Fabio Andrade

Flexible System Integration and Advanced Hierarchical Control Architectures in the Microgrid Research Laboratory of Aalborg University

This paper presents the system integration and hierarchical control implementation in an inverter-based Microgrid Research Laboratory (MGRL) at Aalborg University, Denmark. MGRL aims to provide a flexible experimental platform for comprehensive studies of microgrids. The structure of the laboratory, including the facilities, configurations, and communication network, is first introduced. The complete control system is based on a generic hierarchical control scheme including primary, secondary, and tertiary control. Primary control loops are developed and implemented in digital control platform, while system supervision, advanced secondary, and tertiary management are realized in a microgrid central controller. The software and hardware schemes are described. Several example case studies are introduced and performed to achieve power quality regulation, energy management, and flywheel energy storage system control. Experimental results are presented to show the performance of the whole system.

Multiagent based distributed control for state-of-charge balance of distributed energy storage in DC microgrids

In this paper, a distributed multiagent based algorithm is proposed to achieve SoC balance for DES in the DC microgrid by means of voltage scheduling. Reference voltage given is adjusted instead of droop gain. Dynamic average consensus algorithm is explored in each agent to get the required information for scheduling voltage autonomously. State-space analysis on a single energy storage unit and simulation verification shows that the proposed method has two advantages. Firstly, modifying the reference voltage given has less impact on system stability compared to gain scheduling. Secondly, by adopting multiagent methodology, the proposed distributed control has less communication dependence and more reliable during communication topology changes.

Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side

This work presents an electricity consumption-forecasting framework configured automatically and based on an Adaptative Neural Network Inference System (ANFIS). This framework is aimed to be implemented in industrial plants, such as automotive factories, with the objective of giving support to an Intelligent Energy Management System (IEMS). The forecasting purpose is to support the decision-making (i.e. scheduling workdays, on-off production lines, shift power loads to avoid load peaks, etc.) to optimize and improve economical, environmental and electrical key performance indicators. The base structure algorithm, the ANFIS algorithm, was configured by means of a Multi Objective Genetic Algorithm (MOGA), with the aim of getting an automatic-configuration system modelling. This system was implemented in an independent section of an automotive factory, which was selected for the high randomness of its main loads. The time resolution for forecasting was the quarter hour. Under these challenging conditions, the autonomous configuration, system learning and prognosis were tested with success.

A Combined Methodology of Adaptive Neuro- Fuzzy Inference System and Genetic Algorithm for Short-term Energy Forecasting

1 Abstract—This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short- term load forecasting for the different modeled consumption processes. I. INTRODUCTION With the continuously growing demand of the energy, it is getting more important to develop systems capable to optimize the energy use. Energy management is nowadays a subject of great importance because of the facing problems of the global warming and oil shortage. In the industrial sector, the energy management systems have focused so far on the monitoring and off-line management of energy as it is outlined in (1). The typical energy management systems are based on the collection of information about the plants operation using energy meters. Those systems help to monitor the operation of the installations, collect data and generate reports to identify the possible critical points of the consumptions. However, intelligent systems can improve the operation of the energy management systems, offering further functionalities such as predictive maintenance, energy optimization, fault diagnosis and energy forecast. Different approaches for energy savings and energy prediction have been studied over the past years. Evolutionary algorithms such as Particle Swarms (PSO), Gravitational Search Algorithms (GSA) and Simulated Annealing (SA) have successfully implemented in optimization and control applications (2). An implementation of a model based on Artificial Neural Network (ANN) was presented in (3-4) and (5) in order to estimate the load forecast in an electrical distribution system while in (6) a comparison between ANN and Fuzzy Logic is made on applications of short-term and medium-term load forecasting. An application of Neuronal Networks (NN) is presented in (7) in which it faces Multi-Input-Multi-Output (MIMO) applications with single input and output (SISO) net works. An ANFIS implementation for energy prediction of regional electrical loads in Taiwan was presented in (8), comparing its performance with other similar techniques (i.e., regression models, ANN-based models, Genetic algorithms and hybrid ellipsoidal fuzzy systems). A cellular multi-grid genetic algorithm is presented in (9) to face balancing problems in assembling lines. Techniques based on cultural algorithms are presented in (10) to resolve complex mechanical design optimization problems in an efficient and effective method. Thi s document presents the modeling and prediction algorithms that were developed in order to generate customizable mathematical models for different consumptions, as a way to improve the operation of a general energy monitoring system. The paper is organized as follows: section II describes an overview of the algorithm that has been used for the models training and the energy forecast while section III outlines a brief explanation about the Genetic Algorithms operation. In section IV, the proposed methodology is presented explaining the combination of the two algorithms in order to develop a system capable to train the consumption models autonomously. In Section V, the implementation of the system in the pilot plant is explained, presenting the different results that have been obtained during the test and the evaluation of the system. Finally, section VI summarizes the paper and discusses the different conclusions.

Distributed consensus-based control of multiple DC-microgrids clusters

This paper presents consensus-based distributed control strategies for voltage regulation and power flow control of dc microgrid (MG) clusters. In the proposed strategy, primary level of control is used to regulate the common bus voltage inside each MG locally. An SOC-based adaptive droop method is introduced for this level which determines droop coefficient automatically, thus equalizing SOC of batteries inside each MG. In the secondary level, a distributed consensus based voltage control strategy is proposed to eliminate the average voltage deviation while guaranteeing proper regulation of power flow among the MGs. Using the consensus protocol, the global information can be accurately shared in a distributed way. This allows the power flow control to be achieved at the same time as it can be accomplished only at the cost of having the voltage differences inside the system. Similarly, a consensus-based cooperative algorithm is employed at this stage to define appropriate reference for power flow between MGs according to their local SOCs. The effectiveness of proposed control scheme is verified through detailed hardware-in-the-loop (HIL) simulations.

New Model of a Converter-Based Generator Using Electrostatic Synchronous Machine Concept

In this paper, a new method for modeling converter-based power generators in ac-distributed systems is proposed. It is based on the concept of electrostatic synchronous machines. With this new concept, it is possible to establish a simple relationship between the dc and ac side and to study stability in both the small and large signals of the microgrid by considering a dc-link dynamic and high variation in the power supplied. Also, for the purpose of illustration, a mathematical and electrical simulation is presented, based on MATLAB and PSCAD software. Finally, an experimental test is performed in order to validate the new model.

Microgrid central controller development and hierarchical control implementation in the intelligent microgrid lab of Aalborg University

This paper presents the development of a microgrid central controller in an inverter-based intelligent microgrid (iMG) lab in Aalborg University, Denmark. The iMG lab aims to provide a flexible experimental platform for comprehensive studies of microgrids. The complete control system applied in this lab is based on the hierarchical control scheme for microgrids and includes primary, secondary and tertiary control. The structure of the lab, including the lab facilities, configurations and communication network, is first introduced. Primary control loops are developed in MATLAB/Simulink and compiled to dSPACEs for local control purposes. In order to realize system supervision and proper secondary and tertiary management, a LabVIEW-based microgrid central controller is also developed. The software and hardware schemes are described. An example case is introduced and tested in the iMG lab for voltage/frequency restoration and voltage unbalance compensation. Experimental results are presented to show the performance of the whole system.

Economic power dispatch of distributed generators in a grid-connected microgrid

Grid-connected microgrids with storage systems are reliable configurations for critical loads which can not tolerate interruptions of energy supply. In such cases, some of the energy resources should be scheduled in order to coordinate optimally the power generation according to a defined objective function. This paper defines a generationside power scheduling and economic dispatch of a grid-connected microgrid that supplies a fixed load and then, the scheduling is enhanced by including penalties in order to increase the use of the renewable energy sources and guarantee a high state of charge in the storage system for the next day. Linear models are proposed for the scheduling which are implemented in GAMS. The microgrid model is obtained deploying MATLAB/Simulink toolbox and then downloaded into dSPACE 1006 platform based on real-time simulation to test the economic dispatch. A compromise between cost and use of renewable energy is achieved.

Study of large-signal stability of an inverter-based generator using a Lyapunov function

This document analyses the large-signal stability for an inverter-based generator such as photovoltaic and wind power sources. The objective of this study is to determine the stability region taking into account the electrical and control signal of the generator. The generator uses the concept of the electrostatic machine for the model of the generator. Finally, the applied procedure to find the Lyapunovs function is the Popov method, which not only permits to generate a valid function but also to determine the stability region of the system.

Modeling and studying of power flow in a parking lot with plug-in vehicles and the impact in the public utility

For a massive introduction of electric vehicles to the market is required to have a reliable infrastructure. The Infrastructure needs includes enough charging stations around cities and electric parking lots. Currently the grid is able to support the electric consumption and hardly support recharging the fleet of electric vehicles. Likewise, the public utility has to keep high power quality without blackout, voltage dips, harmonics, etc. This paper studies the electric infrastructure of an electric parking lot. It is considered a high penetration of charging stations. The analysis has been performed by means of vehicle battery and charging station modeling in MATLAB Simulink. The paper analyzes the power flow into the distributed transformer using three different scenarios.