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Abstract For 118 million residential housing units in the U.S., there is currently a gap between the potential energy savings that can be achieved through the use of existing energy efficiency technologies, and the actual level of energy savings realized, particularly for the 37% of housing units that are considered residential rental properties. Additional quantifiable benefits are needed beyond energy savings to help further motivate residential property owners to invest in energy efficiency upgrades. This research focuses on assessing the adoption of energy efficient upgrades in U.S. residential housing and the impact on rental prices. Ten U.S. cities are chosen for analysis; these cities vary in size across multiple climate zones, and represent a diverse set of housing market conditions. Data was collected for over 159,000 rental property listings, their characteristics, and their energy efficiency measures listed in rental housing postings across each city. Following an extensive data quality control process, over thirty different types energy efficient features were identified. The level of adoption was determined for each city, ranging from 5.3% to 21.6%. Efficient lighting and appliances were among the most common, with many features doubling as energy efficient and other desirable aesthetic or comfort improvements. Then using propensity score matching and conditional mean comparison methods, the relative impact on rent charged in each city was calculated, which ranged from a 6% to 14.1% increase in rent for properties with energy efficient features, demonstrating a positive economic impact of these features, particularly for property owners. This was further subdivided into five types of energy efficiency upgrade and three housing types. Single family homes generally demanded higher premiums with energy efficient features, however there was not a consistent pattern across the types of efficient upgrades. The results of this work demonstrate that investment in energy efficient technologies has quantifiable benefits for rental property owners in the U.S. beyond just energy savings. This methodology and results can also be used in other cities and by property owners, utility companies, or others, ultimately encouraging further investment and positive economic impact in residential energy efficiency and in turn improving energy and resource conservation in the building sector.

We propose a methodology to price an insurance contract designed to hedge the volumetric risk associated with weather conditions. Our methodology is based on conditional quantile regressions and adapts Value at Risk (VaR) and Expected Shortfall statistics from the literature on financial econometrics. In our empirical application, we use actual daily meteorological and radiation data for 40 European cities in 13 countries, from April 1, 2011, to December 31, 2021, and calculate the value of the annual insurance premium under reasonable assumptions on the technology of the solar panels and expected energy demand. We consider variables such as temperature, wind speed, and precipitation in our calculations. Our results for the different cities in our sample show that due to the nonlinear impact of weather (mainly temperature and precipitation) on the expected generation losses, it is appropriate to use quantile regressions to calculate the VaR of radiation, conditional on weather factors. Our insurance premium calculations also present a high degree of variation across European, which indicates that risk-diversification opportunities of catastrophic risk due to weather conditions faced by insurance companies exist. This variation is explained by different weather conditions, model adjustment, and the average price of electricity.

Abstract Within residences, normative messaging interventions have encouraged households to engage in various pro-environmental behaviors. In norm-based intervention campaigns, it is hypothesized that more personally relevant reference groups increase norm adherence, thus improving the effectiveness of normative messaging interventions. Advanced energy grid infrastructure, such as smart meters and cloud computing, enables the creation of highly personalized behavioral reference groups in a non-invasive manner by dynamically classifying households into highly similar user groups based on usage patterns. Unfortunately, it remains unclear how readily available data on household energy use and housing characteristics affect the classification performance of dynamic behavioral reference groups. Therefore, this research evaluates the classification performance of dynamic behavioral reference groups using readily available data. An energy-cyber-physical system for personalized normative messaging interventions is trained and tested using one-year of energy use data from 2248 households in Holland, Michigan. Dynamic behavioral reference group classification proved very accurate, 94.7–95.9% for weekly feedback and 89.9–93.1% for monthly feedback using only readily available data. In addition, using more historical energy use data contributes to enhancing classification accuracy. Lastly, high classification performance for each behavioral reference group is achieved at 97.6% of precision, recall and F1-score. With the proposed system, it is possible to dynamically assign highly personalized behavioral reference groups to households every billing cycle even if behavioral patterns are subject to change. Thus, interveners will be able to deploy personalized normative feedback messages on a large scale.

Abstract As our energy systems are transitioning towards low-carbon energy sources and their environmental and economic sustainability are assessed, their potential social impacts must also be determined. These social impacts may be disproportionate to a population, leading to energy justice concerns. The social life cycle assessment framework can be used to comprehensively address energy justice concerns by different stakeholder groups and at all life cycle stages associated with a low-carbon energy system. Indicators for a social life cycle assessment framework that addresses energy justice are introduced and discussed. These indicators are organized by four categories of stakeholders for electrical energy systems: workers, electricity consumers, local communities, and society as a whole. The social life cycle assessment framework allows for variations in justice and equity to be determined not only at the generation stage, but through multiple points in the life cycle of the same energy system, from raw material extraction, through manufacturing, transportation, distribution, electricity generation, and waste management. This framework can address potential energy justice issues along the life cycle of new energy systems and assist in their design and planning for optimizing their social sustainability without overlooking vulnerable populations.

Electric vehicles (EVs) are considered a substitute for fossil-fueled vehicles due to rising fossil fuel prices and accompanying environmental concerns, and their use is predicted to increase dramatically shortly. However, the widespread use of EVs and their large-scale integration into the energy system will present several operational and technological hurdles. In the energy industry, an innovative solution known as the EVs smart parking lot (SPL) is introduced to handle EV charging and discharging electricity and energy supply challenges. This paper investigates social equity access and mobile charging stations (MCSs) for EVs, where the owner of MCSs is the EV parking lot. Accordingly, a new self-scheduling model for SPLs is presented in this paper that incorporates scheduling of the MCSs as temporary charging infrastructures while considering social equity access and optimizes SPL energy generation and storage schedule. The main objectives of this research are to (i) develop MCSs accessibility measures and quantify the equity impacts of MCSs locations by modeling prioritized demand based on several indices; (ii) determine the optimal set-points of SPL components (i.e., combined heat and power (CHP), photovoltaic system, electrical and heat-energy storage, and MCSs) to manage electrical peak demand and to maximize the economic benefits of SPLs. Results indicate that the proposed demand prioritization function model can meet the required EV charging demands for prioritized events, and the self-scheduling model for SPLs satisfies the charging demand of the EVs in the SPL location. Also, the social equity access to the EV charging stations is satisfied by analyzing the operation of MCSs around the prioritized demand of the prioritized events and social equity access indices.

Author(s): Tian, S; He, H; Kendall, A; Davis, SJ; Ogunseitan, OA; Schoenung, JM; Samuelsen, S; Tarroja, B | Abstract: Energy storage systems are critical for enabling the environmental benefits associated with capturing renewable energy to displace fossil fuel-based generation, yet producing these systems also contributes to environmental impacts through their materials use and manufacturing. As energy storage capacity is scaled up to support increasingly renewable grids, the environmental benefits from their use may scale at different rates than the environmental impacts from their production. This implies the existence of capacity thresholds beyond which installing additional storage capacity may be environmentally detrimental. Identifying such thresholds are important for ensuring that energy storage capacity selection in future grids are consistent with net emissions reduction goals, but such thresholds have not been studied in the present literature. To identify such thresholds, here we combine electric grid dispatch modeling with life cycle analysis to compare how the emissions reductions from deploying three different flow battery energy storage types on a future California grid (g80% wind and solar) compare with emissions contributions from producing such batteries as total battery capacity installed on the grid increases. Depending on the type of battery and environmental impact indicator (greenhouse gas or particulate matter emissions), we find that the marginal environmental benefits of storage begin to diminish at deployed capacities of 38–76% of the mean daily renewable generation (256–512 GWh in our California scenarios) and reach zero at 105–284% of mean daily renewable generation (700–1810 GWh). Such storage capacities are conceivable, but upstream impacts of storage must be assessed in evaluating the environmental benefits of large-scale storage deployment, or they could negate the environmental benefits of regional electricity system decarbonization.

Abstract Plug load monitoring and associated occupant behavior interventions can play a critical role in reducing commercial building energy consumption. This study investigates whether the reduction in building energy consumption justify the added cost of plug load monitoring and occupant energy saving interventions. The objective of this study is to conduct deterministic and probabilistic return-on-investment (ROI) analysis of instrumenting workspaces, monitoring plug load usage, and applying interventions to promote building energy reduction. The study uses the findings of actual occupant energy saving intervention investigations conducted with city and federal government offices in which the association between occupant energy savings interventions and energy use risk was evaluated. While the deterministic approach led to a positive net present value, the interventions failed to recapture the initial investment, and operational expenses given the uncertainties in the estimate of costs and energy use. The mean ten-year net present value was −$3914 at a 6% discount rate considering all U.S. states. From the project manager’s perspective, other non-energy benefits can justify the additional resources.

The use of electric vehicles is being promoted to address emerging concerns about global warming associated with emissions from fossil fuels. Besides, in the context of parcel delivery deep growth related to e-commerce, electric vehicle is becoming an alternative to conventional fossil fuel technology. Intrinsically, the charging process implies the interdependence between the transportation and electric power systems. This paper presents a new multistage optimization-based approach that allows linking delivery routing and aggregated demand management in the transportation and electric power systems, respectively. For the routing and charging of each independent electric vehicle, battery degradation, acceleration- and speed-dependent power consumption, penalty for delivery delay, tolls, fixed charging prices and incentives for availability time are considered. An electric vehicle demand aggregator is used to guarantee the synergy between systems. Incentives are included to motivate electric vehicles to remain at charging intersections. However, attractive incentives can create electric power system congestion due to simultaneous charges on nodes. Thus, an iterative decongestion methodology is developed. The resulting model is divided into three stages: delivery allocation, delivery routing for each