Stochastic operation of isolated microgrids: aggregating-rule-based optimization versus standard approaches

Stochastic operation of power systems has risen attention of researchers as fluctuating energy sources like renewables are being increasingly integrated into existing grids. Uncertainties can be higher in small power systems like isolated microgrids, where both renewables and load can be extremely unpredictable, thus causing increasing operating costs and business risks. In the last years, many approaches have been proposed to account for uncertainties in off-grid microgrids, usually simulating several size, load and renewables scenarios. Among them, a simplified stochastic approach, namely Aggregating-Rule-based Stochastic Optimization (ARSO), which decomposes the N-scenario problem into N deterministic subproblems whose solutions are finally processed and aggregated, has been recently proposed with interesting results in terms of optimality of results and computational requirements. In this paper, two ARSO approaches are compared with standard stochastic and deterministic methodologies used to operate isolated microgrids, to assess advantages and drawbacks of all these techniques and their ability in handling uncertainties. The two ARSO methodologies differ in the aggregating rule: to take into account the load and RES forecasting errors, the Improved-ARSO employs a Monte Carlo procedure, whereas the Mixed ARSO technique makes use of statistical rules. A numerical case study for a typical isolated microgrid in Africa is proposed and discussed.