ASHRAE OR-16-C035-2016 Reduction of Campus Greenhouse Gas Emissions through a Hybrid Centralized Energy Distribution System.pdf

1、 Author Chelsea L. Guenette is a Masters of Science candidate in the Department of Mechanical energy models were created and compared. To begin, each building was modeled as it currently operates. The building models were then calibrated using utility data from previous years. This calibration step

2、is vital to the verification of inputs and schedules used in the building energy models. Then the calibrated models were incorporated into a single model for the energy mini-district and the energy system was updated. The natural gas and electricity consumption, source energy use intensity, and gree

3、nhouse gas emission equivalent from both configurations were compared. Figure 2 shows the energy models created for this study. Figure 2 The campus with the created energy models for Leon Johnson Hall, Cooley Laboratory, Lewis Hall, Chemistry Biochemistry Building, Tietz Hall, Montana Hall, Wilson H

4、all and Jabs Hall. Modeling Factors There are modeling factors that are large contributors to the degradation of an energy model. These factors include occupancy definition, plug and equipment loads, weather and infiltration. Considering the buildings being explored in the energy mini-district, occu

5、pancy and equipment loads in the laboratory spaces are important to explore further. ASHRAE has published values in multiple standards that energy modelers may use to corroborate their assumptions about many spaces. In this study values from the ASHRAE Handbook Fundaments (ASHRAE 2013), ANSI/ASHRAE

6、Standard 90.1-2010 (ASHRAE 2010) and ANSI/ASHRAE Standard 62.1-2010 (ASHRAE 2010) were used in the development on the model. While offices fall within the scope of these standards, these unique laboratory spaces are not entirely captured. Electrical equipment definitions in energy models are often a

7、pplied as a power density in W/ft2 (W/m2) which is consistent with published values. The concept is to apply additional power density to represent interior equipment such as printers, copiers, and coffee machines which release heat to their surrounding environment. Similarly the same assumption shou

8、ld be made for laboratory research equipment. A sample list of the types of laboratory equipment identified in the laboratories in consideration include steam autoclaves, centrifuges, imaging lasers, fume hoods, mass spectrometry equipment, and nuclear magnetic resonance equipment. The sample of equ

9、ipment identified points to the need for significant power density assignments in the laboratory spaces to translate the real-world usage characteristics to the virtual building models. The existence of the laboratory research in these spaces is the driving force behind the consideration of this min

10、i-district configuration; it is because of these process loads that the lab buildings experience internally driven load demands. To validate the interior equipment assignments in the energy model versions of these facilities a metering plan was created to establish an interior equipment power densit

11、y for laboratory spaces in this energy mini-district. Metering Plan A metering plan was implemented to quantify the laboratory activity in these buildings for the energy simulation models. There were two primary modes of data acquisition: 1. Electrical consumption related to laboratory equipment 2.

12、Occupancy and lighting usage profiles The measurement of electrical consumption related to laboratory equipment was accomplished by taking current measurements of electrical panels that exclusively served the laboratory spaces. Generally most of the laboratories in these building have a unique elect

13、rical distribution panel, but occasionally there were labs whose panel also fed some corridor lights, common areas, IT closets, or alcoves. It was important to identify labs that had electric service from a panel without any distribution to a common area. The collected electrical data yielded the to

14、tal energy consumption and the peak demand for the laboratory spaces and was used to create usage profiles. Laboratories in both the Chemistry Biochemistry Building and Cooley Laboratory were selected and used as typical laboratory space. The occupancy and lighting usage profile were assessed in the

15、se selected laboratories. Light and occupancy sensors were installed in these spaces. This data yielded usage schedules expressed in percentage of full load for both occupancy and lighting. Data was collected from December 17th, 2013 through February 19th, 2014. This timeframe allowed for data to be

16、 collected during both active school sessions and during holiday breaks. Collecting data during academic breaks was considered advantageous since it allowed for the determination of base load characteristics in these spaces and could be applied to the energy model. Figure 3 shows the resulting labor

17、atory equipment usage schedule and equipment power densities for Cooley Laboratory and Chemistry Biochemistry Building from the collected data. Figure 3 Laboratory equipment usage schedule and equipment power densities created for Cooley Laboratory and Chemistry Biochemistry Building from the collec

18、ted data. Calibrating the Baseline When calibrating the baseline models there were two types of adjustments made, cyclic and periodic. Cyclic factors are based on either annual or diurnal cycles. Examples include insulation R-values, temperature setbacks and infiltration values. For instance, Montan

19、a Hall was built in 1896 and the R-value of the insulation is unknown. Periodic factors are based on operational schedules. Examples include plug loads, lighting levels, ventilation rates and occupancy schedules. These considerations were made when calibrating the individual building models. Interna

20、tional Performance Measurement and Verification Protocol Option D Calibrated Simulation, herein referred to as IPMVP: Option D, has been adopted by building energy performance rating systems (IPMVP Committee, 2002). In this calibration models are based off of monthly utility data and have achieved a

21、 mean biased error (MBE) of 20% when compared to the monthly energy usage. The MBE equation is listed below as equation 1. (1) IPMVP: Option D standards were used when calibrating the baseline models. This means that the energy models achieved a MBE of 20% or better when compared to the monthly ener

22、gy usage. Model calibration is important for the validity of the model but proves to be difficult. Table 3 shows the MBE values for the buildings in this simulation. It should be noted that Jabs Hall is not included on this list because it was still under construction at the time of this study and t

23、here were no utility bills available for calibration. Figure 4 shows the Lewis Hall simulated natural gas from the calibrated energy model and compared to historic utility data. Table 3. Baseline Building Models Mean Biased Error (MBE) Building Name Natural Gas MBE Electricity MBE Leon Johnson Hall

24、(LJ) 9% -8% Cooley Laboratory (CL) 6% 17% Lewis Hall (LH) -10% 6% Chemistry Biochemistry Building (CHBC) -18% -19% Tietz Hall (TZ) 6% -1% Montana Hall (MH) -8% -5% Wilson Hall (WH) 10% 18% Figure 4 Historical and simulated natural gas utility for Lewis Hall. Evaluating the Solution The proposed syst

25、em includes modular heat recovery chillers or central plant heat pumps. There are several benefits to implementing this system including: 1. Expandable and modular systemcurrently there are five 960,000 Btu/h (280 kW) units for a total of 4,800,000 Btu/h (1400 kW) capacity with space to expand to 9,

26、600,000 Btu/h (2800 kW) 2. Ability to simultaneous heat and cool 3. Low-temperature water, allowing the incorporation of source side renewables 4. Uses low ozone depletion factor refrigerant, R-410a Figure 5 is a schematic of the proposed system. The proposed system also includes the incorporation o

27、f solar thermal arrays and additional geothermal bores. There is a 3,216 ft2 (298.8 m2) solar thermal array installed on the roof of Leon Johnson Hall. Analysis yielded that the addition of the solar thermal array would reduce the steam heating demand by 536 MMBtu/yr (157,100 kWh/yr). As part of the

28、 Jabs Hall construction project a geothermal borefield consisting of 52 bores of 500 ft (152.4 m) deep was installed. Design documents show a calculated borefield capacity of approximately one MMBtu/yr (293.1 kWh/yr) for year round heat rejection and absorption. Figure 5 System schematic drawing of

29、the energy mini-district interconnections. MODELING RESULTS As seen in Table 4, this central plant conversion transfers energy from a natural gas source to an electric source; the natural gas usage declines by 69% while the electric usage only increases by 38%. Taking into account the states source

30、energy for electricity production, the source energy use intensity (EUI) decreases by 7.24%. This results in the greenhouse gas emission equivalent (GHGe) reduction of 14.26%. This value takes into account how currently the electricity in the state is created and includes GHGe reduction value. This

31、indicates that there is potential for improving the GHGe by incorporating low carbon source energy resources in the future such as solar thermal, photovoltaics and additional geothermal borefields. Table 4. Proposed and Baseline Energy and Greenhouse Gas Results Baseline Results (Sum of Calibrated M

32、odels) Proposed Results Percent Reductions Source Energy Use Intensity (EUI) 273.6 MBtu/ft2/yr (863.1 kWh/m2/yr) 253.8 MBtu/ft2/yr (800.6 kWh/m2/yr) 7.24% Greenhouse Gas Emission Equivalent (GHGe) 4941.1 MtCO2e/yr 4237.6 MtCO2e/yr 14.26% Natural Gas 21,135 MMBtu/yr (6,194,100 kWh/yr) 12,506 MMBtu/yr

33、 (3,665,160 kWh/yr) 69% Electricity 7,590 MMBtu/yr (2,224,400 kWh/yr) 12,242 MMBtu/yr (3,587,800 kWh/yr) (-38%) CONCLUSION This paper details an alternate configuration the university evaluated and subsequently implemented to reduce greenhouse gas emission on campus. The university interconnected a

34、small group of buildings to form an energy mini-district within the large centralized campus district. This mini-district contains eight buildings totaling just over 400,000 ft2 (37,000 m2) or 14% of the total campus building square footage. This energy mini-district consisted of an approximately ba

35、lanced distribution of buildings with internal and external demand loads. Before implementing the energy mini-district it was common to see some buildings rejecting heat through cooling towers while adjacent buildings were experiencing heating demands. This new configuration uses a centralized heat

36、pump plant with water loops allowing energy to be shared between the buildings. This central plant conversion shifts energy from a solely natural gas source to an electric source partially provided by renewable energy generation; the natural gas usage declines by 69% while the electric usage only in

37、creases by 38%. Taking into account the states source energy for electricity production, the source EUI decreases by 7.24%. This results in the GHGe reduction of 14.26%. The method of evaluating and comparing various campus-wide energy systems can be applied to other institutions. REFERENCES ASHRAE.

38、 2013. Chapter 18, 2013 ASHRAE Handbook-Fundamentals. Atlanta: ASHRAE. ASHRAE. 2010. ANSI/ASHRAE Standard 62.1-2010, Ventilation for Acceptable Indoor Air Quality. Atlanta: ASHRAE. ASHRAE. 2010. ANSI/ASHRAE Standard 90.1-2010, Energy Standard for Buildings Except Low-Rise Residential Buildings. Atla

39、nta: ASHRAE. International Performance Measurement & Verification Protocol (IPMVP) Committee. 2002. Chapter 3 Option D, International Performance Measurement & Verification Protocol, Concepts and Options for Determining Energy and Water Savings Volume I. Montana State University. 2011. “Climate Acti

40、on Plan.” Last modified December 7. http:/www.montana.edu/sustainability/climateactionplan.html. Talbert, Joshua W. “Efficient Energy Modeling: A Low Carbon Source Energy Assessment of Proposed Building Interconnections Based on Emerging Market Modeling Tools.” MS diss., Montana State University, 2014.