Multi‐objective optimal planning of a residential energy hub based on multi‐objective particle swarm optimization algorithm

With the increasing rate of population in big cities around world, tendency to build new buildings suburb main or large apartments has been highlighted. In this regard, building residential complexes seen a dramatic increase these areas as it makes possible number units within reasonable space. Although have brought numerous benefits, they are some challenges regarding their construction processes. One concern associated with is how optimally install energy components such transformers, combined heat and power (CHP) units, boilers etc., shared area complex. To address issue, paper models system complex an hub proposes novel framework obtain optimal planning hub. order conflicting desires complex's builders future residents multi-objective (MO) optimization problem considered proposed method that simultaneously optimizes investment costs, operation reliability supply. Multi-objective Particle Swarm Optimization (MOPSO) algorithm classical linear programming (LP) solve MO problem. demonstrate effectiveness method, case study including 300 considered, implemented study. The numerical results show can appropriately optimize index simultaneously, obtained Pareto frontier gives investors freedom opt for any point from surface.