A risk-averse decision based on IGDT/stochastic approach for smart distribution network operation under extreme uncertainties

Abstract

Renewable generation as well as electrical demand uncertainties cause significant technical challenges in addition to associated financial consequences in smart distribution networks (SDNs), particularly in the electricity markets, which are restructured and are featured by smart grids. In this paper, a risk-averse-strategy-based decision making tool is proposed to help the smart distribution network operator (SDNO) in day-ahead operational practices including optimal unit commitment (UC) and optimal distribution feeder reconfiguration (DFR). The proposed tool aims to reduce the consumers’ electricity prices as well as to optimize the financial transactions with the energy market, reliability of distributed generation (DG), electricity storage system (ESS) dispatch, and planning interruptible electrical demands in order to secure the specified revenue targets for SDNO by means of the risk-averse strategy. A bi-level stochastic optimization problem based on information gap decision theory (IGDT) is considered to preserve the SDNO from the risks of information gap between the predicted and actual uncertainty variables. The bi-level stochastic optimization problem is applied to a single-level problem obtained by Karush–Kuhn–Tucker method. As uncertainty variables compete to expand their enveloped-bounds, the enhanced ε -constraint method is employed to address the multifaceted IGDT-based stochastic optimization problem proposed in the study. Finally, the efficiency and efficacy of the proposed model are evaluated on an IEEE 33-bus SDN.