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

• Published in: Applied energy Vol. 400; p. 126528
• Main Authors: Kotegov, Ruslan, Abokersh, Mohamed, Mateu, Carles, Shobo, Adedamola, Boer, Dieter, Vallès, Manel
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2025
• Subjects: Life-cycle assessment; Multi-objective optimization; Renewable energy systems; Solar district energy systems (SDES); Techno-economic analysis; TRNSYS-Python coupling; STC; TES; MILP; PV; GPR; HP; FRW; TRNSYS-Python coupling; DP; Life-cycle assessment; MTBF; DDDM; GASP; SDH; LFP; SDES; HBT; ECOT; AUX; MW; EP; LCA; LCC; PSO; SEC; RF; Solar district energy systems (SDES); NC; HPC; TOPSIS; DHC; FSOC; NPC; ANN; DHWT; FG; CEPCI; IRENA; Techno-economic analysis; DHW; SH; GBR; LHS; Renewable energy systems; GA; XPS; COL; UHPC; TRNSYS; PVGIS; CST; SOC; IGD; KS; CL; FIS; CV; SST; Multi-objective optimization; BESS
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2025.126528

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. •A hybrid multi-method approach optimizes Solar District Energy Systems (SDES).•The framework balances cost, environmental impact, and market adaptability.•SDES achieves over 90 % solar fraction, minimizing fossil fuel reliance.•Economically viable SDES reduces operating costs by 66.7 %.•Grid export policies strongly influence system sizing and feasibility.