An Integrated Optimization Model For Capital Allocation Of Energy Efficiency Measures Of Existing Buildings: A Case Study Of Bogazici University Kilyos Campus
||Ciochetti, Brian (Tony); Mehmet Emre Camlibel
||An Integrated Optimization Model For Capital Allocation Of Energy Efficiency Measures Of Existing Buildings: A Case Study Of Bogazici University Kilyos Campus
||22nd Annual European Real Estate Society Conference in Istanbul, Turkey
||Buildings are responsible for more than a third of global energy consumption, and emit nearly 40% of all CO2 emissions. A small, but growing body of literature seeks to identify and isolate methods which may be employed in order to reduce the energy consumed in the operation of these structures. __In this study, we develop a decision-making algorithm to mitigate the uncertainty of financial and environmental factors related to energy improvements of existing buildings, and how to efficiently allocate available funds in order to undertake such improvements. We develop a case study, in which forty two energy efficiency measures (EEM) are identified within the existing buildings of a University campus in Turkey. The operations of the buildings are analyzed, and energy consumption, energy costs and carbon emissions are measured. Costs and savings of these specific EEMs are calculated as are a number of their possible combinations. Of the more than four trillion possible combinations of energy improvement packages, the ones providing the greatest savings per unit of investment are computed for a range of limited investment budgets. This optimization problem is solved through the uses of both a Mixed Integer Programming (MIP), and a custom developed heuristics model. __Our findings suggest that over the optimized investment curve, the most efficient use of EEM capital occurs withing a very tight range of allocation, providing the greatest returns in terms of energy savings, energy costs and carbon emission. Retrofitting of existing buildings with an optimized investment budget appears to be a viable investment strategy, providing yearly savings of 33% in energy use, 22% in energy cost and 23% in carbon emission. Our results show that a decision-maker can comfortably use a less sophisticated heuristics approach, which only minimally deviates from an exact MIP solution. Finally, we compare optimized solutions for retrofitting existing buildings against alternative investments of building new energy production plants and demolishing and re-constructing new buildings. In both cases retrofitting proved to be significantly more efficient in terms of investment cost, energy savings and CO2 reduction._
||Energy Efficiency Measures, Sustainability, Mixed Integer Programming, Carbon Emissions
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