Control of Microgrids using an Enhanced Model Predictive Controller (original) (raw)
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Model Predictive Control for Microgrid Functionalities: Review and Future Challenges
Energies , 2021
Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.
Electronics, 2022
In recent times, Microgrids (MG) have emerged as solution approach to establishing resilient power systems. However, the integration of Renewable Energy Resources (RERs) comes with a high degree of uncertainties due to heavy dependency on weather conditions. Hence, improper modeling of these uncertainties can have adverse effects on the performance of the microgrid operations. Due to this effect, more advanced algorithms need to be explored to create stability in MGs’. The Model Predictive Control (MPC) technique has gained sound recognition due to its flexibility in executing controls and speed of processors. Thus, in this review paper, the superiority of MPC to several techniques used to model uncertainties is presented for both grid-connected and islanded system. It highlights the features, strengths and incompetencies of several modeling methods for MPCs and some of its variants regarding handling of uncertainties in MGs. This survey article will help researchers and model devel...
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.
Utilizing flexibility in Microgrids using Model Predictive Control
Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2018), 2018
We derive a control strategy for the operation of Microgrids (MGs) with high shares of Renewable Energy Sources involving Model Predictive Control (MPC). By combining the MPC with an Energy Management System (EMS) utilizing stochastic programming techniques and a sufficiently large temporal optimization window we improve the point of operation of the system regarding both short and long-term operational aspects. We aim for a system operation that allows for the utilization of the MG as a Virtual Power Plant. In this work we focus on the predictive controller design and the incorporation of information derived in the EMS layer.
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.
Designing of Model Predictive Control Algorithm for Active and Reactive Power Control of a Microgrid
2021
Microgrid development is a viable solution for integrating rapidly expanding renewable energy sources. However, the stochastic nature of renewable energies and changeable power demand has caused a slew of issues, including unstable voltage/frequency, difficult power management, and grid interaction. Predictive control has recently shown tremendous potential in microgrid applications due to its fast transient response and flexibility to suit various restrictions. This work examines model predictive control (MPC) in individual and interconnected microgrids, including converterand grid-level control techniques applied to three layers of the hierarchical control architecture. MPC research is just getting started in microgrids, but it's already proving to be a viable alternative to traditional approaches for voltage regulation, frequency control, power flow management, and economic operation optimization. In addition, some of the most significant patterns in MPC development have been...
Predictive Control for Microgrid Applications: A Review Study
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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.
A Model Predictive Control Approach to Microgrid Operation Optimization
IEEE Transactions on Control Systems Technology, 2014
Microgrids are subsystems of the distribution grid, which comprises generation capacities, storage devices, and controllable loads, operating as a single controllable system either connected or isolated from the utility grid. In this paper, we present a study on applying a model predictive control 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 microgrid is employed to assess the performance of the online optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid located in Athens, Greece. The experimental results show the feasibility and the effectiveness of the proposed approach.
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.
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...