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http://hdl.handle.net/10985/17728
Convex formulation of confidence level optimization of DG affine reactive power controllers in distribution grids
BUIRE, Jérôme; DIEULOT, Jean-Yves; COLAS, Frédéric; GUILLAUD, Xavier; DE ALVARO, Léticia
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.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/177282020-01-01T00:00:00ZBUIRE, JérômeDIEULOT, Jean-YvesCOLAS, FrédéricGUILLAUD, XavierDE ALVARO, LéticiaVolatile 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.Coordinated control of active distribution networks to help a transmission system in emergency situation
http://hdl.handle.net/10985/17722
Coordinated control of active distribution networks to help a transmission system in emergency situation
MORIN, J.; COLAS, Frédéric; DIEULOT, Jean-Yves; GRENARD, S.; GUILLAUD, Xavier
This paper addresses the relevance of using reactive power from Medium Voltage (MV) networks to support the voltages of a High Voltage (HV) rural network in real-time. The selection and analysis of different optimal coordination strategies between the HV and several MV grids is investigated. The algorithms will control the reactive powers that can flow between HV/MV networks after a request from the Transmission Network Operator in case of an emergency situation such as a line outage. From a case study, the relevance of the coordination is enlightened and recommendations are given on how to tune and to combine the optimal algorithms with the advanced Volt Var Controllers of the distribution grids.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/177222018-01-01T00:00:00ZMORIN, J.COLAS, FrédéricDIEULOT, Jean-YvesGRENARD, S.GUILLAUD, XavierThis paper addresses the relevance of using reactive power from Medium Voltage (MV) networks to support the voltages of a High Voltage (HV) rural network in real-time. The selection and analysis of different optimal coordination strategies between the HV and several MV grids is investigated. The algorithms will control the reactive powers that can flow between HV/MV networks after a request from the Transmission Network Operator in case of an emergency situation such as a line outage. From a case study, the relevance of the coordination is enlightened and recommendations are given on how to tune and to combine the optimal algorithms with the advanced Volt Var Controllers of the distribution grids.Confidence Level Optimization of DG Piecewise Affine Controllers in Distribution Grids
http://hdl.handle.net/10985/17725
Confidence Level Optimization of DG Piecewise Affine Controllers in Distribution Grids
BUIRE, Jerome; COLAS, Frédéric; DIEULOT, Jean-Yves; DE ALVARO, Leticia; GUILLAUD, Xavier
Distributed generators (DGs) 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%.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/177252019-01-01T00:00:00ZBUIRE, JeromeCOLAS, FrédéricDIEULOT, Jean-YvesDE ALVARO, LeticiaGUILLAUD, XavierDistributed generators (DGs) 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%.Economic supervisory predictive control of a hybrid power generation plant
http://hdl.handle.net/10985/11354
Economic supervisory predictive control of a hybrid power generation plant
DIEULOT, Jean-Yves; DAUPHIN-TANGUY, Geneviève; CHALAL, Lamine; COLAS, Frédéric
This work deals with the development of an economic supervisory predictive control method for the management of a hybrid renewable energy system. The hybrid cell integrates solar panels, a gas microturbine and a storage unit. Tuning the predictive controller is easy: the optimal criterion encom- passes the environmental, fuel, energy delivery and storage costs. Short time predictions of the solar power are embedded in the supervisor which yields smoother battery control and better power management. Real-time experiments are driven in a Hardware-in-the-Loop framework illustrating the relevance of the proposed supervisory predictive control design.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/113542015-01-01T00:00:00ZDIEULOT, Jean-YvesDAUPHIN-TANGUY, GenevièveCHALAL, LamineCOLAS, FrédéricThis work deals with the development of an economic supervisory predictive control method for the management of a hybrid renewable energy system. The hybrid cell integrates solar panels, a gas microturbine and a storage unit. Tuning the predictive controller is easy: the optimal criterion encom- passes the environmental, fuel, energy delivery and storage costs. Short time predictions of the solar power are embedded in the supervisor which yields smoother battery control and better power management. Real-time experiments are driven in a Hardware-in-the-Loop framework illustrating the relevance of the proposed supervisory predictive control design.Stochastic Optimization of PQ Powers at the Interface between Distribution and Transmission Grids
http://hdl.handle.net/10985/17724
Stochastic Optimization of PQ Powers at the Interface between Distribution and Transmission Grids
BUIRE, Jérôme; COLAS, Frédéric; DIEULOT, Jean-Yves; GUILLAUD, Xavier
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.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/177242019-01-01T00:00:00ZBUIRE, JérômeCOLAS, FrédéricDIEULOT, Jean-YvesGUILLAUD, XavierThis 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.Non-Linear Primary Control Mapping for Droop-Like Behavior of Microgrid Systems
http://hdl.handle.net/10985/21617
Non-Linear Primary Control Mapping for Droop-Like Behavior of Microgrid Systems
LEGRY, Martin; DIEULOT, Jean-Yves; COLAS, Frederic; SAUDEMONT, Christophe; DUCARME, Olivier
Interconnecting microgrids in LV power system presents appealing features such as self-healing or power quality. When networked microgrids are not connected to a strong utility grid, their Point of Common Coupling (PCC) voltage and their power reserves vary with the operating point. An external droop control architecture is proposed that allows active and reactive power sharing among the different microgrids, thereby stabilizing the system frequency and PCC voltage, and the maximum achievable droop gains are supplied. Next, the design of appropriate primary controllers for the Distributed Energy Resources inside each microgrid allows to achieve a specified aggregated external droop controller at the connection point with little communication requirements. This methodology is applied to a modified CIGRE benchmark and shows good results while keeping a standard decentralized control architecture.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/216172020-01-01T00:00:00ZLEGRY, MartinDIEULOT, Jean-YvesCOLAS, FredericSAUDEMONT, ChristopheDUCARME, OlivierInterconnecting microgrids in LV power system presents appealing features such as self-healing or power quality. When networked microgrids are not connected to a strong utility grid, their Point of Common Coupling (PCC) voltage and their power reserves vary with the operating point. An external droop control architecture is proposed that allows active and reactive power sharing among the different microgrids, thereby stabilizing the system frequency and PCC voltage, and the maximum achievable droop gains are supplied. Next, the design of appropriate primary controllers for the Distributed Energy Resources inside each microgrid allows to achieve a specified aggregated external droop controller at the connection point with little communication requirements. This methodology is applied to a modified CIGRE benchmark and shows good results while keeping a standard decentralized control architecture.Mixed integer quadratic programming receding horizon microgrid supervisor
http://hdl.handle.net/10985/21618
Mixed integer quadratic programming receding horizon microgrid supervisor
LEGRY, Martin; COLAS, Frédéric; SAUDEMONT, Christophe; DIEULOT, Jean-Yves; DUCARME, Olivier
This paper proposes to optimize the real time operation of a microgrid controlled with a two-layer Model Predictive Controller supervisor. Based on the classical decomposition of control level, the proposed supervisor tracks long-term economic references from a classical economic optimization routine. The optimization problem is formulated as a Mixed Integer Quadratic Problem and uses the different power references as levers to reach this optimum and maintain the state of the microgrid within limitations. In addition, it is able to minimize the grid losses.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/216182019-01-01T00:00:00ZLEGRY, MartinCOLAS, FrédéricSAUDEMONT, ChristopheDIEULOT, Jean-YvesDUCARME, OlivierThis paper proposes to optimize the real time operation of a microgrid controlled with a two-layer Model Predictive Controller supervisor. Based on the classical decomposition of control level, the proposed supervisor tracks long-term economic references from a classical economic optimization routine. The optimization problem is formulated as a Mixed Integer Quadratic Problem and uses the different power references as levers to reach this optimum and maintain the state of the microgrid within limitations. In addition, it is able to minimize the grid losses.Embedding OLTC nonlinearities in predictive Volt Var Control for active distribution networks
http://hdl.handle.net/10985/17730
Embedding OLTC nonlinearities in predictive Volt Var Control for active distribution networks
MORIN, J.; COLAS, Frédéric; DIEULOT, Jean-Yves; GRENARD, S.; GUILLAUD, Xavier
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.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/177302017-01-01T00:00:00ZMORIN, J.COLAS, FrédéricDIEULOT, Jean-YvesGRENARD, S.GUILLAUD, XavierVolatile 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.A Two-layer Model Predictive Control Based Secondary Control with Economic Performance Tracking for Islanded Microgrids
http://hdl.handle.net/10985/21619
A Two-layer Model Predictive Control Based Secondary Control with Economic Performance Tracking for Islanded Microgrids
LEGRY, Martin; COLAS, Frédéric; SAUDEMONT, Christophe; DIEULOT, Jean-Yves; DUCARME, Olivier
This paper proposes a two-layer microgrid supervisor based on Model Predictive Control (MPC). The supervisor in the upper layer relies on an economical optimization that considers the cost of energy and the load and production forecasts to define the State of Charge (SoC) targets for each storage device on a timescale of 15 minutes. The lower layer displays a shorter timescale and aims to control the equipment to ensure the stability of the overall system and SoC tracking while satisfying the economic constraints specified by the upper layer. These two layers require an uniformization of the timestep and of the references in order to behave properly. The main contributions of this paper are the microgrid network modelling embedded in the optimization routine of the lower layer and a discretization for integrating upper-layer references.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/216192018-01-01T00:00:00ZLEGRY, MartinCOLAS, FrédéricSAUDEMONT, ChristopheDIEULOT, Jean-YvesDUCARME, OlivierThis paper proposes a two-layer microgrid supervisor based on Model Predictive Control (MPC). The supervisor in the upper layer relies on an economical optimization that considers the cost of energy and the load and production forecasts to define the State of Charge (SoC) targets for each storage device on a timescale of 15 minutes. The lower layer displays a shorter timescale and aims to control the equipment to ensure the stability of the overall system and SoC tracking while satisfying the economic constraints specified by the upper layer. These two layers require an uniformization of the timestep and of the references in order to behave properly. The main contributions of this paper are the microgrid network modelling embedded in the optimization routine of the lower layer and a discretization for integrating upper-layer references.Event-triggered variable horizon supervisory predictive control of hybrid power plants
http://hdl.handle.net/10985/11885
Event-triggered variable horizon supervisory predictive control of hybrid power plants
DIEULOT, Jean-Yves; DAUPHIN-TANGUY, Geneviève; CHALAL, Lamine; COLAS, Frédéric
The supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s.The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15 %, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12 %.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/118852014-01-01T00:00:00ZDIEULOT, Jean-YvesDAUPHIN-TANGUY, GenevièveCHALAL, LamineCOLAS, FrédéricThe supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s.The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15 %, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12 %.