per MJ of biofuel. After carefully consideration of the current situation in China, we chose to examine three conversion pathways: bio-ethanol (BE), bio-methanol (BM) and bio-diesel (BD). LCA was integrated to a multi-objective supply chain model by dividing each pathway into several individual parts and analyzing each part. The multi-objective MILP problem was solved using a epsilon-constraint method by defining the total annual profit as the optimization objective and assigning the average fossil energy input per MJ biofuel and GHG emissions per MJ biofuel as constraints. This model was then used to design an experimental biofuel supply chain for China. A surface of the Pareto optimal solutions was obtained by linear interpolation of the non-inferior solutions. The optimal results included the choice of optimal conversion pathway, biomass type, biomass locations, facility locations, and network topology structure in the biofuel supply chain. Distributed and centralized systems were also factored into our experimental system design. In addition, the influence of price change on the optimal solutions was investigated. The optimal solutions obtained in this study reveal a tradeoff between the impact of the 3E criteria. These results indicate that our model will be extremely useful for the design and planning of biofuel supply chains in China.
In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms (GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias toward some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously. The proof-of-principle results obtained on three problems used by Schaffer and others suggest that the proposed method can be extended to higher dimensional and more difficult multiobjective problems. A number of suggestions for extension and application of the algorithm are also discussed.
The purpose of this study was to identify a suitable type of mega-solar system from an environmental viewpoint. The authors evaluated six types of 20 different PV modules by life cycle analysis (LCA) with actual equipment data and output. The types were single crystal silicon (sc-Si), amorphous silicon (a-Si)/sc-Si, multicrystalline silicon (mc-Si), a-Si, microcrystalline silicon (µc-Si)/a-Si and CIS. The boundaries of LCA were from the mining stage to that of waste management. Mining, manufacturing and waste management information was taken from an LCA database, while data on transport, construction and amounts of equipment were obtained from actual systems. Since the irradiation figures and electricity output were also actual data, we could avoid the difficulties of making assumptions for values such as the actual output power of thin films. In addition, installation at a single plant provided suitable conditions for comparing PV systems.
The results showed an energy requirement ranging from 19 to 48 GJ/kW and an energy payback time of between 1.4 and 3.8 years. CO2 emissions were from 1.3 to 2.7 t-CO2/kW, and CO2 emission rates ranged from 31 to 67 g-CO2/kWh. The multicrystalline (mc-Si) and CIS types showed good results because mc-Si and CIS PV modules have high efficiency and a lower energy requirement. In particular, the CIS module generated more electricity than expected with catalogue efficiency. The single crystal silicon PV module did not produce good results because, considering their energy requirement, installed sc-Si PV modules do not have high efficiency. However, the operation data used covered only 1 year; data from a longer period should be collected to obtain long-term irradiation figures and clarify degradation. Copyright
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