DOC

Use of Billing Simulation tool for Commissioning

By Marie Marshall,2014-01-20 04:40
8 views 0
Use of Billing Simulation tool for Commissioning

Proceedings 1999 National Commissioning Conference. Page 1

    Presented at National Commissioning Conference, 1999.

    Use of Billing Simulation Tool for Commissioning

    David Robison, Howard Reichmuth

    Stellar Processes, Inc.

    Synopsis

A spreadsheet tool has been developed that allows quick adjustment of a simplified engineering

    model to match actual utility bills. The tool utilizes billing analysis of commercial facilities to:

? Diagnose energy patterns and end use consumption;

    ? Calibrate savings estimates to agree with actual usage;

    ? Verify vendor claims for energy products and services;

    ? Generate performance targets and compare against actual energy bills. This application

    represents a low-cost, simplified commissioning check.

The tool is designed to operate with only simple information about the facility and to focus on

    the HVAC system. It represents one quick approach to treating the facility as an integrated whole.

    Case examples illustrate how the tool is useful in diagnosing energy problems, guiding on-site

    audits, establishing predicted targets for O&M tracking and performance verification.

    About the Authors David Robison and Howard Reichmuth have extensive experience in verifying energy savings in

    commercial facilities. They participated in end-use monitoring of the Energy Edge project and in

    end-use planning for Demand Side programs. They assisted in the development of

    commissioning procedures for PacifiCorp's programs, including field procedures for use of

    short-term dataloggers. Their interest has been to provide techniques that lead to confirmation of

    annual energy savings, as well as the verification of current operations.

Proceedings 1999 National Commissioning Conference. Page 2

    Introduction and Background

    The process of commissioning relies on functional performance tests to verify that equipment is operating correctly. Such tests may involve review of control settings, one-time measurements, short-term monitoring or specific equipment tests. Skilled engineering consultants are required to conduct the test and interpret the results. As a result, the process of commissioning is an expense difficult to justify for small projects.

    The commissioning process is silent on another important issue -- do the expected energy savings actually occur? Since the commissioning takes place at one point in time, commissioning agents are careful to point out that they cannot quantify savings that occur during a different season or under different operating conditions. All the commissioning can do is to point out that the equipment is operating as designed -- one must then assume that the overall energy savings will occur. "Building squirm" or change in operating conditions is one possibility that interferes with assuming actual savings will match expectations. A commissioning verification of the whole building performance has been difficult to do, short of a complex re-modeling exercise. Yet, verification of the actual amount of savings may be very important to certain customers that need to justify the financial investment or that have performance-based contracts.

    From the customer's standpoint, utility bills are where the “rubber hits the road”. Yet commissioning agents use billing data in only a cursory manner for two reasons. First, if commissioning is done shortly after the installation, there is no post-retrofit billing data available. Second, both analysts and customers are skeptical of simply comparing energy bills. Bills are sensitive to weather and the length of the metering interval -- they rarely match predictions exactly. While energy accounting programs normalize for weather variations, the methodology is not transparent to the user. Normalizing corrections are primarily statistical and without relationship to building physics. For a commissioning of conservation measures, billing analysis needs to be tied to the engineering and control parameters of a building.

    The Billing Simulation Approach

    As one approach to resolve these problems, we developed a "billing simulation" tool. This tool is a spreadsheet that ties together whole-building level billing data and a simplified engineering simulation model of a commercial facility. The tool is designed to quickly "tune" or calibrate the engineering model to match the bills, using actual site weather. The tuning process often provides diagnostic insight toward identifiable operation problems. Of course, the tuned model can provide calibrated estimates of conservation savings on an integrated basis. More importantly, the tuned model can be used to predict future billings, taking into account the actual, local weather and operations. The predictions represent performance targets. Comparing the post-retrofit bills to the targets provides a first-order commissioning check at low-cost. For small projects, this check may be the only affordable methodology. The comparison may facilitate performance-based contracting by providing answers in a format the customer can understand. Or it can be used for on-going quality assurance -- to make sure that measures persist over time.

The key to this process is the “tuning graph” shown in Figure A. This graph shows an example

    of how the billing data may identify operational problems. The data points are billed

Proceedings 1999 National Commissioning Conference. Page 3

    consumption normalized for differences in duration of the billing period and for building size.

    They are plotted on the y-axis as average energy usage versus average temperature on the x-axis.

    We refer to this visual as a "tuning" graph because it also facilitates quick adjustment of the

    engineering model to match actual bills. This example shows a case where the economizers were

    locked in the full open position during the heating season. After the problem was fixed,

    consumption fell to the lower line.

    Two Year Billings

    10First Year Electric

    Bills8Second Year6Electric Bills

    4Basecase Electric

    Modelwatts/ft22Comparison CaseNormalized Power, 0Electric Model

    20406080

    Mean Monthy Temp, Deg F

Figure A. Example of Operational or "Tuning" Plot

This visual image presents the building energy use (electric, gas, or key enduses) in a picture of

    average operations versus mean temperature. Billing data typically form a reliable pattern in this

    analysis space. These patterns are intuitively comprehensible and they bear an engineering

    relationship to enduse interactions of building energy use. Review of this plot provides the first

    level of an operations check -- is the building performing as expected? Under all temperature

    conditions? Using this type of plot, one can often identify operational errors for specific

    equipment problems, even though the data are collected at the whole-building level. Since these

    utility data are readily available, they provide a low-cost checking mechanism.

Once the tuning graph has been properly matched to be accurately modeling the facility, the

    model can be used to generate consumption estimates for any desired conditions. One such

    application is provides the calendar presentation of energy use as demonstrated in Figure B. The

    calendar presentation can be used to compare directly to monthly bills. In this example, the black

    line represents what the baseline building would have used under the same weather and

    occupancy as actually occurred post-retrofit. The bars show respectively the predicted and the

    actual electric bills.

Figure B shows an example of computing performance targets for a community college campus.

    The college utilized an ESCO contractor to install a series of efficiency improvements. Even

    though, these measures were all carefully commissioned, the college balked at purchasing a

    second round of measures. The college wanted to see that the first round actually produced

    savings before investing in a second round. The ESCO contractor had monitoring logs and

    commissioning tests, but these were not understandable to the customer. The billing analysis

    provided a result they could understand. In part, because the tuning plot was transparent to the

    customer for any adjustment due to weather or operations. Also the analysis related directly to

Proceedings 1999 National Commissioning Conference. Page 4

    what the customer perceived as the result -- their own monthly bills. This graph convinced the

    customer that the project was on-track for savings and they then entered into discussions with the

    ESCO for a second round of measures.

    Predicted and Actual Energy Consumption

    Using Actual Weather and Occupancy

    700,000Predicted600,000Consumption

    500,000Actual400,000Consumption300,000

    Baseline200,000Consumption100,000

    Electricity Usage, kWh/Month0

    Jan-Feb-Mar-Apr-May-Jun-Jul-Aug-Sep-Oct-Nov-Dec-

    969695959595959595959595

Figure B. Example of Performance Verification or "Commissioning" Plot

This example shows that an initial level of commissioning can be provided based on the utility

    bills instead of functional tests. If the building is on-target for savings, the measures must be

    operating correctly. We refer to this visual as a "commissioning" graph. Together the tuning plot

    and the commissioning graph provide a crosscheck on both the physical (temperature) and time

    varying energy use of a building. The methodology supports a surprisingly detailed functional

    understanding of building energy use.

The simulation model has been shown to provide results consistent with DOE-2, from which it

    was derived. Figure C shows results from a benchmark comparison of the two modeling

    techniques. Since the size of these projects spanned a wide range, results are presented as the

    Realization Rate or the ratio of actual, "tuned" savings estimates to the initial design estimates.

    In some cases, extended operating hours or increased floor area in the "as-built" case provided

    realization rates of greater than 100%.

Use of an engineering model provides certain advantages. Starting with little more than the

    utility bills, the model provides an estimate of energy end uses within the facility. Inclusion of

    sophisticated HVAC options ensures that the model offers the following:

? A comprehensive analysis approach, including interaction effects between end uses;

    ? Explicit inputs allowing changes for operations or equipment efficiency;

    ? Results based on the actual local weather, not average weather;

    ? Graphic outputs that are readily understood by the customer.

Proceedings 1999 National Commissioning Conference. Page 5

    Benchmark Comparison of

     Realization Rates

    3.0

    2.5

    2.0

    1.5

    1.0

    Simulation Tool0.5

    0.0

    0.01.02.03.0

    DOE-2 Model

Figure C. Benchmark Comparison

    Verification Cases

The following examples illustrate some of the ways the tool can be useful.

Large Office

    Predicted and Actual Energy Consumption

    Using Actual Weather and Occupancy

    1,200,000Predicted

    Consumption1,000,000

    800,000Actual

    Consumption600,000

    400,000Baseline

    Consumption200,000

    Electricity Usage, kWh/Month0

    Jan-Feb-Mar-Apr-May-Jun-Jul-Aug-Sep-Oct-Nov-Dec-

    929292929292929292929292

Figure D. Large Office

This retrofit project was extensively commissioned including functional performance tests of

    equipment as installed, review of trend logs and short-term monitoring. The monitoring revealed

    that some initial modeling assumptions were incorrect. Specifically, plug loads and night fan

Proceedings 1999 National Commissioning Conference. Page 6

    usage were higher than assumed. It was, however, not feasible to redo the expensive DOE2

    model for such small changes. Despite the detailed information, the service company was not

    able to provide the customer with a concise statement of exactly what monthly savings were

    accomplished.

The simplified model in Figure D was corrected for the changes revealed by monitoring but

    otherwise matches the DOE-2 model. Results show that actual savings are about 33% rather than

    the predicted 41%, with the difference explained by the monitored changes. The simplified

    model is better able to show the comparison because it provides results based on the actual

    weather compared to the actual post-retrofit bills.

Supermarket

    Predicted and Actual Energy Consumption

    Using Actual Weather and Occupancy

    180,000Predicted160,000Consumption140,000

    120,000Actual100,000Consumption80,000

    60,000Baseline40,000Consumption20,000Electricity Usage, kWh/Month0

    Jan-Feb-Mar-Apr-May-Jun-Jul-Aug-Sep-Oct-Nov-Dec-

    989898989898989797979797

Figure E. Initial Supermarket Billings

This example shows the commissioning graph for a supermarket that conducted lighting retrofit.

    At the same time, they also added a number of additional energy-efficient refrigeration cases.

    The customer notes that his bills have not changed and wonders if the efficiency measures have

    been effective. The results in Figure E are ambiguous. Any decrease in the monthly bill is small

    due to the added equipment and the variability of operations.

Using the model, we are able to estimate what the old store would have used with the old

    lighting and the old type of refrigeration for the new cooler cases. This "hypothetical" baseline

    provides a better representation of what the customer's bills would have been for purposes of

    estimating savings. In Figure F, the difference between the hypothetical basecase and the actual

    bills is more apparent. Based on this graph, the efficiency measures appear to be effective.

Proceedings 1999 National Commissioning Conference. Page 7

    Predicted and Actual Energy Consumption

    Using Actual Weather and Occupancy

    200,000Predicted180,000Consumption160,000

    140,000Actual120,000Consumption100,000

    80,000

    60,000Baseline40,000Consumption

    20,000Electricity Usage, kWh/Month0

    Jan-Feb-Mar-Apr-May-Jun-Jul-Aug-Sep-Oct-Nov-Dec-

    989898989898989797979797

Figure F. Revised Supermarket

It must be noted that the customer may be skeptical of introducing a hypothetical baseline. The

    key to this approach lies in first demonstrating with the operational plot or tuning graph, that the

    modeler has accurately and fairly represented the building's performance. It is important that the

    methodology be transparent to the customer so that the extrapolating to a revised baseline will

    appear fair to both parties.

Retail Store

    Two Year Billings

    4First Year Electric

    Bills3Second Year

    Electric Bills2Basecase Electric

    Model1watts/ft2Comparison CaseNormalized Power, 0Electric Model

    20406080

    Mean Monthy Temp, Deg F

Figure G. Retail Store Tuning Graph

The retail store in this example conducted a successful lighting retrofit and dramatically reduced

    electric bills. They took advantage of the savings to restore air conditioning, which they had

    earlier chose to minimize. As Figure G shows, operations in the store have changed so that one

    can no longer directly compare pre- and post-retrofit bills in order to compute savings. However,

    the model can be used to estimate what bills would have been had air conditioning been in use

    during the pre-retrofit period.

Proceedings 1999 National Commissioning Conference. Page 8

    Small Office

    Predicted and Actual Energy Consumption

    Using Actual Weather and Occupancy

    300Predicted250Consumption

    200Actual

    Consumption150

    100Baseline

    Consumption50Gas Usage, Therm/Month