Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification

Building heat electrification is central to economy-wide decarbonization efforts and directly affects energy infrastructure planning through increasing electricity demand and reduces the use of gas infrastructure that also serves the power sector. However, the simultaneous effects on both the power and gas systems have yet to be rigorously evaluated. Offering two key contributions, we develop a modeling framework to project end-use demand for electricity and gas in the buildings sector under various electrification pathways and evaluate their impact on co-optimized bulk power-gas infrastructure investments and operations under deep decarbonization scenarios. Applying the framework to study the U.S. New England region in 2050 across 20 weather scenarios, we find high electrification of the residential sector can increase sectoral peak and total electricity demands by up to 62-160% and 47-65% respectively relative to business-as-usual trajectories. Employing demand-side measures like building envelope improvements under high electrification, however, can reduce the magnitude and weather sensitivity of peak load as well as reduce combined power and gas demand by 29-31% relative to the present day. Notably, a combination of high electrification and envelope improvements yields the lowest bulk power-gas system cost outcomes. We also find that inter-annual weather-driven variations in demand result in up to 20% variation in optimal power sector investments, which highlights the importance of capturing weather sensitivity for planning purposes

Demonstration of fault detection and diagnosis methods for air-handling units (ASHRAE 1020-RP)

Results are presented from controlled field tests of two methods for detecting and diagnosing faults in HVAC equipment. The tests were conducted in a unique research building that featured two air-handling units serving matched sets of unoccupied rooms with adjustable internal loads. Tests were also conducted in the same building on a third air handler serving areas used for instruction and by building staff. One of the two fault detection and diagnosis (FDD) methods used first-principles-based models of system components. The data used by this approach were obtained from sensors typically installed for control purposes. The second method was based on semiempirical correlations of submetered electrical power with flow rates or process control signals. Faults were introduced into the air-mixing, filter-coil, and fan sections of each of the three air-handling units. In the matched air-handling units, faults were implemented over three blind test periods (summer, winter, and spring operating conditions). In each test period, the precise timing of the implementation of the fault conditions was unknown to the researchers. The faults were, however, selected from an agreed set of conditions and magnitudes, established for each season. This was necessary to ensure that at least some magnitudes of the faults could be detected by the FDD methods during the limited test period. Six faults were used for a single summer test period involving the third air-handling unit. These fault conditions were completely unknown to the researchers and the test period was truly blind. The two FDD methods were evaluated on the basis of their sensitivity, robustness, the number of sensors required, and ease of implementation. Both methods detected nearly all of the faults in the two matched air-handling units but fewer of the unknown faults in the third air-handling unit. Fault diagnosis was more difficult than detection. The first-principles-based method misdiagnosed several faults. The electrical power correlation method demonstrated greater success in diagnosis, although the limited number of faults addressed in the tests contributed to this success. The first-principles-based models require a larger number of sensors than the electrical power correlation models, although the latter method requires power meters that are not typically installed. The first-principles-based models require training data for each subsystem model to tune the respective parameters so that the model predictions more precisely represent the target system. This is obtained by an open-loop test procedure. The electrical power correlation method uses polynomial models generated from data collected from “normal” system operation, under closed-loop control.Both methods were found to require further work in three principal areas: to reduce the number of parameters to be identified; to assess the impact of less expensive or fewer sensors; and to further automate their implementation. The first-principles-based models also require further work to improve the robustness of predictions

Improving air quality in high-density cities by understanding the relationship between air pollutant dispersion and urban morphologies

10.1016/j.buildenv.2013.10.008BUILDING AND ENVIRONMENT71245-25

Fault Detection Based on Motor Start Transients and Shaft Harmonics Measured at the RTU Electrical Service

Non-intrusive load monitoring (NILM) is accomplished by sampling voltage and current at high rates and reducing the resulting start transients or harmonic content to concise load “signatures. ” Changes in these signatures can be used to detect, and possibly diagnose, equipment and component faults associated with roof-top cooling units. NILM-based fault detection and diagnosis (FDD) is important because 1) it complements other FDD schemes that are based on thermo-fluid sensors and analyses and 2) it is minimally intrusive (one measuring point in the relatively protected confines of the control panel) and therefore inherently reliable. This paper describes changes in the power signatures of fans and compressors that were found, experimentally and theoretically, to be useful for fault detection

Anthropogenic Heat of Power Generation in Singapore: analyzing today and a future electromobility scenario

This report studies the anthropogenic heat emissions of Singapore’s power generation sector and evaluates the potential future emissions with electromobility across the island. We thus developed a power plant dispatch model to downscale the total heat released by the power sector in 2016. Taking electricity demand and fuel prices as inputs, the model was based on an energy-only model of the National Electricity Market of Singapore. Generation companies were assumed to bid at marginal cost and discount the value of cogeneration heat. This led to a higher correlation of electricity prices and demand than in reality, and sensitivity to fuel prices. The model is capable of calculating the dispatch, fuel consumption, cogeneration heat and waste heat streams of each plant. These heat profiles would then serve as inputs to a WRF mesoscale model of Singapore. The model was calibrated with the monthly fuel mix and annual fuel consumption in 2016 via hyperparameter optimization. An RMSE of 4.67 ktoe was achieved in the electricity produced per month and per fuel, and the total released heat was within 1.88% of the energy statistics. Simulation of the baseline electricity demand showed that CCGT PNG plants emit over half of the waste heat (1796 ktoe of 3282 ktoe), with the Senoko power plant releasing half of this. Cogeneration CCGT plants released about 882 ktoe of waste heat, while producing as much as 1813 ktoe of process heat. As much as 47% of the total waste heat is released into the air as sensible heat, and 27% as latent heat, with the rest released into the sea. Based on data from a previous study on the anthropogenic heat emissions in the transportation sector, we simulated a scenario wherein the road transportation in Singapore was fully electrified. This scenario could have an additional waste heat of 248 ktoe, and an additional electricity demand of 369 ktoe. This additional demand represents a reduction of vehicle heat on the roads by a factor of six, and more heat is emitted at far-away and efficient cogeneration plants. Overall, the estimated reduction in total anthropogenic heat is 1473 ktoe, or about 7% less than in 2016