原文信息：
Decarbonizing all-electric communities via carbon-responsive control of behind-the-meter resources
原文链接：
https://www.sciencedirect.com/science/article/pii/S2666792423000185
Abstract
The progression of electrification in the building and transportation sectors brings new opportunities for energy decarbonization. With higher dependence on the grid power supply, the variation of the grid carbon emission intensity can be utilized to reduce the carbon emissions from the two sectors. Existing coordinated control methods for buildings with distributed energy resources (DERs) either consider electricity price or renewable energy generation as the input signal, or adopt optimization in the decision-making, which is difficult to implement in the real-world environment. This paper aims to propose and validate an easy-to-deploy rule-based carbon responsive control framework that facilitates coordination between all-electric buildings and electric vehicles (EVs). The signals of the grid carbon emission intensity and the local photovoltaics (PV) generation are used for shifting the controllable loads. Extensive simulations were conducted using a model of an all-electric mixed-use community in a cold climate to validate the control performance with metrics such as emissions, energy consumption, peak demand, and EV end-of-day state-of-charge (SOC). Our study identifies that 4.5% to 27.1% of annual emission reduction can be achieved with limited impact on energy costs, peak demand, and thermal comfort. Additionally, up to 32.7% of EV emission reduction can be obtained if the EV owners reduce the target SOC by less than 21.2%.
Keywords
Decarbonization
Electrification
Control
Electric vehicle charging
HVAC
All-electric community
Graphics
Fig. 1. The workflow of this paper, which involves building community energy models with EV loads in URBANopt and optimizing the DERs using REopt. OpenStudio measures are then used to implement control algorithms, and annual energy simulations are conducted to evaluate the results of the coordinated control scenario against the baseline scenario.
Fig. 4. Three-dimensional rendering map of the mixed-use case study community located in Denver, Colorado, United States. The community is planned to have 148 buildings, most of which are large commercial buildings. Figure was first used in Wang et al. [20].
Fig. 10. Color plots of annual average zone mean PMV values per building before and after the implementation of the emission reduction control. Each color block represents one building. The emission reduction control has slightly lowered the community average PMV value by 0.02, indicating a slightly colder indoor environment, but the adoption of the control will not impact the occupants’ thermal comfort with the design parameters proposed in this work.
Fig. 7. The results of ultrasonic testing using different battery charging methods at 15 ◦C
Fig. A.1. The 2022 annual grid carbon intensity profile used in the study, ranging from 0 to 2991.7 kg/MWh, with a mean value of 983.5 kg/MWh. The maximum carbon intensity drops to 948.0 kg/MWh, and the mean value drops to 278.8 kg/MWh for 2050 under the same scenario.
Fig. A.2. EV profiles for one building of each building type of the community on three summer days (one weekday and two weekends). The x-axis represents the time in hour, and the y-axis represents the power (kW) of the EV charger. It can be seen that after a buildings normal operation hours, much less EV charging power occur.
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