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This paper explains the use of a convolutional neural network (CNN) to segment solar panels in a satellite image containing solar arrays, and extract associated metadata from the arrays. A novel unsupervised technique is introduced to estimate the azimuth of each individual solar panel from the predicted mask of the convolutional neural network. This pipeline was developed with the aim of extracting necessary metadata for a solar installation, using only a set of latitude–longitude coordinates. Azimuth prediction results for 669 individual solar installations associated with 387 sites located across the United States are provided. A mean average error and median average error of 21.65 degrees and 1.0 degrees were obtained, respectively, when predicting the azimuth of the solar fleet data set, with about 80% of the results within an error of zero degrees of the ground truth azimuth value and about 85% within an error of 25 degrees. The predicted azimuth was then used to estimate the energy conversion of the solar arrays. Results show a 90.9 and 90.6 R-squared value for estimating alternating current (AC) and direct current (DC) energy, respectively, and a mean absolute percentage error (MAPE) of 1.70% in estimating the alternating current (AC) energy using the fully automated algorithm. [Display omitted] • A novel unsupervised technique is used to estimate the azimuth of solar arrays. • Segmentation of solar panels in the satellite image, using transfer learning and a CNN. • The proposed approach is can be used with high resolution satellite or aerial imagery. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to summarize the findings reported recently in the literature on non-energy benefits attributable to the weatherizing of low-income homes. Non-energy benefits are divided into three major categories: (1) ratepayer benefits; (2) household benefits; and (3) societal benefits. The ratepayer benefits can be divided into two main subcategories: payment-related benefits and service-provision benefits. Similarly, there are two key types of household benefits: those associated with affordable housing and those related to safety, health and comfort. Societal benefits can be classified as either environmental, social, or economic. Our study found the total lifetime value for all non-energy benefit-categories to be $3346 (in 2001 dollars) per household, which is slightly greater than the average value of energy savings for houses heated by natural gas, and substantially higher than the total cost per low-income weatherization. Societal benefits were much larger than either ratepayer or household benefits. [Copyright &y& Elsevier]

Many energy systems models have sought to develop pathways for deep decarbonization of the global energy system. Most often, these pathways minimize system costs or greenhouse gas emissions; with few exceptions, they ignore the constraints imposed by political, social, and economic factors that slow transition processes, making them prone to producing implausible decarbonization pathways. This paper integrates a key socio-technical factor—social acceptance of low-carbon nuclear power—into an energy systems model to illustrate how it alters the optimal energy generation mix. The United States was chosen as the example, but the approach itself is designed to be general and applicable to any region of interest. An empirically grounded risk tolerance model is developed to characterize acceptance of nuclear power and estimate an upper-bound deployment limit for the technology. Illustrative scenarios are presented to improve our understanding of how the socio-technical constraints that exist in the real world can alter deep decarbonization pathways. The cost-optimal generation portfolio to achieve net zero CO 2 emissions by 2050 primarily relies on nuclear power. If risk tolerance concerns constrain nuclear deployment to socially acceptable levels, deep decarbonization scenarios are up to 11% more expensive than the reference scenario and require low-carbon options to be available and replace the reduced nuclear share. Results from this novel framework improve our representation of the effect of social acceptance on the adoption and diffusion of energy technologies. They also contribute to a growing literature that seeks to firmly embed the social sciences in climate and energy policy. • Plausible decarbonization pathways must also account for social constraints. • We combine a social risk tolerance model with an energy system optimization model. • We assess the role of social acceptance in constraining nuclear power's deployment. • Social acceptance limits nuclear's share by as much as 71% in CO 2 reduction pathways. • Decision-making can be strengthened with integration of socio-technical analyses. [ABSTRACT FROM AUTHOR]

Efficient electric heat pumps have the potential to significantly reduce greenhouse gas emissions from heating and cooling buildings. However, heat pumps' initial costs can be prohibitively high and their lifetime costs are only situationally competitive with incumbent technologies. Here we show that a business model based on heat purchase agreements could lower these barriers to heat pump adoption. In this business model, a user hosts a heat pump owned by an aggregator. The aggregator installs the heat pump at low or no initial cost to the user. The user buys the heat pump's heat or cooling output from the aggregator. The aggregator buys the heat pump's input electricity in the wholesale energy market and sells the flexibility of their aggregate electrical load in ancillary service markets. This paper presents the first economic analysis of heat purchase agreements as a third-party ownership model for electric heat pumps. We derive conditions under which a heat purchase agreement is mutually beneficial to the user and the aggregator. We also provide a method to fairly price heat and cooling. A case study of a typical United States home shows that a heat purchase agreement could more than double the value of a heat pump investment relative to the incumbent business model. The potential impact of this work is to reduce emissions both directly, by accelerating replacement of fossil-fueled or inefficient heating or cooling equipment, and indirectly, by helping power system operators reliably integrate wind and solar generation. • We analyze a new business model for efficient electric heat pumps. • The model reduces or eliminates the user's initial cost. • It also enables access to wholesale electricity markets, reducing lifetime costs. • The model could reduce emissions by accelerating heat pump adoption. • It could also facilitate the integration of renewable generation into power systems. [ABSTRACT FROM AUTHOR]

Distributed energy resources have increased considerably in the United States and the world in the last decade. The proliferation of prosumers generates the opportunity to have a more decentralized and open energy market. Given this opportunity, the Peer-to-Peer (P2P) trading energy paradigm appears, where consumers and prosumers can exchange energy without the need for an intermediary. Because P2P energy trading plays a fundamental role in the proliferation of renewable energies and the system flexibility for a low-carbon energy transition, this paper provides a review of the P2P energy trading that is necessary to understand the current approaches, challenges, and future research that should be conducted in this area. As a result, areas for consideration were identified and grouped into the following six topics: (1) trading platform, (2) blockchain, (3) game theory, (4) simulation, (5) optimization, and (6) algorithms. The study concludes by identifying several challenges that may give way to future research, such as integrating generation, transmission, and distribution into studies, large-scale studies, and modeling of consumer and prosumer complex behavior. Given P2P energy trading is a relatively new topic, there is still much work to be done to successfully implement the real-world model. [ABSTRACT FROM AUTHOR]

• A novel approach to harness offshore renewable energy sources is proposed. • Cycle technical description from cryogen generation to end-use of energy is provided. • Using liquid air improves thermal cycle efficiency and allows waste heat recovery. • Liquid air favors indirect integration of renewable energy sources in the electric grid. • A Round-Trip Efficiency of up to 70% is possible with the proposed decoupled system. A novel approach using decoupled processes to harness offshore renewable energy, from marine hydrokinetics, ocean waves, wind, and solar to produce liquid air, is presented in this paper. Offshore renewables interconnection using submarine medium- and high-voltage direct current technologies are used to produce liquid air that can be transported to end-use locations using repurposed liquefied natural gas tankers. Two important possibilities arise from using the proposed technology. The first possibility allows the integration with conventional thermal cycles to improve efficiency. This approach can be used to leverage efficiencies of thermal systems that have already reached a plateau in the maximum achievable efficiency via design and operation optimization. The second possibility is related to the incorporation of low temperature cycles to recover waste heat from other thermal processes. This is important considering that waste heat accounts for more than 60% of the consumed energy in the United States. A detailed technical description of the complete cycle from cryogen generation to end-use of energy is provided. Estimation of efficiency enhancement for thermal plants using liquid air including waste heat recovery is presented. [ABSTRACT FROM AUTHOR]

• Develop physics-based model for an electric-boosting glass furnace. • Define demand response (DR) strategy balancing electricity and natural gas. • Optimal DR decisions for two case studies of practical interest are analyzed. The glass industry is highly energy-intensive, accounting for 1% of total industrial energy consumption in the United States. Most of the energy consumption in the glass manufacturing process is attributable to the significant heat required to melt raw materials. An electric boosting system which can transfer extra heat (5%–20% of total energy) to the glass melt in addition to the energy from natural gas combustion can be implemented in a glass furnace. Electric boosting is thermally efficient, reduces direct pollutant emissions, and prolongs furnace superstructure lifespan. However, a high level of electric boost is not always economically desirable, considering the volatility of electricity prices. Balancing between natural gas and electricity consumption in a demand response strategy can reduce the energy cost and mitigate strain on the electrical grid. In this paper, a physics-based model is developed to describe the dynamic behavior of a prototype electric boosting glass furnace. We present a dynamic optimization strategy to optimally balance between using natural gas and electric power under electricity price fluctuations. Case studies on the effect of varying energy prices and emissions regulations are analyzed. [ABSTRACT FROM AUTHOR]

• Data availability and cost can be prohibitive barriers for energy modeling. • We use a statistical method to predict power plant parameters using available data. • We apply two learning methods: linear regression and k-nearest-neighbors. • These approaches are applied in the context of U.S. and Canadian power plant data. • Results show reasonable predictions of heatrates and, to a lesser extent, costs. Energy systems models are used to perform energy and environmental policy analysis, inform company strategy, and understand implications of technological change. Although open-source models can promote transparency and reproducibility, data availability and cost can be prohibitive barriers for researchers and other stakeholders. This paper presents a novel application of a statistical approach to predict unknown power plant parameters in Canada using available data from the United States, which can be applied in other settings where critical model inputs are missing. We apply two statistical learning methods, linear regression and k -nearest-neighbors, and compare their performance on unseen portions of the United States data before applying the learned functions to unknown Canadian data. Results indicate that reasonable predictions of heatrates and, to a lesser extent, operation and maintenance costs are possible even with limited data about age, capacity, and power plant types. The nearest-neighbor approach generally outperforms linear regressions for the datasets and applications to power plant parameters investigated here. [ABSTRACT FROM AUTHOR]

• We examine the emissions impact of battery storage across sensitivities and regions. • Existing studies focus on dispatch; we show how investment effects drive emissions. • Emissions impacts of storage vary based on policy and market assumptions. • Storage emissions reductions are more likely when wind and solar are lower cost. Battery storage technologies have attracted attention from policymakers for their potential to reduce electric sector emissions by enabling greater wind and solar penetration. Yet existing studies indicate that adding energy storage actually increases emissions. The difference may be explained by the absence in the previous literature of "investment effects" – changes in deployment of other generation technologies after storage is deployed. Our paper tests this hypothesis by evaluating battery-storage-induced emissions changes using a model that accounts for both long-run investment and dispatch effects simultaneously across regions in the United States, which can be used to separate the relative magnitudes of these two effects. Model results indicate that the investment effect dominates the dispatch effect under a range of sensitivities. We find that emissions may increase or decrease with battery storage, depending on policy and market assumptions, with reductions more likely in environments where wind and solar are more economically competitive relative to natural-gas-fired generation. These results suggest that existing studies that only account for operational impacts likely underestimate potential emissions changes from battery storage deployment. [ABSTRACT FROM AUTHOR]

• 1.1% of United States households could find it privately economic to grid defect. • Low load customers will find it easiest to defect from the grid. • Defection up to 7% is possible if customers accept slightly less reliable service. • Limiting inefficient load defection via fixed charges could lead to grid defection. The cost declines of solar and storage technologies have led to concerns about customers disconnecting from utility service and self-supplying electricity. Prior research addressing this issue focused on average electricity tariffs, solar profiles, and demand without considering detailed customer heterogeneity. This paper fills the gap by analyzing how electricity tariffs that shift cost recovery away from variable charges towards fixed charges influence a customer's decision to disconnect from utility service. A linear optimization model is developed to size an off-grid solar/storage system. Technology cost and reliability parameters of the optimization model are then adjusted to calculate a range of off-grid costs. Unique rate structure information for over 2000 utilities in the United States is then used to compare grid costs to off-grid costs. The results show limited ability for solar/storage systems to economically substitute for grid services. However, 1% of households might disconnect from utility service in a scenario that accounts for future off-grid costs and updated tariff designs. We find that 3% of households in the Southwest and California have private economics that favor defection in this scenario, rising to as much as 7% in Hawaii. Grid defection could increase from 1% to 7% of United States households in a slightly reduced reliability scenario. These results indicate that utilities and regulators seeking to limit rooftop solar adoption by lowering variable charges face a significant possibility that the corresponding increase in fixed charges could lead to inefficient grid defection. [ABSTRACT FROM AUTHOR]

• Temporal and spatial features are used for wind speed forecasting. • A novel probabilistic deep learning model is proposed for wind speed forecasting. • The proposed model shows the best performance compared with other models. • Temporal and spatial uncertainty of the wind speed are estimated based on the model. Reliable and accurate probabilistic forecasting of wind speed is of vital importance for the utilization of wind energy and operation of power systems. In this paper, a probabilistic spatiotemporal deep learning model for wind speed forecasting is proposed. The underlying wind turbines are embedded into a grid space, which fully expresses the spatiotemporal variation process of the airflow. Thus, advanced image recognition methods can be employed to solve the spatiotemporal wind speed forecasting problem. The proposed model is based on a spatial–temporal neural network (STNN) and variational Bayesian inference. The proposed STNN combines the convolutional GRU model and 3D Convolutional Neural Network and uses an encoding-forecasting structure to generate the spatiotemporal predictions. Variational Bayesian inference is employed to obtain the approximated posterior parameter distribution of the model and determine the probability of the prediction. The proposed model is applied in two real-world case studies in United States. The experimental results demonstrate that the proposed model significantly outperforms other models in both forecast skill and forecast reliability. The uncertainty estimation is also shown and it demonstrates that the proposed model is able to provide effective uncertainty estimation in both the time level and space level. [ABSTRACT FROM AUTHOR]