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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. 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

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  1. SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE Presented by: Walter Booysen ICUE 2012 - Stellenbosch

  2. 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

  3. PRESENT METHODS SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE Baseline Implementation Assessment Impact Power What would it have been? Time Project impact = Baseline – Actual profile

  4. 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

  5. PRESENT METHODS – REGRESSION MODELS SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE Power (kW) Temperature (Deg C)

  6. PRESENT METHODS – REGRESSION MODELS SIMPLIFIED 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.)

  7. PRESENT METHODS – REGRESSION MODELS SIMPLIFIED 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.)

  8. 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

  9. 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

  10. 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

  11. 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

  12. REGRESSION MODELS – DIFFERENT IMPACTS SIMPLIFIED 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

  13. 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

  14. 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)

  15. PRESENT METHODS – REGRESSION MODELS SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE Power (kW) Flow (kg/s) Power Time

  16. EXAMPLE – MINE COMPRESSED AIR CONTROL SIMPLIFIED METHODS TO EVALUATE INDUSTRIAL DSM PROJECT PERFORMANCE Power Adjusted Baseline Baseline Time

  17. 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

  18. SUMMARY SIMPLIFIED 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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