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This paper addresses the design and implementation of a model predictive control framework for temperature control in buildings zones via direct control of their thermal energy inputs. Comfort-centric approach in ensured by selecting building thermal zones to be equal to the physical building rooms. The framework integrates different identification and estimation technologies, machine learning and model predictive control to assure systematic handling of non-modelled disturbances and offset-free control. It is envisioned as the lowest level in the hierarchical decomposition of building subsystems responsible for comfort and shaping the overall thermal energy consumption in building zones. The paper shows how it is deployed on a full scale occupied skyscraper building. To enable optimization of the whole building behaviour a special focus is put on developing the possibility for interaction and coordination with other building subsystems or energy distribution grids. This ensures the scalability of the approach, computational relaxation, technology independency, cost-effective implementation and enables upscaling towards the smart grid and smart city concepts where buildings play decisive roles. [Display omitted] • Direct control of thermal energy per zone. • Enabled interaction with other building subsystems. • Integral part for upscaling towards smart grid and smart city concepts. • Deployment and verification on a scale of the whole skyscraper building. • Modular service built on top of the existing building automation infrastructure. [ABSTRACT FROM AUTHOR]

In the Smart Grid context, the involvement of end-users is a key element for the implementation of demand response, as a way to enhance the energy efficiency of the electricity infrastructure also enabling to cope with the intermittency of renewable energy sources. Although the participation of end-users may result in higher complexity of system management, it may have a positive impact on mitigating the volatility of electricity prices. End-users may also be a key component in the provision of ancillary services using demand side resources to offer the system operator additional means to enhance system flexibility, robust planning, constraint management and operation scheduling, contributing to the balance between load and supply under a load follows supply paradigm. Demand response is seen as an effective and reliable strategy for the successful integration of renewable energy sources, in a perspective of integrated energy resource management, handling the demand curve using load flexibility whenever the system requires it. This embodies the possibility of changing/controlling the load profile by optimally time deferring the use of some equipment. This paper focuses on the role of energy management systems aggregators, both concerning actual practice in industry as well as research, which can be seen as relevant players contributing to that endeavour. A review of recent literature and projects is made, putting in perspective the role of energy management systems aggregators in the Smart Grid context, in association with demand response programs and technologies, involving the participation of end-users in the provision of ancillary services. The aim is recognizing trends, opportunities, challenges and potential barriers regarding the creation of energy management systems aggregators to improve overall system performance, characterizing the services provided by aggregators and identifying potential research gaps. [ABSTRACT FROM AUTHOR]

This paper intends to present a cooperative control framework for a connected cluster of microgrids with multi-smart greenhouses creating a smart local electric grid in the framework of smart grids. Each microgrid comprises renewable generators, pumps, advanced communication and metering infrastructure, water reservoir, energy storage device, and a set of greenhouses where each one includes heating, ventilation and air conditioning (HVAC), CO 2 injector, artificial lighting, sensors, local pump, and fans. The key objective is to formulate a coordinated optimization framework embedded in a model predictive control (MPC) scheme to optimally control the operation of the clustered microgrids and manage the power flows exchange ensuring a high quality of service. The microgrids are connected permitting the power exchanges to enhance the utilization of local renewable generations. Furthermore, the cluster is connected to the main grid through a power link permitting power exchange in excess/shortage case. The cooperation is achieved throughout a bidirectional communication infrastructure, where a centralized controller is responsible of managing the different control signals. A comprehensive scheduling optimization algorithm is developed and implemented to effectively control the clustered microgrids operation considering the operational constraints, where the purpose is to enhance energy efficiency, and manipulating effectively the microclimate variables defining the optimal environment for crops development in all greenhouses. An MPC-based energy management framework is implemented and applied to a case study to demonstrate its performance and effectiveness through extensive numerical simulations. • We present a control framework for a cluster of microgrids with multi-greenhouses. • We formulate a coordinated optimization framework embedded in a MPC scheme. • The purpose is to optimally control the operation of the clustered microgrids. • An MPC-based algorithm is developed to control the microclimate in each greenhouse. • The algorithm is applied to a case study to demonstrate its performance. [ABSTRACT FROM AUTHOR]

Abstract: In an increasingly constrained context with severe requirements of quality, safety and environment respect, a building-integrated photovoltaic (BIPV) involves advanced local energy management rather than photovoltaic (PV) power permanent grid injection. This paper focus on building-integrated microgrid (BIMG) design and implementation applied to BIPV system with energy storage and smart grid communication. The goal is to propose power balancing strategy with smart grid interaction, aiming at reducing grid peak consumption, avoiding undesirable grid power injection, making full use of local PV production. The energy management is carried out considering grid time-of-use tariffs, grid access limits, storage capacity, load and PV power shedding if necessary. Based on experimental platform, the results show that the system maintains stability and confirm the relevance of the proposed local energy management giving perspectives on better integration of small PV plant in power grid. [Copyright &y& Elsevier]

Smart grid technologies are a catalyst for the modernisation of the electrical system whilst satisfying all electrical power stakeholders. The application of intelligent systems results in more flexibility and reliability. This paper presents a dynamic energy management system for a microgrid connected to a grid for residential application. The system models a smart metering system to collect data from different components of the electrical system. A grid-tied photovoltaic and energy storage system model is optimally designed. The model uses the framework of a smart grid based on demand response and energy pricing to coordinate the energy flow of a home. Three optimal control scenarios are formulated, where the opportunity energy is considered to be injected to the main grid. These scenarios are two linear methods (open and closed-loop models) and a quadratic approach based on model predictive control. It was observed that the energy storage system plays an essential role in the context of energy-saving and gain from the demand side. The models provide benefits in terms of energy-saving and energy cost. The performance of dynamic modelling is validated with the experimental data from the smart metering system. • Formulates linear and quadratic model of smart home energy coordination for the intelligent microgrid application. • Designs optimal energy flow control strategies by the use of a smart metering system based on wireless communication. • Compares the dynamic performance of the energy management schemes based on demand response. • Provides the consumer with an opportunity of selling the energy to the utility for a microgrid connected to the main grid. • Applies system analysis on the energy components of a residential home. [ABSTRACT FROM AUTHOR]