Abstract:
Existing buildings are responsible for a third of the global energy consumptions, as well as CO2 emissions. A decision-making algorithm is developed to mitigate the uncertainty of financial and environmental returns of energy improvements in existing buildings and to most properly spend the available funds. As a case study, forty two energy efficiency measures (EEM) are identified in existing buildings of a university campus. Energy consumption, energy cost and carbon emissions are measured. Costs and savings of EEMs are calculated and their possible combinations are studied. Out of over four trillion possible combinations of energy improvement packages, the ones providing the most bang for the buck are computed for given limited investment budgets. The optimization problem is solved alternatively with the more accurate Mixed Integer Programming (MIP) and a custom developed heuristics. Along the optimized investment curve, a sweet spot is identified at around 100000 USD providing highest returns in terms of savings in energy, energy cost and carbon emission. Retrofitting of existing buildings with an optimized investment budget appear to be a viable investment tool providing yearly savings of 33% in energy use, 22% in energy cost and 23% in carbon emission. Optimization results show that the decision maker can comfortably use the less sophisticated heuristics approach, which deviates minimal from the exact MIP solution. Finally, optimized solutions for retrofitting existing buildings are compared 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.
