Toggle Main Menu Toggle Search

Open Access padlockePrints

Chance-Constrained Optimization for Multienergy Hub Systems in a Smart City

Lookup NU author(s): Da HuoORCiD



This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2019.

For re-use rights please refer to the publisher's terms and conditions.


The energy hub is a powerful conceptualization of how to acquire, convert, and distribute energy resources in the smart city. However, uncertainties such as intermittent renewable energy injection present challenges to energy hub optimization. This paper solves the optimal energy flow of adjacent energy hubs to minimize the energy costs by utilizing the flexibility of energy resources in a smart city with uncertain renewable generation. It innovatively models the power and gas flows between hubs using chance constraints, thus permitting the temporary overloading acceptable on real energy networks. This novelty not only ensures system security but also helps reduce or defer network investment. By restricting the probability of chance constraints over a specific level, the energy hub optimization is formulated as a multiperiod stochastic problem with the total generation cost as the objective. Cornish–Fisher expansion is utilized to incorporate the chance constraints into the optimization, which transforms the stochastic problem into a deterministic problem. The interior-point method is then applied to resolve the developed model. The proposed chance-constrained optimization is demonstrated on a three-hub system and results extensively illustrate the impact of chance constraints on power and gas flows. This work can benefit energy hub operators by maximizing renewable energy penetration at the lowest cost in a smart city.

Publication metadata

Author(s): Huo D, Gu C, Ma K, Wei W, Xiang Y, Le Blond S

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Industrial Electronics

Year: 2019

Volume: 66

Issue: 2

Pages: 1402-1412

Print publication date: 01/02/2019

Online publication date: 14/08/2018

Acceptance date: 21/07/2018

Date deposited: 30/10/2019

ISSN (print): 0278-0046

ISSN (electronic): 1557-9948

Publisher: IEEE


DOI: 10.1109/TIE.2018.2863197


Altmetrics provided by Altmetric