Impact of Financial Assumptions on the Cost Optimality towards Nearly Zero Energy Buildings- A case study

The energy efficiency challenge in Europe is mainly concerned with existing buildings and the investment scenarios to implement deep renovations. The cost-optimal approach imposed on EU-Member states by the European Energy Performance of Buildings Directive aims to identify the investment gap and challenges to transform existing buildings into nearly Zero Energy Buildings (nZEBs). The investment gap is function of several volatile financial parameters including discount rate (r), developing of energy price (e), decline rate of technology price (d), as well as nZEB’s incentives like feed-in-tariff (FiT) and investment grant (iG). In this context, the decision making process of individuals or investment institutions is hindered by complexity and uncertainty. In order to assist the decision making process and improve the visibility of financial energy benefits, a novel optimization-based parametric analysis scheme (OptnZEB-I) is developed. The scheme is designed to investigate a large number of economic scenarios (i.e., combinations of financial assumptions) in a short computational time while a holistic optimization approach is adopting for exploring all possible design options including energy conservation measures (ESMs); renewable energy sources (RETs) and mechanical systems (Sys). For demonstration, the scheme is applied to analyse the impact of several financial parameters on the cost-optimal energy performance level (CO-EPL) of a single family house in Finland. In line with the EU-directive, a large number of possible design options (∼3 × 109million) are optimized for 4608 cases of economic scenarios. The results of the address case study show that, in average, the CO-EPL ranges from 90 to 160 [kWh/m2]. The range has most frequent value of 145 kWh/m2. The CO-EPL is significantly sensitive to the e, f, then i, respectively. Less sensitivity is found to the other financial parameters. The robustness of the optimization results are verified by solving the addressed design problem by using four different optimization algorithms (i.e., pattern search, interior-point, simulated annealing and genetic algorithms)