Multi-objective design optimization of PV–battery sizing and panel orientation toward efficient zero-energy offices

This study proposes a scenario-based, multi-objective optimization framework for designing grid-connected, zero-energy campus offices that integrate photovoltaic (PV) generation, battery storage, and PV panel orientation. A novel Multi-objective Age of Exploration–Inspired Optimizer (MOAEIO) is developed using a clustering-based mechanism that adaptively assigns exploration- and exploitation-oriented strategies to search agents. The algorithm is validated against dominance-, decomposition-, and indicator-based optimization methods using benchmark functions. The framework is applied to a university campus office in Taipei, Taiwan, under three scenarios: annual energy balance, techno-economic and operational constraints, and renewable-energy utilization with curtailment penalties. The preferred designs differ substantially, including 34 PV panels with a 28.2 kWh battery, 35 panels with an 8.1 kWh battery, and 24 panels with a 30 kWh battery. The results demonstrate that economic constraints and renewable-energy utilization objectives strongly influence optimal PV–battery configurations for zero-energy office design.