Bi-Objective and Stochastic Approach for Optimal Device Sizing in a Multi-Carrier Energy System for a Community of Net-Zero-Energy Buildings

Among different sectors, buildings represent the largest contributors to global final energy consumption and CO 2 emissions. In this regard, Net-Zero Energy Buildings (NZEBs), which are defined as buildings that generate as much renewable-based energy as they consume annually, have emerged as a promising solution. However, determining the optimal sizing of energy system components in the buildings remains a significant challenge, as decision-makers must address the trade-off between economic performance and environmental sustainability. To tackle this challenge, this paper proposes a modified Mixed-Integer Linear Programming (MILP) model to determine the optimal sizing of electrical, thermal, and hydrogen-based devices within a community of NZEBs integrated into a multi-carrier energy system. The problem is formulated as a bi-objective optimization model that simultaneously minimizes economic cost and environmental impact. To efficiently solve the problem, a lexicographic method combined with the augmented ε-constraint approach is employed, and a fuzzy decision-making method is used to identify the best compromise solution. Moreover, the model accounts for the impact of demand response programs and input data uncertainty on the optimal device configuration. The results indicate that implementing demand response strategies can reduce the cost of electricity purchased from the main grid by 16.59%, thereby contributing to lower environmental impact. Furthermore, as decision-makers place greater emphasis on environmental concerns, total investment costs increase. At the same time, more green technologies are integrated into the system, enhancing the system's energy independence from the main grid.