  In order to reduce the negative impact of the uncertainty of load and
renewable energies outputs on microgrid operation, an optimal scheduling model
is proposed for isolated microgrids by using automated reinforcement
learning-based multi-period forecasting of renewable power generations and
loads. Firstly, a prioritized experience replay automated reinforcement
learning (PER-AutoRL) is designed to simplify the deployment of deep
reinforcement learning (DRL)-based forecasting model in a customized manner,
the single-step multi-period forecasting method based on PER-AutoRL is proposed
for the first time to address the error accumulation issue suffered by existing
multi-step forecasting methods, then the prediction values obtained by the
proposed forecasting method are revised via the error distribution to improve
the prediction accuracy; secondly, a scheduling model considering demand
response is constructed to minimize the total microgrid operating costs, where
the revised forecasting values are used as the dispatch basis, and a spinning
reserve chance constraint is set according to the error distribution; finally,
by transforming the original scheduling model into a readily solvable mixed
integer linear programming via the sequence operation theory (SOT), the
transformed model is solved by using CPLEX solver. The simulation results show
that compared with the traditional scheduling model without forecasting, this
approach manages to significantly reduce the system operating costs by
improving the prediction accuracy.
