Optimal design and scheduling of integrated energy systems considering the uncertainties of renewable energy and loads demand: A long and short-term objectives model

The inherent limitations of fixed-order-based operation strategies hinder the full exploitation of the performance potential in integrated energy systems (IESs). To address this issue, this study develops a novel stochastic optimization framework that integrates both long-term and short-term objectives, while explicitly accounting for the uncertainties associated with renewable energy sources and building load demands. Environmental and demand uncertainties are modeled through parameterized probability distributions, enabling a more realistic representation of system variability. The optimization problem is solved using a hybrid algorithm that combines multi-objective tuna swarm optimization with mixed integer linear programming (MILP), aiming to achieve optimal economic, energy, and environmental (3E) performance. Comparative analysis between the proposed framework and conventional fixed-order-based operation modes, namely electricity priority strategy (EPS), thermal priority strategy (TPS), and hybrid management strategy (HMS), is conducted across multiple system confidence levels. Results demonstrate that the proposed model consistently outperforms fixed-order-based modes in terms of component sizing and 3E performance. Notably, when the system confidence level is relaxed from 0.99 to 0.50, the proposed model yields average reductions of 21.77% in economic cost, 15.08% in primary energy consumption, and 14.84% in CO2 emissions compared to EPS. Among the fixed-order strategies, EPS offers superior design outcomes relative to TPS and HMS. Furthermore, the proposed long- and short-term objectives model enables the IES to maintain off-grid capability and high operational efficiency under varying uncertainty conditions.