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

TL;DR

The paper tackles co-planning of distribution-network (DN) expansion and renewable charging stations (RCS) under coupled uncertainties from EV routing/charging and renewable generation. It introduces a tri-level two-stage stochastic-robust optimization (SRO) framework that treats decision-dependent uncertainty (DDU) in the middle level and decision-independent uncertainty (DIU) in the lower level, solved via a novel Adaptive inexact Column-and-Constraint Generation (A-iC&CG) algorithm with theoretical finite-convergence guarantees. Case studies on Shenzhen's 47-node DN and 68-hub transport network show that PV-EV-RCS configurations are cost-optimal and tend to be sited near substations and high-flow hubs, with A-iC&CG outperforming benchmark methods in both speed and solution quality. The work demonstrates that integrating transportation flows and population density into co-planning, along with adaptive uncertainty handling, yields realizable, robust DN and RCS investments while maintaining acceptable voltage and operation performance.

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.

Figures (9)

• Figure 1: Framework of the tri-level stochastic-robust planning problem.
• Figure 2: Traffic-distribution coupled network with renewable charging stations.
• Figure 3: Transportation network modeling illustration.
• Figure 4: Variation of problem bounds based on upper and lower-level solutions.
• Figure 5: Annualized costs ($10^{4}$ CNY ¥) and solution time.