1. OPTIMAL PLANNING OF ENERGY EFFICIENCY PROGRAMS WITH THE PAID-FROM-SAVINGS STRATEGY Seyedmohammadhossein Hosseinian Ph.D. Student, Zachry Department of Civil Engineering Luca Quadrifoglio Associate Professor, Zachry Department of Civil Engineering April 2015

2. Outline o Introduction Energy Efficiency programs The paid-from-savings financing strategy o Problem statement o Methodology The dynamic KP Integer Linear Programming formulation o Case Study Formulation Results o Conclusion

3. Introduction Energy Efficiency Programs o Environmental impact of fossil fuels is a major concern. o Building sector accounts for about 40% of the U.S. total energy consumption (41% in 2014). Existing Buildings vs. New Buildings Renewable Energy vs. Energy Use Reduction

4. Introduction Energy Efficiency Programs o Energy Efficiency Programs*: Reducing energy consumption in existing buildings through installation of energy efficient equipment and material e.g. Thermal insulation, Building Automation System (BAS), etc. * It usually involves multiple buildings to be improved over a period of time. Source: U.S. Green Building Council http://www.centerforgreenschools.org/docs/paid-from-savings-guide-exec-summary.pdf

5. Introduction: Financial Viewpoint Paid-from-savings Financing Strategy o The program COST is a major barrier to implementing Energy Efficiency measures. o Inadequate access to capital is yet a problem. o Incremental improvement with the paid-from-savings strategy is a solution to overcome the hardship of raising capital. o “Paid-from-savings” is a financial strategy that justifies the cost.

6. Introduction: Financial Viewpoint Paid-from-savings Financing Strategy o Incremental improvement with the paid-from-savings strategy + + + + R  Advantage:start with partial capital  Disadvantage:less energy saving than the full-cost funding [energy saving gap]

7. Problem Statement Optimal Planning o Sequence of improvement (i.e. prioritization) has a great influence on the performance of the program. o Given an Energy Efficiency program, involving N units (buildings), and a certain amount of upfront budget: What are the units that should be improved using the initial fund? What is the optimal sequence of the remaining units to be improved over time? in order to maximize the energy saving (minimize the gap) in a certain period of time? And more importantly: How much is the maximum energy saving (compared to the full-cost funding scenario) under such optimal planning?

8. Methodology o Finding the optimal sequence of improvement is a dynamic counterpart of the Knapsack Problem (KP). The classic static form of the KP represents the problem of allocating a limited available capacity (resources) to a subset of items (options), such that the total profit of the selected items is maximized. The paid-from-savings feedback loop gives a dynamic nature to the problem, since the available resources at each time step depend on the energy cost savings of the units improved before.

9. Methodology Integer Linear Programming o Suppose the problem is studied in a period of T time steps. (e.g. months) o Any solution to this problem can be written as a T x N Boolean matrix: Units with the value of 1 in the first row are to be improved by the initial fund.

10. Methodology Integer Linear Programming

11. Case Study TAMU Energy Efficiency Program TAMU Utility and Energy Services o College Station campus ( 2011 ) o 4.3 million SF ( 12 office buildings + 5 parking garages ) o $10 million ( provided by Texas State Energy Conservation Office ) o The program mainly involved: Building Automation System (BAS) upgrades Lighting retrofits o The Utility Assessment Report estimated energy consumption/saving in terms of : Electricity (Kwh) Chilled water (mmbtu) Hot water (mmbtu)

12. Energy Saving Estimate (ESE) matrix Case Study TAMU Energy Efficiency Program Energy Saving Integration (ESI) matrix Cost Saving Integration (CSI) matrix [$/unit] Integrated Saving

13. Case Study TAMU Energy Efficiency Program o The ILP problem was solved for different scenarios of Initial Funding, varying from $1 million (10% of the full-cost funding) to $10 million (100% of the full-cost funding) : Number of units (buildings):N = 17 Study period:T = 120 months Number of variables:2040 17 x 120 = 2040 Number of constraints:2383 (3 x 120)+[(120-1) x 17] = 2383 Solver:TOMLAB/CPLEX

14. I.F. = $1 million Case Study TAMU Energy Efficiency Program I.F. = $4 million T = 120 months

15. I.F. = $7 million Case Study TAMU Energy Efficiency Program I.F. = $10 million T = 120 months

16. Case Study TAMU Energy Efficiency Program

17. o Implementing the strongest feedback loop in planning the TAMU Energy Efficiency program (i.e. optimal planning) would lead to great amount of energy saving even if the program started with a partial initial fund.

18. Conclusion o Incremental improvement with the paid-from-savings strategy is a practical way to initiate Energy Efficiency programs without access to the full-cost capital. o In such programs, the prioritization of units (buildings) plays a big role in the program performance. o This work presented an Integer Linear programming formulation to find the optimal sequence of improvement (i.e. optimal planning). The optimal solution leads to the strongest paid-from-savings feedback loop, and results in the maximum possible energy saving under the limitation of partial capital. o Results of the analysis on a case study showed that such a “strongest feedback loop” would lead to great amount of energy saving even if the program started with partial funding.

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