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Valenzuela, P. E., Ebadat, A., Everitt, N. & Parisio, A. (2020). Closed-Loop Identification for Model Predictive Control of HVAC Systems: From Input Design to Controller Synthesis. IEEE Transactions on Control Systems Technology, 28(5), 1681-1695
Open this publication in new window or tab >>Closed-Loop Identification for Model Predictive Control of HVAC Systems: From Input Design to Controller Synthesis
2020 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 28, no 5, p. 1681-1695Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
Keywords
HVAC, Heating systems, Ventilation, Computational modeling, Buildings, Temperature measurement, Controller synthesis, heating, air conditioning (HVAC) systems, input design, model predictive control (MPC), system identification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-279888 (URN)10.1109/TCST.2019.2917675 (DOI)000557701400005 ()2-s2.0-85089816259 (Scopus ID)
Note

QC 20200915

Available from: 2020-09-15 Created: 2020-09-15 Last updated: 2024-03-18Bibliographically approved
Parisio, A., Wiezorek, C., Kyntaja, T., Elo, J., Strunz, K. & Johansson, K. H. (2017). Cooperative MPC-Based Energy Management for Networked Microgrids. IEEE Transactions on Smart Grid, 8(6), 3066-3074
Open this publication in new window or tab >>Cooperative MPC-Based Energy Management for Networked Microgrids
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2017 (English)In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 8, no 6, p. 3066-3074Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Model predictive control, flexibility services, energy management systems, demand response, distributed optimization, mixed integer linear programming
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-243568 (URN)10.1109/TSG.2017.2726941 (DOI)000413244600052 ()2-s2.0-85029007511 (Scopus ID)
Funder
Swedish Energy AgencyVINNOVAKnut and Alice Wallenberg Foundation
Note

QC 20190206

Available from: 2019-02-06 Created: 2019-02-06 Last updated: 2024-03-18Bibliographically approved
Wiezorek, C., Parisio, A., Kyntaja, T., Elo, J., Gronau, M., Johannson, K. H. & Strunz, K. (2017). 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, Helsinki. IET Generation, Transmission & Distribution, 11(12), 3134-3143
Open this publication in new window or tab >>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, Helsinki
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2017 (English)In: IET Generation, Transmission & Distribution, ISSN 1751-8687, E-ISSN 1751-8695, Vol. 11, no 12, p. 3134-3143Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
IET - Institution of Engineering and Technology, 2017
Keywords
smart power grids, laboratories, telecommunication security, distributed power generation, energy management systems, predictive control, power generation control, virtual instrumentation, virtual private networks, multilocation virtual smart grid laboratory, secure communication analysis, remote co-simulation, Berlin, Stockholm, Helsinki, VSGL, communication platform, geographically distributed laboratories, smart grid communication network testing, distributed laboratory resources, distributed energy resources, energy management system, model predictive control, MPC, communication links
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-243515 (URN)10.1049/iet-gtd.2016.1578 (DOI)000411402800021 ()2-s2.0-85029752774 (Scopus ID)
Note

QC 20190211

Available from: 2019-02-11 Created: 2019-02-11 Last updated: 2024-03-18Bibliographically approved
Parisio, A. & Jones, C. N. (2015). A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand. Omega: The International Journal of Management Science, 53, 97-103
Open this publication in new window or tab >>A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand
2015 (English)In: Omega: The International Journal of Management Science, ISSN 0305-0483, E-ISSN 1873-5274, Vol. 53, p. 97-103Article in journal (Refereed) Published
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.

Keywords
Decision making/process, Stochastic programming, Scheduling, Decision support systems, Integer programming, Operational/OR, Optimization, Resource management
National Category
Economics and Business Control Engineering
Identifiers
urn:nbn:se:kth:diva-163939 (URN)10.1016/j.omega.2015.01.003 (DOI)000350936000011 ()2-s2.0-84921713375 (Scopus ID)
Note

QC 20150507

Available from: 2015-05-07 Created: 2015-04-13 Last updated: 2024-03-18Bibliographically approved
Parisio, A., Wiezorek, C., Kyntaja, T., Elo, J. & Johansson, K. H. (2015). An MPC-based Energy Management System for Multiple Residential Microgrids. In: 2015 INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE): . Paper presented at IEEE International Conference on Automation Science and Engineering (CASE), AUG 24-28, 2015, Gothenburg, SWEDEN (pp. 7-14).
Open this publication in new window or tab >>An MPC-based Energy Management System for Multiple Residential Microgrids
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2015 (English)In: 2015 INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2015, p. 7-14Conference paper, Published 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.

National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-191069 (URN)10.1109/CoASE.2015.7294033 (DOI)000380453000002 ()2-s2.0-84952765730 (Scopus ID)978-1-4673-8183-3 (ISBN)
External cooperation:
Conference
IEEE International Conference on Automation Science and Engineering (CASE), AUG 24-28, 2015, Gothenburg, SWEDEN
Note

QC 20160825

Available from: 2016-08-25 Created: 2016-08-23 Last updated: 2024-03-18Bibliographically approved
Paridari, K., Parisio, A., Sandberg, H. & Johansson, K. H. (2015). Demand response for aggregated residential consumers with energy storage sharing. In: : . Paper presented at IEEE 54th Annual Conference on Decision and Control (CDC), 2015 (pp. 2024-2030). IEEE
Open this publication in new window or tab >>Demand response for aggregated residential consumers with energy storage sharing
2015 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2015
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-182382 (URN)10.1109/CDC.2015.7402504 (DOI)000381554502030 ()2-s2.0-84962003798 (Scopus ID)978-1-4799-7884-7 (ISBN)
Conference
IEEE 54th Annual Conference on Decision and Control (CDC), 2015
Note

QC 20160419

Available from: 2016-02-18 Created: 2016-02-18 Last updated: 2024-03-18Bibliographically approved
Paridari, K., Parisio, A., Sandberg, H. & Johansson, K. H. (2015). Robust Scheduling of Smart Appliances in Active Apartments With User Behavior Uncertainty. IEEE Transactions on Automation Science and Engineering, 13(1), 247-259
Open this publication in new window or tab >>Robust Scheduling of Smart Appliances in Active Apartments With User Behavior Uncertainty
2015 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 13, no 1, p. 247-259Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
IEEE Press, 2015
Keywords
Demand response, mixed-integer linear programming, multi-objective robust optimization, robust scheduling of smart appliances, user behavior uncertainty
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-182340 (URN)10.1109/TASE.2015.2497300 (DOI)000374443300025 ()2-s2.0-85034965186 (Scopus ID)
Funder
Swedish Energy AgencyKnut and Alice Wallenberg FoundationVINNOVA
Note

QC 20160226

Available from: 2016-02-18 Created: 2016-02-18 Last updated: 2024-03-18Bibliographically approved
Parisio, A., Rikos, E. & Glielmo, L. (2014). A Model Predictive Control Approach to Microgrid Operation Optimization. IEEE Transactions on Control Systems Technology, 22(5), 1813-1827
Open this publication in new window or tab >>A Model Predictive Control Approach to Microgrid Operation Optimization
2014 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 22, no 5, p. 1813-1827Article in journal (Refereed) Published
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.

Keywords
Microgrids, mixed logical dynamical systems, mixed-integer linear programming (MILP), model predictive control (MPC), optimization
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-139020 (URN)10.1109/TCST.2013.2295737 (DOI)000345574100011 ()2-s2.0-84905244928 (Scopus ID)
Funder
EU, FP7, Seventh Framework ProgrammeStandUp
Note

QC 20140131

Available from: 2013-12-24 Created: 2013-12-24 Last updated: 2024-03-18Bibliographically approved
Parisio, A., Fabietti, L., Varagnolo, D., Molinari, M. & Johansson, K. H. (2014). Control of HVAC systems via scenario-based explicit MPC. In: 2014 IEEE 53rd Annual Conference on Decision and Control (CDC): . Paper presented at 2014 IEEE 53rd Annual Conference on Decision and Control (CDC), 15-17 Dec. 2014, Los Angeles, CA, USA (pp. 5201-5207). Institute of Electrical and Electronics Engineers (IEEE), Article ID 7040202.
Open this publication in new window or tab >>Control of HVAC systems via scenario-based explicit MPC
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2014 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2014
Series
Proceedings of the IEEE Conference on Decision and Control, ISSN 0743-1546 ; 2015
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-165780 (URN)10.1109/CDC.2014.7040202 (DOI)000370073805060 ()2-s2.0-84940423528 (Scopus ID)978-1-4799-7746-8 (ISBN)
Conference
2014 IEEE 53rd Annual Conference on Decision and Control (CDC), 15-17 Dec. 2014, Los Angeles, CA, USA
Funder
Swedish Research CouncilKnut and Alice Wallenberg Foundation
Note

QC 20150430

Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2024-03-18Bibliographically approved
Paridari, K., Parisio, A., Sandberg, H. & Johansson, K. H. (2014). Energy and CO2 efficient scheduling of smart appliances in active houses equipped with batteries. In: Automation Science and Engineering (CASE), 2014 IEEE International Conference on: . Paper presented at 2014 IEEE International Conference on Automation Science and Engineering (CASE)18-22 Aug. 2014, Taipei (pp. 632-639). IEEE conference proceedings
Open this publication in new window or tab >>Energy and CO2 efficient scheduling of smart appliances in active houses equipped with batteries
2014 (English)In: Automation Science and Engineering (CASE), 2014 IEEE International Conference on, IEEE conference proceedings, 2014, p. 632-639Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-164081 (URN)10.1109/CoASE.2014.6899394 (DOI)2-s2.0-84939633153 (Scopus ID)
Conference
2014 IEEE International Conference on Automation Science and Engineering (CASE)18-22 Aug. 2014, Taipei
Note

This paper was one of the Best student paper award finalist.

QC 20150427

Available from: 2015-04-13 Created: 2015-04-13 Last updated: 2024-03-18Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-8633-1641

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