Electric vehicles charge optimization for the electric system, taking into account drivers behavior

Publication Type:

Conference Paper

Source:

Gerpisa colloquium, Paris (2019)

Abstract:

The interest on electric vehicles integration to the grid (V2G) has been mainly focused on the reduction of greenhouse gas emissions coming from the transport sector and the contribution that EV can offer to the electricity balance as a regulable way to stock energy. The research about the subject date now from some years ago and it is principally based on electrical technical fundamentals for batteries charge and discharge optimization. Different topics around V2G have emerged more recently such as renewable energies integration and the possible revenues for drivers and power producers. Today, supported on a big size of welfares brought by the EV to the grid (from a technical, economical and environmental point of view) and in the middle of the energy transition context, involved actors do efforts for toggling the general vision of EV as energy consumers to make them become revenues producers (Sovacool et al. 2017).

Technological advances have allowed to have a digital solution for optimizing individually the vehicles charge. We propose here, an economic model approach which is able to respond to technical criteria for optimizing the EV batteries usage by means of a particular signal (price or environmental i.e.). All this, aiming the evaluation of an economic collective surplus for involving and stimulating different actors of the sector (manufacturers, grid system operators and drivers). The proposed model looks for the battery charge optimization as a function of vehicles requirements while smoothing the electrical charge curve in countries such as France or Germany and enhancing the renewable energies operation bringing the facility for the intermittence management. The approach will describe the optimal way of getting profits from the intelligent batteries charge and discharge and then, it will describe the optimal way to distribute these profits among the involved actors.

Batteries are the first key of V2G and many questions emerge around. The grid integration call upon batteries for an extra number of charging cycles, what causes an accelerated degradation of lifetime and therefore an additional cost. Aiming the V2G economic viability, several researches related to batteries longevity optimization have been developed. All this resulting in an intelligent control system of charge and discharge that preserves batteries lifetime as if, in the worst case, they were not connected to the grid (Redondo-Iglesias et al. 2019, Uddin et al. 2018). Our future model will take into account batteries capacity looking for the lifetime preservation, will take into account the electric grid and de the power plants capacity and situation and will take into account the drivers behavior. The whole includes clearly a considerable number of random variables that lead us to use a stochastic optimization method.

The V2G is a way of bringing flexibility to electric systems. Therefore, we are facing the design of a direct competitor model with some other flexibility methods already presents in the system such as thermal centrals, hydraulic barrages or load management. The electricity market rules and the remuneration policies will have an important place in the model for getting the proper acceptation and integration and for responding to particular needs of a specific country (Chèze et al. 2015). Different V2G market structures may be proposed and evaluated and some estimations of probable revenues have been already presented for diverse scenarios. Obtained values go from hundreds to some thousands dollars (Han et al. 2012, Petit & Perez, 2013). In the model we will define what will be remunerated, how it will be remunerated and who will remunerate it without never forget the main function of a car: transport his owner.

Once the model will be defined we will look for two different simulations: at first, the adaptation for a national electrical system taking into account particularities of a country and his markets. Then, the adaptation for a local grid taking into account particular households or community behaviors and including self-consumption structures. In both cases, we will add value to changes on the load curve (peak shaving and valley filling), the operation reserve and frequency and voltage regulations. For each simulation a characteristic period will be chosen through a partitioning algorithm that will bring us nearer the proper results.

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