Stochastic Day-ahead Optimal Scheduling of Active Distribution Networks with Dispersed Energy Storage and Renewable Resources

This paper focuses on the problem of the probabilistic optimal day-ahead scheduling of energy resources in Active Distribution Networks (ADNs). These resources include both dispersed energy storage systems (DESSs) and volatile renewable embedded generators. Technical constraints related to both energy resources and electrical network are modeled and taken into account in the proposed optimization problem. The paper first proposes a convex formulation of a specific optimal power flow (OPF) used to compute the resources schedule. Its objective function aims at achieving the minimum of the following quantities: network and DESSs losses, energy cost imported from the external grid, and deviations from the day-ahead scheduled power flow with the same external grid. In addition, the ability of using the substation transformer tap-changer is incorporated into the problem with a suitable cost function. The initial OPF formulation is then enhanced thanks to the use of the Mixed Integer Second Order Cone Programming approach in order to formulate a stochastic AC-OPF. The uncertainties of the problem are due to the forecast errors of the PV generation, load consumption and energy prices. The applicability and the effectiveness of the proposed scheduling approach are tested by using a modified version of the IEEE 34 buses test feeder.