Distributed photovoltaics (PV) have played a critical role in the deployment of solar energy, currently making up roughly half of the global PV installed capacity. However, there remains significant unused economically beneficial potential. Estimates of the total technical potential for rooftop PV systems in the United States calculate a generation comparable to approximately 40% of the 2016 total national electric-sector sales. To best take advantage of the rooftop PV potential, effective analytic tools that support deployment strategies and aggressive local, state, and national policies to reduce the soft cost of solar energy are vital. A key step is the low-cost automation of data analysis and business case presentation for structure-integrated solar energy. In this paper, the scalability and resolution of various methods to assess the urban rooftop PV potential are compared, concluding with suggestions for future work in bridging methodologies to better assist policy makers.

The rooftop solar industry in the United States has experienced dramatic growth—roughly 50% per year since 2012, along with steadily falling prices. Although the opportunities this affords for clean, reliable power are transformative, the benefits might not accrue to all individuals and communities. Combining the location of existing and potential sites for rooftop photovoltaics (PV) from Google’s Project Sunroof and demographic information from the American Community Survey, the relative adoption of rooftop PV is compared across census tracts grouped by racial and ethnic majority. Black- and Hispanic-majority census tracts show on average significantly less rooftop PV installed. This disparity is often attributed to racial and ethnic differences in household income and home ownership. In this study, significant racial disparity remains even after we account for these differences. For the same median household income, black- and Hispanic-majority census tracts have installed less rooftop PV compared with no majority tracts by 69 and 30%, respectively, while white-majority census tracts have installed 21% more. When correcting for home ownership, black- and Hispanic-majority census tracts have installed less rooftop PV compared with no majority tracts by 61 and 45%, respectively, while white-majority census tracts have installed 37% more. The social dispersion effect is also considered. This Analysis reveals the racial and ethnic injustice in rooftop solar participation.

This paper implements a framework that catalogs and maps opportunities at a city level to support the electrification of the transportation system in an equitable way. Leveraging on the use of hyper-local air quality data, paired with sociodemographic indicators, we evaluate the framework in the City of Oakland, CA under a context of public and private transport areas to prioritize interventions and incentives, while reducing environmental hazard exposure under an equity focus. Following the recognition of racial and socio-economic disparities, the subsequent steps of implementing operational strategies are to identify specific bus routes in the city in which decarbonization would render the highest impact with respect to the distribution of environmental burden, and to identify regions in which private vehicle interventions would render the highest impact with respect to racial and social equity and mobility.

Renewable energy is often presented as a partisan issue. However, solar incentives have made rooftop PV attractive to many consumers, regardless of political affiliation. This paper quantifies the role of political affiliation in rooftop PV adoption for the Democratic-leaning state of New York and Republican-leaning state of Texas. High resolution rooftop PV installation data from Google Project Sunroof is combined with election data from the Harvard Election Data Archive. We applied a bootstrapped LOWESS model to fit local linear relationships between rooftop PV adoption and both voting behavior and household income. In doing so, we find that Republican-majority communities are installing rooftop PV at equal or greater rates than Democraticmajority communities. While Republican politicians may be less likely than Democrats to support renewable energy, this partisan division is not found at the voter level.

As global political discourse is taking place where the need for a cleaner energy mix is constantly highlighted, manufacturing strategies are becoming more relevant. Thus, the photovoltaics system design is a crucial aspect related with the overall sustainability. In fact, various countries are considering the potential to locally manufacture different elements of the photovoltaics (PV) value chain and the strategies to incentivize a local manufacturing base. This paper develops a mathematical programming approach for the optimal design of a PV manufacturing value chain considering diverse criteria linked to economic and environmental performance such as minimum sustainable price, transportation capacity, among others, and considering uncertainty. In addition, the proposed methodology involves the dependence over time of supply chain variables and economic parameters such as inflation, electricity cost, and weighted average cost of capital, to determine the manufacturing system topology under uncertain conditions. Our results highlight the importance of planning models to develop markets policies related to supply chains, production level changes and imposed tariffs all while involving uncertainty in economic parameters, which is an improvement compared to planning models that use deterministic formulations. Finally, the proposed methodology and results can encourage decision-making considering probable variations in different parameters.

Amid climate change and public health concerns, world economies are seeking to reduce the greenhouse gas emissions and local air pollution from transportation. Population growth in cities worldwide will further increase demand for clean and affordable transportation. We propose a city-specific environmental justice mapping index, inspired by a similar index used in California, that highlights promising areas for clean transportation interventions in Greater Mexico City to reduce greenhouse gas emissions and local pollution. This novel approach leverages highly spatially resolved population, pollution, and transportation data. The proposed index score is designed as an open source, updateable point of orientation for decisionmakers as they consider investment in transportation electrification from the standpoint of overlapping atmospheric pollution and social vulnerability.

Carbon dioxide has increased drastically in the last decades due to energy production, exacerbating the global warming problem. To address this issue, researchers have focused on developing energy production technologies from renewable sources. From the renewable energy sources, solar has shown great promise chiefly due to its high availability. The conversion of solar energy into electricity (photovoltaics) requires specialized equipment such as solar cells, and a coordinated supply chain to be able to manufacture this technology in a sustainable way and at low cost. Therefore, this chapter proposes an approach based on mathematical programming for the optimal design of a solar photovoltaics manufacturing system considering diverse criteria linked to economic and environmental variables such as minimum sustainable price, transportation costs, and technical limits. In addition, the dependence of the minimum sustainable price over inflation, electricity price, and weighted average capital cost is analyzed, showing that a variation of minimum sustainable price could significantly change the manufacturing supply chain topology.

After undergoing an unprecedented energy reform, Mexico is poised to significantly grow its renewable energy share, and evolve from 80 MW installed solar photovoltaics (PV) capacity in 2015, to more than 6 GW by 2022. The impact on the overall electrical grid is therefore a crucial element to determine where the investments should be allocated -both in generation, and transmission-, to be optimally deployed. In this contribution, we model the generation, transmission and distribution assets in Mexico and optimize the investment in capacity to meet policy goals under three different plausible scenarios.

Solar photovoltaics (PV) manufacturing has experienced dramatic worldwide growth in recent years, enabling a reduction in module costs, and a higher adoption of these technologies. Continued sustainable price reductions, however, require strategies focused in further technological innovation, minimization of capital expenditures, and optimization of supply chain flows. We present a framework: Techno-economic Integrated Tool For Tariff And Transportation (TIT-4-TAT), that enables the study of these different strategies by coupling a techno-economic model with a tariff and transportation algorithm to optimize supply chain layouts for PV manufacturing under equally-weighted objectives. We demonstrate the use of this framework in a set of interacting countries (Mexico, China, USA, and Brazil) and two extreme tariff scenarios: no tariffs, and high tariff levels imposed. Results indicate that introducing tariffs between countries significantly increase the minimum sustainable price for solar PV manufacturing, alter the optimal manufacturing locations, and render a more expensive final solar PV module price which can hinder the adoption rates required to mitigate climate change. Recommendations for stakeholders on the optimization process, and techno-economic drivers are presented based on our results. This framework may be utilized by policymakers for the spatially-resolved planning of incentives, labor and manufacturing programs, and proper import tariff designs in the solar PV market.

Today resource adequacy is most often maintained by installing natural gas plants to meet the peak load. In California, the current risk of inadequate electricity supply is highest around sunset in late summer. In a zero-carbon grid, resource adequacy will increasingly require adequate stored energy throughout the entire year. Here we seek to develop an intuition about the times of the year when resource adequacy may be most challenged for a solar-dominant system. We use a simplified approach and show that the month of the biggest challenge occurs in winter and can shift by more than two months depending on the amount of solar and storage that are built.