A hierarchical genetic algorithm and mixed-integer linear programming-based stochastic optimization of the configuration of integrated trigeneration energy systems

Facing the growing pressure of climate change and environmental protection, integrated energy systems (IESs), which comprise different sources, have become promising candidates for future systems. However, capacity configuration each source remains challenging due to various couplings, randomness renewables numerical optimization difficulty. In this paper, a hierarchical framework is proposed determine component capacities trigeneration IESs, i.e., involving combined cooling, heating power (CCHP) generation. The potential variation in demand renewable resource availability are considered stochastic factors captured as scenarios generated according probability function. first level, with defined, mixed-integer linear programming (MILP) problem formulated minimize total system cost. Then, second Monte Carlo method applied calculate expectation by feeding into MILP sampling minimal costs. Finally, level returns expected value cost considering given capacities, genetic algorithm adopted third search optimal capacities. Compared conventional deterministic method, reduces annual operational while allowing wider range. addition, it revealed that inclusion heat storage grid connections yields notable benefits terms IES reduction.