Giuseppe Tommaso Costanzo

A System Architecture for Autonomous Demand Side Load Management in Smart Buildings

This paper presents a system architecture for load management in smart buildings which enables autonomous demand side load management in the smart grid. Being of a layered structure composed of three main modules for admission control, load balancing, and demand response management, this architecture can encapsulate the system functionality, assure the interoperability between various components, allow the integration of different energy sources, and ease maintenance and upgrading. Hence it is capable of handling autonomous energy consumption management for systems with heterogeneous dynamics in multiple time-scales and allows seamless integration of diverse techniques for online operation control, optimal scheduling, and dynamic pricing. The design of a home energy manager based on this architecture is illustrated and the simulation results with Matlab/Simulink confirm the viability and efficiency of the proposed framework.

Testing of a Predictive Control Strategy for Balancing Renewable Sources in a Microgrid

This paper presents the design of a control strategy for the energy management of a grid-connected microgrid with local distributed energy resources as: 10-kW photovoltaic plant, 11-kW wind turbine, and 15-kW-190-kWh vanadium-based electric storage system. According to future regulations, the renewable energy producers will also have to provide a day-ahead hourly production plan. The overall idea is, by knowing the meteorological forecasts for the next 24 h, to dispatch the microgrid in order to be able to grant the scheduled hourly production by means of proper management of the storage system. The usage of the storage system is, however, minimized by the energy management strategy. The system design is validated by experimental testing carried out in SYSLAB, a distributed power system test facility at Riso Campus, Technical University of Denmark.

An overview of demand side management control schemes for buildings in smart grids

The increasing share of distributed energy resources and renewable energy in power systems results in a highly variable and less controllable energy production. Therefore, in order to ensure stability and to reduce the infrastructure and operation cost of the power grid, flexible and controllable demand is needed. The research area of demand side management is still very much in flux and several options are being presented which can all be used to manage loads in order to achieve a flexible and more responsive demand. These different control schemes are developed with different organization of the power sector in mind and thus can differ significantly in their architecture, their integration into the various markets, their integration into distribution network operation and several other aspects. This paper proposes a classification of load control policies for demand side management in smart buildings, based on external behavior: direct, indirect, transactional and autonomous control; internal operation: decision support system scope, control strategy, failure handling and architecture. This classification assists in providing an overview of the control schemes as well as different ways of representing a building.

Grey-box modeling for system identification of household refrigerators: A step toward smart appliances

This paper presents the grey-box modeling of a vapor-compression refrigeration system for residential applications based on maximum likelihood estimation of parameters in stochastic differential equations. Models obtained are useful in the view of controlling refrigerators as flexible consumption units, which operation can be shifted within temperature and operational constraints. Even if the refrigerators are not intended to be used as smart loads, validated models are useful in predicting units consumption. This information can increase the optimality of the management of other flexible units, such as heat pumps for space heating, in order to smooth the load factor during peak hours, enhance reliability and efficiency in power networks and reduce operational costs.

Power Admission Control With Predictive Thermal Management in Smart Buildings

This paper presents a control scheme for thermal management in smart buildings based on predictive power admission control. This approach combines model predictive control with budget-schedulability analysis in order to reduce peak power consumption as well as ensure thermal comfort. First, the power budget with a given thermal comfort constraint is optimized through budget-schedulability analysis which amounts to solving a constrained linear programming problem. Second, the effective peak power demand is reduced by means of the optimal scheduling and cooperative operation of multiple thermal appliances. The performance of the proposed control scheme is assessed by simulation based on the thermal dynamics of a real eight-room office building located at Danish Technical University.

Day-ahead scheduling of a photovoltaic plant by the energy management of a storage system

The paper discusses and describes a system for energy management of a 10 kW PV plant coupled with a 15 kW-190 kWh storage system. The overall idea is, by knowing the meteorological forecast for the next 24h, to dispatch the PV system and to be able to grant the scheduled hourly energy profile by a proper management of the storage. Due to forecast inaccuracies, the energy manager controls the storage in order to ensure that the plan for hourly energy production is respected, minimizing the storage itself usage. The experimental study is carried out in SYSLAB, a distributed power system test facility at DTU Riso Campus and part of PowerLabDK. Both the PV and the storage are connected to the local network and are fully controllable through the SCADA system. The control management and the models are implemented in Matlab-Simulink, which can be interfaced with SYSLAB.

A Distributed Model Predictive Control approach for the integration of flexible loads, storage and renewables

This paper presents an innovative solution based on distributed model predictive controllers to integrate the control and management of energy consumption, energy storage, PV and wind generation at customer side. The overall goal is to enable an advanced prosumer to auto-produce part of the energy he needs with renewable sources and, at the same time, to optimally exploit the thermal and electrical storages, to trade off its comfort requirements with different pricing schemes (including real-time pricing), and apply optimal control techniques rather than sub-optimal heuristics.

Experimental Validation of Energy Resources Integration in Microgrids via Distributed Predictive Control

This paper presents an innovative control scheme for the management of energy consumption in commercial buildings with local energy production, such as photovoltaic panels or wind turbine and an energy storage unit. The presented scheme is based on distributed model predictive controllers, which manage the storage system and the building space heating and cooling. The proposed approach is implemented and tested in SYSLAB, the experimental facility for distributed energy systems at the Technical University of Denmark, Risø Campus. The experimental setup consists of wind and solar renewable sources, a vanadium redox battery system, resistive load, and a point of common coupling to the national grid. Several experiments are carried to assess the performance of the control scheme in managing local energy production and consumption.

A Hardware-In-The-Loop Simulation Architecture for Integration of Smart Buildings and Distributed Energy Resources in Micro Grids

Abstract This paper presents the design, implementation and test of hardware in the loop simulation architecture for integration of Smart Buildings and distributed energy resources in Micro Grids. The rationale for this work is the integration of off-the-shelves devices in a HIL simulation setup within software development. The architecture and preliminary results are presented, together with a case study for integrating flexible units, such as a space heating system and Electric Vehicles in a Smart Building equipped with local solar energy production.