High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation

Achieving net zero energy heating by integrating the building envelope as a thermal battery

Photovoltaic (PV) heating is a promising technology for achieving fossil fuel-free heating and carbon neutrality in the building sector. Cost-effective energy storage plays a critical role in PV heating to solve the temporal mismatch between supply and demand. Herein, we propose the concept of using a building envelope as an active energy-storage device for a PV heating system, thus transforming the building envelope into a thermal battery. Experimental results show that the energy storage capacity of 142 kW h/m2, which is higher than that of conventional thermal storage systems. We developed a top-down macro performance assessment model to quantify the contribution of a PV heating system using a building envelope as energy storage. By our estimation, the envelope-embedded system can reduce heating-related CO2 emissions by 7435.7 tons annually in northern China. Our study provides insights into innovative energy-saving building energy storage systems that can help achieve global carbon neutrality and sustainability.

Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050

Assessment of current and future growth in the global rooftop area is important for understanding and planning for a robust and sustainable decentralised energy system. These estimates are also important for urban planning studies and designing sustainable cities thereby forwarding the ethos of the Sustainable Development Goals 7 (clean energy), 11 (sustainable cities), 13 (climate action) and 15 (life on land). Here, we develop a machine learning framework that trains on big data containing ~700 million open-source building footprints, global land cover, road, and population datasets to generate globally harmonised estimates of growth in rooftop area for five different future growth narratives covered by Shared Socioeconomic Pathways. The dataset provides estimates for ~3.5 million fishnet tiles of 1/8 degree spatial resolution with data on gross rooftop area for five growth narratives covering years 2020-2050 in decadal time steps. This single harmonised global dataset can be used for climate change, energy transition, biodiversity, urban planning, and disaster risk management studies covering continental to conurbation geospatial levels.

Environmental implications and levelized cost analysis of E-fuel production under photovoltaic energy, direct air capture, and hydrogen

The ecological transition in the transport sector is a major challenge to tackle environmental pollution, and European legislation will mandate zero-emission new cars from 2035. To reduce the impact of petrol and diesel vehicles, much emphasis is being placed on the potential use of synthetic fuels, including electrofuels (e-fuels). This research aims to examine a levelised cost (LCO) analysis of e-fuel production where the energy source is renewable. The energy used in the process is expected to come from a photovoltaic plant and the other steps required to produce e-fuel: direct air capture, electrolysis and Fischer-Tropsch process. The results showed that the LCOe-fuel in the baseline scenario is around 3.1 €/l, and this value is mainly influenced by the energy production component followed by the hydrogen one. Sensitivity, scenario and risk analyses are also conducted to evaluate alternative scenarios, and it emerges that in 84% of the cases, LCOe-fuel ranges between 2.8 €/l and 3.4 €/l. The findings show that the current cost is not competitive with fossil fuels, yet the development of e-fuels supports environmental protection. The concept of pragmatic sustainability, incentive policies, technology development, industrial symbiosis, economies of scale and learning economies can reduce this cost by supporting the decarbonization of the transport sector.

Can Solar Photovoltaic Poverty Alleviation Policies Reduce Carbon Emissions and Increase Income in China?

China implemented a solar photovoltaic (PV) poverty alleviation (PVPA) policy of building nearly 0.24 million PVPA power plants in 2014-2020 to fight poverty. However, our current knowledge of its effects, encompassing not only primary poverty alleviation but also secondary objectives such as carbon emission-reduction, remains comparatively constrained. Here, we present a comprehensive assessment of the emission-reducing and income-increasing effects of the PVPA policy using estimated carbon emission factors and a staggered difference-in-difference model based on integrated data from almost all actual PVPA plants in China. Our analysis revealed the co-benefits of emission-reduction and poverty alleviation, with PVPA policy boosting villagers' per capita net income by 2-3% in villages with PV plants. A nonlinear, inverted U-shaped relationship between income and PVPA plant investment was identified with a $2.21 million inflection point. Spatial heterogeneity was observed in the income-increasing effect, with centralized village-level plants proving more effective than rooftop household plants. China's PVPA plants reduced carbon emissions by nearly 3% in 2020 and are projected to generate 774 billion kW h of electricity by 2045, mitigating 715.75 million tons of carbon emissions. The findings from this research offer insights for optimizing antipoverty and climate change policies to facilitate sustainable development goal achievement in China and other developing nations.

The momentum of the solar energy transition

Decarbonisation plans across the globe require zero-carbon energy sources to be widely deployed by 2050 or 2060. Solar energy is the most widely available energy resource on Earth, and its economic attractiveness is improving fast in a cycle of increasing investments. Here we use data-driven conditional technology and economic forecasting modelling to establish which zero carbon power sources could become dominant worldwide. We find that, due to technological trajectories set in motion by past policy, a global irreversible solar tipping point may have passed where solar energy gradually comes to dominate global electricity markets, without any further climate policies. Uncertainties arise, however, over grid stability in a renewables-dominated power system, the availability of sufficient finance in underdeveloped economies, the capacity of supply chains and political resistance from regions that lose employment. Policies resolving these barriers may be more effective than price instruments to accelerate the transition to clean energy.

Inherent spatiotemporal uncertainty of renewable power in China

Solar and wind resources are vital for the sustainable energy transition. Although renewable potentials have been widely assessed in existing literature, few studies have examined the statistical characteristics of the inherent renewable uncertainties arising from natural randomness, which is inevitable in stochastic-aware research and applications. Here we develop a rule-of-thumb statistical learning model for wind and solar power prediction and generate a year-long dataset of hourly prediction errors of 30 provinces in China. We reveal diversified spatiotemporal distribution patterns of prediction errors, indicating that over 60% of wind prediction errors and 50% of solar prediction errors arise from scenarios with high utilization rates. The first-order difference and peak ratio of generation series are two primary indicators explaining the uncertainty distribution. Additionally, we analyze the seasonal distributions of the provincial prediction errors that reveal a consistent law in China. Finally, policies including incentive improvements and interprovincial scheduling are suggested.

Splashing of tungsten-based anode during arc discharge

A unique mechanism of splashing from a tungsten-based anode was identified during arc discharge. Splashing occurred by breakoff of a liquid metal column, which elongates after a local concavity formed on the molten anode surface. Blue-violet luminescence, emitted by cerium ions originating from additives in the tungsten-based anode, was captured before the concavity formation. The surface temperature exceeded the boiling point of the additives at the time of splashing. The measured droplet speeds suggested that an electromagnetic force contributes the high-speed ejections. Energy dispersive spectrometry mapping also exhibited a remnant of the additives on the longitudinal cross-section of the anode after arc discharge. Based on these experimental facts, the mechanism of anode splashing in arc discharge was deduced as follows: bubble formation of additives at temperatures above their boiling point, bubble bursting at the surface, micro-plasma jet generation, liquid-column elongation and breakoff under an electromagnetic force, and consequent high-speed droplet ejection.

Carbon mitigation potential afforded by rooftop photovoltaic in China

Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km² rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries.

Overcoming the coupled climate and biodiversity crises and their societal impacts

Earth's biodiversity and human societies face pollution, overconsumption of natural resources, urbanization, demographic shifts, social and economic inequalities, and habitat loss, many of which are exacerbated by climate change. Here, we review links among climate, biodiversity, and society and develop a roadmap toward sustainability. These include limiting warming to 1.5°C and effectively conserving and restoring functional ecosystems on 30 to 50% of land, freshwater, and ocean "scapes." We envision a mosaic of interconnected protected and shared spaces, including intensively used spaces, to strengthen self-sustaining biodiversity, the capacity of people and nature to adapt to and mitigate climate change, and nature's contributions to people. Fostering interlinked human, ecosystem, and planetary health for a livable future urgently requires bold implementation of transformative policy interventions through interconnected institutions, governance, and social systems from local to global levels.

The Multiplicity of π-π Interactions of Fused-Ring Electron Acceptor Polymorphs on the Exciton Migration and Charge Transport

Efficient long-range exciton migration and charge transport are the key parameters for organic photovoltaic materials, which strongly depend on the molecular stacking modes. Herein, we extracted the stacked structures of the archetype fused-ring electron acceptor molecule, ITIC, based on the information on four polymorphic crystals and investigated the relationship between molecular stacking modes and exciton migration/charge transport properties through the intermolecular Coulomb coupling and charge transfer integral calculation. Experimentally, the thin film texture is crystallized through a post-annealing treatment through grazing-incidence wide-angle X-ray scattering (GIWAXS) measurements, which lead to the enhanced exciton migration through exciton-exciton annihilation in the femtosecond transient absorption (fs-TA) measurements. This work demonstrates the relationship between the molecular arrangement and the exciton migration and electron transport and highlights the significance of optimizing molecular stacking for the development of high-performance electron acceptor materials.

Reconciling climate action with the need for biodiversity protection, restoration and rehabilitation

Globally, we are faced with a climate crisis that requires urgent transition to a low-carbon economy. Simultaneously, the biodiversity crisis demands equally urgent action to prevent further species loss and promote restoration and rehabilitation of ecosystems. Climate action itself must prevent further pressures on biodiversity and options for synergistic gains for both climate and biodiversity change mitigation and adaptation need to be explored and implemented. Here, we review the key potential impacts of climate mitigation measures in energy and land-use on biodiversity, including the development of renewable energy such as offshore and onshore wind, solar, and bioenergy. We also assess the potential impacts of climate action driven afforestation and native habitat rehabilitation and restoration. We apply our findings to Ireland as a unique case-study as the government develops a coordinated response to climate and biodiversity change through declaration of a joint climate and biodiversity emergency and inclusion of biodiversity in key climate change legislation and the national Climate Action Plan. However, acknowledgement of these intertwined crises is only a first step; implementation of synergistic solutions requires careful planning. We demonstrate how synergy between climate and biodiversity action can be gained through explicit consideration of the effects of climate change mitigation strategies, such as energy infrastructure development and land-use change, on biodiversity. We identify several potential "win-win" strategies for both climate mitigation and biodiversity conservation. For Ireland, these include increasing offshore wind capacity, rehabilitating natural areas surrounding onshore wind turbines, and limiting the development of solar photovoltaics to the built environment. Ultimately, climate mitigation should be implemented in a "Right Action, Right Place" framework to maximise positive biodiversity benefits. This review provides one of the first examples of how national climate actions can be implemented in a biodiversity-conscious way to initiate discussion about synergistic solutions for both climate and biodiversity.

Opportunity of rooftop solar photovoltaic as a cost-effective and environment-friendly power source in megacities

Rooftop solar photovoltaics (RSPV) are critical for megacities to achieve low-carbon emissions. However, a knowledge gap exists in a supply-demand-coupled analysis that considered simultaneously RSPV spatiotemporal patterns and city-accommodation capacities, a pivotal way to address solar PV intermittency issues. Here, we developed an aggregated model for an RSPV + system by linking building-level potential assessment to dynamic optimization of building-related flexible loads. Taking Beijing, the capital city of China, as case in point, we show that annual RSPV potential in Beijing’s Greater-Metropolitan area amounts to 15.4 TWh, all of which could be accommodated environmentally friendly and cost-effectively through the smart operation of electric vehicles and air conditioners equipped with thermal energy storage (TES). Additionally, the RSPV + system would reduce the 8.6 GW transmission capacity otherwise required for increasing electricity demand for 2035 in Beijing. The analysis offers an important reference for sustainable RSPV development in mega-cities in China and other countries globally.

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Develop a building-level RSPV potential assessment model for mega-cities

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Show the chance to fully use RSPV potential cost-effectively and environment-friendly

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The RSPV potential in the Beijing GM area amounts to 20.8% of the power demand

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Deployment of RSPV + system would reduce the required new transmission capacity

Improving Distributed PV Integration with Dynamic Thermal Rating of Power Distribution Equipment

The rapid development of distributed photovoltaic (PV) systems poses great challenges to the integration capability of distribution networks. Traditionally, the transfer capacity of power distribution equipment is calculated as the maximum loading that prevents overheating under the assumption of extreme weather conditions. Dynamic thermal rating (DTR), which evaluates equipment capacity based on real-time weather conditions, could enhance the transfer capacity to improve distributed PV integration. Through case studies in Texas, Switzerland, and China, we show that the application of DTR on power distribution equipment could increase installed PV capacities by 15%–27% and improve net revenues by 4%–27%. We also find that the application of DTR would be positively affected by climate change and is more profitable under the PV policies with higher tariffs for the surplus generation fed into the grid. Compared to energy storage systems, DTR provides a more cost-competitive option to enhance the integration capability of distribution networks.

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Dynamic thermal rating improves PV integration capability of distribution networks

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The improving effects would be positively affected by climate change

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Dynamic thermal rating is more profitable with high tariffs for feed-in PV generation

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Dynamic thermal rating is more cost-competitive than energy storage systems

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation.

Comparing the Energy System of a Facility with Uncertainty about Future Internal Carbon Prices and Energy Carrier Costs Using Deterministic Optimisation and Two-Stage Stochastic Programming

For an organisation, one aspect on the path to a decarbonised future is the cost-optimal decarbonisation of their facilities’ energy systems. One method to guide the decarbonisation is internal carbon pricing. However, the design process of decarbonisation pathways, guided by internal carbon prices, can be challenging, since the energy system environment consists of many uncertainties. Despite the numerous uncertainties and existing methods to address uncertainties during the optimisation process, the optimisation of a facility’s energy system is often done by assuming perfect knowledge of all relevant input parameters (deterministic optimisation). Since real-world decisions can never be based on perfect knowledge and certain decisions might lead to path dependencies, it is important to consider the robustness of a solution in the context of developments that vary from the assumed scenarios. So far, no academic work has analysed the potential benefits of using an optimisation method that considers uncertainty about future CO2 prices and energy carrier cost as two important input parameters during the optimisation process. This publication closes the knowledge gap by optimising a real-world energy system of a manufacturing site with two-stage stochastic programming and comparing it with methods of deterministic optimisation. The results show considerably more robust results for the solutions generated by stochastic programming. The total cost deviation does not exceed 52%, while the deviation of the deterministic results reaches up to 96%. The results also indicate that organisations should not analyse their energy systems by only considering uncertain internal carbon prices, but should examine the effects together with other important but uncertain parameters.

Estimation of Rooftop Solar Power Potential by Comparing Solar Radiation Data and Remote Sensing Data—A Case Study in Aichi, Japan

There have been significant advances in the shift from fossil-based energy systems to renewable energies in recent years. Decentralized solar photovoltaic (PV) is one of the most promising energy sources because of the availability of rooftop areas, ease of installation, and reduced cost of PV panels. The current modeling method using remote sensing data based on a geographic information system (GIS) is objective and accurate, but the analysis processes are complicated and time-consuming. In this study, we developed a method to estimate the rooftop solar power potential over a wide area using globally available solar radiation data from Solargis combined with a building polygon. Our study also utilized light detection and ranging (LiDAR) data and AW3D to estimate rooftop solar power potential in western Aichi, Japan, and the solar radiation was calculated using GIS. The estimation using LiDAR data took into account the slope and azimuth of rooftops. A regression analysis of the estimated solar power potential for each roof between the three methods was conducted, and the conversion factor 0.837 was obtained to improve the accuracy of the results from the Solargis data. The annual rooftop solar power potential of 3,351,960 buildings in Aichi Prefecture under Scenario A, B, and C was 6.92 × 107, 3.58 × 107, and 1.27 × 107 MWh/year, estimated using Solargis data after the adjustment. The estimated solar power potential under Scenario A could satisfy the total residential power demand in Aichi, revealing the crucial role of rooftop solar power in alleviating the energy crisis. This approach of combining Solargis data with building polygons can be easily applied in other parts of the world. These findings can provide useful information for policymakers and contribute to local planning for cleaner energy.

Trends in Renewable Electricity Generation in the G20 Countries: An Analysis of the 1990–2020 Period

The use of electricity from non-renewable sources increases environmental impacts. Therefore, several countries have committed to increase the use of renewable sources. Considering the importance of the Group of Twenty (G20), this article aims to identify trends in renewable electricity supply in these countries. The data collected are from the International Energy Agency (IEA) between 1990 and 2020. The methods measured the production of each type of electricity in the G20 matrices and identified the influence of the population, Gross Domestic Product (GDP), and CO2 emissions in renewable electricity production using multiple linear regression. In terms of results, Brazil and Canada have the most renewable electricity matrices and higher per capita renewable production than non-renewable. Saudi Arabia presented the smallest renewable matrix throughout the analyzed period. All 20 countries have varied electrical production, with different amounts of solar, wind, hydro, biomass, geothermal, and tidal energy. Countries with the highest GDP are not necessarily the largest producers of renewable electricity. Hydroelectric energy, the biggest highlight in renewable production, is making room for other sources such as wind and solar, which grew the most in terms of participation in the electrical matrices. The waste, geothermal, and tidal energy participation have shown a timid but constant growth.