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  • 1. Oldewurtel, Frauke
    et al.
    Jones, Colin Neil
    Parisio, Alessandra
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Morari, Manfred
    Stochastic Model Predictive Control for Building Climate Control2014In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 22, no 3, p. 1198-1205Article in journal (Refereed)
    Abstract [en]

    In this brief paper, a Stochastic Model Predictive Control formulation tractable for large-scale systems is developed. The proposed formulation combines the use of Affine Disturbance Feedback, a formulation successfully applied in robust control, with a deterministic reformulation of chance constraints. A novel approximation of the resulting stochastic finite horizon optimal control problem targeted at building climate control is introduced to ensure computational tractability. This work provides a systematic approach toward finding a control formulation which is shown to be useful for the application domain of building climate control. The analysis follows two steps: 1) a small-scale example reflecting the basic behavior of a building, but being simple enough for providing insight into the behavior of the considered approaches, is used to choose a suitable formulation; and 2) the chosen formulation is then further analyzed on a large-scale example from the project OptiControl, where people from industry and other research institutions worked together to create building models for realistic controller comparison. The proposed Stochastic Model Predictive Control formulation is compared with a theoretical benchmark and shown to outperform current control practice for buildings.

  • 2.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Parisio, Alessandra
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Demand response for aggregated residential consumers with energy storage sharing2015Conference paper (Refereed)
    Abstract [en]

    A novel distributed algorithm is proposed in this paper for a network of consumers coupled by energy resource sharing constraints, which aims at minimizing the aggregated electricity costs. Each consumers is equipped with an energy management system that schedules the shiftable loads accounting for user preferences, while an aggregator entity coordinates the consumers demand and manages the interaction with the grid and the shared energy storage system (ESS) via a distributed strategy. The proposed distributed coordination algorithm requires the computation of Mixed Integer Linear Programs (MILPs) at each iteration. The proposed approach guarantees constraints satisfaction, cooperation among consumers, and fairness in the use of the shared resources among consumers. The strategy requires limited message exchange between each consumer and the aggregator, and no messaging among the consumers, which protects consumers privacy. Performance of the proposed distributed algorithm in comparison with a centralized one is illustrated using numerical experiments.

  • 3.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Parisio, Alessandra
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Energy and CO2 efficient scheduling of smart appliances in active houses equipped with batteries2014In: Automation Science and Engineering (CASE), 2014 IEEE International Conference on, IEEE conference proceedings, 2014, p. 632-639Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a novel method for scheduling smart appliances and batteries, in order to reduce both the electricity bill and the CO2 emissions. Mathematically, the scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which can be solved by using standard algorithms. A case study is performed to assess the performance of the proposed scheduling framework. Numerical results show that the new formulation can decrease both the CO2 emissions and the electricity bill. Furthermore, a survey of studies that deal with scheduling of smart appliances is provided. These papers use methods based on MILP, Dynamic Programming (DP), and Minimum Cut Algorithm (MCA) for solving the scheduling problem. We discuss their performance in terms of computation time and optimality versus time discretization and number of appliances.

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  • 4.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Parisio, Alessandra
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Robust Scheduling of Smart Appliances in Active Apartments With User Behavior Uncertainty2015In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 13, no 1, p. 247-259Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a robust approach for scheduling of smart appliances and electrical energy storages (EESs) in active apartments with the aim of reducing both the electricity bill and the CO2 emissions. The proposed robust formulation takes the user behavior uncertainty into account so that the optimal appliances schedule is less sensitive to unpredictable changes in user preferences. The user behavior uncertainty is modeled as uncertainty in the cost function coefficients. In order to reduce the level of conservativeness of the robust solution, we introduce a parameter allowing to achieve a trade-off between the price of robustness and the protection against uncertainty. Mathematically, the robust scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which is solved by using standard algorithms. The numerical results show effectiveness of the proposed approach to increase both the electricity bill and CO2 emissions savings, in the presence of user behavior uncertainties. Mathematical insights into the robust formulation are illustrated and the sensitivity of the optimum cost in the presence of uncertainties is investigated. Although home appliances and EESs are considered in this work, we point out that the proposed scheduling framework is generally applicable to many use cases, e.g., charging and discharging of electrical vehicles in an effective way. In addition, it is applicable to various scenarios considering different uncertainty sources, different storage technologies and generic programmable electrical loads, as well as different optimization criteria.

  • 5.
    Parisio, Alessandra
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fabietti, Luca
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Varagnolo, Damiano
    Technical University of Luleå, Sweden.
    Molinari, Marco
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Control of HVAC systems via scenario-based explicit MPC2014In: 2014 IEEE 53rd Annual Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 5201-5207, article id 7040202Conference paper (Refereed)
    Abstract [en]

    Improving energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems is a primary objective for the society. Model Predictive Control (MPC) techniques for HVAC systems have recently received particular attention, since they can naturally account for several factors, such as weather and occupancy forecasts, comfort ranges and actuation constraints. Developing effective MPC based control strategies for HVAC systems is nontrivial, since buildings dynamics are nonlinear and affected by various uncertainties. Further, the complexity of the MPC problem and the burden of on-line computations can lead to difficulties in integrating this scheme into a building management system.

  • 6.
    Parisio, Alessandra
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Glielmo, L.
    Stochastic Model Predictive Control for economic/environmental operation management of microgrids2013In: 2013 European Control Conference, ECC 2013, IEEE , 2013, p. 2014-2019Conference paper (Refereed)
    Abstract [en]

    Microgrids are subsystems of the distribution grid which comprises generation capacities, storage devices and controllable loads, which can operate either connected or isolated from the utility grid. In this work, microgrid management system is developed in a stochastic framework. Uncertainties due to fluctuating demand and generation from renewable energy sources are taken into account and a two-stage stochastic programming approach is applied to efficiently optimize microgrid operations while satisfying a time-varying request and operation constraints. Mathematically, the stochastic optimization problem is stated as a mixed-integer linear programming problem, which can be solved in an efficient way by using commercial solvers. The stochastic problem is incorporated in a Model Predictive Control (MPC) scheme to further compensate the uncertainty though the feedback mechanism. Simulations show the effective performance of the proposed approach.

  • 7.
    Parisio, Alessandra
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jones, Colin Neil
    A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand2015In: Omega: The International Journal of Management Science, ISSN 0305-0483, E-ISSN 1873-5274, Vol. 53, p. 97-103Article in journal (Refereed)
    Abstract [en]

    This paper describes an employee scheduling system for retail outlets; it is a constraint-based system that exploits forecasts and stochastic techniques to generate schedules meeting the demand for sales personnel. Uncertain scenarios due to fluctuating demand are taken into account to develop a stochastic operational optimization of staffing levels. Mathematically, the problem is stated as a mixed-integer linear programming problem. Simulations with store data belonging to a major Swiss retailer show the effective performance of the proposed approach. The schedule quality is assessed through comparison with a deterministic scheduling package, which has been used at several outlets in Switzerland.

  • 8.
    Parisio, Alessandra
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Molinari, Marco
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Varagnolo, Damiano
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A Scenario-based Predictive Control Approach to Building HVAC Management Systems2013In: IEEE International Conference on Automation Science and Engineering, 2013, p. -435Conference paper (Refereed)
    Abstract [en]

    We present a Stochastic Model Predictive Control (SMPC) algorithm that maintains predefined comfort levels in building Heating, Ventilation and Air Conditioning (HVAC) systems while minimizing the overall energy use. The strategy uses the knowledge of the statistics of the building occupancy and ambient conditions forecasts errors and determines the optimal control inputs by solving a scenario-based stochastic optimization problem. Peculiarities of this strategy are that it does not make assumptions on the distribution of the uncertain variables, and that it allows dynamical learning of these statistics from true data through the use of copulas, i.e., opportune probabilistic description of random vectors. The scheme, investigated on a prototypical student laboratory, shows good performance and computational tractability.

  • 9.
    Parisio, Alessandra
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rikos, Evangelos
    Glielmo, Luigi
    A Model Predictive Control Approach to Microgrid Operation Optimization2014In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 22, no 5, p. 1813-1827Article in journal (Refereed)
    Abstract [en]

    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.

  • 10.
    Parisio, Alessandra
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rikos, Evangelos
    Tzamalis, George
    Glielmo, Luigi
    Use of Model Predictive Control for Experimental Microgrid Optimization2013In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 115, p. 37-46Article in journal (Refereed)
    Abstract [en]

    In this paper we deal with the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. Microgrids are subsystems of the distribution grid comprising sufficient generating resources to operate in isolation from the main grid, in a deliberate and controlled way. The Model Predictive Control (MPC) approach is applied for achieving economic efficiency in microgrid operation management. The method is thus applied to an experimental microgrid located in Athens, Greece: experimental results show the feasibility and the effectiveness of the proposed approach.

  • 11.
    Parisio, Alessandra
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Varagnolo, Damianno
    Technical University of Luleå.
    Molinari, Marco
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Pattarello, Giorgio
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fabietti, Luca
    University of Padova.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Implementation of a Scenario-based MPC for HVAC Systems: an Experimental Case Study2014In: Proceedings of the 19th IFAC World Congress, 2014, Elsevier BV , 2014, Vol. 47, p. 599-605Conference paper (Refereed)
    Abstract [en]

    Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and air quality levels. Model Predictive Control (MPC) techniques are known to bring significant energy savings potential. Developing effective MPC-based control strategies for HVAC systems is nontrivial since buildings dynamics are nonlinear and influenced by various uncertainties. This complicates the use of MPC techniques in practice. We propose to address this issue by designing a stochastic MPC strategy that dynamically learns the statistics of the building occupancy patterns and weather conditions. The main advantage of this method is the absence of a-priori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the practical implementation of the proposed MPC controller on a student laboratory, showing its effectiveness and computational tractability.

  • 12.
    Parisio, Alessandra
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Varagnolo, Damiano
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Risberg, Daniel
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Pattarello, Giorgio
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Molinari, Marco
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Randomized Model Predictive Control for HVAC Systems2013In: BuildSys'13 Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, 2013Conference paper (Refereed)
    Abstract [en]

    Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and Indoor Air Quality (IAQ) levels, essentials for occupants well-being. Since performing this task implies high energy requirements, there is a need for improving the energetic efficiency of existing buildings. A possible solution is to develop effective control strategies for HVAC systems, but this is complicated by the inherent uncertainty of the to-be-controlled system. To cope with this problem, we design a stochastic Model Predictive Control (MPC) strategy that dynamically learns the statistics of the building occupancy and weather conditions and uses them to build probabilistic constraints on the indoor temperature and CO2 concentration levels. More specifically, we propose a randomization technique that finds suboptimal solutions to the generally non-convex stochastic MPC problem. The main advantage of this method is the absence of apriori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the proposed approach by means of numerical simulations and real tests on a student laboratory, and show its practical effectiveness and computational tractability.

  • 13.
    Parisio, Alessandra
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Wiezorek, Christian
    Kyntaja, Timo
    Elo, Joonas
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control.
    An MPC-based Energy Management System for Multiple Residential Microgrids2015In: 2015 INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2015, p. 7-14Conference paper (Refereed)
    Abstract [en]

    In this study we present a Model Predictive Control (MPC) approach to Energy Management Systems (EMSs) for multiple residential microgrids. The EMS is responsible for optimally scheduling end-user smart appliances, heating systems and local generation devices at the residential level, based on end-user preferences, weather-dependent generation and demand forecasts, electric pricing, technical and operative constraints. The core of the proposed framework is a mixed integer linear programming (MILP) model aiming at minimizing the overall costs of each residential microgrid. At each time step, the computed optimal decision is adjusted according to the actual values of weather-dependent local generation and heating requirements; then, corrective actions and their corresponding costs are accounted for in order to cope with imbalances. At the next time step, the optimization problem is re-computed based on updated forecasts and initial conditions. The proposed method is evaluated in a virtual testing environment that integrates accurate simulators of the energy systems forming the residential microgrids, including electric and thermal generation units, energy storage devices and flexible loads. The testing environment also emulates real-word network medium conditions on standard network interfaces. Numerical results show the feasibility and the effectiveness of the proposed approach.

  • 14.
    Parisio, Alessandra
    et al.
    Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England..
    Wiezorek, Christian
    Berlin Univ Technol, SENSE Lab, D-10587 Berlin, Germany..
    Kyntaja, Timo
    VTT Tech Res Ctr Finland, Espoo 02044, Finland..
    Elo, Joonas
    VTT Tech Res Ctr Finland, Espoo 02044, Finland..
    Strunz, Kai
    Berlin Univ Technol, SENSE Lab, D-10587 Berlin, Germany..
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Cooperative MPC-Based Energy Management for Networked Microgrids2017In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 8, no 6, p. 3066-3074Article in journal (Refereed)
    Abstract [en]

    Microgrids are subsystems of the distribution grid operating as a single controllable system either connected or isolated from the grid. In this paper, a novel cooperative model predictive control (MPC) framework is proposed for urban districts comprising multiple microgrids sharing certain distributed energy resources (DERs). The operation of the microgrids, along with the shared DER, are coordinated such that the available flexibility sources are optimised and a common goal is achieved, e.g., minimizing energy exchanged with the distribution grid and the overall energy costs. Each microgrid is equipped with an MPC-based energy management system, responsible for optimally controlling flexible loads, heating systems, and local generation devices based on end-user preferences, weather-dependent generation and demand forecasts, energy prices, and technical and operational constraints. The proposed coordination algorithm is distributed and guarantees constraints satisfaction, cooperation among microgrids and fairness in the use of the shared resources, while addressing the issue of scalability of energy management in an urban district. Furthermore, the proposed framework guarantees an agreed cost saving to each microgrid. The described method is implemented and evaluated in a virtual testing environment that integrates accurate simulators of the microgrids. Numerical experiments show the feasibility, the computational benefits, and the effectiveness of the proposed approach.

  • 15.
    Song, Meng
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Alvehag, Karin
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Widén, Joakim
    Parisio, Alessandra
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Estimating the impacts of demand response by simulating household behaviours under price and CO2 signals2014In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 111, p. 103-114Article in journal (Refereed)
    Abstract [en]

    To facilitate the implementation of demand response (DR), it is necessary to establish proper methods to estimate and verify the load impacts of it. This paper develops a simulation model to investigate the joint influence of price and CO2 signals in a DR program in the ex ante evaluation. It consists of a Markov-chain load model for forecasting the power demands of residential consumers and a scheduling program for providing optimal schedules for smart appliances. A case study of the Stockholm Royal Seaport project is analysed to demonstrate how to apply the simulation model to assess a DR program by simulating consumers' behaviour change in response to the DR signals. The results show that consumers' attitude to the signals and willingness to change (expressed by weight), and time preference) largely affect the load shift, bill saving and emission reduction. Moreover, by observing the load shifts over different lengths of the testing period, the model could also provide suggestions on the required testing period to get sufficient load data to distinguish the load patterns between consumers in different testing groups.

  • 16.
    Valenzuela, Patricio Esteban
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Ebadat, Afrooz
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Everitt, Niklas
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Parisio, Alessandra
    Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England..
    Closed-Loop Identification for Model Predictive Control of HVAC Systems: From Input Design to Controller Synthesis2020In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 28, no 5, p. 1681-1695Article in journal (Refereed)
    Abstract [en]

    Heating, ventilation, and air conditioning (HVAC) systems are responsible for maintaining occupants' thermal comfort and share a large portion of the overall building energy use. Hence, it is of great interest to improve the performance of HVAC control systems and thus the building energy efficiency. Model predictive control (MPC) has been proved to be a promising control strategy to be employed in this field. However, MPC implementation relies on the model of the system, and inaccurate models can deteriorate the control performance, while overly complicated models can lead to the prohibitive computational burden. Because of this, existing models do not usually allow the MPC controller to adjust multiple set points (e.g., both temperature and flow rates) and do not include the dynamics of the heating and ventilation subsystems with their local controllers. In this paper, we address the challenge of developing more reliable HVAC models for MPC controllers based on the experimental data. Data are obtained from an experiment designed using a graph theoretical technique, which guarantees maximum information content in the data. The resulting models are employed to design local controllers of the heating and ventilation subsystems, which are experimentally tested in a real HVAC test bed. A supervisory MPC controller that incorporates the closed-loop models of the heating and ventilation subsystems is then developed. This can lead to a control strategy able to more effectively adapt key HVAC set points based on weather conditions, occupancy, and actual thermal comfort, as shown by a numerical study based on the data from the HVAC test bed.

  • 17.
    Wiezorek, Christian
    et al.
    Tech Univ Berlin, Fac Elect Engn & Comp Sci, Einsteinufer 11 EMH 1, Berlin, Germany..
    Parisio, Alessandra
    KTH, School of Electrical Engineering (EES), Automatic Control. Univ Manchester, Sch Elect & Elect Engn, Ferranti C5, Manchester M13 9PL, Lancs, England..
    Kyntaja, Timo
    Teknol Tutkimuskeskus VTT Oy, FI-02044 Helsinki, Finland..
    Elo, Joonas
    Teknol Tutkimuskeskus VTT Oy, FI-02044 Helsinki, Finland..
    Gronau, Markus
    Tech Univ Berlin, Fac Elect Engn & Comp Sci, Einsteinufer 11 EMH 1, Berlin, Germany..
    Johannson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Strunz, Kai
    Tech Univ Berlin, Fac Elect Engn & Comp Sci, Einsteinufer 11 EMH 1, Berlin, Germany..
    Multi-location virtual smart grid laboratory with testbed for analysis of secure communication and remote co-simulation: concept and application to integration of Berlin, Stockholm, Helsinki2017In: IET Generation, Transmission & Distribution, ISSN 1751-8687, E-ISSN 1751-8695, Vol. 11, no 12, p. 3134-3143Article in journal (Refereed)
    Abstract [en]

    The process of advancement and validation of smart grid technologies and systems calls for the availability of diverse expertise and resources. In response to this consideration, the virtual smart grid laboratory (VSGL) was developed as described in this study. At the core of the VSGL is a novel communication platform for seamlessly connecting geographically distributed laboratories with distinct competences. The platform has the dual purpose of opening access to resources of remote partner laboratory sites and offering the capability to emulate, analyse, and test smart grid communication networks involved in linking the distributed laboratory resources. The VSGL implementation is validated through a use case, in which the resources of R&D laboratories in three European countries are connected to form an aggregated system of distributed energy resources. The operation of the latter was coordinated through an energy management system based on model predictive control (MPC). The VSGL was found to be very suitable to meet the communication-specific requirements of such type of study. In addition, for this particular case the effectiveness of the MPC subject to diverse implementations of communication links was substantiated.

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