The increasing penetration of Electric Vehicles (EVs) presents challenges to the distribution grid, due to more volatile power profiles and higher peak demand. One key research question is how to accommodate EVs with limited-capacity grid equipment, such as transformers and lines. However, uncertainties from the EV side and the complexity of grid equipment models challenge the performance of the control strategies implemented. Moreover, the thermal loading of the transformer is often neglected. In this work, we propose a fully model-free, safe Deep Reinforcement Learning (DRL)- based grid-to-vehicle management strategy to avoid electric and thermal overloading of the transformer and power grid constraint violation. The management strategy is based on Projection-based Constraint Policy Optimization (PCPO) and takes only the observable information from the grid and vehicles. The target is to maximize energy delivery to the EV fleet while considering safe constraints, such as transformer thermal loading, voltage magnitude limits, and line loading limits. We compared the proposed strategy with conventional DRL and other safe DRL methods and investigated its robustness against higher ambient temperatures. The results show that the proposed strategy can deliver 92 % energy and reduce violations of the grid and transformers, while the other benchmarks deliver less than 80 %. The robustness test demonstrates that the proposed strategy is effective in various temperature. Moreover, the proposed strategy can effectively reduce at most 90 % of the transformer aging incurred by the thermal stress, compared with the uncontrolled charging. • Proposed a model-free safe DRL strategy for EV charging management in distribution grids. • Addressed unknown EV departure times and charging energy demands at stations. • Considered thermal stress of transformers, voltage and line constraints in grid operations.

This work-in-progress paper shares our experience and student feedback based on the first two years of our international research internship program at Grenoble Institute of Technology in France made available by Virginia Tech and supported through an NSF IRES grant. This program, IRES: Track I: U.s.- France Program for INverter-based And Cyber-secure Control and Communication for eLEctric power system (PINNACLE), trains and sends about 18 U.S. students (6 each year) for 8 weeks to Grenoble to engage in research and extracurricular activities, including language instruction, several industry visits, and integration with an existing international internship program, in its G2Elab. The overarching theme of students' research projects is to enable a massively inverter-based electric power system while addressing control, communication, and cybersecurity requirements and challenges. Students' exposure to these problems, especially in a European context, is expected to help them think of innovative solutions to the U.S.'s similar challenges. Additionally, G2Elab has active collaborations with several industry partners, which facilitates industry tours and field trips. This program is mutually beneficial and strengthens our existing collaboration by providing a framework for conducting research projects of common interest. We recruit nationally for this program. The program alumni are a cohort of individuals with highly desired skills for industry and graduate programs.

Control and management in smart grids are facing many challenges such as scalability, heterogeneity and technology innovation. This requires a transformation from the traditional centralised paradigm into a distributed one. In this article, a new distributed programming methodology, called Centralised Programming and Distributed Execution (CPDE), is proposed. CPDE relies on (i) the abstraction of the whole system as a distributed database; (ii) the use of the Smartlog declarative and reactive rule based language for expressing data manipulation; and (iii) the automatic Smartlog rule distribution according to data distribution. It thus provides a simple and straightforward mean for distributed programming. A centralised algorithm of fair over-voltage regulation of PV systems is used as a typical smart grids study case to validate the methodology and to compare it with centralized implementations. The experiments are implemented in a real-time simulation platform with a network of Raspberry Pis. In addition to showing its correctnes and ease of use, the performance of the CPDE implementation is studied, as well as its sensitivity to the increasing number of computing units and the data distribution. Results are promising and show the clear benefits of this methodology compared to more classical implementations.

This paper proposes an observer-based current controller for a virtual synchronous generators (VSG) to improve its operational performances. First, the model and the controllers are analytically described. Two controller are compared to the proposed one (PI integrating a state observer): a traditional PI and a PI integrating virtual impedances for filtering. Secondly, the advantage of the observer in the current controller loop for the VSG application is studied and compared to the other controllers. Finally, experimental results validate the capacity of the VSG-based inverter to supply unknown and unpredictable loads, thanks to the integration of the observer in the current control loop.

In the control and management of smart grids, from steady state to real-time, the objective is to handle and to treat any change in the system as fast as possible, with as less resources as possible. In this context, this paper proposes a new language, called Smartlog, designed as declarative programming. Smartlog is developed for distributed computing in real-time and distributed database management. Compared to imperative programming, based on anticipation rather than reaction, the interest is to not ignore the meaning of some data, or to collect and analyze data without interest, and thus loose bandwidth and computational time. Smartlog is designed for operating smart grids, which are defined as abstract structures of large and scalable distributed databases. After its definition, the main features of the Smartlog language are its compactness, its simplicity and its scalability are shown. The language is tested on the application of a frequency and voltage secondary control of an islanded micro-grid in an experimental test-case, using a realtime simulator connected to Raspberry Pis. The characteristics of Smartlog are illustrated thanks to a comparison with an imperative programming implementation of the same regulation.

Smart energy and electricity networks are a crucial component in building smart city architectures; their consistent and harmonized inclusion in the smart city design should be carefully considered through a detailed analysis of the impacts (environmental, energy, economic, societal) and the implementation of cost benefit analysis (CBA), not only in terms of managing the grid itself but also in a wider perspective that includes environmental, security, and social aspects. This paper first discusses the main impact that smart grid deployment has, in different respects, in smart cities and then presents a methodology for an extended CBA, able to go beyond the strictly financial aspects. It is based on previous developments at the European level. The methodology conceptually illustrated can naturally be extended to the assessment of proposals for the development of smart cities.

With the rapid increase of mobile data demand, Mobile Network Operators (MNOs) have started to pay close attention to wireless network energy consumption and CO2 emissions. In the current context of energy transition, Renewable Energy (RE) represents a great potential for MNOs to use environment-friendly power supply and reduce the energy expenses. Moreover, the Smart Grid (SG) offers important services in terms of Demand Side Management and decentralized production, which make rethinking the power usage within mobile networks a necessity. In this paper, we propose a fuzzy Q-Learning based energy controller for a small cell powered by local RE, local storage, and the SG to simultaneously minimize electricity expenditures of the MNOs and enhance the life span of the storage device. Simulation results show that the proposed solution achieves important cost reduction with respect to simpler approaches and performs very closely to the ideal strategy based on a perfect knowledge of the stochastic variables.