Adjustable robust optimization in enabling optimal day-ahead economic dispatch of CCHP-MG considering uncertainties of wind-solar power and electric vehicle

At present, electric vehicles (EVs), small-scale wind power, and solar power have been increasingly integrated into modern system via the combined cooling heating based microgrid (CCHP-MG). However, inside uncertainties of EVs charging, significantly impact economy CCHP-MG operation. Therefore to improve deteriorated by uncertainties, this paper presents a two-stage adjustable robust optimization achieve minimal operational cost for CCHP-MG. Before realizations day-ahead stage as first decides an strategy that can withstand worst-case uncertainties. As long are observed, real-time second adjusts units compensate errors caused strategy. Due difficulties model solution, further adopts duality theory, Big-M method, column-and-constraint generation (C & CG) decomposition convert two tractable mixed integer linear programming (MILP) problems. Further, C CG iteration algorithm is also employed solve MILPs, which ultimately provide optimal economic dispatch capable handling The experimental results demonstrate effectiveness presented approach.