An iterative algorithm for regret minimization in flexible demand scheduling problems

A major challenge to develop optimal strategies for allocation of flexible demand toward the smart grid paradigm is uncertainty associated with real-time price and electricity demand. This article presents a regret-based model novel iterative algorithm which solves minimax regret optimization problem. algorithms exhibits low computational burden compared traditional linear programming methods affords convergence through updates feasible power schedules, thus enabling scalable parallel implementation large device populations. Specifically, our approach seeks minimize induced worst-case over all scenarios charging strategy electrical devices. The method optimality computed solution justified some numerical simulations are discussed case single operating under different types realizations bounds.