Optimal Bilevel Operation-Planning Framework of Distributed Generation Hosting Capacity Considering Rival DISCO and EV Aggregator

Abstract

The aggressive goal of having a 100% renewable energy system requires preparing an appropriate infrastructure for deploying as much distributed generation (DG) as possible into the electricity network. This article aims at proposing a new framework to maximize the hosting capacity (HC) of DGs in a distribution network. This operation-planning framework provides a suitable and interactive environment for the electricity market, distribution company (DISCO), electric vehicle (EV) aggregator, and rival DISCOs to increase the HC. The problem is modeled via a bilevel conditional-value-at-risk-constrained stochastic programming approach. The DISCO, in the upper level (UL), tries to maximize the HC and minimize the operating costs while interacting with a passive electricity market and an EV aggregator. The aggregator, in the lower level, aims to maximize their profit by interacting with the primary DISCO in the UL and the rival DISCO, to satisfy the EV owners. The Karush–Kuhn–Tucker conditions are used to recast the model into an equivalent single level. The proposed framework is tested on a 33-node distribution network. Results show how an interactive-based framework can contribute to maximizing the HC of DGs.