Multi-method optimization of solar district energy systems with battery and thermal energy storage via real-time TRNSYS-Python coupling

Transitioning to sustainable energy is vital for decarbonizing energy systems. Solar District Energy Systems (SDES) offer a viable alternative to fossil fuels, but face challenges related to cost, intermittency, and optimization. This study proposes a high-fidelity, fully automated optimization framework for SDES that integrates TRNSYS simulations with a dynamic Python-based controller to jointly minimize life cycle cost and environmental impact. The core innovation lies in the seamless, real-time coupling of simulation and optimization using a hybrid multi-method strategy – combining metaheuristic, heuristic, and stochastic algorithms – without reliance on surrogate models or manual intervention. A Feature Importance Scoring (FIS) module adaptively prioritizes influential variables, enabling efficient convergence and reduced computational cost. The framework is applied to a real Mediterranean case study, assessing PV, battery, and thermal storage integration under economic and environmental criteria. Results show that the proposed SDES achieves a solar fraction above 90 %, ensuring long-term sustainability with minimal fossil fuel reliance. The most cost-effective solution cuts operating costs by 66.7 %, reaching €70.8 million over the system's lifetime, while the environmentally optimal configuration lowers the baseline environmental impact by 29.8 %. Sensitivity analysis reveals that electricity prices strongly influence cost and system sizing, whereas natural gas prices have minimal effect. Overall, the method yields significant improvements over traditional deterministic or surrogate-based approaches, demonstrating its potential to support scalable, cost-effective energy planning in low-carbon urban districts.