304 research outputs found

    Smart Microgrids: Overview and Outlook

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    The idea of changing our energy system from a hierarchical design into a set of nearly independent microgrids becomes feasible with the availability of small renewable energy generators. The smart microgrid concept comes with several challenges in research and engineering targeting load balancing, pricing, consumer integration and home automation. In this paper we first provide an overview on these challenges and present approaches that target the problems identified. While there exist promising algorithms for the particular field, we see a missing integration which specifically targets smart microgrids. Therefore, we propose an architecture that integrates the presented approaches and defines interfaces between the identified components such as generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid Worksho

    Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

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    Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

    Resilient Distributed Energy Management for Systems of Interconnected Microgrids

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    In this paper, distributed energy management of interconnected microgrids, which is stated as a dynamic economic dispatch problem, is studied. Since the distributed approach requires cooperation of all local controllers, when some of them do not comply with the distributed algorithm that is applied to the system, the performance of the system might be compromised. Specifically, it is considered that adversarial agents (microgrids with their controllers) might implement control inputs that are different than the ones obtained from the distributed algorithm. By performing such behavior, these agents might have better performance at the expense of deteriorating the performance of the regular agents. This paper proposes a methodology to deal with this type of adversarial agents such that we can still guarantee that the regular agents can still obtain feasible, though suboptimal, control inputs in the presence of adversarial behaviors. The methodology consists of two steps: (i) the robustification of the underlying optimization problem and (ii) the identification of adversarial agents, which uses hypothesis testing with Bayesian inference and requires to solve a local mixed-integer optimization problem. Furthermore, the proposed methodology also prevents the regular agents to be affected by the adversaries once the adversarial agents are identified. In addition, we also provide a sub-optimality certificate of the proposed methodology.Comment: 8 pages, Conference on Decision and Control (CDC) 201

    A Comprehensive Review of Control Strategies and Optimization Methods for Individual and Community Microgrids

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    © 2022 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.Community Microgrid offers effective energy harvesting from distributed energy resources and efficient energy consumption by employing an energy management system (EMS). Therefore, the collaborative microgrids are essentially required to apply an EMS, underlying an operative control strategy in order to provide an efficient system. An EMS is apt to optimize the operation of microgrids from several points of view. Optimal production planning, optimal demand-side management, fuel and emission constraints, the revenue of trading spinning and non-spinning reserve capacity can effectively be managed by EMS. Consequently, the importance of optimization is explicit in microgrid applications. In this paper, the most common control strategies in the microgrid community with potential pros and cons are analyzed. Moreover, a comprehensive review of single objective and multi-objective optimization methods is performed by considering the practical and technical constraints, uncertainty, and intermittency of renewable energies sources. The Pareto-optimal solution as the most popular multi-objective optimization approach is investigated for the advanced optimization algorithms. Eventually, feature selection and neural network-based clustering algorithms in order to analyze the Pareto-optimal set are introduced.This work was supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (MICINN)–Agencia Estatal de Investigación (AEI), and by the European Regional Development Funds (ERDF), a way of making Europe, under Grant PGC2018-098946-B-I00 funded by MCIN/AEI/10.13039/501100011033/.Peer ReviewedPostprint (published version

    Experiments on a real-time energy management system for islanded prosumer microgrids

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    This paper presents an experimental demonstration of a novel real-time Energy Management System (EMS) for inverter-based microgrids to achieve optimal economic operation using a simple dynamic algorithm without offline optimization process requirements. The dynamic algorithm solves the economic dispatch problem offering an adequate stability performance and an optimal power reference tracking under sudden load and generation changes. Convergence, optimality and frequency regulation properties of the real-time EMS are shown, and the effectiveness and compatibility with inner and primary controllers are validated in experiments, showing better performance on optimal power tracking and frequency regulation than conventional droop control power sharing techniques

    Optimized Hierarchical Control for an AC Microgrid Under Attack

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    Context: An inverter-based microgrid working in islanded mode can suffer cyber- attacks, these can be done against either the local controller or the communication links among the inverters. Secondary control is able to reject those attacks, however, a tertiary control action is necessary in order to stabilize the power flow among the microgrid. Method: Confidence factor technique allows to reject attacks in a microgrid acting directly over the secondary control, however, this technique omits other factor related to the power available. In this case, secondary control was complemented with a tertiary control that includes optimization criteria. Results: An inverter-based microgrid is simulated in Matlab for different scenarios and under cyberattack, this allows checking the correct response of the controller under attacks and the effective powersharing among inverters. Conclusions: The tertiary control allows stabilizing the active power of the system after the rejection of a cyber-attack by the secondary control. Each inverter supplies active power according to its máximum power rating without affecting the stability of the whole system

    Distributed transactive control in distribution systems with microgrids

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    Microgrids are considered as a cornerstone in the evolution to a smarter grid. However, this evolution brings some critical challenges for the control in a real-time implementation. We present two control algorithms to operate a power system with microgrids and other two to operate microgrids in order to reach the optimal social welfare. We consider three types of agents: photovoltaic generators, conventional generators and smart loads. These agents can be aggregated into a microgrid or interact directly in the power system depending on their power. To optimize the microgrids, we use two strategies. First one is based on projected consensus algorithm, where each agent iteratively optimizes its local utility function based on local information obtained from its neighbors and global information obtained through a distributed finite-time average algorithm. The second one is based on populations game theory; specifically we use a centralized replicator dynamics where a central agent iteratively optimizes the system status. To optimize the whole power system we use two strategies, first an asynchronous algorithm based on primal-dual optimization is proposed, where we consider that agents update the primal variables and a "virtual agent" updates the dual variables. Our last algorithm is a distributed transactive control algorithm based on populations games to dynamically manage the distributed generators and smart loads in the system to reach the optimum social welfare. Agents are considered non-cooperative, and they are individually incentive-driven. The proposed algorithm preserve stability while guarantee optimality conditions considering several constraints in the system on the real-time operation. We show numerical results of the proposed control strategies.Resumen: Las microrredes están consideradas como la piedra angular de la evolución hacia una red más inteligente. Sin embargo, esta evolución trae consigo algunos retos importantes para el control en la implementación en tiempo real. Presentamos dos algoritmos de control para operar un sistema de energía con microrredes y otros dos para operar microrredes con el fin de alcanzar el bienestar social óptimo. Consideramos dos tipos de agentes: generadores convencionales y cargas inteligentes. Estos agentes pueden ser agregados en una microred o interactuar directamente en el sistema de energía dependiendo de su potencia. Para optimizar las microrredes utilizamos dos estrategias, la primera se basa en un algoritmo de consenso proyectado, donde cada una de ellas optimiza iterativamente su función de utilidad local a partir de la información local obtenida de sus vecinos y la información global obtenida a través de un algoritmo distribuido de tiempo finito promedio. El segundo se basa en la teoría de juegos de poblaciones, específicamente usamos una dinámica de replicador centralizada donde un agente central optimiza iterativamente el estado del sistema. Para optimizar todo el sistema de potencia utilizamos dos estrategias, la primera es proponer un algoritmo asíncrono basado en la optimización prima-dual, donde consideramos que los agentes actualizan las variables primarias y un ”agente virtual” actualiza las variables duales. Nuestro último algoritmo es un algoritmo de control transaccional distribuido basado en juegos de poblaciones para gestionar dinámicamente los generadores distribuidos y las cargas inteligentes en el sistema para alcanzar el bienestar social óptimo. Se considera que los agentes no cooperan y se basan en incentivos individuales. El algoritmo propuesto preserva la estabilidad a la vez que garantiza condiciones óptimas considerando varias restricciones en el sistema sobre la operación en tiempo real. Se muestran los resultados numéricos de las estrategias de control propuestas.Maestrí

    Coordinated Active Power Dispatch for a Microgrid via Distributed Lambda Iteration

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