A parallel computing simulation-based multi-objective optimization framework for economic analysis of building energy retrofit: A case study in Iran

Abstract

The building sector represents a large share of rising global energy demand. Improving energy efficiency in existing building stock is a crucial strategy. Adopting the best energy retrofitting strategy in a specific building is a challenging task due to a plethora of possible combinations of retrofit measures and mutually contrasting objective functions. In addition, peculiar conditions of Iran, such as extremely subsidized energy prices, and step utility tariffs, escalate the challenges of building energy retrofit. Accordingly, the current study presents a simulation-based multi-objective optimization framework characterized by parallel processing structure and results-saving archive. The framework is implemented by integrating MATLAB® as an optimization engine with EnergyPlus as a dynamic energy simulator to minimize primary energy consumption and discounted payback period while maximizing the net present value. The algorithm explores a vast domain of possible solutions including, building envelope, cooling and heating systems, and renewable energy sources. The framework is applied to a single-family residence located in Iran. Three different scenarios are examined with reference to prospective energy pricing policies to evaluate their effect on the attractiveness of energy retrofit projects. For each scenario, final solutions are selected from respective Pareto fronts according to cost-optimality and energy-efficiency criteria and considering budget constraints. The results indicate that even though significant reductions in primary energy consumption can be achieved, implementing energy retrofit under the current energy pricing policy in Iran would not yield economic benefits. However, the elimination of subsidies along with offering incentives for building energy retrofits presents promising outcomes.