Evaluating the social benefits and network costs of heat pumps as an energy crisis intervention

Fuel poverty, a pressing issue affecting social prosperity, has been exacerbated during the energy crisis triggered by the Russia-Ukraine conflict. This problem can be more severe for off-gas regions. Our study investigates heat pumps (HPs) as a cost-effective alternative to off-gas heating to alleviate fuel poverty in England and Scotland. We analyze regional fuel poverty rates and the associated greenhouse gas emission reduction by replacing all off-gas heating with HPs, observing positive effects under pre-crisis and crisis conditions, with existing government support for HP upfront costs. HP rollout can burden distribution networks especially for certain regions, but our correlation analysis shows that high benefits do not always come with network costs at the regional level, and we identify "priority" regions with low costs and high benefits. These findings provide valuable insights for policymakers to address fuel poverty and reach decarbonization. The methodology is adaptable to other countries with appropriate datasets.

Mechanism that determines the economics of 100% renewable power systems

Many studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain unclear, making this issue still debatable. This study presents a mathematical formulation of the mechanism that only the demand and power generation profiles determine the optimal capacities of generation and storage and their trade-off relationship. Furthermore, this study demonstrates the comprehensive quantification of the corresponding relationships among the factor cost of technologies, their optimal capacities, and total system cost. Based on these findings, the study also shows that hybrid systems comprising multiple renewable energy sources and different types of storage, including long-duration energy storage, are critical to reducing the total system cost by using actual profile data for multiple years and regions in Japan. This suggests that large-scale deployment of current-level power-to-gas technologies, such as water electrolysis, can contribute to the economics of 100% renewable power systems.

Co-adoption pathways toward a low-carbon energy system

Low-carbon technology adoption is an essential element of energy transitions toward net-zero emissions around the world. To exploit the full potential of low-carbon technologies, households should ideally co-adopt multiple low-carbon technologies. Whereas previous research primarily investigated predictors of single-technology adoption in isolation, here we focus on the co-adoption of multiple low-carbon technologies, including solar photovoltaics, stationary batteries, heat pumps, and electric vehicles, to examine the interconnections between adoption decisions and the potential of certain technologies to serve as "entry points" for the co-adoption of multiple low-carbon technologies. Based on a sample of 1967 homeowners, we identified unique demographic and psychological variables associated with co-adoption. We moreover observed specific co-adoption patterns across time in that the adoption of one technology increased the likelihood of adopting another technology. This effect, however, was primarily driven by co-adoption in close temporal proximity, pointing to opportunities for targeted policies that support technology bundles.

Unintended consequences of curtailment cap policies on power system decarbonization

As countries pursue power system decarbonization, a well-intentioned strategy being pursued in jurisdictions like China is the strict integration target, often in the form of a curtailment cap. The effects of these curtailment caps have not been systematically studied. Here, we evaluate the effects of these caps on the decarbonization of one provincial power system using a capacity expansion model. Results reveal that curtailment caps yield deleterious effects that do not align with the stated goals of these policies. Capping curtailment significantly increases storage capacity (+43% with a 5% curtailment cap) and reduces renewable capacity (-17%). Even with the increase in flexible storage capacity, the policy still jeopardizes power system reliability by increasing occurrences of over or under generation. It also suppresses power generation from hydropower and reduces energy storage utilization while increasing fossil fuel utilization. Capping curtailment increases economic costs (+6% with a 5% curtailment cap) and CO2 emissions (+7%).

Comprehensive assessment for different ranges of battery electric vehicles: Is it necessary to develop an ultra-long range battery electric vehicle?

Some automotive companies develop battery electric vehicles (BEVs) with an ultra-long range to address consumers' range anxiety. However, ultra-long-range BEVs have many problems, and whether they can truly solve consumers' range anxiety has not been studied. Thus, we build a technology-rich, bottom-up approach model to evaluate BEVs' performance, economy, and total cost of ownership (TCO) to reveal the necessity of developing ultra-long-range BEVs. The results show that the ultra-long-range BEVs' dynamic, safety, and economy performances are poor compared to short-range BEVs. Based on the TCO analysis considering battery replacement and alternative transportation costs, 400 km is the optimal range of BEVs for consumers. In addition, consumers' range anxiety is essentially anxiety about energy replenishment. Ultra-long-range BEV cannot really solve consumers' range anxiety except by reducing charging frequency. In the case of gradually improving the charging and swapping infrastructure, we believe that automotive companies do not need to develop ultra-long-range BEVs.

China's ambitious low-carbon goals require fostering city-level renewable energy transitions

Cities in China, as elsewhere, are increasingly playing a crucial role in mitigating climate change. We developed a panel dataset on renewable energy transition in Chinese cities, and assessed the CO₂ emissions reduction of city-level renewable energy transition. We found that city-level renewable energy transition only reduced 446 million tonnes of CO₂ emissions from 2005 to 2019. Moreover, the 2030 carbon peak target will be missed in the business-as-usual scenario. The CO₂ emissions reduction of city-level renewable energy transition will significantly increase in the policy constraint scenario and in the technology breakthrough scenario, and the 2030 carbon peak target will likely be reached in both these scenarios, with a range of possible CO₂ emissions in 2030 equal to 8.34-10.43 and 8.00-10.07 billion tonnes, respectively. In this study, we were the first to assess the historical contribution and prospective trajectory of CO₂ emissions reduction of China's city-level renewable energy transition.

LCA model validation of SAGD facilities with real operation data as a collaborative example between model developers and industry

There has been a notable disagreement between life cycle GHG emission estimates reported by research communities and key energy sector stakeholders as many LCA models are not validated against real operation data. This is originated from lack of collaboration and knowledge exchange between model developers and company experts. We present a pragmatic procedure for engaging company experts to advance the assumptions, models, and information used in an open-source LCA simulator (OPGEE). Using real operation and local emission factor data, two oil sands SAGD fields GHG emissions are compared rigorously against the scope 1 and 2 reported emissions. By introducing consistent region-specific input data, system boundaries, and assumptions, OPGEE carbon intensity estimates are within 1%-5% of reported data by companies. The system boundary expansion (e.g., expanding from direct emissions to also include offsite emissions from natural gas co-production, diluent source emission) impacts the GHG intensities estimates for both fields.

Multi-objective parameter optimization of CNC plane milling for sustainable manufacturing

Energy modeling and cutting parameter optimization of the machining process have been recognized as powerful and effective ways to save energy. However, in the actual machining process, technologists often use empirical methods to determine the final cutting parameters. Due to the lack of theoretical support and optimization tools, this method is difficult to fully consider the constraints of machine tool capability, cutting tool performance, and workpiece material, which affects the overall performance of the machine tool to give full play. To address this problem, a multi-objective parameter optimization method of computer numerical control (CNC) plane milling for sustainable manufacturing was proposed in this paper. More specifically, three tasks were carried out: (1) an accurate milling energy model considering transient processes such as spindle acceleration was established; (2) a multi-objective parameter optimization model of CNC plane milling was established with cutting parameters as optimization variables and considering various complex constraints; (3) by drawing 3D surface maps, the internal relationship between the cutting parameters and the optimization index was presented in detail and intuitively. Finally, a case study was carried out in the XHK-714F vertical machining center. The results showed that the processing efficiency is improved by 21.0%, the energy consumption is reduced by 15.3%, and the surface roughness is reduced by 5.5% through the optimization of cutting parameters, which verified the effectiveness and feasibility of the proposed model and method.

Hierarchical approach to evaluating storage requirements for renewable-energy-driven grids

Energy storage can accelerate the decarbonization of the electrical grid. As useful energy storage technologies are developed, investors and manufacturers want to determine the needs for storage in a wide range of scenarios. In this study, we introduce a strategy for identifying the types of storage that will be most valuable to the grid given specific generation and load profiles. This method estimates the annual minimum number of cycles for each storage, how long each holds the charge, and charging and discharging rates for an idealized system, giving insight into tomorrow's complex systems. We demonstrate the proposed hierarchical approach and quantify how many fewer times wind-driven grids cycle the storage at night compared with solar-driven grids, as well as how winter-dominant wind generation and latitude-tilt solar may reduce the need for seasonal storage. Also, we quantify how higher discharging rates are required for energy storage products that cycle most frequently.

An overview of deterministic and probabilistic forecasting methods of wind energy

In recent years, a variety of wind forecasting models have been developed, prompting necessity to review the abundant methods to gain insights of the state-of-the-art development status. However, existing literature reviews only focus on a subclass of methods, such as multi-objective optimization and machine learning methods while lacking the full particulars of wind forecasting field. Furthermore, the classification of wind forecasting methods is unclear and incomplete, especially considering the rapid development of this field. Therefore, this article aims to provide a systematic review of the existing deterministic and probabilistic wind forecasting methods, from the perspectives of data source, model evaluation framework, technical background, theoretical basis, and model performance. It is expected that this work will provide junior researchers with broad and detailed information on wind forecasting for their future development of more accurate and practical wind forecasting models.

Reducing energy system model distortions from unintended storage cycling through variable costs

Energy system models are used for policy decisions and technology designs. If not carefully used, models give implausible outputs and mislead decision-making. One implausible effect is "unintended storage cycling", which is observable as simultaneous storage charging and discharging. Methods to remove such misleading effects exist, but are computationally inefficient and sometimes ineffective. Through 124 simulations, we find that determining appropriate levels of variable costs depends on the variable cost allocation to certain components and the solver accuracy used for the optimization. For the latter, if the accuracy is set too loosely, the solver prevents the removal of unintended storage cycling. We further provide a list of recommended variable cost model inputs as well as a minimum threshold that can significantly reduce the magnitude and likeliness of unintended storage cycling. Finally, our results suggest that our approach can remove other similar misleading effects such as unintended line cycling or sector cycling.

Optimal deployment for carbon capture enables more than half of China's coal-fired power plant to achieve low-carbon transformation

Carbon capture, utilization and storage (CCUS) technology is critical to global net-zero emission goal, whereas actual deployment is well below expectations. This study constructs a comprehensive framework, integrating nonlinear dynamic optimization, real option and technology learning curve, to explore optimal CCUS deployment for China's coal-fired power plant toward carbon neutrality. The commercialization application will occur in 2030-2035, with the optimal potential ranging between 248.54 GW and 564.90 GW. East China has the greatest potential, reaching 196.85 GW, followed by North China with the potential of 116.29 GW. The cost of second-generation capture technology will decrease from 219 CNY/ton CO₂ to 165 CNY/ton CO₂ during 2030-2031.The annual corporate expenditure (R&D investment and capture cost) and government expenditure (subsidy) will peak at 23.92 billion CNY in 2035 and 63.71 billion CNY in 2044, respectively. The financial burden can be lessened by carbon trading market and third-party intervention in the later period.

Construction and performance analysis of a solar thermophotovoltaic system targeting on the efficient utilization of AM0 space solar radiation

Solar thermophotovoltaic (STPV) has great potential as efficient power supply source for spacecraft to meet the demand of spacecraft miniaturization. In this work, a novel space STPV system is proposed to achieve the efficient utilization of the AM0 space solar radiation. Metamaterial structures were designed and FDTD method is used to analyze their radiation regulation mechanism. A multi-layer cylindrical periodic structure is used as the absorber which realizes a total absorptance of 0.9283 to AM0 radiation. A cylindrical periodic structure is used as the emitter to reshape the broadband thermal radiation as narrowband to match with the Si/InGaAsSb tandem cell, which realizes a highest TPV efficiency of 51.36%. System performance analysis is conducted and the system presents a highest STPV efficiency of 40.86% and good adaptability under wide range of operating parameters, which reveals its great potential to realize the efficient utilization of AM0 solar radiation for space power supply.

•

A novel space STPV system for AM0 space solar radiation is proposed

•

Metamaterial structures are designed for spectrum reshaping of AM0 solar radiation

•

The total absorptance of AM0 radiation is 0.9283 and the TPV efficiency is 51.36

•

The highest energy conversion efficiency of the space STPV system reaches 40.86

Requirements for a maritime transition in line with the Paris Agreement

The shipping industry is a hard-to-abate sector in today's society. Although past studies have looked at levels of carbon pricing, fuel savings, and the upscaling of green fuel availability separately, we combine these critical parameters for a green transition of the shipping industry to show what it takes to reach sectoral emissions reduction targets in line with the Paris Agreement. We utilize a least-cost optimization model drawing on data on, e.g., emissions with lifecycle elements and the costs of green fuel production. We find that reaching maritime reduction targets for a green transition requires high growth rates for green fuel availability, carbon pricing beyond 300EUR/tCO₂eq, and at least 50% in fuel demand savings compared to today's demand projection for 2050. The results show the importance of immediate climate action if maritime emissions reduction goals are to be achieved.

Improving the effectiveness and equity of fuel economy regulations with sales adjustment factors

Larger vehicles, such as sports utility vehicles, consume more energy than cars. Their increasing popularity runs contrary to the goal of fuel economy regulations to reduce fossil fuel consumption and greenhouse gas emissions and can be explained by consumer preference and lower regulation stringency, which is due to footprint, truck classification, and the omission of heterogenous lifetime vehicle distance traveled among vehicle classes. This study shows that, for both the US and China, large vehicles travel more, last longer, and are owned by higher income consumers. This means large vehicles and their high-income owners use more fuel and emit more pollutants than represented by current policy and thus raises both policy effectiveness and energy equity concerns. We propose and estimate Sales Adjustment Factors that weigh fuel economy standards based on vehicle lifetime usage and demonstrate the resultant significant improvements in the effectiveness and equity of fuel economy regulations.

•

Current fuel economy regulations ignore vehicle usage heterogeneity

•

Large vehicles travel more, last longer, and are owned by higher income consumers

•

Sales Adjustment Factors (SAF) are formulated and estimated as a policy improvement

•

SAF impacts the industry by $0.4–$1.2 billion and consumers by up to $800/vehicle

The costs of replacing coal plant jobs with local instead of distant wind and solar jobs across the United States

To further a just energy transition, jobs lost at retiring coal plants could be replaced by jobs at wind and solar plants. No research quantifies the feasibility and costs of such an undertaking across the United States. Complicating such an undertaking are workers’ place-based preferences that could prevent them from moving long distances, e.g. to high renewable resource regions. We formulate a bottom-up optimization model to quantify the technical feasibility and costs of replacing coal plant jobs with local versus distant jobs in the renewables sector. For the contiguous United States, we find replacing coal generation and employment with local wind and solar investments is feasible. Siting renewables local to instead of distant from retiring coal plants increases replacement costs by 5%–33% across sub-national regions and by $83 billion, or 24%, across the United States. These costs are modest relative to overall energy transition costs.

•

We quantify costs of replacing coal plant with local versus distant renewable jobs

•

Local wind and solar can replace electricity and jobs from every U.S. coal plant

•

Replacing coal with local versus distant renewables increases U.S. costs by 24%

•

Cost increases are less than 10% for at least some coal plants in most U.S. regions

Renewable energy targets and unintended storage cycling: Implications for energy modeling

To decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artifact of excessive electricity storage use. We introduce this phenomenon as “unintended storage cycling”, which can be detected in case of simultaneous storage charging and discharging. In this paper, we provide an analytical representation of different approaches for implementing minimum renewable share constraints in energy models, and show how these may lead to unintended storage cycling. Using a parsimonious optimization model, we quantify related distortions of optimal dispatch and investment decisions as well as market prices, and identify important drivers of the phenomenon. Finally, we provide recommendations on how to avoid the distorting effects of unintended storage cycling in energy modeling.

•

Renewable energy constraints may cause excessive storage use in energy models

•

We investigate how this artifact is related to different constraint implementations

•

We quantify distortions of model outcomes with a parsimonious optimization model

•

We discuss how to avoid unintended storage cycling in energy models

Modeling the energy consumption of potable water reuse schemes

Potable reuse of municipal wastewater is often the lowest-energy option for increasing the availability of fresh water. However, limited data are available on the energy consumption of potable reuse facilities and schemes, and the many variables affecting energy consumption obscure the process of estimating energy requirements. By synthesizing available data and developing a simple model for the energy consumption of centralized potable reuse schemes, this study provides a framework for understanding when potable reuse is the lowest-energy option for augmenting water supply. The model is evaluated to determine a representative range for the specific electrical energy consumption of direct and indirect potable reuse schemes and compare potable reuse to other water supply augmentation options, such as seawater desalination. Finally, the model is used to identify the most promising avenues for further reducing the energy consumption of potable reuse, including encouraging direct potable reuse without additional drinking water treatment, avoiding reverse osmosis in indirect potable reuse when effluent quality allows it, updating pipe networks, or using more permeable membranes. Potable reuse already requires far less energy than seawater desalination and, with a few investments in energy efficiency, entire potable reuse schemes could operate with a specific electrical energy consumption of less than 1 kWh/m³, showing the promise of potable reuse as a low-energy option for augmenting water supply.

A mathematical programming formulation for long-term infrastructure investment planning in Small Island Developing States

Mixed-integer programming is a common method used in electricity generation and transmission optimization models. However, the size of the problem can result in extraordinarily long run times. Solve time also increases exponentially with the number of variables to optimize. There is therefore a constant trade-off between a realistic representation of the network and computational tractability. Additionally, actual data and publicly available, real-world application are scare. This is particularly true for Small Island Developing States. This paper bridges these gaps by describing a customized mathematical formulation for co-optimizing generation and transmission infrastructure investments. Data from the island of Jamaica and program scripts are available for reproduction. Key customizations to a mixed-integer programming model for long-term generation and transmission infrastructure investment planning include:•Hours are treated as representative hour categories and multiplied by the number of hour types within a given period.•Simulated construction is limited to every other year.•While fossil fuel plants are treated as discrete variables, renewable energy plants are treated as continuous variables.

An energy and time prediction model for remanufacturing process using graphical evaluation and review technique (GERT) with multivariant uncertainties

The rising energy price and stringent energy efficiency-related legislations encourage decision makers to concern more about energy efficiency in current manufacturing competition. In this regard, a quick and accurate prediction of the energy consumption and makespan in the manufacturing process has been a prerequisite for energy optimization. Given the various types of uncertainties in the remanufacturing system such as stochastic, fuzzy, and grey factors, the present study developed a prediction model that forecasts the energy consumption, completion time, and probability of processing routes. It adopted the graphical evaluation and review technique (GERT) to convert remanufacturing process into an uncertain network, considering multivariant uncertainties instead of merely stochastic uncertainty in prior works. We provided a generic seven steps to implement this approach. The energy consumption and completion time of remanufacturing process were determined in conjunction with Mason's rule and chance-constrained programming. Connecting rod reprocessing was presented as a numerical example. Based on the GERT network, we conducted an Arena simulation to validate the feasibility and effectiveness of this approach. In addition, we adopted the concept of criticality index to conduct sensitivity analysis and examine the predominant factors affecting the concerned indicators. This study would enable remanufacturers to perform a quick prediction of energy use and makespan in remanufacturing process.

Modeling household energy consumption and adoption of energy efficient technology

This study develops a model to study household energy use behavior that can impose common preferences for feasible demand estimation with multiple discrete technology choices and multiple continuous energy consumption uses. The model imposes fixed proportions production and additivity of uses for plausible estimation feasibility while adopting a second-order translog flexible functional form to focus on flexibility in identification of consumer preferences that determine interactions among energy uses and between short-run and long-run choices. Using a unique household-level dataset from California, the model is applied to estimate short-run household demand for electricity and natural gas and the long-run technology choices with respect to clothes washing, water heating, space heating, and clothes drying. The estimation results support commonality of underlying preferences except in one case that is explained by an unavailable variable.

Modeling impacts of sea-level rise, oil price, and management strategy on the costs of sustaining Mississippi delta marshes with hydraulic dredging

Over 25% of Mississippi River delta plain (MRDP) wetlands were lost over the past century. There is currently a major effort to restore the MRDP focused on a 50-year time horizon, a period during which the energy system and climate will change dramatically. We used a calibrated MRDP marsh elevation model to assess the costs of hydraulic dredging to sustain wetlands from 2016 to 2066 and 2016 to 2100 under a range of scenarios for sea level rise, energy price, and management regimes. We developed a subroutine to simulate dredging costs based on the price of crude oil and a project efficiency factor. Crude oil prices were projected using forecasts from global energy models. The costs to sustain marsh between 2016 and 2100 changed from $128,000/ha in the no change scenario to ~$1,010,000/ha in the worst-case scenario for sea level rise and energy price, an ~8-fold increase. Increasing suspended sediment concentrations, which is possible using managed river diversions, raised created marsh lifespan and decreased long term dredging costs. Created marsh lifespan changed nonlinearly with dredging fill elevation and suspended sediment level. Cost effectiveness of marsh creation and nourishment can be optimized by adjusting dredging fill elevation to the local sediment regime. Regardless of management scenario, sustaining the MRDP with hydraulic dredging suffered declining returns on investment due to the convergence of energy and climate trends. Marsh creation will likely become unaffordable in the mid to late 21st century, especially if river sediment diversions are not constructed before 2030. We recommend that environmental managers take into consideration coupled energy and climate scenarios for long-term risk assessments and adjust restoration goals accordingly.