Tri-Level Stochastic-Robust Co-Planning of Distribution Networks and Renewable Charging Stations With an Adaptive iC&CG Algorithm

TL;DR

This paper introduces a tri-level stochastic-robust optimization framework for co-planning distribution networks and renewable charging stations, accounting for uncertainties in EV loads and renewable generation, solved efficiently by a novel adaptive algorithm.

Contribution

It develops a new tri-level stochastic-robust co-planning model with decision-dependent and independent uncertainties, and proposes an adaptive inexact C&CG algorithm with proven convergence.

Findings

PV-EV hybrid stations are cost-optimal.

RCS siting concentrates near substations and high-flow hubs.

The proposed algorithm outperforms benchmarks in case studies.

Abstract

Renewable charging stations (RCSs) that co-locate electric-vehicle (EV) charging with distributed generation (DG) can raise renewable utilization and improve distribution-network (DN) efficiency, yet their variability and the siting-dependent charging demand can overload feeders if placed poorly. This paper proposes a tri-level, two-stage stochastic-robust optimization (SRO) co-planning framework that jointly determines RCS siting and DN expansion while accounting for transportation flows and population density. The model distinguishes two uncertainty classes: (i) decision-dependent uncertainty (DDU), under which EV charging loads vary with RCS siting; and (ii) decision-independent uncertainty (DIU), under which load fluctuations and renewable-generation variability do not depend on the RCS locations or the DN topology. At the upper level, the framework selects RCS sites and DN expansions. At the middle level, EV routing and charging are dispatched given the RCS siting to produce charging loads DDU. At the lower level, DN operation minimizes annualized loss costs under the worst-case DIU, reformulated via Karush-Kuhn-Tucker (KKT) conditions. To solve the resulting problem efficiently, we develop an adaptive inexact column-and-constraint generation (A-iC&CG) algorithm and prove finite-iteration convergence. Case studies on a 47-node DN coupled with a 68-hub transportation network in Shenzhen, China, show that A-iC&CG outperforms benchmark algorithms and that PV-EV hybrid stations are cost-optimal, with RCS siting concentrated near substations and high-flow hubs.