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Shared Autonomous Electric Vehicles (SAEVs) offer a transformative solution to bridge the mobility gap in suburban regions where public transportation means are scarce. Integration of SAEVs into the current electrical grid system poses operational challenges due to the anticipated surge in electricity demand for their charging. This paper proposes a strategy based on the Vehicle Scheduling Problem (VSP) for SAEVs to fulfill passenger travel demand and provide optimal charge scheduling using location based charging prices derived from Time of Use (TOU) rates. A significant portion of this study also investigates the fiscal benefits of utilization of local renewable energy for charging SAEVs. A multi-objective function to minimize charging costs, mobility costs and waiting time for passengers is formulated using mixed-integer linear programming (MILP). The proposed strategy is simulated and analyzed on a coupled traffic and low voltage suburban power grid of the French region considering coordinated charging strategy in the presence and absence of renewable energy. The comparison of results shows that the algorithm optimally schedules charging to maximize the utilization of renewable energy while serving passenger requests.

The widespread deployment of renewable energy sources and the increasing uptake of electric vehicles contribute to greater complexity in managing power flows within distribution grids. Understanding system dynamics and implementing control actions are crucial for effective grid management. While smart meters, accurate sensors, communication networks facilitate measurements at specific voltage nodes, obtaining measurements at all nodes across a network is costly and, in some cases, impractical. In response to these challenges, this paper firstly compares different state estimation methods based on available measurements and secondly, explains why an artificial neural network-based state estimation method is the most effective. The proposed method utilizes data-driven techniques to deliver precise estimations of voltages and currents within distribution networks, offering valuable insights into flexibility management and control processes. This approach also considers the phase angle effect of voltage and holds significant promise in improving awareness of the situation for power system operators, enhancing their understanding of evolving grid dynamics. The robustness and effectiveness of this method are validated through missing data cases, demonstrating its adaptability to diverse operational conditions.

Microgrids play a crucial role in modernizing the power grid by facilitating the integration of renewable energy sources. However, these sources exhibit high intermittency and stochastic behaviour, leading to challenges in effectively managing a microgrid amidst varying load demand and unexpected grid events. To address these uncertainties, local advanced control methods that leverage real-time data and enhanced computing capabilities are required. Frequency droop controllers generate additional power faster than the allocated power reserve to achieve an instantaneous balance of the electrical system. The automatic frequency restoration consists in making other generators participate gradually after 30 seconds. This paper proposes an intelligent control technique designed to enhance the static frequency droop controller, aiming to achieve active power balancing while minimizing CO2 emissions and operating costs. Consequently, an artificial neural network-based adaptive module is developed to anticipate and substitute the diesel-based reserve with a low carbon footprint reserve. This module considers new influencing input factors to anticipate and adjust the power setpoints of a stationary storage unit. The effectiveness of the proposed method is demonstrated on an islanded AC microgrid. The real-time simulation is conducted and validated on Opal-RT simulator, showing improved active power balancing while reducing both costs and CO2 emissions.

The use of autonomous electric vehicles has gained significant attention due to the growing interest in sustainable transportation solutions. Shared autonomous vehicles also have the potential to minimize market investments, improve transit systems, and reduce local environmental impact. To address the challenges of scheduling and coordinating shared AEV fleets while considering their integration with power networks, a digital twin-based platform is proposed. The digital twin concept involves creating a virtual replica of a complex system, enabling real-time monitoring, analysis, and optimization. This paper introduces an adaptive digital twin model falling within the third phase of digital twin evolution, integrating transportation and power networks for efficient shared AEV scheduling. The platform utilizes real-time simulators for transportation and power networks, connected through communication protocols. The proposed system aims to enhance scheduling algorithms, consider power grid conditions, and optimize AEV charging infrastructure.

Renewable energy sources like photovoltaic systems (PV) often result in significant active power imbalances due to their variable nature. Despite their predictability, the uncertainty in their power production increases the complexity of planned power generation and necessitates the activation of costly and polluting power reserves commonly provided by conventional generators. This paper proposes an intelligent control technique to predict and replace diesel-based power reserves by faster, more economical and environmentally friendly reserves provided by a battery energy storage system (BESS). Following a power imbalance, a frequency droop controller ensures the primary reserve support from a BESS within a few seconds. Building upon the assumption that the variations of PV power are persistent within a 10-minute window, a secondary reserve for the next 5 minutes is commanded from an artificial neural network (ANN)-based adaptive module. This module predicts battery power references with a 1-minute time increment based on the PV and diesel generator power measured 5 minutes earlier. The proposed method's efficacy is showcased through its application on an islanded AC microgrid. Real-time simulations are performed using the Opal-RT simulator, revealing enhanced power balancing along with reductions in both operating costs and CO2 emissions.

The growing integration of renewable energy sources and the widespread adoption of electric vehicles add complexity to power flows in distribution systems. Understanding the system dynamics and implementing control actions are imperative for effective grid management. While measurements in specific nodes are facilitated by smart meters, accurate sensors, communication networks, and electric vehicles, it is costly and, in some cases, impossible to obtain measurements at all nodes. In response to these challenges, this paper introduces a machine learning-based state estimation method, utilizing available measurements and estimating others. By harnessing data-driven techniques, the proposed method aims to provide accurate estimations of voltages and currents in distribution networks, offering valuable insights into flexibilities management and control processes. Specific case study is considered, confirming the efficiency of the suggested state estimation approach and providing insights into its flexibility across a range of scenarios. In addition to the mentioned benefits, the results validate the proposed method, demonstrating a small margin of error. This approach holds significant promises in improving situational awareness for power system operators and enhancing their understanding of the evolving grid dynamics.

In the era of autonomous electric vehicles (AEVs), the emergence of Shared AEVs presents a unique set of challenges and opportunities. This paper introduces a new graph-based routing algorithm aimed at optimizing the allocation of AEVs and recharging them. The algorithm takes into account multiple factors, including energy requirements, charging infrastructure availability, time constraints, and trip distances, to efficiently respond to passenger requests. In addition, when AEVs face energy constraints, proposed algorithm selects the most cost-effective charging point to minimize charging costs, considering factors like electricity prices. By bridging the transportation network and the power grid, this algorithm can also address more complex constraints like weather situations, road conditions, and battery degradation. Our case studies demonstrate the algorithm's effectiveness in finding optimal routes and managing low state of charge situations. This research opens the door to further exploration in complex urban environments, dynamic request prioritization, and user involvement, offering a promising future for AEV routing and optimization.

The increasing integration of renewable energy sources, and the widespread use of electric vehicles have introduced greater complexity and dynamics in power flows in distribution systems. With bidirectional power flows enhancing the observability of the grid becomes essential to identify critical feeders. In this context, data-driven methods offer a promising approach to enhance situational awareness for power system operators and improve their understanding of the system behavior under variabilities. This paper proposes a voltage estimation method based on a Machine Learning algorithm to address this challenge. By leveraging data-driven techniques, the proposed method aims to provide accurate and efficient voltage estimations in distribution networks, contributing to the effective management and control of modern power systems.

L'intégration croissante des sources d'énergie renouvelables et l'adoption généralisée des véhicules électriques complexifient les flux d'énergie dans les réseaux de distribution. Il est impératif de comprendre la dynamique de ces flux et de mettre en oeuvre des actions de contrôle pour une gestion efficace du réseau. Bien que les mesures dans les réseaux électriques soient facilitées par les compteurs intelligents et les réseaux de communication, il est coûteux et, dans certains cas, impossible d'obtenir des mesures précises à tous les noeuds d'un réseau en temps réel. En réponse à ces défis, cet article présente une méthode d'estimation de l'état basée sur un réseau neuronal artificiel, qui utilise les mesures remontées par les bornes de recharge de véhicules électriques et celles au poste source. La méthode proposée vise à fournir des estimations précises des tensions et des courants dans les réseaux de distribution, offrant ainsi des informations précieuses pour le pilotage des flexibilités. Cette approche intègre l'effet de l'angle des tensions, améliorant la compréhension de la dynamique du réseau par les opérateurs électriques. Elle est validée par les résultats obtenus avec plusieurs études de cas.