# Chance-Constrained Shrunken-Primal-Dual Subgradient (CC-SPDS) Approach for Decentralized Electric Vehicle Charging Control

## TL;DR

This paper introduces a decentralized EV charging control framework using a novel chance-constrained shrunken-primal-dual subgradient algorithm to optimize valley-filling while respecting network constraints and individual needs.

## Contribution

It presents a new CC-SPDS algorithm for decentralized control under stochastic constraints, ensuring convergence and effectiveness in distribution network management.

## Key findings

- The CC-SPDS algorithm effectively manages EV charging with chance constraints.
- The method achieves valley-filling while satisfying network and individual constraints.
- Convergence of the proposed algorithm is verified through simulations.

## Abstract

In this paper, we develop a chance-constrained decentralized electric vehicle (EV) charging control framework to achieve "valley-filling" meanwhile meeting individual charging requirements and satisfying distribution network constraints. The control design is formulated as an optimization problem with a stochastic non-separable objective function and globally coupled chance constraints. We propose a novel chance-constrained shrunken-primal-dual subgradient (CC-SPDS) algorithm to support the chance-constrained decentralized control scheme and verify its efficacy and convergence with a representative distribution network model.