Design of a risk-averse decision making tool for smart distribution network operators under severe uncertainties: An IGDT-inspired augment ε-constraint based multi-objective approach

Abstract

In the context of restructured electricity market and smart grid, uncertainties including renewable generation, load demand, and electricity price would significantly affect the technical and financial aspects of smart distribution networks. This paper presents a risk-averse decision making tool to help distribution network operator (DNO) in short-term operational activities. The objective is to optimize hourly sale prices offered to the customers, transactions (purchase/sale) with the wholesale market, commitment of distributed generation, dispatch of energy storage systems, and planning of interruptible loads in a way that a target profit for the risk-averse DNO is guaranteed. A bi-level information gap decision theory (IGDT) inspired problem is developed to hedge the DNO against risk imposed by the information gap between the forecasted and actual uncertain variables. The bi-level problem is recast into its equivalent single level problem driven by Karush-Kuhn-Tucker optimality conditions. Since the uncertain variables compete with each other to maximize their enveloped-bounds, the augmented ε-constraint method is used to solve the proposed IGDT-inspired multi-objective optimization problem. A Monte Carlo simulation based after-the-fact analysis is conducted to verify the robust performance of the operational decisions. The effectiveness and efficiency of the proposed model are evaluated on the 33-bus and the 118-bus modified test networks.