Real-time enforcement of local energy market transactions respecting distribution grid constraints

International audienceFuture electricity distribution grids will host a considerable share of the renewable energy sources needed for enforcing the energy transition. Demand side management mechanisms play a key role in the integration of such renewable energy resources by exploiting the flexibility of elastic loads, generation or electricity storage technologies. In particular, local energy markets enable households to exchange energy with each other while increasing the amount of renewable energy that is consumed locally. Nevertheless, as most ex-ante mechanisms, local market schedules rely on hour-ahead forecasts whose accuracy may be low. In this paper we cope with forecast errors by proposing a game theory approach to model the interactions among prosumers and distribution system operators for the control of electricity flows in real-time. The presented game has an aggregative equilibrium which can be attained in a semi-distributed manner, driving prosumers towards a final exchange of energy with the grid that benefits both households and operators, favoring the enforcement of prosumers' local market commitments while respecting the constraints defined by the operator. The proposed mechanism requires only one-to-all broadcast of price signals, which do not depend either on the amount of players or their local objective function and constraints, making the approach highly scalable. Its impact on distribution grid quality of supply was evaluated through load flow analysis and realistic load profiles, demonstrating the capacity of the mechanism ensure that voltage deviation and thermal limit constraints are respected

Scaling of Distributed Multi-Simulations on Multi-Core Clusters

International audienceDACCOSIM is a multi-simulation environment for continuous time systems, relying on FMI standard, making easy the design of a multi-simulation graph, and specially developed for multi-core PC clusters, in order to achieve speedup and size up. However, the distribution of the simulation graph remains complex and is still the responsibility of the simulation developer. This paper introduces DACCOSIM parallel and distributed architecture, and our strategies to achieve efficient multi-simulation graph distribution on multi-core clusters. Some performance experiments on two clusters, running up to 81 simulation components (FMU) and using up to 16 multi-core computing nodes, are shown. Performances measured on our faster cluster exhibit a good scalability, but some limitations of current DACCOSIM implementation are discussed

Toward an Accurate and Fast Hybrid Multi-Simulation with the FMI-CS Standard

International audienceMulti-simulation in the context of future smart electrical grids consists in associating components modeling different physical domains, but also their local or global control. Our DACCOSIM multi-simulation environment is based on the version 2.0 of the FMI-CS (Functional Mock-up Interface for Co-Simulation) standard maintained by the Modelica Association. It has been specifically designed to run large-scale and complex systems on a single PC or a cluster of multicore nodes. But it is quite challenging to accurately simulate FMUs-composed systems involving predictable and unpredictable events while preserving the system overall performance. This paper presents some additions to the FMI-CS standard aiming to improve the accuracy and the performance of distributed multi-simulations involving a mix of both time steps and various kinds of events. The proposed FMI-CS primitives are explained, as well as the Master Algorithm strategies to exploit them efficiently

Real-time enforcement of local energy market transactions respecting distribution grid constraints

International audienceFuture electricity distribution grids will host a considerable share of the renewable energy sources needed for enforcing the energy transition. Demand side management mechanisms play a key role in the integration of such renewable energy resources by exploiting the flexibility of elastic loads, generation or electricity storage technologies. In particular, local energy markets enable households to exchange energy with each other while increasing the amount of renewable energy that is consumed locally. Nevertheless, as most ex-ante mechanisms, local market schedules rely on hour-ahead forecasts whose accuracy may be low. In this paper we cope with forecast errors by proposing a game theory approach to model the interactions among prosumers and distribution system operators for the control of electricity flows in real-time. The presented game has an aggregative equilibrium which can be attained in a semi-distributed manner, driving prosumers towards a final exchange of energy with the grid that benefits both households and operators, favoring the enforcement of prosumers' local market commitments while respecting the constraints defined by the operator. The proposed mechanism requires only one-to-all broadcast of price signals, which do not depend either on the amount of players or their local objective function and constraints, making the approach highly scalable. Its impact on distribution grid quality of supply was evaluated through load flow analysis and realistic load profiles, demonstrating the capacity of the mechanism ensure that voltage deviation and thermal limit constraints are respected

Identification and Characterization of Power Quality Disturbances affecting MV Distribution Networks

La reconnaissance des perturbations survenant sur les réseaux HTA est une problématique essentielle pour les clients industriels comme pour le gestionnaire du réseau. Ces travaux de thèse ont permis de développer un système d’identification automatique. Il s’appuie sur des méthodes de segmentation qui décomposent de manière précise et efficace les régimes transitoires et permanents des perturbations. Elles utilisent des filtres de types Kalman linéaire ou anti-harmoniques pour extraire les régimes transitoires. La prise en compte des variations harmoniques et de la présence de transitoires proches se fait à l’aide de seuils adaptatifs. Des méthodes de correction du retard a posteriori permettent d’améliorer la précision de la décomposition. Des indicateurs adaptés à la dynamique des régimes de fonctionnement analysés sont utilisés pour caractériser les perturbations. Peu sensibles aux erreurs de segmentation et aux perturbations harmoniques, ils permettent une description fiable des phases des perturbations. Deux types de systèmes de décision ont également été étudiés : des systèmes experts et des classifieurs SVM. Ces systèmes ont été mis au point à partir d’une large base de perturbations simulées. Leurs performances ont été évaluées sur une base de perturbations réelles : ils déterminent efficacement le type et la direction des perturbations observées (taux de reconnaissance moyen > 98%).The recognition of disturbances affecting MV networks is essential to industrials and distribution system operators. The aim of this thesis work is to design a near real-time automatic system able to detect and identify disturbances from their waveforms. Segmentation methods split the disturbed waveforms into transient and steady-state intervals. They use Kalman filters or anti-harmonic filters to extract the transient intervals. Adaptive thresholding methods increase the detection capacity while a posterior delay compensation methods improve the accuracy of the decomposition. Indicators adapted to the disturbance dynamic are used to characterize its steady-state and transient phases. They are robust to segmentation inaccuracies as well as to steady-state disturbances such as harmonics. Two distinct decision systems are also studied: expert recognition systems and SVM classifiers. During the learning stage, a large simulated event database is used to train both systems. Their performances are evaluated on real events: the type and direction of the measured disturbances are determined with a recognition rate over 98%

Identification et caractérisation des perturbations affectant les réseaux électriques HTA.

The recognition of disturbances affecting MV networks is essential to industrials and distribution system operators. The aim of this thesis work is to design a near real-time automatic system able to detect and identify disturbances from their waveforms. Segmentation methods split the disturbed waveforms into transient and steady-state intervals. They use Kalman filters or anti-harmonic filters to extract the transient intervals. Adaptive thresholding methods increase the detection capacity while a posterior delay compensation methods improve the accuracy of the decomposition. Indicators adapted to the disturbance dynamic are used to characterize its steady-state and transient phases. They are robust to segmentation inaccuracies as well as to steady-state disturbances such as harmonics. Two distinct decision systems are also studied: expert recognition systems and SVM classifiers. During the learning stage, a large simulated event database is used to train both systems. Their performances are evaluated on real events: the type and direction of the measured disturbances are determined with a recognition rate over 98%.La reconnaissance des perturbations survenant sur les réseaux HTA est une problématique essentielle pour les clients industriels comme pour le gestionnaire du réseau. Ces travaux de thèse ont permis de développer un système d’identification automatique. Il s’appuie sur des méthodes de segmentation qui décomposent de manière précise et efficace les régimes transitoires et permanents des perturbations. Elles utilisent des filtres de types Kalman linéaire ou anti-harmoniques pour extraire les régimes transitoires. La prise en compte des variations harmoniques et de la présence de transitoires proches se fait à l’aide de seuils adaptatifs. Des méthodes de correction du retard a posteriori permettent d’améliorer la précision de la décomposition. Des indicateurs adaptés à la dynamique des régimes de fonctionnement analysés sont utilisés pour caractériser les perturbations. Peu sensibles aux erreurs de segmentation et aux perturbations harmoniques, ils permettent une description fiable des phases des perturbations. Deux types de systèmes de décision ont également été étudiés : des systèmes experts et des classifieurs SVM. Ces systèmes ont été mis au point à partir d’une large base de perturbations simulées. Leurs performances ont été évaluées sur une base de perturbations réelles : ils déterminent efficacement le type et la direction des perturbations observées (taux de reconnaissance moyen > 98%)

Impact of Waveform Segmentation Accuracy on Disturbance Recognition Reliability

International audiencePower Quality disturbances can be identified by modern waveform analyzers using several recognition indicators. The indicators are computed after segmentation of the signal during either steady-state intervals or transient ones. This paper proposes indicators for the recognition of capacitor bank switching and transformer energizing events. These indicators computed over transient intervals prove to be robust to noise and harmonic disturbances. We also investigate their sensitivity to inaccurate detection instants. For each indicator, a validity interval is identified and its properties determined. Constraints on the segmentation stage are deduced from this analysis: a standard deviation less than 2.5 ms is required on the segmentation delay for reliable recognition of transformer energizing event, while a deviation less than 0.25 ms is necessary for capacitor switching recognition. For both event types, a near zero mean segmentation delay is to be expected

MECSYCO-DACCOSIM Coupling

DACCOSIM implements a FMI v2 CS master, it does not offer precise event notifications. MECSYCO is designed to coordinate the simulation of DEVS-compliant models, in particular event-based simulators. To integrate DACCOSIM into MECSYCO we propose an "exploration mechanism" to localize state events in DACCOSIM simulations