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Abstract Bioproducts from biomass feedstocks and organic wastes have shown great potential to address challenges across food-energy-water systems. However, bioproducts production is at an early, nascent stage that requires new inventions and cost-reducing approaches to meet market needs. Biochar, a byproduct of the pyrolysis process, derived from nutrient-rich biomass feedstocks (e.g., cattle manure and poultry litter) is one of these bioproducts that has numerous applications, such as improving soil fertility and crop productivity. This study investigates the market opportunity and sustainability benefits of converting manure to biochar on-site, using a portable refinery unit. Techno-economic and environmental impact assessments are conducted on a real case study in Twin Falls, Idaho, USA. The techno-economic analysis includes a stochastic optimization model to calculate the total cost of biochar production and distribution. The environmental study employs a life cycle assessment method to evaluate the global warming potential of manure-to-biochar production and distribution network. The total cost of biochar production from cattle manure near the feedlots is approximately $237 per metric ton, and total emission is 951 kg CO2 eq. per metric ton. The on-site operation and manure moisture content are two key parameters that can reduce biochar unit price and carbon footprint of manure management. It is concluded that converting cattle manure, using the presented strategy and process near the collection sites can address upstream and midstream sustainability challenges and stimulate the biochar industry.

Abstract 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.

Abstract Reliability of lithium-ion (Li-ion) rechargeable batteries used in implantable medical devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, physicians, and patients. To ensure Li-ion batteries in these devices operate reliably, it is important to be able to assess the battery health condition by estimating the battery capacity over the life-time. This paper presents a data-driven method for estimating the capacity of Li-ion battery based on the charge voltage and current curves. The contributions of this paper are three-fold: (i) the definition of five characteristic features of the charge curves that are indicative of the capacity, (ii) the development of a non-linear kernel regression model, based on the k-nearest neighbor (kNN) regression, that captures the complex dependency of the capacity on the five features, and (iii) the adaptation of particle swarm optimization (PSO) to finding the optimal combination of feature weights for creating a kNN regression model that minimizes the cross validation (CV) error in the capacity estimation. Verification with 10 years’ continuous cycling data suggests that the proposed method is able to accurately estimate the capacity of Li-ion battery throughout the whole life-time.

Nowadays, the photovoltaic technology level development makes it the best option for its building integration as energy supplier. Nevertheless, its aesthetic appearance plays a relevant role because architect requirements go beyond the simple installation of solar devices on terraces. In order to fulfill their requirements, the typical white backsheet uses to be replaced by a black one. This simple change leads to a huge PV module performance reduction. In this work, it has been demonstrated that a suitable material selection allows to fabricate photo voltaic modules with a high architectonic integration, but without power reduction with respect to the most commercial solar devices. The former consists in the replacement of the typical glass front cover and the white backsheet by an antireflective glass and a black backsheet respectively. All the study has been developed on real size photovoltaic modules fabricated in an automatic line. The obtained results determine that those modules where black backsheets are used, suffer a power reduction equal to 8.66 W per fabricated module. Nevertheless, when in addition of the black backsheet an antireflective coating glass is implemented, the resulted PV modules present a more aesthetic presence without a detrimental of their power performance when they are compared to the standard PV modules. Additionally, the fabricated solar devices using the proposed configuration successfully overcome the most common aging tests. Política de acceso abierto tomada de: https://openpolicyfinder.jisc.ac.uk/id/publication/11131

Abstract The optimized performance of two advanced CO2 capture processes is compared to that of a monoethanolamine (MEA) baseline for a gas-powered CO2 capture retrofit of an existing coal-fired facility. The advanced temperature-swing processes utilize piperazine and mixed-salt solvents. The mixed-salt treatment involves the use of ammonia for CO2 absorption and potassium carbonate primarily to control ammonia slip. The processes are represented in terms of energy duty requirements within a modular heat integration code developed for CO2 capture modeling and optimization. The model includes a baseload coal plant, a gas-fired subsystem containing gas turbines and a heat recovery steam generator (HRSG), and a CO2 capture facility. A formal bi-objective optimization procedure is applied to determine the design (e.g., detailed HRSG components and pressure levels, gas turbine capacity, CO2 capture capacity) and time-varying operations of the facility to simultaneously maximize net present value (NPV) and minimize total capital requirement (TCR), while meeting a maximum CO2 emission intensity constraint. For a realistic scenario constructed using historical data, optimization results indicate that both advanced processes outperform MEA in both objectives, and the mixed-salt process in turn outperforms the piperazine process. Specifically, for the scenario considered, the base case mixed-salt process achieves 16% greater NPV and 14% lower TCR than the MEA process, and 10% greater NPV and 5% lower TCR than the piperazine process. A five-case sensitivity study of the mixed-salt process indicates that it is competitive with the piperazine process and consistently outperforms the MEA process.

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.