Coordinated Deliverable Energy Flexibility from EV Aggregators in Distribution Networks

TL;DR

This paper introduces a coordinated optimization framework for EV aggregators to provide energy flexibility in distribution networks, effectively managing uncertainties and system constraints through hybrid robust/stochastic models.

Contribution

It proposes a novel two-level optimization approach combining robust/stochastic methods for flexibility estimation and EV charging scheduling.

Findings

Effective energy flexibility estimation from EV aggregators.

Robust/stochastic approach manages load and renewable uncertainties.

Numerical results demonstrate framework efficiency on IEEE network.

Abstract

This paper presents a coordinated framework to optimize electric vehicle (EV) charging considering grid constraints and system uncertainties. The proposed framework consists of two optimization models. In particular, the distribution system operator (DSO) solves the first model to optimize the amount of deliverable energy flexibility that can be obtained from EV aggregators. To address the uncertainties of loads and solar energy generation, a hybrid robust/stochastic approach is employed, enabling the transformation of uncertainty-related constraints into a set of equivalent deterministic constraints. Once the DSO has computed the optimal energy flexibility, each aggregator utilizes the second optimization model to optimize the charging schedule for its respective fleet of EVs. Numerical simulations are performed on a modified IEEE 33-bus distribution network to illustrate the efficiency of the proposed framework.