Efficient and Dynamic ENergy Management for Large-Scale Smart Grids – EDEN4SG
Smart piloting of large-scale EV fleets
Climate change as well as geopolitical tensions have led a large number of countries to target a massive integration of renewables in their energy mix. This will be achieved among others by increasing the electrification rate of several sectors such as transport. In this context, the wide-scale deployment of electrical vehicles (EVs) represents a challenge as well as an opportunity to render more efficient and affordable the transformation of the current power system into a smarter grid.
It is necessary to develop more efficient, decentralised, and dynamic energy management methods able to ensure coordination of large-scale fleets of EVs with conflicting objectives/constraints.
This represents a challenge as strong couplings exist at different levels (local/global) between the smart grid actors which belong to an increasingly uncertain environment (e.g. growing dependence of electric production to photovoltaics and wind). This leads to a highly-coupled, multi-scale and multi-criteria grid management problem presenting multiple sources of uncertainty. Such a complex optimization challenge cannot be addressed by conventional methods.<br /><br />In addition, the energy management of power systems closer-and-closer to real-time will require the intensive use of pervasive information and communication technologies (ICT). These technologies may suffer from an imperfect quality of service (QoS) (e.g. delays) which may greatly decrease the performance of smart grids. They also have an energy and environmental impact. Hence, the project will consider the «cost of information«.
The project targets to develop methods for the intelligent coordination of large-scale EV fleets and as well to determine the associated cost of information for piloting the required smart grid.
The project targets to develop methods for the intelligent coordination of large-scale EV fleets and as well to determine the associated cost of information for piloting the required smart grid.
A highly scalable method for the coordination of a large-scale fleet of EVs will be developed, and a methodology for determining the cost of information associated with a smart grid will be defined.
The results, computer code and data are intended to be provided as open-source as much as possible.
The wide-scale deployment of electrical vehicles (EVs) represents a challenge as well as an opportunity to render more efficient and affordable the transformation of the current power system into a smarter grid. This will be achieved by the safe and efficient coordination of EV fleets to be mutualized for different services (smart charging, ancillary services, self-consumption, etc). However, the current energy management strategies present several pitfalls. Centralized strategies, being not scalable, do not allow to control a sufficiently large number of EVs to reach economic viability. On the contrary, decentralized strategies present a scalability potential. However, the currently used methods are either not sufficiently scalable, or are highly conservative and do not exploit fully the flexibility potential of EV fleets. Hence, it is necessary to develop more efficient, decentralized, and dynamic energy management methods able to ensure the satisfaction of all stakeholders having potentially conflicting objectives and constraints (e.g. safe and cost-effective balancing of the power system, congestion avoidance, mobility needs, etc.).
In addition, the energy management of power systems closer-and-closer to real-time will require the intensive use of pervasive information and communication technologies (ICT). However, the associated economic and environmental “cost of information” may outweigh in some cases the benefits obtained from the smart grid performance increase.
Hence, the first objective of the EDEN4SG project consists in developing an efficient, decentralized energy management method applicable to a real life-sized, heterogeneous fleets of electric vehicles, managed with various levels of ICT quality of service (QoS). It will feed the second objective which consists in developing a methodology for assessing the economic and environmental cost of information in the smart grid.
Project coordination
Anne BLAVETTE (Centre national de la recherche scientifique)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
Partner
IRIT Université Toulouse 3 - Paul Sabatier
SATIE Centre national de la recherche scientifique
ORANGE
IRISA Institut de Recherche en Informatique et Systèmes Aléatoires
EDF ELECTRICITE DE FRANCE
SRD
Help of the ANR 583,692 euros
Beginning and duration of the scientific project:
January 2023
- 48 Months
