Two-Stage Distributionally Robust Optimization for Energy Hub Systems

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

Research output: Contribution to journalArticlepeer-review

93 Citations (SciVal)
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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
Article number8820049
Pages (from-to)3460-3469
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Issue number5
Early online date29 Aug 2019
Publication statusPublished - 31 May 2020


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