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Erschienen in:

2015 | OriginalPaper | Buchkapitel

5. Risk Averse Energy Hub Management Considering Plug-in Electric Vehicles Using Information Gap Decision Theory

verfasst von : Alireza Soroudi, Andrew Keane

Erschienen in: Plug In Electric Vehicles in Smart Grids

Verlag: Springer Singapore

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Abstract

The energy hub is defined as the multi-input multi-output energy converter. It usually consists of various converters like thermal generators, combined heat and power (CHP), renewable energies and energy storage devices. The plug-in electric vehicles as energy storage devices can bring various flexibilities to energy hub management problem. These flexibilities include emission reduction, cost reduction, controlling financial risks, mitigating volatility of power output in renewable energy resources, active demand side management and ancillary service provision. In this chapter a comprehensive risk hedging model for energy hub management is proposed. The focus is placed on minimizing both the energy procurement cost and financial risks in energy hub. For controlling the undesired effects of the uncertainties, the Information gap decision theory (IGDT) technique is used as the risk management tool. The proposed model is formulated as a mixed integer linear programming (MILP) problem and solved using General Algebraic Modeling System (GAMS). An illustrative example is analyzed to demonstrate the applicability of the proposed method.

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Vorheriges Kapitel An Optimal and Distributed Control Strategy for Charging Plug-in Electrical Vehicles in the Future Smart Grid

Nächstes Kapitel Integration of Distribution Grid Constraints in an Event-Driven Control Strategy for Plug-in Electric Vehicles in a Multi-Aggregator Setting

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Titel: Risk Averse Energy Hub Management Considering Plug-in Electric Vehicles Using Information Gap Decision Theory
verfasst von: Alireza Soroudi
Andrew Keane
Verlag: Springer Singapore
Buch: Plug In Electric Vehicles in Smart Grids
Print ISBN: 978-981-287-301-9

Electronic ISBN: 978-981-287-302-6

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-981-287-302-6_5

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