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SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE. Presented by: Walter Booysen ICUE 2012 - Stellenbosch. INTRODUCTION. Energy saving projects are becoming more commonplace Use of official M&V teams Increased complexity, increased risk

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simplified methods to evaluate industrial dsm project performance

SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE

Presented by: Walter Booysen

ICUE 2012 - Stellenbosch

simplified methods to evaluate industrial dsm project performance1
INTRODUCTION
  • Energy saving projects are becoming more commonplace
  • Use of official M&V teams
  • Increased complexity, increased risk
  • ESCO requires simple methods to evaluate performance
SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE
simplified methods to evaluate industrial dsm project performance2
PRESENT METHODSSIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE

Baseline

Implementation

Assessment

Impact

Power

What would it have been?

Time

Project impact = Baseline – Actual profile

simplified methods to evaluate industrial dsm project performance3
COST OF M&V
  • PRESENT METHODS
  • Regression
  • Econometric
  • Fuzzy logic
  • Neural networks
  • Support vector regression
  • Particle Swarm optimisation
  • BASELINE ADJUSTMENT MODLES:
  • CHANGES PROMPTING ADJUSTMENT:
  • Physical
  • Utilisation
  • Occupancy
  • Hours of operation
  • Volume heated/cooled
  • Equipment (Amount/Use)
  • Environmental conditions
  • Production
  • Maintenance principles
SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE

Cost

Uncertainty

simplified methods to evaluate industrial dsm project performance5
PRESENT METHODS – REGRESSION MODELSSIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE

Before linear model (yB = mBx + cB)

After linear model (yA= mAx + cA)

Power

Independent variable (temperature, occupation, area etc.)

simplified methods to evaluate industrial dsm project performance6
PRESENT METHODS – REGRESSION MODELSSIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE

Before linear model (yB = mBx + cB)

What it would have been

After linear model (yA= mAx + cA)

Specific Impact

Power

Actual

Independent variable (temperature, occupation, area etc.)

simplified methods to evaluate industrial dsm project performance7

Model estimates “what it would have been”

  • Model developed using independent variables
  • Applicable where independent variables (occupation, ambient temp, floor space etc.) are quantifiable
  • PRESENT METHODS – REGRESSION MODELS
SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE
simplified methods to evaluate industrial dsm project performance8
FACTORS COMPLICATING INDUSTRIAL MODELS
  • Nature of Building and Industrial systems
  • Limited selection of variables available
  • Various buffers in system, unknown delays
  • Difference in methods used to achieve savings
SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE
simplified methods to evaluate industrial dsm project performance9
EXAMPLE – MINE COMPRESSED AIR CONTROL SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE

Flow (kg/s)

Baseline

Optimised 1

Time

Reduced wastage, improved equipment and system efficiency

simplified methods to evaluate industrial dsm project performance10
EXAMPLE – MINE COMPRESSED AIR CONTROL SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE

Flow (kg/s)

Baseline

Optimised 2

Time

Improved control matching user demand

simplified methods to evaluate industrial dsm project performance11
REGRESSION MODELS – DIFFERENT IMPACTSSIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE

Before linear model (yB = mBx + cB)

What it would have been

After linear model (yA= mAx + cA)

Power (kW)

Total impact

Actual

Change in variable

Option 1: Impact known

Option 2: Impact unknown

Flow (kg/s)

Assume that driver behind air consumption remains constant

simplified methods to evaluate industrial dsm project performance12
SIMPLIFIED METHOD – USE OF DEPENDENT VARIABLES
  • Use dependent variables for regression model
  • Identify period that will remain unaffected
  • Use data from this period only
  • Clean data to represent “Business as usual”
  • Develop regression model using data set
SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE
simplified methods to evaluate industrial dsm project performance13
EXAMPLE – MINE COMPRESSED AIR CONTROL SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE

Unaffected period

Power (kW)

Flow (kg/s)

Baseline

Time

Time

Power (kW)

Flow (kg)

simplified methods to evaluate industrial dsm project performance16
EXAMPLE – MINE COMPRESSED AIR CONTROL SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE

Power

AdjustedBaseline

Actual

Baseline

Time

Reduced wastage, improved equipment and system efficiency

simplified methods to evaluate industrial dsm project performance17
SUMMARYSIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE
  • Proposed model utilises dependant variables
  • Variables are typically already part of control feedback
  • Regression model indicated what the power would have been for a specific portion of the day
  • Baseline can be adjusted to match the value
  • Adjusted baseline indicates what the power would have been
  • Project impact can be determined by comparing adjusted baseline and actual profile
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