Abstract
Energy hubs, which integrate multiple energy vectors through converters, can enhance the value of Integrated Local Energy Systems (ILES) via increased flexibility and reduced costs. However, uncertain renewable energy and the non-convex, non-linear properties of energy flows complicate the modelling and operation of energy hub systems. This paper develops chance-constrained optimization methods for planning and operation of energy hub systems under uncertainty. The non-linear formulations of power and gas flows are relaxed by convexification methods, leading to a formulation of Second Order Cone Problem (SOCP), which can be efficiently solved to global optimality. The correlation between geographically close wind generators connected to the hub systems is modelled by establishing their relation using Gaussian copula. The proposed chance-constrained optimization is demonstrated on a six-hub system within a multi-vector energy distribution network with 7 electrical buses and 7 gas nodes. The value of different levels of system integration through the installation of energy hubs is investigated. The results show that by combining system integration via energy hubs with chance constrained operation, the proposed method can reduce operating costs and increase renewable energy yields, thereby benefitting hub system operators and customers with reduced energy infrastructure investment and energy costs.
Original language | English |
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Article number | 107153 |
Number of pages | 12 |
Journal | International Journal of Electrical Power and Energy Systems |
Volume | 132 |
Early online date | 15 May 2021 |
DOIs | |
Publication status | Published - Nov 2021 |
Funder
European Union’s Horizon 2020 research and innovation programme under grant agreement No 824386Keywords
- Chance-constrained programming
- Copula
- Correlation
- Distribution network
- Energy hub
- Integrated Local Energy Systems
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering