Control of an isolated microgrid using hierarchical economic model predictive control (original) (raw)
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Model Predictive Control Strategies in Microgrids: A Concise Revisit
IEEE Access
The world is rapidly integrating renewable energy resources into the existing grid systems. However, the unpredictable nature of renewables and uncertain load profiles cause issues such as poor power quality, lower system reliability, complex power management, battery degradation, high operating costs, and lower efficiency. Microgrids can help smart grid technology overcome several problems associated with renewable energy integration. Distant locations can obtain electricity without building extensive transmission infrastructure, cutting development costs, or transmission losses. The intermittent nature of renewable energy sources contributes to microgrid problems such as poor power quality, decreased reliability, and high operating costs. Model predictive control (MPC) is an effective method to address challenging industrial and scientific issues. Advancements in MPC that accept different system constraints have solved multiple concerns in uncertain microgrid systems. MPC applied to three hierarchal control layers in a microgrid resolves the problems of power quality, power sharing, energy management, and economic optimization. This study demonstrates that MPC microgrid control is suitable for low-cost operation, improved management, and reliable control. The shortcomings of recent model predictive control techniques for microgrids are reviewed, and future research directions for MPC microgrids are identified. INDEX TERMS Microgrids, renewable energy resources, model predictive control, power quality enhancement, energy management system, hybrid energy storage system, demand side management, demand response, distributed systems.
Energy efficient microgrid management using Model Predictive Control
IEEE Conference on Decision and Control and European Control Conference, 2011
Microgrids are subsystems of the distribution grid which comprises small generation capacities, storage devices and controllable loads, operating as a single controllable system that can operate either connected or isolated from the utility grid. In this paper we present a preliminary study on applying a Model Predictive Control (MPC) approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using Mixed-Integer Linear Programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a typical microgrid is employed to assess the performance of the on-line optimization-based control strategy: simulation results show the feasibility and the effectiveness of the proposed approach.
Energies
This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that ...
Predictive control of a renewable energy microgrid with operational cost optimization
Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE, 2013
A proposal for optimal control of a microgrid powered by solar energy with mixed temporary storage based on a battery bank and hydrogen is presented and evaluated here by means of simulations. The proposal is based on a Predictive Controller that optimizes the operational costs by taking into account the value of the energy generated, the cost of locally storing energy, the aging of the components and the operational constraints. The Constrained Mixed-Integer Predictive Control problem obtained is then solved using a formulation based on the duration of the states, which simplifies the numerical solution. Some simulation results are presented to show the validity of the proposed approach.
Energies
Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.
Predictive Control for Microgrid Applications: A Review Study
Energies
Microgrids need control and management at different levels to allow the inclusion of renewable energy sources. In this paper, a comprehensive literature review is presented to analyse the latest trends in research and development referring to the applications of predictive control in microgrids. As a result of this review, it was found that the application of predictive control techniques on microgrids is performed for the three control levels and with adaptations of the models in order to include uncertainties to improve their performance and dynamics response. In addition, to ensure system stability, but also, at higher control levels, coordinated operation among the microgrid’s components and synchronised and optimised operation with utility grids and electric power markets. Predictive control appears as a very promising control scheme with several advantages for microgrid applications of different control levels.
Effective Energy Management System in Microgrid Employing Model Predictive Controller
International Journal of Electrical and Electronics Research (IJEER) , 2024
The primary focus of this study is to develop an energy management system that regulates the energy transfers between the hybrid microgrid system and the loads connected to it, and the grid via MATLAB/Simulink so as to model the flow of energy. The secondary aim is to make recommendations aimed at the charging and discharging of what is referred to as the hybrid energy storage system (HESS). The results indicate that the proposed algorithm successfully carried out the requir ed task of bridging the HESS charging to discharging ratio in relation to the different operating conditions as well as power management between the microgrid and the network. In this application, a stronger charging power might be employed on the HESS. It has been seen that the HESS is more likely to complete charging within a short time than the greater charging power. A more advanced and efficient energy management system is critical to the microgrid system so that the generation can keep pace with the requirem ents of the load profile. It is important to take account of load forecasting with regard to power planning and executing so as to know the most suitable action that should be taken. To achieve a general reduction in the cost of operation, in this paper, we propose the use of an advanced Energy Management System (EMS), Model Predictive Control (MPC), to effectively manage the allocation of power in the microgrid.
A Predictive Control Strategy for Energy Management in Micro-Grid Systems
Electronics, 2021
The integration of renewable energy sources (RES) was amplified, during the past decades, in order to tackle the challenges related to energy demands and CO2 increases. Recently, many initiatives have been taken by promoting the deployment and the usage of micro-grids (MG) in buildings, as decentralized systems, for energy production. However, the variable nature of RESs and the limited size of energy storage systems require the deployment of adaptive control strategies for efficient energy balance. In this paper, a generalized predictive control (GPC) strategy is introduced for energy management (EM) in MG systems. Its main objective is to efficiently connect the electricity generators and consumers in order to predict the most suitable actions for energy flow management. In fact, based on energy production and consumption profiles as well as the availability of energy storage systems, the proposed EM will be able to select the best suitable energy source for supplying the building...
Modeling and Energy Management of a Microgrid Based on Predictive Control Strategies
Solar
This work presents the modeling and energy management of a microgrid through models developed based on physical equations for its optimal control. The microgrid’s energy management system was built with one of the most popular control algorithms in microgrid energy management systems: model predictive control. This control strategy aims to satisfy the load demand of an office located in the CIESOL bioclimatic building, which was placed in the University of Almería, using a quadratic cost function. The simulation scenarios took into account real simulation parameters provided by the microgrid of the building. For case studies of one and five days, the optimization was aimed at minimizing the input energy flows of the microgrid and the difference between the energy generated and demanded by the load, subject to a series of physical constraints for both outputs and inputs. The results of this work show how, with the correct tuning of the control strategy, the energy demand of the build...
Control of Microgrids using an Enhanced Model Predictive Controller
11th International Conference on Power Electronics, Machines and Drives (PEMD 2022), 2022
Renewable energy sources have been widely adopted to stop global warming. This growing adaptation has led to a significant change in topologies of traditional power networks, and now we have the concept of a microgrid. Model Predictive Control is an advanced method that is used to control power systems while satisfying several constraints to achieve an optimal solution based on various criteria. Although, Model Predictive Control is robust and has several advantages, its implementation is often very complex and requires high computational power. On the other hand, ε-variables based control strategies, which are practical methods to model control strategies in microgrids, are able to simplify the control structure allowing more scalability and even resilience. This paper presents, a hybrid method to simplify the implementation of Model Predictive Control using ε-variables and make it more effective on complicated energy systems. Our results demonstrate that combining Model Predictive Control with ε-variables can significantly simplify the control structure and hence allow for more complicated control strategies to be employed in order to provide extra benefits to the energy system like scalability and robustness.