Two-Stage Distributionally Robust Optimization for Energy Hub Systems

Pengfei Zhao, Chenghong Gu, Da Huo, Yichen Shen, Ignacio Hernando Gil

Research output: Contribution to journalArticle

20 Downloads (Pure)

Abstract

Energy hub system (EHS) incorporating multiple energy carriers, storage and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation.
This paper proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This paper proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where i) the first stage optimizes EHS operation cost, and ii) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modelled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this paper justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multi-energy carriers.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Early online date29 Aug 2019
DOIs
Publication statusE-pub ahead of print - 29 Aug 2019

Cite this

Two-Stage Distributionally Robust Optimization for Energy Hub Systems. / Zhao, Pengfei; Gu, Chenghong; Huo, Da; Shen, Yichen; Hernando Gil, Ignacio.

In: IEEE Transactions on Industrial Informatics, 29.08.2019, p. 1-9.

Research output: Contribution to journalArticle

@article{4de1b40573d74b558800675ba5154b2a,
title = "Two-Stage Distributionally Robust Optimization for Energy Hub Systems",
abstract = "Energy hub system (EHS) incorporating multiple energy carriers, storage and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation. This paper proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This paper proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where i) the first stage optimizes EHS operation cost, and ii) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modelled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this paper justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multi-energy carriers.",
author = "Pengfei Zhao and Chenghong Gu and Da Huo and Yichen Shen and {Hernando Gil}, Ignacio",
year = "2019",
month = "8",
day = "29",
doi = "10.1109/TII.2019.2938444",
language = "English",
pages = "1--9",
journal = "IEEE Transactions on Industrial Informatics",
issn = "1551-3203",
publisher = "IEEE",

}

TY - JOUR

T1 - Two-Stage Distributionally Robust Optimization for Energy Hub Systems

AU - Zhao, Pengfei

AU - Gu, Chenghong

AU - Huo, Da

AU - Shen, Yichen

AU - Hernando Gil, Ignacio

PY - 2019/8/29

Y1 - 2019/8/29

N2 - Energy hub system (EHS) incorporating multiple energy carriers, storage and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation. This paper proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This paper proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where i) the first stage optimizes EHS operation cost, and ii) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modelled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this paper justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multi-energy carriers.

AB - Energy hub system (EHS) incorporating multiple energy carriers, storage and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation. This paper proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This paper proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where i) the first stage optimizes EHS operation cost, and ii) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modelled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this paper justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multi-energy carriers.

U2 - 10.1109/TII.2019.2938444

DO - 10.1109/TII.2019.2938444

M3 - Article

SP - 1

EP - 9

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

SN - 1551-3203

ER -