Stochastic energy community trading model for day-ahead and intraday coordination when offering DER’s reactive power as ancillary services

Abstract

A two-stage stochastic programming energy trading model is presented in this article to measure the distributed energy resources’ capability to provide reactive power as ancillary services from an energy community to the distribution system operator. The formulation proposed models the day-ahead and the intraday markets as first and second-stage decisions, respectively, using the second-order cone relaxation of the optimal power flow to represent the network limitations. In addition, the model considers that the energy community trades energy operating under a collaborative scheme and minimizing the global cost. Likewise, the formulation (i) includes community batteries to study the effect on the total cost, (ii) identifies the extra energy charged and discharged by the agents’ batteries to face the uncertainty related to the intraday market, as an agents’ flexibility service for the community, and (iii) prevents the simultaneous buying and selling of energy in the local energy market by an agent. The model has been programmed in Python-Pyomo and tested in three radial distribution systems under three different scenarios showing that the DERs can (i) self-satisfy the reactive power demand, (ii) provide reactive power to the DSO, and (iii) decrease the community cost significantly in an eventual ancillary services market.