Matt Gudorf UCI Energy Manager

Transcription

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2 Matt Gudorf UCI Energy Manager

3 UNIVERSITY OF CALIFORNIA, IRVINE Comprehensive research university 22,216 Undergraduate Students $330 million sponsored research 27 lab buildings operating 24/7 ~7.5 Million square feet served by campus microgrid 8 24 Megawatts load

4 WHY ARE WE DOING THIS? Financial Reliability Environmental Future Ready M&V of energy projects Breaker capacity Adding renewables to meet load Microgrid Control Tariff analysis Backup generation / UPS sizing Carbon emissions Automated demand response Understanding peak demand Power quality RPS Requirement for imported electricity Energy storage and dispatch Demand response Load shedding (planning) Human Comfort EV charging

5 HARDWARE CHALLENGES 1. Complex, multi-vendor architectures 2. Multiple points of failure 3. Requires integrator services Vendor #4 Vendor #1 Ethernet Interface Vendor #3 Vendor #2

6 SOFTWARE CHALLENGES 1. Multiple users of the energy data 2. Varying complexity of the data presented 3. Network and system security Consultants, engineers, and building operators Students and the community Research

7 UC IRVINE S SOLUTION: THE ENERGY INFORMATION PLATFORM One Vendor Hardware Software Communications Integration Storage

8 HIGH DEFINITION METERING Typical UC Irvine Metering Install 1-5 Meters Phases Phases 15 1Hz Minute Data Data Plug Loads Data Centers Main Switch Board Motor Control Center HVAC Systems Lighting More points, more often allows us to see Do Not the energy Reproduce picture Without more clearly! Permission

9 REAL TIME INFORMATION Grid Weather Other Environmental & Other Sensors Pulse Counters Power / Water / Gas Meters Ethernet Bacnet/IP Modbus/IP SNMP Other Plug Loads Data Centers Main Switch Board Motor Control Center HVAC Systems Lighting Grid, weather, and other data like class schedules completes the program

10 UC IRVINE S SOLUTION: METERING Gateway Specifications 12 or 24 Channel (CT sensors) per gateway. Gateway accuracy ± 0.5% TCP/IP, 10 base T, 100 base TX Linux OS 256MB onboard Current Transformer Specifications Sensor accuracy ± 1% Current transformers range from 60 to 3000 Amps Split core and flexible transformers

11 EXAMPLE OF SUB-METERING POINT SELECTION Installed Gateways: 6 12 Channel Basement (1) First Floor (2) Second Floor (1) Third Floor (2) 1 24 Channel Roof (1) Metering Points

12 2 METER, 36 POINT INSTALL AT UC IRVINE Flexible and split core CTs Non-Invasive No Power Down Required (Typ.)

13 UNDERSTANDING THE UTILITY BILL How much, how fast, and when? When? How How Fast Fast How Much

14 UNDERSTANDING THE UTILITY BILL How much, how fast, and when? Peak (kw) demand for the Time month of use How - When Fast Area = kwh How Much

15 HISTORICAL METRICS Buildings sorted by kwh consumption per year Which buildings use the most energy Year over year comparison may not capture energy conservation efforts, change in use, or space allocation. Answers how much but fails to provide how fast, and when

16 HISTORICAL METRICS Buildings sorted by kwh/sqft consumption per year Which buildings use the most energy Year over year comparison may not capture energy conservation efforts, change in use, or space allocation. Answers how much more accurately, but still fails to provide how fast, and when

17 DIVING IN ON 4 LAB BUILDINGS Four lab buildings selected for further analysis: McGaugh Hall Natural Sciences 2 Rowland Hall Gross Hall These buildings were in the top 10 highest consuming and highest EUI charts seen previously. All buildings are traditional laboratory buildings that contain wet and dry lab spaces. The sub-metering installed is not only permanent but considered our standard. This allows for continuous commissioning and tuning.

18 SELECTED BUILDING CHARACTERISTICS McGaugh Hall 213,717 ft 2, built in 1991 Biological Sciences Building Natural Sciences II 84,440 ft 2, built in 2003 Chemical Sciences Building Rowland Hall 196,652 ft 2, built in 1968 Physical Sciences Building Gross Hall 97,221 ft 2, built in 2010 Stem Cell Research Building All Four Lab Buildings Office and Lab space 100% Outside air for lab wings District chilled water District high temp. water (except Gross Hall) Variable air volume Direct digital controls Various energy conservation measures completed in last 5 years.

19 ANALYSIS SPECIFICS Analysis conducted over the following time range: Start = January 1, 2014 End = September 1, 2014 Data is collected from 129 Points for a total of 14,049,288 total data points Data is collected at 1 minute intervals 1Hz resolution is possible but only needed for specific research or diagnostics.

20 ANALYSIS Amount of data required the use of more advanced data analysis tools. Analysis conducted using the python programming language and open source libraries (software packages).

21 CONSUMPTION BY DAY TYPE Calendar Breakdown Holidays 3.6% Off Days 27% Work Days 69% Does the energy consumption of the building beat the calendar average? 1. Check building scheduling 2. Verify controls are working 3. Add additional controls Targets or goals for energy use: 80% of consumption on workdays 18% of consumption on weekends 2% of consumption on holidays This new analytic helps determine which buildings are operating efficiently based on schedule Do or occupancy. Not Reproduce when Without Permission

22 CONSUMPTION BY HOUR TYPE Hourly Breakdown Work Hour 28% After Hour 40% Off Hours 27% Holiday 3% Does the energy consumption of the building beat the hourly average? 1. Check building scheduling 2. Verify controls are working 3. Add additional controls Targets or goals for energy use: 35% consumption work hours 38% consumption after hours 25% consumption off hours 2% consumption holidays This new analytic helps determine which buildings are operating efficiently based on schedule Do Not or occupancy. Reproduce Without Permission

23 CLUSTER CHARTS Gross Hall Quickly identify when and how fast by hour type. Rowland Hall

24 ENERGY USE HEAT MAP Run hours beyond occupied hours 11pm to 1am run for cleaning staff? Building Running 24/7

25 TIME OF USE PEAK DEMAND EVENT ANALYTIC Work Day Histograms Profiles Targeted examination of 1-2PM to understand what is causing building peak when

26 DEMAND DISTRIBUTION How dynamic is the building? What impact does adding controls, have? Quickly shows when, and how fast and how often Adding more automated controls, widens the buildings distribution profile.

27 LOAD DURATION CURVES Profiles show a high baseload and slowly increasing demand.

28 WEATHER NORMALIZATION Weather normalization typically yields poor results when applied to whole building data for buildings comprised of high percentages of complex space such as labs. Sub-metering to allow for lighting, plug, and process loads to be excluded and then normalizing only the HVAC energy provides actionable data.

29 WEATHER NORMALIZATION Weather normalization of the air handler data yields the best fit, however it should be noted that fan energy will be driven by air change rates, fume hood use, and process loads. In this example the majority of the data points above 85º F are above the curve. One action that could be considered is for days with a predicted temperature greater than 85º F the window shades should be closed.

30 CHILLED WATER AND HIGH TEMPERATURE WATER DATA COLLECTION Supply and Return Temp Pulling this data into the metering platform and not standing it on a BMS allows for complete Copyright energy analysis of Caltech

31 CHILLED WATER AND HIGH TEMPERATURE WATER DATA COLLECTION T Pulling this data into the metering platform and not standing it on a BMS allows for complete Copyright energy analysis of Caltech

32 CHILLED WATER AND HIGH TEMPERATURE WATER DATA COLLECTION Consumption vs External Temperature Clearly a problem exists! Must look at valve position, and supply/return temperatures. Consumption may also not be dependent on external temperature, consumption may be driven by process load.

33 NATURAL GAS CONSUMPTION AND WEATHER DATA Natural gas consumption is slightly better correlated. Gas consumption does decrease as exterior temperature increases, this could lend itself to simultaneous heating and cooling occurring. This building has a vivarium and the cage wash and autoclaves cause the gas usage to fluctuate without correlation to outside temperature.

34 SUB-METERING BY CATEGORY Looking at the use data by day things look ok, but what about an hourly breakdown?

35 SUB-METERING BY CATEGORY Consumption graph is skewed due to the number of hours in each day type!

36 SUB-METERING BY CATEGORY Lighting after hours may be too close to work hours?

37 SUB-METERING BY CATEGORY

38 WHY ARE WE DOING THIS? Reliability Breaker capacity Backup generation / UPS sizing Power quality Load shedding (planning)

39 Increased reliability and resiliency of the system! GROSS HALL EMERGENCY CIRCUIT LOAD DURATION CURVE Emergency generator or UPS check to ensure backup capabilities have not been exceeded using actual demand data

40 BREAKER CAPACITY REPORTS PREVENTING LOST RESEARCH Are the circuits in your lab overloaded? Gross Hall Capacity Report Breaker Ratings = 50A Breakers are nowhere near their maximum rating, even on the day with the highest current draw! Increased reliability and resiliency of the system!

41 WHY ARE WE DOING THIS? Financial M&V of energy projects Demand response

42 GROSS HALL DEMAND DRILL DOWN Total building load for 24 hours

43 GROSS HALL DEMAND DRILL DOWN Emergency Lighting Total building load for 24 hours

44 GROSS HALL DEMAND DRILL DOWN Basement Load Emergency Lighting Load Total building load for 24 hours

45 GROSS HALL DEMAND DRILL DOWN First Floor Load Basement Load Emergency Lighting Load Total building load for 24 hours

46 GROSS HALL DEMAND DRILL DOWN Second Floor Load First Floor Load Basement Load Emergency Lighting Load Total building load for 24 hours

47 GROSS HALL DEMAND DRILL DOWN Third Floor Load Second Floor Load First Floor Load Basement Load Emergency Lighting Load Total building load for 24 hours

48 GROSS HALL DEMAND DRILL DOWN HVAC Load Third Floor Load Second Floor Load First Floor Load Basement Load Emergency Lighting Load Total building load for 24 hours

49 GROSS HALL HVAC

50 GROSS HALL - HVAC

51 GROSS HALL - HVAC

52 GROSS HALL - HVAC

53 GROSS HALL - HVAC

54 GROSS HALL - HVAC

55 GROSS HALL - HVAC

56 GROSS HALL AIR HANDLING UNITS

57 GROSS HALL AIR HANDLING UNITS

58 GROSS HALL AIR HANDLING UNITS

59 GROSS HALL AIR HANDLING UNITS

60 GROSS HALL AIR HANDLING UNITS

61 GROSS HALL AIR HANDLING UNITS After hours office HVAC savings for supply and return fan

62 GROSS HALL AIR HANDLING UNITS Potential demand response load shed

63 BIG DATA ALLOWS DEEP DIVING Metering only tells a portion of the story PV Generation Building load Analytics Building BMS Weather Data Results Occupancy Energy Cost

64 CONCLUSIONS AND TAKEAWAYS 1. When making the case for metering, submetering, and energy management, buying and selling on Energy Savings is far too simplistic. 2. The platform must be robust, with the ability to interface with existing metering, building automation systems, lighting controls, distributed generation, energy storage, and other future systems. 3. Flexibility to adapt to a changing market place with real time pricing, automated demand response, and an ancillary services market can produce financial savings and shield you from financial impact. FUTURE OF THIS PROGRAM 1. Automate the analysis presented in the form of a monthly report for the campus, each building, and each load type. 2. Create work orders based on the report to reduce energy use, save money, and increase reliability.

65 QUESTIONS?