Contract researcher in Smart Charging Infrastructure

Université de Technologie de Compiègne is seeking a contract researcher to work in Smart Charging Infrastructure for Electric Vehicles and Buses in Public Transportation (SIR-V2G) project in the laboratory AVENUES (EA 7284), Urban Engineering Department (GU).

This recruitment is funded via Regional research program EE.4.0 of CPER. as part as SIR-V2G project.

Mission

The successful applicant will actively contribute to the activities of the SIR-V2G project wich primary objective is to address the challenges arising from the large-scale integration of electric vehicles (EVs) into distribution networks by developing smart solutions that maximize operational efficiency, minimize network impacts, and ensure the long-term sustainability of the proposed approaches. The project investigates optimized EV charging strategies aligned with renewable energy generation, the utilization of EVs as distributed energy storage through Vehicle-to-Everything (V2X) technologies, and coordinated control mechanisms—such as aggregators—to mitigate network congestion. Particular attention is given to the influence of user behavior, including mobility patterns and building occupancy, on the management and operation of distribution networks.

Mains activities

• Projet management (20%)

– Ensure effective communication and coordination among all project partners and stakeholders.

– Prepare, maintain, and update the project work plan, including timelines, milestones, and deliverables.

– Participate in and organize consortium meetings, technical workshops, and dissemination events.

– Supervise and mentor internship, master’s, or doctoral projects associated with the project activities.

– Assist in the dissemination of project results through presentations, publications, and stakeholder engagement.

• Development of algorithms (80%)

– Conduct a comprehensive review of the scientific literature and market frameworks to identify opportunities and barriers for aggregator-based market integration and the deployment of V2X services.

– Design and propose novel optimization and control algorithms that create value for multiple stakeholders, including EV users, aggregators, grid operators, and energy markets.

– Develop advanced co-simulation platforms coupling distribution network load-flow analysis with (multi-)objective optimization algorithms for EV charging and discharging.

– Investigate challenges related to optimality, robustness, and reproducibility of the proposed methods through sensitivity, uncertainty, and scalability analyses.

– Enhance the computational efficiency of large-scale optimization problems by exploring decomposition techniques, parallelization, and approximation methods to enable fast and accurate decision-making for real-time or near-real-time EV charging and discharging operations.

Scientific background

Smart charging infrastructures are designed to pursue multiple objectives beyond cost minimization, ensuring compliance with regulatory frameworks and supporting a full transition to electric mobility. In this context, EVs are considered small, distributed energy storage units that can provide additional flexibility to the power system. Through Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), and more broadly Vehicle-to-X (V2X) technologies, EVs can supply electricity when required, enhancing cost-effectiveness, enabling self-consumption, and supporting grid operations. The integration of V2X services allows vehicle owners to actively participate in grid management by discharging energy to satisfy local demand or deliver ancillary services. Accordingly, the project focuses on three main use cases: (i) the determination of variable charging requirements at charging stations, (ii) the provision of V2X flexibility enabled by advanced metering infrastructure, and (iii) the synchronization of photovoltaic generation with EV charging demand.

Despite these opportunities, the large-scale deployment of EVs presents significant challenges for distribution networks, necessitating careful planning and coordination to prevent overloads and operational imbalances. As EV penetration increases, the number of decision variables in charging optimization problems grows substantially, highlighting the importance of decentralized decision-making frameworks that allow individual actors to operate autonomously. In this context, coordination among self-interested stakeholders must be achieved through regulatory mechanisms or via third-party entities known as aggregators. Aggregators play a central role by coordinating consumers, producers, and grid operators, facilitating optimal collaboration, balancing supply and demand, and improving overall system efficiency and reliability.

While addressing economic and environmental objectives—often represented through price signals or emission coefficients—and consider only connection power limits, a more comprehensive assessment is required. Evaluating the actual impact of these strategies on grid performance and system reliability necessitates detailed power flow analyses based on realistic distribution network models. Accordingly, the project objectives are defined as follows:

– Develop and validate smart EV charging strategies that account for network constraints, renewable energy availability, and user behavior.

– Design and assess V2G/V2X control mechanisms enabling EVs to act as flexible, distributed energy storage resources.

– Investigate coordination frameworks based on aggregators to manage large populations of EVs and mitigate congestion in distribution networks.

– Analyze the impact of decentralized decision-making on system performance, scalability, and reliability.

– Synchronize photovoltaic generation with EV charging demand to enhance self-consumption and reduce grid stress.

Evaluate economic, environmental, and technical trade-offs through detailed power flow analyses using realistic distribution network models.

ADDITIONAL INFORMATION

Application dates

From 01/30/2026 to 03/01/2026

Type of contract and expected recruitment dates

Fixed-term contract – expected duration of 20 months – end date of contract no later than 08/31/2027 - to be filled imediatly

Gross monthly salary

Around €2800, depending on funding

Hours

1607 hours/year

Work environment and context

The activity takes place in Avenues laboratory - Urban Engineering Department. Currently, three faculty member (one associate professor, and two professors) with two doctoral students, one researcher and one research engineer are working on the related research activities and on the operation of the experimental platform (STELLA : Micro-grid dedicated to EV charging stations and powering a building).

The main components of the STELLA experimental platform are:

–      84 Sunpower SPR X21-345 photovoltaic panels;

–      4 connections for AC and/or DC charging stations;

–      6 inverters (FRONIUS) SYMO 3.7-3-M;

–      1 digital weather station (FRONIUS) for measuring: solar irradiation, the air temperature, cell temperature and wind speed (anemometer);

–      1 integrated real-time power system (THREE-PHASE) 60kW (1 DC/DC converter) 15kW, 3 x 5kW DC/DC converters, 1 x 15kW three-phase grid-tied inverter and 1 stand-alone inverter three-phase 15kW);

–      1 stockage Li-ion (E4V) 7,2kWh 48V/150Ah;

–      1 storage plomb-acide (Sonnenschein Solar) 37.4kWh 288V/130Ah;

–      1 stockage supercapacitif (Maxwell Technologies) 0,294kWh 300V/23,5F;

–      2 programmable electronic loads 1 dial (POWER +) 6kW 500V-50A;

The recruited person will have access to STELLA platform (https://avenues.utc.fr/recherche/plateformes-technologiques/living-lab-stella/) as well as high performance computing unit PILCAM (https://pilcam2.utc.fr/) of UTC.

Qualifications, training and accreditation

Qualification: Doctorate or PhD

Field of study: Electrical Engineering, Energy Engineering, or a closely related field

Skills

C1-C2 level written English (Common European Framework of Reference for Languages) essential

Strong programming skills in scientific computing languages (e.g. Python, C++)

Solid knowledge of microgrid concepts and distribution network operation

Knowledge of electric vehicle technologies and charging infrastructures

Familiarity with aggregator concepts, electricity markets, and V2G/V2X frameworks

Experience in developing research-grade code

Proven experience in formulating, implementing, and solving complex optimization problems (e.g. linear, nonlinear, mixed-integer, or multi-objective optimization)

Experience in energy management of renewable energy sources coupled with energy storage systems and flexible assets