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.

Load prediction is at the core of many smart grid topics because its accuracy affects the resilience and optimal operation of the power grid. Short term load forecasting (STLF) specific to buildings is needed for both demand and production sides management, to help reducing peak electricity demand, optimizing energy planning, management, and conservation among other. In this paper, we analyze and compare five state of art regressive STLF algorithms relying on deep learning techniques. Algorithms were chosen based on their performances in the context of load prediction. These methods are compared in the context of smart buildings with real (large) data-set, leading to a comparison on quality metrics and a precise evaluation. The load forecasting is deployed with and without taking into account environmental features to better evaluate the benefits of reaching for external information. The idea is to help determine which of the five forecasting algorithm could be considered in the optimization loop of an energy management systems, either at the level of a district (local energy communities, or smart districts) or directly at the level of individual households (smart residential buildings) using quality metrics and need of exogenous data. Results show that the algorithms will perform better if we accept a larger training time and also present a high sensitivity on the choice of influencing parameters.

This paper focuses on a simplified expression of the load flow constraints for the optimal planning of distributed generation (DG) in radial grids. The proposed approach lies on the conventional DistFlow equations improved with the consideration of linearized bi-directional losses and their impact on the voltage profiles. The method is validated on various radial test cases and allows to run time coupled power flows in order to consider dynamic constraints (e.g. battery state of chare) in short computational times. With the help of the proposed method, a DG placement problem is solved in less than 2 minutes with an exhaustive search over a 69 buses system and the operation is simulated along a single representative day. A scalability test is finally performed with longer time horizons and greater numbers of connected DG. The developed approach allows to run the operation of a 69 buses system over 2 months and with 20 connected DGs in less than 5 minutes.

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.

Non-intrusive load monitoring (NILM) deals with the identification and subsequent energy estimation of the individual appliances from the smart meter data. The state of the art applications typically runs once per day and reports the detected appliances. In this work, data driven models are implemented for two different sampling rates (10 seconds and 15 minutes). The models are trained for 20 houses in the Netherlands and tested for a period of 4-weeks. The results indicate that the disaggregation methods is applicable for both sampling cases but with different use-case.

As the number of Electric Vehicles (EVs) has continuously increased in the last years, due to environmental issues, the interest is to find the maximal penetration rate of EVs, which can meet both, technical and environmental constraints. The technical impacts can be reduced by applying an Energy Management Strategy (EMS), which in the present study increases the maximal allowed EVs' penetration rate with over 41%. The energetic mix has a great importance concerning the environmental impacts. If the renewable energy is injected in the grid as distributed energy, it also has benefic impacts regarding the technical constraints. Consequently, a multi-objective and multi-variable optimization is performed, in order to find an optimal energetic mix for a maximal number of EVs, with an appropriate EMS.

The article tackles the issues related to the identification of electrical appliances inside residential buildings. Each appliance can be identified from the aggregate power readings at the meter panel. The possibility of applying a temporal multilabel classification approach in the domain of nonintrusive load monitoring is explored (nonevent-based method). A novel set of metafeatures is proposed. The method is tested on sampling rates based on the capabilities of current smart meters. The proposed approach is validated over a dataset of energy readings at residences for a period of a year for 100 houses containing different sets of appliances (water heater, washing machines, etc.). This method is applicable for the demand side management of households in the current limitation of smart meters; from the inhabitants or from the grid operator's point of view.

Energy management for residential homes and/or offices requires both identification and prediction of the future usages or service requests of different appliances present in the buildings. The aim of this work is to identify residential appliances from aggregate reading at the smart meter and to predict their states in order to minimize their energy consumption. For this purpose, our work is divided in two distinct modules: Appliance identification and future usage prediction. Both identification and prediction are based on multi-label learners which takes inter-appliance co-relation into account. The first part of the paper concerns the identification of electrical appliance usages from the smart meter monitoring. The main objective is to be able to identify individual loads from the aggregate power consumption in a non-intrusive manner. In this work, high energy consuming appliances are identified at 1-hour sampling rate using novel set of meta-features for this domain. The second part of the paper concerns future usage prediction. A comparison of algorithms for future appliance usage prediction using identification and direct consumption reading is presented. This work is based on a real residential dataset, called IRISE: 100 houses monitored every 10 minutes to one hour during one year (including weather informations).

Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical if the task is only based on energy consumption data and no other sensors are used. The proposed model tries to formalize such an approach using a time-series based multi-label classifier which takes into account correlation between different appliances among other factors. In this work, prediction results are shown for 1-hour in the future but this approach can be extended to predict more hours in the future as per the requirement(with restrictions). The learned models and decision tree showing the important factors in the input information is also discussed.