A novel stochastic method to dispatch microgrids using Monte Carlo scenarios

•Novel stochastic optimization to enable fast rolling-horizon procedures.•Classification of methodologies as Aggregating-Rule-based Stochastic Optimization.•Decoupling a N-scenario problem into N deterministic sub-problems.•Cost-based aggregator to select the final dispatching strategy.•Numerical case study comparing the new approach with standard stochastic procedures. Stochastic management strategies have proven to achieve cheaper resource scheduling both in large power systems and microgrids, but suffer from high computational requirements with respect to traditional deterministic approaches; therefore, using stochastic formulations in advanced infra-daily operating strategies is quite challenging, especially in isolated hybrid energy systems with limited computational assets. This paper proposes a methodology for the microgrid operation based on a novel two-stage formulation that decomposes the stochastic problem into several deterministic subproblems, whose solutions are afterwards aggregated by the aggregator using simulations and a cost-based rule. In the first stage, every subproblem is solved, then each optimal dispatching is simulated in the second stage to evaluate the corresponding expected operating cost, which is used by the aggregator to select the final optimal scheduling. When compared to traditional methods for a rural microgrid in Uganda, the proposed approach not only achieves interesting savings in operational costs, up to 5%, but also sharply reduces the computational requirements, even more than 5–100 times with respect to traditional stochastic approaches. The paper also proposes a review and first classification of this kind of methodologies, to highlight the novelties of the approach.