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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.

Feature-based machine learning models for capacity and internal resistance (IR) curve prediction have been researched extensively in literature due to their high accuracy and generalization power. Most such models work within the high frequency of data availability regime, e.g., voltage response recorded every 1–4 s. Outside premium fee cloud monitoring solutions, data may be recorded once every 3, 5 or 10 min. In this low-data regime, there are little to no models available. This literature gap is addressed here via a novel methodology, underpinned by strong mathematical guarantees, called ‘path signature’. This work presents a feature-based predictive model for capacity fade and IR rise curves from only constant-current (CC) discharge voltage corresponding to the first 100 cycles. Included is a comprehensive feature analysis for the model via a relevance, redundancy, and complementarity feature trade-off mechanism. The ability to predict from subsampled ‘CC voltage at discharge’ data is investigated using different time steps ranging from 4 s to 4 min. It was discovered that voltage measurements taken at the end of every 4 min are enough to generate features for curve prediction with End of Life (EOL) and its corresponding IR values predicted with a mean absolute percentage error (MAPE) of approximately 13.2% and 2.1%, respectively. Our model under higher frequency (4 s) produces an improved accuracy with EOL predicted with an MAPE of 10%. Full implementation code publicly available.

Abstract The pressure-loss coefficient for a duct junction of square cross-section was determined using computational fluid dynamics (CFD). The predicted junction pressure-loss coefficient for combining flows was generally in good agreement with experimental data from the literature. The junction pressure-loss coefficient was associated with the flow from the side branch to the duct carrying the total flow.

Abstract Most of the current Stirling-type pulse tube refrigerators (PTRs) adopt inertance tubes with large reservoirs for phase shifting. Recovering the acoustic power dissipated in the inertance tube provides a great potential for improving the efficiency of a PTR. In this study, an inertance tube PTR is modified by replacing the dissipative inertance tube and reservoir with a mass-spring displacer directly coupled to a compression space. Numerical simulations are conducted on both the PTRs based on a validated one-dimensional computational fluid dynamics model. Optimization of the inertance tube PTR shows that the coefficient of performance (COP) is limited within 0.103 at the cooling temperature of 77 K. The simulation of the PTR with the feedback mechanism indicates that COP can be significantly improved due to the extra power recovered by the mass-spring displacer. The parametric analyses of the moving mass, spring stiffness, mechanical resistance, piston diameter, and working frequency of the mass-spring displacer are finally performed. The phase relations at both ends of the regenerator are significantly influenced by the geometric and operating parameters, which further affect the performance. The designing parameters have been optimized, COP reaches about 0.13–0.14 with the relative Carnot COP of around 0.4. It demonstrates that adopting the mass-spring displacer to feed the expansion power back into the compression space is an effective way of improving the performance of PTRs. This work provides comprehensive understanding of the mechanisms and characteristics of the PTRs with the mass-spring displacer. It would be helpful for future designs of such systems.

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.

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.