Toward optimal operation of multienergy home-microgrids for power balancing in distribution networks: a model predictive control approach

The energy policy objectives of the German government regarding renewable energy sources and energy efficiency will lead to a significantly increase in the share of photovoltaics, storage systems, CHP plants, and heat pumps, especially at the distribution grid level. In the future, inside a household, such systems must be coordinated in such a way that they can respond to variable network conditions as a single flexible unit. This dissertation defines home-microgrid as a residential building with integrated distributed energy resources, and follows a bottom-up approach, based on the cellular approach, which aims at improving local balancing in low-voltage grids by using the flexibilities of home-microgrids. For this purpose, the dissertation develops optimization-based strategies for the coordination of multienergy home-microgrids, focusing on the use of model predictive control. The main core of the work is the formulation of the underlying optimization problems and the investigation of coordination strategies for interconnected home-microgrids. In this context, the work presents the use of the dual decomposition and the alternating direction method of multipliers for hierarchical-distributed coordination strategies. Finally, this dissertation introduces a framework for the co-simulation of electrical networks with penetration of multienergy home-microgrids

Grid-connected Microgrids to Support Renewable Energy Sources Penetration

Abstract Distributed generation systems and microgrids are instrumental for a greater penetration of renewables to achieve a substantial reduction on carbon emissions. However, microgrids performances and reliability strongly depend on the continuous interaction between power generation, storage and load requirements, highlighting the importance in developing a proper energy management strategy and the relative control system. In this work a Model predictive Control (MPC) strategy, based on a Mixed Linear Integer Programming framework, has been applied to a residential microgrid case. Theoretical results obtained confirm that grid connected microgrids have potential capabilities in grid balancing allowing for a larger penetration of fluctuating renewable energy sources and thus producing economic benefits for both end-user and grid operators. A microgrid test bench to reproduce previous microgrid model is also presented in the paper. The experimental setup has been used to validate results obtained from simulation. Results obtained confirm the potential of this solution and its real applicability

Model predictive control of interconnected microgrids and electric vehicles

[ES] La microrred como elemento agregador de fuentes de generación, cargas y sistemas de almacenamiento de energía aparece como tecnología clave para dotar a los sistemas eléctricos de suficiente flexibilidad para una transición energética basada en fuentes renovables. Sin embargo, el problema de control para la gestión de energía se vuelve complejo cuando se incrementa el número de sistemas conectados a una misma microrred. De igual forma, se requiere flexibilidad para integrar a los vehículos eléctricos. La interacción entre las distintas microrredes y los vehículos hacen necesarias herramientas avanzadas de control para resolver el problema de optimización. El objeto del presente trabajo es presentar distintas herramientas de control predictivo basado en el modelo (Model Predictive Control, MPC) para resolver el problema de control asociado a este tipo de sistemas. En concreto, se abordan dos problemas: la conexión de vehículos eléctricos a la microrred y la interconexión de varias microrredes. Para el primer caso se analizan dos escenarios, según que el intercambio de energía sea uni o bidireccional y se presenta la forma de optimizar la operación usando MPC. En el segundo caso se aborda el problema usando técnicas de control distribuido.[EN] Microgrids, as aggregators of sources, loads and energy storage systems, appear as key technology to provide the required flexibility to electric power systems to carry out an energy transition based on renewable sources. Nevertheless, the control problem becomes complex when the number of connected components to the same microgrid increases. Also, the system requires flexibility to integrate electric vehicles. The complexity given by the associated control problem to optimize the energy exchange between microgrids and the electric vehicles makes necessary the development of advanced control tools. In this work, dierent Model Predictive Control (MPC) strategies are introduced in order to face the challenge of the control problem formulation of this kind of systems. Specifically, two problems are addressed: the connection of electric vehicles to the microgrid and the interconnection of several microgrids. For the first case, two scenarios are analyzed, depending on whether the energy exchange is uni or bidirectional, the way to optimize the operation using MPC is presented and examples of use are shown. For the second case, the problem isaddressed using distributed control techniques.Este trabajo ha sido realizado parcialmente gracias al apoyo del Ministerio de Econom´ıa, Industria y Competitividad de Espana mediante el proyecto CONFIGURA (DPI2016-78338-R) y por la Comision Europea, en el proyecto AGERAR (0076- ´ AGERAR-6-E), dentro del programa Interreg Spain-Portugal (POCTEP).Bordons, C.; Garcia-Torres, F.; Ridao, M. (2020). 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Distributed MPC for coordinated energy efficiency utilization in microgrid systems

To improve the renewable energy utilization of distributed microgrid systems, this paper presents an optimal distributed model predictive control strategy to coordinate energy management among microgrid systems. In particular, through information exchange among systems, each microgrid in the network, which includes renewable generation, storage systems, and some controllable loads, can maintain its own systemwide supply and demand balance. With our mechanism, the closed-loop stability of the distributed microgrid systems can be guaranteed. In addition, we provide evaluation criteria of renewable energy utilization to validate our proposed method. Simulations show that the supply demand balance in each microgrid is achieved while, at the same time, the system operation cost is reduced, which demonstrates the effectiveness and efficiency of our proposed policy.Accepted manuscrip

Optimal Economic Schedule for a Network of Microgrids With Hybrid Energy Storage System Using Distributed Model Predictive Control

Artículo Open Access en el sitio web el editor. Pago por publicar en abierto.In this paper, an optimal procedure for the economic schedule of a network of interconnected microgrids with hybrid energy storage system is carried out through a control algorithm based on distributed model predictive control (DMPC). The algorithm is specifically designed according to the criterion of improving the cost function of each microgrid acting as a single system through the network mode operation. The algorithm allows maximum economical benefit of the microgrids, minimizing the degradation causes of each storage system, and fulfilling the different system constraints. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled with the framework of mixed logic dynamic. The DMPC problem is solved with the use of mixed integer linear programming using a piecewise formulation, in order to linearize a mixed integer quadratic programming problem.Ministerio de Economía, Industria y Competitivadad DPI2016-78338-RComisión Europea 0076-AGERAR-6-

Decentralized energy management of power networks with distributed generation using periodical self-sufficient repartitioning approach

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a decentralized model predictive control (MPC) method as the energy management strategy for a large-scale electrical power network with distributed generation and storage units. The main idea of the method is to periodically repartition the electrical power network into a group of self-sufficient interconnected microgrids. In this regard, a distributed graph-based partitioning algorithm is proposed. Having a group of self-sufficient microgrids allows the decomposition of the centralized dynamic economic dispatch problem into local economic dispatch problems for the microgrids. In the overall scheme, each microgrid must cooperate with its neighbors to perform repartitioning periodically and solve a decentralized MPC-based optimization problem at each time instant. In comparison to the approaches based on distributed optimization, the proposed scheme requires less intensive communication since the microgrids do not need to communicate at each time instant, at the cost of suboptimality of the solutions. The performance of the proposed scheme is shown by means of numerical simulations with a well-known benchmark case. © 2019 American Automatic Control Council.Peer ReviewedPostprint (author's final draft

A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409

Dynamic Reactive Power Control of Isolated Power Systems

This dissertation presents dynamic reactive power control of isolated power systems. Isolated systems include MicroGrids in islanded mode, shipboard power systems operating offshore, or any other power system operating in islanded mode intentionally or due to a fault. Isolated power systems experience fast transients due to lack of an infinite bus capable of dictating the voltage and frequency reference. This dissertation only focuses on reactive control of islanded MicroGrids and AC/DC shipboard power systems. The problem is tackled using a Model Predictive Control (MPC) method, which uses a simplified model of the system to predict the voltage behavior of the system in future. The MPC method minimizes the voltage deviation of the predicted bus voltage; therefore, it is inherently robust and stable. In other words, this method can easily predict the behavior of the system and take necessary control actions to avoid instability. Further, this method is capable of reaching a smooth voltage profile and rejecting possible disturbances in the system. The studied MicroGrids in this dissertation integrate intermittent distributed energy resources such as wind and solar generators. These non-dispatchable sources add to the uncertainty of the system and make voltage and reactive control more challenging. The model predictive controller uses the capability of these sources and coordinates them dynamically to achieve the voltage goals of the controller. The MPC controller is implemented online in a closed control loop, which means it is self-correcting with the feedback it receives from the system