Microgrid energy management system for smart home using multi-agent system

This paper proposes a multi-agent system for energy management in a microgrid for smart home applications, the microgrid comprises a photovoltaic source, battery energy storage, electrical loads, and an energy management system (EMS) based on smart agents. The microgrid can be connected to the grid or operating in island mode. All distributed sources are implemented using MATLAB/Simulink to simulate a dynamic model of each electrical component. The agent proposed can interact with each other to find the best strategy for energy management using the java agent development framework (JADE) simulator. Furthermore, the proposed agent framework is also validated through a different case study, the efficiency of the proposed approach to schedule local resources and energy management for microgrid is analyzed. The simulation results verify the efficacy of the proposed approach using Simulink/JADE co-simulation

Exploring a Direct Policy Search Framework for Multiobjective Optimization of a Microgrid Energy Management System

With an increasing focus on integration of distributed energy resources, it is likely that microgrids will proliferate globally. Microgrid systems will be expected to achieve multiple stakeholder objectives, motivating the study of microgrid operations using a multiobjective framework. A multiobjective perspective has the potential balance the trade-offs implicit to efficient use of available resources. To address this challenge, this paper proposes a simulation based parametric approach for multiobjective optimization for microgrid energy management. The methodology generates a Pareto-approximate set of control policies, to provide a microgrid controller with diverse alternative strategies for utilizing resources to balance competing objectives. The policies also help to illustrate the complex relationships between the objectives, and the consequences of compromises across performance. The methodology is implemented on a test microgrid and the potential benefits are demonstrated with a set of illustrative case studies

Cooperative energy management for a cluster of households prosumers

© 2016 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 worksThe increment of electrical and electronic appliances for improving the lifestyle of residential consumers had led to a larger demand of energy. In order to supply their energy requirements, the consumers have changed the paradigm by integrating renewable energy sources to their power grid. Therefore, consumers become prosumers in which they internally generate and consume energy looking for an autonomous operation. This paper proposes an energy management system for coordinating the operation of distributed household prosumers. It was found that better performance is achieved when cooperative operation with other prosumers in a neighborhood environment is achieved. Simulation and experimental results validate the proposed strategy by comparing the performance of islanded prosumers with the operation in cooperative modePeer ReviewedPostprint (author's final draft

MICROGRID ENERGY MANAGEMENT SYSTEM WITH ANCILLARY SERVICES TO THE GRID

We propose a microgrid energy management system (µGEMS) that optimally plans the operations, and control the distributed energy resources (DERs) while committing, holding, dispatching, and maintaining different ancillary services for the grid in a reliable and economical manner. Reserve, regulation, and voltage support services can be supplied simultaneously via the µGEMS. The proposed µGEMS may be used to commit the services a Day-Ahead (DA) in advance to dispatch, or in Real-Time (RT) (i.e., DA and RT Commitments). Commitment rules that the µGEMS can consider include minimum acceptable capacities, required time to respond, and required time to maintain. We model the µG as an AC network using the current formulation to obtain a model that is mostly linear. Bus voltage, circuit loading, and point of common coupling (PCC) power factor limits are enforced during the commitment, the holding, the dispatching, and the maintaining stages of services. The proposed µGEMS consists of a collection of interacting optimization problems each with a certain task, planning horizon, and frequency of solve. The optimization problems are generally mixed-integer quadratically constrained programming (MIQCP) problems. A solution methodology for the optimization problems is proposed based on successive linear programming (SLP) which promises efficient handling of discrete variables.Ph.D

Functional Analysis of the Microgrid Concept Applied to Case Studies of the Sundom Smart Grid

The operation of microgrids is a complex task because it involves several stakeholders and controlling a large number of different active and intelligent resources or devices. Management functions, such as frequency control or islanding, are defined in the microgrid concept, but depending on the application, some functions may not be needed. In order to analyze the required functions for network operation and visualize the interactions between the actors operating a particular microgrid, a comprehensive use case analysis is needed. This paper presents the use case modelling method applied for microgrid management from an abstract or concept level to a more practical level. By utilizing case studies, the potential entities can be detected where the development or improvement of practical solutions is necessary. The use case analysis has been conducted from top-down until test use cases by real-time simulation models. Test use cases are applied to a real distribution network model, Sundom Smart Grid, with measurement data and newly developed controllers.. The functional analysis provides valuable results when studying several microgrid functions operating in parallel and affecting each other. For example, as shown in this paper, ancillary services provided by an active customer may mean that both the active power and reactive power from customer premises are controlled at the same time by different stakeholders.© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Optimal Decentralized Energy Management of Electrical and Thermal Distributed Energy Resources and Loads in Microgrids Using Reinforcement Learning

In this paper, a decentralized energy management system is presented for intelligent microgrids with the presence of distributed resources using reinforcement learning. Due to the unpredictable nature of renewable energy resources, the variability of load consumption, and the nonlinear model of batteries, the design of a microgrid energy management system is associated with many challenges. In addition, centralized control structures in large-scale systems increase computational volume and complexity in control algorithms. In this paper, a fully decentralized multi-agent structure for a microgrid energy management system is proposed and the Markov decision process is used to model the stochastic behavior of agents in the microgrid. Electrical and thermal distributed resources, batteries, and consumers are considered intelligent and independent agents. They have the learning ability to explore and exploit the environment in a fully decentralized manner and achieve their optimal policies. The proposed method for hourly microgrid management is model-independent and based on learning. The method maximizes the profits of all manufacturers, minimizes consumer costs, and reduces the dependence of the microgrid on the maingrid. Finally, using real data from renewable energy sources and consumers, the accuracy of the proposed method in the Iranian electricity market is simulated and verified

Chalmers Campus as a Testbed for Intelligent Grids and Local Energy Systems

This paper presents an overview of a testbed for intelligent distribution grids, local energy systems, and energy flexible buildings, which is being developed at the campus of Chalmers University of Technology in Gothenburg, Sweden. It describes the test sites, the functionalities, and the planned demonstration activities within the scope of on-going research projects. The proposed demonstrations include a local energy market platform, energy management solutions for microgrids and smart buildings, as well as voltage control in distribution grids. The paper aims to show how the physical energy supply systems of the university are being adapted to integrate the communication and control set-ups that provide the technical requirements for smart grid interoperability. As an example, the on-site implementation of remote battery control is presented, where initial results show the feasibility and potential benefits of the external control. Finally, challenges and lessons learned during the development of the testbed are highlighted