• Energy Research

China has emerged as a leading electric vehicle (EV) market, accounting for approximately half of the global EV sales volume. We employed an atmospheric chemistry model to evaluate the air quality impacts from multiple scenarios by considering various EV penetration levels in China and assessed the avoided premature mortality attributed to fine particulate matter and ozone pollution. We find higher fleet electrification ratios can synergistically deliver greater air quality, climate and health benefits. For example, electrifying 27% of private vehicles and a larger proportion of certain commercial fleets can readily reduce the annual concentrations of fine particulate matter, nitrogen dioxide and summer concentrations of ozone by 2030. This scenario can reduce the number of annual premature deaths nationwide by 17,456 (95% confidence interval: 10,656–22,160), with the Beijing–Tianjin–Hebei, Yangtze River Delta and Pearl River Delta regions accounting for ~37% of the total number. The high concentration of health benefits in populous megacities implies that their municipal governments should promote more supportive local incentives. This study further reveals that fleet electrification in China could have more health benefits than net climate benefits in the next decade, which should be realized by policymakers to develop cost-effective strategies for EV development. Wide adoption of electric vehicles can contribute to mitigate climate change and air pollution. Here the authors develop various fleet electrification scenarios in China to evaluate the associated air quality and health impacts to inform sound policies.

Abstract This paper explores the potential financial return for using plug-in hybrid electric vehicles as a grid resource. While there is little financial incentive for individuals when the vehicle-to-grid (V2G) service is used exclusively for peak reduction, there is a significant potential for financial return when the V2G service is used for frequency regulation. We propose that these two uses for V2G technology are not mutually exclusive, and that there could exist a “dual-use” program that utilizes V2G for multiple uses simultaneously. In our proposition, V2G could be used for regulation on a daily basis to ensure profits, and be used for peak reduction on days with high electricity demand and poor ambient air quality in order to reap the greatest environmental benefits. The profits for the individual in this type of dual-use program are close to or even higher than the profits experienced in either of the single-use programs. More importantly, we argue that the external benefits of this type of program are much greater as well. At higher V2G participation rates, our analysis shows that the market for regulation capacity could become saturated by V2G-based regulation providers. At the same time, there is plenty of potential for widespread use of V2G technology, especially if the demand for regulation, reserves, and storage grows as more intermittent renewable resources are being incorporated into the power systems.

Abstract The black carbon (BC) emitted from heavy-duty diesel vehicles (HDDVs) is an important source of urban atmospheric pollution and creates strong climate-forcing impacts. The emission ratio of BC to total particle mass (PM) (i.e., BC/PM ratio) is an essential variable used to estimate total BC emissions from historical PM data; however, these ratios have not been measured using portable emission measurement systems (PEMS) in order to obtain real-world measurements over a wide range of driving conditions. In this study, we developed a PEMS platform by integrating two Aethalometers and an electric low pressure impactor to realize the joint measurement of real-world BC and PM emissions for ten HDDVs in China. Test results showed that the average BC/PM ratio for five HDDVs equipped with mechanical fuel injection (MI) engines was 0.43 ± 0.06, significantly lower (P

Abstract Heating and cooling buildings cause a large and growing portion of the world’s carbon emissions. These emissions could be reduced by replacing inefficient or fossil-fueled equipment with efficient electric heat pumps. Recent research has shown that variable-speed heat pumps (VSHPs) could also provide a variety of power system services, such as price- or carbon-based load shifting, peak demand reduction, emergency demand response, and frequency regulation. By providing some or all of these services, VSHPs could facilitate the integration of wind and solar power into the grid, accelerating decarbonization of other economic sectors. However, the provision of power system services poses new and challenging VSHP control problems. Model predictive control (MPC) is a promising approach to these problems. MPC combines weather and load predictions, online optimization, and a mathematical model of a VSHP’s performance under varying ambient conditions. With few exceptions, existing VSHP models neglect two important features that arise in practice: (1) dependence of efficiency and capacity on compressor speed and indoor and outdoor temperatures, and (2) inability of VSHPs to operate at low compressor speeds. In this paper, we develop a new VSHP control model that considers both features. The model expresses the VSHP’s power consumption and heat production as affine functions of the driving temperatures and compressor speed. The model also includes a binary variable that constrains the VSHP to be either turned off, or operating within a range of acceptable compressor speeds using mixed-integer programming (MIP). This VSHP model and optimization strategy is combined with neural network based heat load prediction in MPC simulations. These simulations suggest that this approach could reduce energy costs by 9–22% and carbon emissions by up to 22%, relative to MPC with existing VSHP models.

Abstract Domestic hot water (DHW) heating accounts for up to 30% of average household energy use. Compared to gas fired water heaters, electric water heaters (EWH) can be powered by renewable generation resources, thus making it a potential renewable heating option. Furthermore, with the growing need for energy storage, incorporation of renewable resources, and initiatives worldwide, the electrification of DHW heating is expected to continue the rapid growth. However, many commercial EWH products with monitoring and alerting functionalities lack the intelligence to optimize and perform predictive control with data; on the other hand, research studies with refined models and simulations come short in incorporating real-time data and providing robust optimal controls under uncertainties in real-world settings. This paper presents a EWH Smart Scheduling and Control System using data-driven disturbance forecasts in a robust Model Predictive Control (MPC) to accomplish various demand side management objectives. Testing with a real-world EWH dataset and a two-state EWH model, robust MPC simulations are conducted on a central EWH supplying DHW for a multi-unit apartment building with quantified prediction uncertainty. Results show that the proposed system is capable of anticipating DHW demand with an uncertainty interval covering up to 97% of the actual demand during the test days and reducing electricity cost up to 33.2% as well as maintaining a desired DHW temperature without affecting user comfort. Further, the flexibility of the system to alter load profiles under different Demand Response (DR) programs is demonstrated. Reductions in both power and water consumption can be accomplished. The proposed system can create an implementable solution of forecasting DHW usage and optimizing controls as a part of a robust and reliable building energy management and control system in real-world settings.

The introduction of plug-in hybrid electric vehicles (PHEVs) is expected to have a significant impact on regional power systems and pollutant emissions. This paper analyzes the effects of various penetrations of PHEVs on the marginal fuel dispatch of coal, natural gas and oil, and on pollutant emissions of CO2, NOx, SO2 in the New York Metropolitan Area for two battery charging scenarios in a typical summer and winter day. A model of the AC transmission network of the Northeast Power Coordinating Council (NPCC) region with 693 generators is used to realistically incorporate network constraints into an economic dispatch model. A data-based transportation model of approximately 1 million commuters in NYMA is used to determine battery charging pattern. Results show that for all penetrations of PHEVs network-constrained economic dispatch of generation is significantly more realistic than unconstrained cases. Coal, natural gas and oil units are on the margin in the winter, and only natural gas and oil units are on the margin in the summer. Hourly changes in emissions from transportation and power production are dominated by vehicular activity with significant overall emissions reductions for CO2 and NOx, and a slight increase for SO2. Nighttime regulated charging produces less overall emissions than unregulated charging from when vehicles arrive home for the summer and vice versa for the winter. As PHEVs are poised to link the power and transportation sectors, data-based models combining network constraints and economic dispatch have been shown to improve understanding and facilitate control of this link.

Abstract Nitrogen oxides (NOx) and black carbon (BC) emission factors (EF) of 440 on-road diesel trucks were determined by conducting on-road chasing studies in Beijing and Chongqing, China, in the winter 2010. NOx and BC EF distributions are reported. The median NOx EFs for trucks sampled on level roads in Chongqing and Beijing are 40.0 and 47.4 g kg-fuel−1, respectively. The median BC EFs are 1.1 and 0.4 g kg-fuel−1in Chongqing and Beijing, respectively. In addition, a clear downward trend of BC EFs of on-road diesel trucks sampled in Beijing since 2008 is observed. Moreover, Beijing-registered trucks had the lowest BC EFs among the entire sample. These observations appear to reflect the effectiveness of emission standard and fuel quality standard implemented in Beijing (China IV) and nationwide (China III) in reducing BC (and likely overall particulate matter) emission. However, NOx EFs for Beijing-registered trucks did not show lower value than those from other regions. Unlike black carbon, there is no clear correlation between emission controls and NOx emissions from the sampled on-road trucks. Further analysis shows that trucks with high BC EFs do not usually have high NOx EFs, and vice versa, indicating that the current emission standards implemented in Beijing and nationwide have only limited impact on NOx emissions control. Therefore, effective multi-pollutant control strategies and in-use compliance programs are imperative to reduce the overall emissions from the transportation sector.

Abstract While model predictive control (MPC) is a widely studied tool for integrating residential heat pumps with the smart grid, modeling difficulties and hardware requirements are significant barriers to adoption. In response, we present a nonintrusive plug-and-play methodology for model identification and model predictive control using only two increasingly popular smart-home devices: a smart thermostat and smart electricity meter, which makes the overall approach highly scalable. However, this minimal hardware approach requires new methods to overcome modeling challenges due to the lack of data diversity. A data-driven heat pump power model is identified without the need for submetering by using energy disaggregation. By incorporating the heat pump control data from the smart thermostat, we provide implicit load classification data to the model, substantially simplifying the signal decomposition task. Building and heat pump model parameters are then identified by a novel algorithm that combats overfitting caused by limited state excitation. Finally, we show the advantage of this scalable approach in an aggregate model predictive controller enabled by the communication ability provided by the smart thermostat. Results show that this aggregate control approach can provide improved grid services, such as reduced peak demand, while at the same time reducing energy consumption and improving thermal comfort. The proposed method has the potential to greatly lower the barrier for residential energy aggregation.

Biomass facilities have received increasing attention as a strategy to increase the use of renewable fuels and decrease greenhouse gas emissions from the electric generation and heating sectors, but these facilities can potentially increase local air pollution and associated health effects. Comparing the economic costs and public health benefits of alternative biomass fuel, heating technology, and pollution control technology options provides decision-makers with the necessary information to make optimal choices in a given location.For a case study of a combined heat and power biomass facility in Syracuse, New York, we used stack testing to estimate emissions of fine particulate matter (PM2.5) for both the deployed technology (staged combustion pellet boiler with an electrostatic precipitator) and a conventional alternative (wood chip stoker boiler with a multicyclone). We used the atmospheric dispersion model AERMOD to calculate the contribution of either fuel-technology configuration to ambient primary PM2.5 in a 10km×10km region surrounding the facility, and we quantified the incremental contribution to population mortality and morbidity. We assigned economic values to health outcomes and compared the health benefits of the lower-emitting technology with the incremental costs.In total, the incremental annualized cost of the lower-emitting pellet boiler was $190,000 greater, driven by a greater cost of the pellet fuel and pollution control technology, offset in part by reduced fuel storage costs. PM2.5 emissions were a factor of 23 lower with the pellet boiler with electrostatic precipitator, with corresponding differences in contributions to ambient primary PM2.5 concentrations. The monetary value of the public health benefits of selecting the pellet-fired boiler technology with electrostatic precipitator was $1.7 million annually, greatly exceeding the differential costs even when accounting for uncertainties. Our analyses also showed complex spatial patterns of health benefits given non-uniform age distributions and air pollution levels.The incremental investment in a lower-emitting staged combustion pellet boiler with an electrostatic precipitator was well justified by the population health improvements over the conventional wood chip technology with a multicyclone, even given the focus on only primary PM2.5 within a small spatial domain. Our analytical framework could be generalized to other settings to inform optimal strategies for proposed new facilities or populations.