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2020 | Book

Model Predictive Control of Microgrids

Authors: Prof. Carlos Bordons, Dr. Félix Garcia-Torres, Dr. Miguel A. Ridao

Publisher: Springer International Publishing

Book Series : Advances in Industrial Control


About this book

The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids.
The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. The authors also provide a modular simulator to be run in MATLAB/Simulink®, for readers to create their own microgrids using the blocks supplied, in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids.
Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management.
Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Table of Contents

Chapter 1. Microgrid Control Issues
The evolution from the existing energy system based on fossil fuels to a new scheme with high penetration of renewable energy and electric transport systems introduces new challenges in architecture, control, and management of the electrical grid. This situation demands new schemes for the future electricity grids, where distributed generation, demand response, and energy storage systems may be easily integrated. The novel paradigm of microgrid that intends to provide a solution to these issues is presented in this chapter. The new control challenges that appear in microgrids are introduced, proposing Model Predictive Control (MPC) as a powerful tool to face them. This chapter presents an overview of the main topics on automatic operation and control of microgrids that will be tackled along the book, showing the most appropriate MPC technique to deal with them.
Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao
Chapter 2. Model Predictive Control Fundamentals
This book is focused on Model Predictive Control (MPC) techniques, which will be used to solve different control issues in microgrids. Although there are many techniques that can be used for the control of microgrids, MPC provides a general framework to solve most of the issues using some common ideas in an integrated way. MPC replaces offline determination of a control law by online solution of an optimal control problem that provides the current control action. This chapter presents the fundamentals of this technique. The main ideas and formulations are described here as well as some of the most representative techniques. MPC based on state-space models is detailed, since it will be extensively used along the book. Other techniques such as finite state MPC and MPC for hybrid systems are described too. The chapter also tackles two important issues for the application of MPC in microgrids: disturbances and constraints. Based on the methods presented in this chapter, the most relevant topics related to the control of microgrids will be addressed along the rest of the book.
Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao
Chapter 3. Dynamical Models of the Microgrid Components
This chapter describes the main components of a microgrid, focusing on their dynamical behavior, a key concept in control engineering and particularly in MPC. Mathematical models of renewable generation devices (photovoltaic panels or wind turbines), and also energy storage systems with high penetration in microgrids (batteries, ultracapacitors, and hydrogen-based systems) are presented in detail in the chapter. These models are the base for the development of the software included in the companion toolbox \(Sim\mu grid\). Brief descriptions of alternative storage systems, such as flywheels or compressed air, are also included. Operational issues in energy storage systems to avoid non-adequate use, prevent the degradation of the devices, and improve their performance, reliability, and lifespan are also addressed. These concepts are of considerable importance for the design of MPC controllers, and they will be widely used throughout the book.
Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao
Chapter 4. Basic Energy Management Systems in Microgrids
This chapter addresses the basic Energy Management System (EMS) for microgrids, which aims to balance generation and demand using storage or the external grid, and corresponds to secondary control, as presented in Chap. 1. This is also known as power sharing or power dispatch, whose purpose is to drive the dispatchable units (Distributed Energy Resources, DERs) to supply local loads in an appropriate way. A basic MPC algorithm is developed in this chapter, which can solve the problem using only continuous variables. In order to illustrate the concept and methodology, the design and implementation of the basic EMS on a pilot-scale microgrid is presented. Simulated and experimental tests are performed under realistic scenarios, showing how MPC can be customized to a particular microgrid. Other issues such as schedule, consideration of degradation and maintenance costs, integration of energy tariffs, or connection to the electrical market will be addressed in Chap. 5.
Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao
Chapter 5. Energy Management with Economic and Operation Criteria
In this chapter, the basic Energy Management System (EMS) presented in the previous chapter is extended to consider operational and degradation costs. The chapter introduces a formulation to integrate the terms related to operational and degradation issues associated to hybrid storage systems in an MPC-based EMS. The participation of microgrids in the different stages of the electrical markets is described. First, a two-step MPC-based algorithm corresponding to the tertiary and secondary control level of the microgrid it is developed. Later on, based on the stages of the electrical markets, MPC controllers are proposed to follow the operation rules in the day-ahead, intraday, and real-time markets interacting with both the market operator and the system operator. The proposed formulation optimizes the final cost of the energy consumption in the microgrid by means of improving its participation in the different stages of the market but also minimizing the degradation issues, as well as the operational costs of hybrid energy storage systems. The formulation requires to deal with both logic and continuous variables. For this reason, the different operation modes in the microgrid are modeled with the Mixed Logic Dynamical (MLD) framework and the MPC controller is formulated as a Mixed-Integer Quadratic Programming (MIQP) problem. Different results based on simulation and experiments are exposed.
Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao
Chapter 6. Demand-Side Management and Electric Vehicle Integration
This chapter extends the energy management systems developed in previous chapters to the case of controllable loads and electric vehicles. EVs are loads for the microgrid but, due to their storage capability, they can also supply energy to the microgrid when needed and thus they can be considered as prosumers. An appropriate management of loads and EV charging can help improve the operation of the microgrid. The concept of Demand-Side Management (DSM) is introduced, and the main Demand Response (DR) techniques are described and illustrated. The integration of EVs in the microgrid is approached, customizing the MPC techniques to this situation and contemplating the notion of Vehicle-to-Grid (V2G). The chapter presents some simulations to illustrate load shifting and curtailment and several experiments performed in a pilot-scale microgrid to demonstrate V2G capabilities.
Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao
Chapter 7. Uncertainties in Microgrids
Uncertainties in the supply or load is an important issue that must be tackled in Energy Management Systems (EMS) of a microgrid. Renewable generation (solar or wind) and consumer loads typically are not controllable but a forecast of their time evolution is of great interest, especially if control techniques as MPC are applied, where the prediction in a future time horizon plays a crucial role. Prediction of renewable production is an active field of research, based on weather forecast and historical data, analyzed by a range of statistical methods or alternatives as neural networks, machine learning, etc. Nevertheless, uncertainty in these values is unavoidable, and the approach in this chapter is the explicit characterization and introduction in the control problem of those uncertainties, that is, the deterministic decision-making of conventional controllers is replaced by a stochastic process. MPC is essentially a deterministic approach, and can be troublesome in systems where uncertainty is an important topic. This chapter is devoted to the application of Stochastic MPC (SMPC) to the EMS problem. SMPC is based on an explicit statistical representation of the uncertainties, i.e., probabilistic distribution, and including it in the optimization problem formulation. Also, constraints can be defined stochastically and some violations are allowed with a determined probability criteria. Next sections describe some of these stochastic MPC algorithms and its application to a laboratory-scale microgrid.
Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao
Chapter 8. Interconnection of Microgrids
This chapter is devoted to the energy management problem of several interconnected microgrids. EMS of a network of microgrids must determine the power flows inside each microgrid and with the main grid (as in Chap. 4), but also the energy interchange among them. This is an extension of a single microgrid EMS and MPC is an alternative to solve it. The control of these systems presents mainly two problems to be solved by a global controller: first, different microgrids typically are managed by different agents making difficult or even impossible to use a unique controller for the whole system and the second problem is the computational burden due to the dimension of the system when a high number of microgrids are considered. In this situation, Distributed Model Predictive Control (DMPC) is the technique used in this chapter to reduce the complexity. This chapter describes several methods to solve the EMS using MPC in a distributed fashion. Alternatives are tested and compared in a system with three connected microgrids.
Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao
Chapter 9. Microgrids Power Quality Enhancement
Power quality is one of the major issues in electrical grids due to the extended use of power electronics, renewable energy generation, and nonlinear electronic loads. Usually, the elements of the microgrid are interfaced with power converters which are finally responsible for the power quality levels in the microgrid. For this reason, control of power converters becomes a relevant topic in microgrids. This chapter introduces the main aspects of power quality in microgrids and the basic principles of operation of MPC applied to power converters. The two main MPC methods for power converters, Continuous Control Set MPC (CCS-MPC), and Finite Control Set MPC (FCS-MPC) are described and their application to a Voltage Source Inverter (VSI) is shown in order to demonstrate their capabilities. Finally, an MPC-based algorithm to enhance the power quality in microgrids in presence of nonlinear and unbalanced loads is introduced. It works in both modes, islanded and grid-connected, providing the capability of fast transition between modes when required.
Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao
Model Predictive Control of Microgrids
Prof. Carlos Bordons
Dr. Félix Garcia-Torres
Dr. Miguel A. Ridao
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