Model predictive control for microgrid functionalities: review and future challenges

ABSTRACT: 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.info:eu-repo/semantics/publishedVersio

The State of the Art in Model Predictive Control Application for Demand Response

Demand response programs have been used to optimize the participation of the demand side. Utilizing the demand response programs maximizes social welfare and reduces energy usage. Model Predictive Control is a suitable control strategy that manages the energy network, and it shows superiority over other predictive controllers. The goal of implementing this controller on the demand side is to minimize energy consumption, carbon footprint, and energy cost and maximize thermal comfort and social welfare.  This review paper aims to highlight this control strategy\u27s excellence in handling the demand response optimization problem. The optimization methods of the controller are compared. Summarization of techniques used in recent publications to solve the Model Predictive Control optimization problem is presented, including demand response programs, renewable energy resources, and thermal comfort. This paper sheds light on the current research challenges and future research directions for applying model-based control techniques to the demand response optimization problem

GEOTABS concept and design : state-of-the-art, challenges and solutions

GEOTABS refers to the combination of a geothermal heat pump with thermally activated building systems, and is applied in low temperature heating and high temperature cooling of buildings. TABS is a radiant heating and cooling system and is beneficial in terms of thermal comfort and energy efficiency. When combined with a geothermal heat pump, it allows to make efficient use of low grade renewable energy sources. In this paper the benefits and opportunities of GEOTABS are explained. From current practice challenges are identified that prevent the system to be operated at an optimal efficiency and to be widely implemented. Key challenges are (1) to maintain thermal comfort when sudden and significant changes in heating or cooling loads appear, (2) to maintain the thermal balance of the ground, (3) to design and control the system optimally, and (4) to decrease investment, design and commissioning costs. In the hybridGEOTABS H2020 project, three solutions are proposed and developed to tackle these challenges: (1) to integrate GEOTABS with secondary emission and heating/cooling generation systems, (2) to develop a robust and adaptive model predictive control and a toolchain that allows to derive the model architecture and parameters semi-automatically, and (3) to develop a holistic and easy-to-use design procedure that allows optimal integration, sizing and controlling of GEOTABS and secondary systems while avoiding case-bycase simulation work. This integrated solution will allow a near-optimal design and energy-efficient operation of hybridGEOTABS buildings within the boundaries of good thermal comfort and economic feasibility