Microgrid Energy Management Combining Sensitivities, Interval and Probabilistic Uncertainties of Renewable Generation and Loads

This paper proposes an optimal energy management approach combining sensitivities, interval, and probabilistic uncertainties of wind and solar power sources and loads in microgrid. Affine arithmetic (AA) is used to model the interval uncertainties and sensitivities in nodal power injections. However, all the elements in the interval solutions of AA-optimal power flow may not be significant in view of the probabilistic nature of statistical data. So, those elements which are significant with a desired confidence level are boxed using probability boxes obtained by deriving best fitting discrete state probability distribution functions (PDFs) for load and renewable power injections. Thus, the original hard bounded affine intervals are made soft bounded using the derived joint PDFs, forming new less conservative and more feasible intervals of cost and power flow variables. The minimization of the operational cost is taken care of by stochastic weight tradeoff particle swarm optimization. The method is tested in CIGRE LV benchmark microgrid with fuel cell, microturbine, diesel generator, wind, and solar power sources.