Robust optimization based energy dispatch in smart grids considering simultaneously multiple uncertainties: load demands and energy prices

Solving the problem of energy dispatch in a heterogeneous complex system is not a trivial task. The problem becomes even more complex considering uncertainties in demands and energy prices. This paper discusses the development of several Economic Model Predictive Control (EMPC) based strategies for solving an energy dispatch problem in a smart micro-grid. The smart grid components are described using control-oriented model approach. Considering uncertainty of load demands and energy prices simultaneously, and using an economic objective function, leads to a non-linear non-convex problem. The technique of using an affine dependent controller is used to convexify the problem. The goal of this research is the development of a controller based on EMPC strategies that tackles both endogenous and exogenous uncertainties, in order to minimize economic costs and guarantee service reliability of the system. The developed strategies have been applied to a hybrid system comprising some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices interconnected via a DC Bus. Additionally, a comparison between the standard EMPC, and its combination with MPC tracking in single-layer and two-layer approaches was also carried out based on the daily cost of energy production.Postprint (published version

Economic model predictive control for energy dispatch of a smart micro-grid system

© 2017 IEEE. Personal use of this ma terial is permitted. Permission from IEEE must be obtained for al l other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, f or resale or redistribution to se rvers or lists, or reuse of any copyrighted compone nt of this work in other worksThe problem of energy dispatch in heterogeneous complex systems such as smart grids cannot be efficiently solved using classical control or ad-hoc methods. This paper proposes the application of Economic Model Predictive Control (EMPC) for the management of a smart micro-grid system connected to an electrical power grid. The system comprises several subsystems, namely some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices (batteries). The batteries are charged with the energy from the PV panels, wind and hydroelectric generators, and they are discharged whenever the generators produce less energy than needed. The subsystems are interconnected via a DC Bus, from which load demands are satisfied. Assuming the load demand and the energy prices to be known, this study shows that EMPC is economically superior to other Model Predictive Control (MPC) based strategies (a standard tracking MPC, and their cascaded version in form of hierarchical two-layer approach).Postprint (author's final draft

Robust Economic Model Predictive Control of Smart Grids

Tesis doctoral presentada para lograr el título de Doctor por la Universidad Politécnica de Cataluña.--2021-09-13Esta tesis propone un diseño de Control de Modelo Predictivo Económico Robusto (REMPC) basado en un enfoque determinista para optimizar los costos económicos de producción y despacho de energía en redes eléctricas inteligentes. Se desarrollan estrategias robustas de control de modelo predictivo económico para sistemas lineales o linealizados que incluyen restricciones algebraicas.Las contribuciones de esta tesis son múltiples: • El desarrollo de estrategias de Control de Modelo Predictivo Económico Robusto para sistemas lineales o linealizados, incluidas las restricciones algebraicas. • El desarrollo de estrategias EMPC basadas en Robust Optimization para la gestión de redes eléctricas inteligentes que incorporan varios tipos de incertidumbres. • El uso de técnicas de optimización robusta para mejorar la robustez y confiabilidad del EMPC estándar. • Evaluar la eficiencia y aplicabilidad del EMPC estándar, así como sus variantes jerárquicas y de una sola capa para resolver los problemas de despacho de energía económicos de las redes eléctricas inteligentes. • El desarrollo y la aplicación de un novedoso sistema de ajuste de restricciones min-max EMPC robusto basado en la descomposición de las entradas y estados de control en componentes dependientes e independientes para abordar problemas inciertos de distribución de energía en redes eléctricas inteligentes. • El desarrollo y aplicación de un método EMPC robusto basado en aritmética de intervalos, y Zonotopas desde la perspectiva de enfoques basados en tubos para abordar problemas inciertos de despacho de energía en redes eléctricas inteligentes.• El desarrollo de una estrategia de control predictivo de modelo económico robusto para sistemas lineales con restricciones algebraicas.Las estrategias de EMPC robustas desarrolladas se clasifican en tres grupos: EMPC de una sola capa, EMPC de una sola capa con seguimiento de pseudo-referencia y EMPC jerárquico que consta de la capa superior de EMPC y la capa inferior de seguimiento de MPC. Se desarrolla un modelo matemático sistemático orientado al control de redes inteligentes basado en la teoría en flujo de redes. Además, también se propone una función objetivo genérica para EMPC para resolver el problema de despacho de energía de las redes inteligentes. El modelo matemático desarrollado se utiliza para desarrollar varias formulaciones de MPC económico que abarcan funciones de costos multiobjetivo que permite abordar incertidumbres como perturbaciones inesperadas aditivas y fluctuaciones de costos de energía con dinámicas que varían periódicamente en el tiempo. En primer lugar, las estrategias de MPC económicas desarrolladas se aplican a sistemas de micro-redes inteligentes sometidas a demandas de carga periódica nominal y precios de energía fijos, con el fin de evaluar la viabilidad recursiva y la estabilidad del sistema. A continuación, sobre la base de técnicas de optimización robustas, en particular el enfoque del peor caso (min-max), las formulaciones de MPC económicas desarrolladas se generalizan para abordar la incertidumbre de las demandas de carga, los precios de la energía y las fuentes de energía intermitentes. También se desarrolla un enfoque de control min-max de lazo cerrado que integra la técnica de dependencia afín en el enfoque min-max para eliminar la no linealidad y la no convexidad de los problemas de despacho de energía inciertos. Habiendo descubierto la complejidad computacional del min-max basado en la técnica de dependencia afín, se proponen dos métodos novedosos que permiten mejorar los métodos EMPC robustos basados en la descomposición de entradas de control y estados en componentes para abordar problemas inciertos de despacho de energía en redes eléctricas inteligentes

Robust optimization based energy dispatch in smart grids considering simultaneously multiple uncertainties: Load demands and energy prices

Trabajo presentado al 20th IFAC (International Federation of Automatic Control) World Congress, celebrado en Toulouse (Francia) del 9 al 14 de julio de 2017.Solving the problem of energy dispatch in a heterogeneous complex system is not a trivial task. The problem becomes even more complex considering uncertainties in demands and energy prices. This paper discusses the development of several Economic Model Predictive Control (EMPC) based strategies for solving an energy dispatch problem in a smart micro-grid. The smart grid components are described using control-oriented model approach. Considering uncertainty of load demands and energy prices simultaneously, and using an economic objective function, leads to a non-linear non-convex problem. The technique of using an affine dependent controller is used to convexify the problem. The goal of this research is the development of a controller based on EMPC strategies that tackles both endogenous and exogenous uncertainties, in order to minimize economic costs and guarantee service reliability of the system. The developed strategies have been applied to a hybrid system comprising some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices interconnected via a DC Bus. Additionally, a comparison between the standard EMPC, and its combination with MPC tracking in single-layer and two-layer approaches was also carried out based on the daily cost of energy production.This work was funded by the Ministerio de Economía, Industria y Competitividad (MEICOMP) of the Spanish Government and FEDER through the  project HARCRICS (ref. DPI2014-58104-R) and the grant IJCI-2014-20801.  Peer Reviewe

Optimal energy dispatch in a smart micro-grid system using economic model predictive control

The problem of energy dispatch in heterogeneous complex systems such as smart grids cannot be efficiently addressed using classical control or ad hoc methods. This article discusses the application of economic model predictive control to the management of a smart micro-grid system connected to an electrical power grid. The considered system is composed of several subsystems, namely, some photovoltaic panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices (batteries). The batteries are charged with the energy from the photovoltaic panels, wind and hydroelectric generators, and they are discharged whenever the generators produce less energy than needed. The subsystems are interconnected via a DC Bus, from which load demands are satisfied. Modeling smart grids components is based on the generalized flow-based networked systems paradigm, and assuming energy generators to be stable, load demands and energy prices are known. This study shows that economic model predictive control is economically superior to a two-layer hierarchical model predictive control.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECOP) and FEDER through the project HARCRICS (ref. DPI2014-58104- R) and through the grant IJCI-2014-20801 and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656

Economic model predictive control for energy dispatch of a smart microgrid system

Trabajo presentado a la 4th International Conference on Control, Decision and Information Technologies (CoDIT), celebrada en Barcelona (España) del 5 al 7 de abril de 2017.The problem of energy dispatch in heterogeneous complex systems such as smart grids cannot be efficiently solved using classical control or ad-hoc methods. This paper proposes the application of Economic Model Predictive Control (EMPC) for the management of a smart micro-grid system connected to an electrical power grid. The system comprises several subsystems, namely some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices (batteries). The batteries are charged with the energy from the PV panels, wind and hydroelectric generators, and they are discharged whenever the generators produce less energy than needed. The subsystems are interconnected via a DC Bus, from which load demands are satisfied. Assuming the load demand and the energy prices to be known, this study shows that EMPC is economically superior to other Model Predictive Control (MPC) based strategies (a standard tracking MPC, and their cascaded version in form of hierarchical two-layer approach).This work was supported by Spanish Government (MINISTERIO ECNONOMIA Y COMPETITIVIDAD) and FEDER under project DPI2014-58104-R (HARCRICS).Peer Reviewe

Robust economic model predictive control based on a zonotope and local feedback controller for energy dispatch in smart-grids considering demand uncertainty

Electrical smart grids are complex MIMO systems whose operation can be noticeably affected by the presence of uncertainties such as load demand uncertainty. In this paper, based on a restricted representation of the demand uncertainty, we propose a robust economic model predictive control method that guarantees an optimal energy dispatch in a smart micro-grid. Load demands are uncertain, but viewed as bounded. The proposed method first decomposes control inputs into dependent and independent components, and then tackles the effect of demand uncertainty by tightening the system constraints as the uncertainty propagates along the prediction horizon using interval arithmetic and local state feedback control law. The tightened constraints’ upper and lower limits are computed off-line. The proposed method guarantees stability through a periodic terminal state constraint. The method is faster and simpler compared to other approaches based on Closed-loop min–max techniques. The applicability of the proposed approach is demonstrated using a smart micro-grid that comprises a wind generator, some photovoltaic (PV) panels, a diesel generator, a hydroelectric generator and some storage devices linked via two DC-buses, from which load demands can be adequately satisfied.This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the projects DEOCS (ref. MINECO DPI2016-76493) and SCAV (ref. MINECO DPI2017-88403-R). This work has also been partially funded by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482). J. Blesa acknowledges the support from the Serra Húnter program

Robust optimization based energy dispatch in smart grids considering demand uncertainty

Trabajo presentado a la 13th European Workshop on Advanced Control and Diagnosis (ACD 2016).In this study we discuss the application of robust optimization to the problem of economic energy dispatch in smart grids. Robust optimization based MPC strategies for tackling uncertain load demands are developed. Unexpected additive disturbances are modelled by defining an affine dependence between the control inputs and the uncertain load demands. The developed strategies were applied to a hybrid power system connected to an electrical power grid. Furthermore, to demonstrate the superiority of the standard Economic MPC over the MPC tracking, a comparison (e.g average daily cost) between the standard MPC tracking, the standard Economic MPC, and the integration of both in one-layer and two-layer approaches was carried out. The goal of this research is to design a controller based on Economic MPC strategies, that tackles uncertainties, in order to minimise economic costs and guarantee service reliability of the system.This work was supported by Spanish Government (MINISTERIO ECNONOMIA Y COMPETITIVIDAD) and FEDER under project DPI2014-58104-R (HARCRICS).Peer Reviewe

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American Indian art media will be highlighted during the 26th annual Gallery of the Plains Indian Art Show this Labor Day weekend in Colony

Robust optimization based energy dispatch in smart grids considering simultaneously multiple uncertainties: load demands and energy prices

Solving the problem of energy dispatch in a heterogeneous complex system is not a trivial task. The problem becomes even more complex considering uncertainties in demands and energy prices. This paper discusses the development of several Economic Model Predictive Control (EMPC) based strategies for solving an energy dispatch problem in a smart micro-grid. The smart grid components are described using control-oriented model approach. Considering uncertainty of load demands and energy prices simultaneously, and using an economic objective function, leads to a non-linear non-convex problem. The technique of using an affine dependent controller is used to convexify the problem. The goal of this research is the development of a controller based on EMPC strategies that tackles both endogenous and exogenous uncertainties, in order to minimize economic costs and guarantee service reliability of the system. The developed strategies have been applied to a hybrid system comprising some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices interconnected via a DC Bus. Additionally, a comparison between the standard EMPC, and its combination with MPC tracking in single-layer and two-layer approaches was also carried out based on the daily cost of energy production