Data-driven joint TEP-BESS co-planning scheme to relieve transmission lines congestion: A min-max regret method

Abstract

Transmission lines congestion has recently been a vital challenge in power systems operation due to the intermittent outputs of renewable energy sources (RES). Therefore, an efficient transmission congestion management (TCM) method should be defined to deal with the congestion issue. This paper aims to propose a simultaneous linearized two-stage TEP-BESS co-planning optimization model to relieve transmission lines congestion. In doing so, a novel TCM structure is suggested in a pool-based deregulated data-driven optimal power flows (d-OPF) by the computationally effective min-max regret method to consider future scenarios of generating units and demanded loads. To improve the efficiency of the proposed algorithm, an accurate cost design for module-based BESS considering RES uncertainties is proposed; also, a regional classification is considered to simulate different weathers, specialized for the TCM measurement. The major goal is to efficiently trade-off between performing the TCM and minimizing system total cost as conflicting objectives. The output of the proposed model is to optimally find the best expansion plans by applying the computationally effective min-max regret method where performing the TCM and minimizing the system total cost. The effectiveness of the proposed model is examined on the wind-integrated regional RTS test system considering an uncertainty set for the intermittent outputs of RES. The results of this fast-converged model show the difficulty of selecting an expansion plan as the best options can be selected to relieve the transmission lines congestion while increasing the system total cost.