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Volatile productions and consumptions generate a stochastic behavior of distribution grids and make its supervision difficult to achieve. Usually, the Distributed Generators reactive powers are adjusted to perform decentralized voltage control. Industrial controllers are generally equipped with a local affine feedback law, which settings are tuned at early stage using local data. A centralized and more efficient tuning method should aim to maximize the probability that all the node voltages of distribution grids remain within prescribed bounds. When the characteristics of the stochastic power forecasts are known, the centralized algorithm allows to update the settings on a regular time basis. However, the method requires to solve stochastic optimization problem. Assuming that stochastic variables have Gaussian distributions, a procedure is given which guarantees the convergence of the stochastic optimization. Convex problems drastically reduce the difficulty and the computational time required to reach the global minimum, compared to nonconvex optimal power flow problems. The linear controllers with optimized parameters are compared to traditional control laws using simulations of a real distribution grid model. The results show that the algorithm is reliable and moreover fast enough. Hence, the proposed method can be used to update periodically the control parameters.

This paper addresses the volt-var control of distribution grids embedding many distributed generators (DGs). Specifically, it focuses on the compliance of powers to specified PQ diagrams at the high voltage/medium voltage (HV/MV) interface while the voltages remain well controlled. This is achieved using a two-stage optimization corresponding to two different classes of actuators. The tap position of capacitor banks is selected on a daily basis, given a stochastic model of the input powers prediction, which allows infrequent actuation and increases the device lifespan. In a second stage, a confidence level optimization problem allows to tune on an hourly basis the parameters of the DGs reactive power affine control laws. Results on a real-size grid show that the combined tuning of these actuators allows the ability to comply with European grid codes while the control effort remains reasonable.

Distributed generators (DG) reactive powers are controlled to mitigate voltage overshoots in distribution grids with stochastic power production and consumption. Classical DGs controllers may embed piecewise affine laws with dead-band terms. Their settings are usually tuned using a decentralized method which uses local data and optimizes only the DG node behavior. It is shown that when short-term forecasts of stochastic powers are Gaussian and the grid model is assumed to be linear, nodes voltages can either be approximated by Gaussian or sums of truncated Gaussian variables. In the latter case, the voltages probability density functions (pdf) that are needed to compute the overvoltage risks or DG control effort are less straightforward than for normal distributions. These pdf are used into a centralized optimization problem which tunes all DGs control parameters. The objectives consist in maximizing the confidence levels for which voltages and powers remain in prescribed domains and minimizing voltage variances and DG efforts. Simulations on a real distribution grid model show that the truncated Gaussian representation is relevant and that control parameters can easily be updated even when extra DGs are added to the grid. The DG reactive power can be reduced down to 50 % or node voltages variances can be reduced down to 30 %.

The insertion of stochastic renewable energies in distribution grids generates important voltage fluctuations and reactive power exchange between distribution grids and transmission grids. These variations need to comply with European grid codes. The control strategy involves the actuation of the On Load Tap Changer and Capacitor Banks tap position, and the reactive powers of the DGs. This paper focuses on the stochastic estimation of the powers at the interface and the influence of the Capacitor Bank tap position. The main results are that, using a simplified stochastic model of the grid, one can obtain the distribution of the powers, and select, on a daily basis, the tap position which minimizes the level of the prescribed PQ diagram violation risk. This study paves the way for a volt-var stochastic optimization of the distribution grid.

The insertion of stochastic renewable energies in distribution grids generates important voltage fluctuations. However, the influence of the On Load Tap Changer and Distributed Generators (DGs) controllers, and specifically the existence of dead-bands in the control laws, has been seldom evaluated. Under the assumptions of Gaussian inputs and a linear model of the grid, it is shown that node voltages can be approximated either by Gaussian variables or sums of truncated Gaussian variables. A procedure is necessary to select the Probability Density Function (PDF) which fits best each node voltage. A signal based method and another algorithm relying on the grid topology are presented and compared when the modeling is applied to a real distribution grid. The model is accurate and can be used for confidence level or chance-constrained optimization of control parameters.

This paper deals with a centralized tuning of the local controllers parameters in a distribution grid with many Distributed Generators. The optimal controllers settings are obtained by minimizing the confidence level of voltage specification violations. The confidence level optimization problem uses Gaussian uncertainties of short-term forecasting and OLTC errors along with an accurate linear power flow approximation. Considering these assumptions, the optimization problem is shown to be convex and the characteristics of uncertainties are reduced to their means and standard deviations. The proposed method optimizes the full network while keeping the droop-like industrial structure of the controllers and allows to consider feeders which can have low and high voltages. The merits of the method are illustrated via a modified real distribution network showing a decrease of voltage variances and violations.

European Grid codes define new network management rules. In order to answer these decrees, it is important to estimate accurately the voltages and powers inside distribution networks. The intermittent nature of renewable sources leads to consider stochastic variables in power flow algorithms. A review of power flow methods and their ability to comply with these requirements is done, which shows that computationally demanding nonlinear methods have to be discarded when dealing with stochastic data and considering limited calculation time. A combination of linear methods is proposed, for which average errors in power and voltage are quite low, when applied to a real-life distribution network. The validity domain of the method is also presented.

The optimal control of electrical drives necessitates to take into account current and voltage limits that are imposed by the power electronics and the electrical machines. Let’s cite for example the flux-weakening operation of electrical drives or propulsion. If the control of classical three-phase drives under voltage and current limits are known for a long time, the specific characteristics of multiphase drives open the way to researches on their control under such constraints. This paper aims to explain what are the main differences between three-phase and multiphase drives when they run under voltage and current constraints and try to show what are the scientific and technical problems to be solved. Some first results are given in order to show that Model Predictive Control (MPC) is expected to be a good candidate to answer the proposed challenge.

L’évolution des techniques de gestion des réseaux de distribution imposée par les codes de réseaux européens nécessite une connaissance de plus en plus approfondie de leur comportement. La gestion prévisionnelle de ces réseaux implique de prendre en compte la volatilité des énergies renouvelables et les capacités de prévision à l’horizon J-1 de la consommation et de la production. En effet, les valeurs déterministes les plus probables des prévisions ne sont plus suffisantes pour pouvoir prédire et gérer à l’avance un réseau. Une modélisation et une optimisation stochastiques permettent un choix, au plus juste, de paramètres de contrôle, par rapport au risque acceptable de surtensions. Les travaux de thèse se concentrent sur l’étude et la prise en compte, dans la modélisation et l’optimisation des réseaux, des incertitudes matérielles et fonctionnelles des réseaux de distribution. Une modélisation stochastique de réseau est proposée, elle intègre les incertitudes liées au régleur en charge et aux prévisions de consommation et de production. En fonctionnement, les tensions aux nœuds et la puissance réactive transitant vers le réseau de transport varient de manière non uniforme. Les contrôleurs des productions réactives des générateurs, le régleur en charge et les gradins de condensateurs permettent de limiter ces fluctuations et de respecter les exigences contractuelles. Industriellement, les contrôleurs locaux des générateurs sont caractérisés par des lois de commande linéaires ou linéaires par morceaux. En effectuant des hypothèses sur la nature stochastique des prévisions de production et de consommation, on peut montrer que les tensions aux nœuds sont des variables gaussiennes ou des sommes de variables gaussiennes par morceaux. Une optimisation stochastique basée sur ces modèles permet de choisir les paramètres des contrôleurs qui minimisent les risques de surtension et des efforts de générateurs, sans avoir à mettre en œuvre des méthodes coûteuses en temps de calcul de type Monte Carlo.