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Advanced Models and Simulation Tools to Address Electric Vehicle Power System Integration (Steady-State and Dynamic Behavior)

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Book cover Electric Vehicle Integration into Modern Power Networks

Abstract

This chapter is intended to identify grid operational management and control strategies that should be available to deal with a large-scale deployment of electric plug-in vehicles (EVs). EVs are high flexible loads that can be used as mobile storage devices, thus being capable of providing several power system services [1]. In fact, EV batteries when in charging mode can behave as controllable loads, providing spinning reserves as a result of a load decrease or even providing power back to the grid under the so-called vehicle-to-grid (V2G) mode, helping peak load demand management. In this way, the growing prospects of an EV market expansion may strengthen the concepts that aim at the active grid management.

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Notes

  1. 1.

    As the V2G mode of operation (described in Sect. 6.2.1) is the most aggressive mode for charging EV, due to possible implications with EV batteries’ life cycle, this option is not likely to be a reality neither in the short run nor in the medium term. Only in the very long term, when battery technology has reached a high maturation stage, this strategy may be adopted. For this reason, the V2G mode of operation was neither considered in the development of Methodologies 1 and 2 nor in the implementation of these approaches in the steady-state algorithms presented.

  2. 2.

    The lower electricity price period assumed was that of the dual tariff policy currently implemented in Portugal: 22–8 h. More information can be found in http://www.edpsu.pt/pt/particulares/tarifasehorarios/ (in Portuguese).

  3. 3.

    All the required power flows were run using the PSS/E software.

  4. 4.

    The optimization problem was solved using LINGO 13.0, which is an optimization modeling software that includes a set of built-in solvers for linear, nonlinear, quadratic, quadratically constrained, second-order cone, stochastic, and integer optimization. More information can be found in http://www.lindo.com/index.php?option=com_content&view=article&id=2&Itemid=10

  5. 5.

    “Flexible EVs” are the EVs whose owners adhered to the smart charging scheme.

  6. 6.

    “Inflexible EVs” are the EVs whose owners adhered to the dumb charging or multiple tariff schemes.

  7. 7.

    “Mandatory load” is the conventional load of the network plus the load from the EV whose owners adhered to the dumb charging or multiple tariff schemes.

  8. 8.

    All the required power flows were run using the PSS/E software.

  9. 9.

    Fortran is a general-purpose, procedural, imperative programming language that is especially suited to numeric computation and scientific computing developed by IBM in the 1950s.

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Soares, F.J., Almeida, P.M.R., Lopes, J.A.P. (2013). Advanced Models and Simulation Tools to Address Electric Vehicle Power System Integration (Steady-State and Dynamic Behavior). In: Garcia-Valle, R., Peças Lopes, J. (eds) Electric Vehicle Integration into Modern Power Networks. Power Electronics and Power Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0134-6_6

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  • DOI: https://doi.org/10.1007/978-1-4614-0134-6_6

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