Heuristic optimization for grid-interactive net-zero energy building design through the glowworm swarm algorithm

Abstract

As buildings account for a substantial amount of energy worldwide, many developers encourage self-sustained net-zero energy buildings (NZEBs) to minimize buildings’ energy consumption and negative impacts on the environment. In contrast to regular buildings, NZEBs have interactive and complex energy systems that require synthetical evaluation of their complicated interplay with the grid. Many studies implemented various optimization approaches to improve the NZEBs energy system designed with a global optimal sizing solution. However, the optimal design is often infeasible due to the availability of proper equipment sizing on the markets. In addition, due to the uncertainty caused by different climate conditions, government feed-in-tariff subsidies, and availability of renewable systems, there could exist multiple distinctive design solutions for the same building. Therefore, this study intends to develop a heuristic multiple objectives algorithm based on the glowworm swarm mechanism to refine the optimization of the grid-interactive NZEB design. To validate the proposed method, a case study of the Hong Kong Zero Carbon Building was examined and three design variables (area of the photo-voltage system, installation power of the wind turbine system, and capacity of the thermal energy storage system) were optimized. The results suggest that the optimized design can outperform the baseline design in terms of operating costs and grid energy consumption. In addition, multiple local optima design solutions that represent different design strategies can be identified using the proposed non-linear model.