Digital Twin Based Optimal Design of a Grid-Connected Hybrid Renewable Energy Microgrid Using Improved Multi-Objective Optimization: A Case Study

This study investigates the optimal sizing of a grid-connected hybrid renewable energy microgrid. The optimization, employing a multi-objective artificial hummingbird algorithm (MOAHA) combined with fuzzy decision-making (FDM), aims to minimize the cost of energy while maximizing renewable energy utilization. MOAHA is used to generate a well-distributed Pareto front, while FDM identifies the preferred configuration under the specified decision preference. However, the preferred solution obtained is a static configuration. Most existing studies focus on such static planning, with limited attention to dynamic mapping and validation of the optimized configuration. To bridge this gap, a digital twin architecture is further proposed for hybrid renewable energy microgrids, and a corresponding digital twin system is also developed to achieve virtual representation, dynamic state mapping, operational visualization, and configuration validation. An industrial park microgrid in Urumqi is selected as the case study. The results indicate that the preferred configuration achieves a cost of energy of 0.065 $/kWh and a renewable energy utilization of 0.675. Comparative results demonstrate that the proposed framework outperforms benchmark methods in terms of convergence, solution diversity, and computational efficiency. Meanwhile, the developed digital twin system effectively supports time-series state visualization and feasibility checking of the optimized configuration.