Computer-Assisted Management of Building Climate and Microgrid with Model Predictive Control
With 40% of total world energy consumption,
building systems are developing into technically complex large
energy consumers suitable for application of sophisticated power
management approaches to largely increase the energy efficiency
and even make them active energy market participants. Centralized
control system of building heating and cooling managed by
economically-optimal model predictive control shows promising
results with estimated 30% of energy efficiency increase. The research
is focused on implementation of such a method on a case study
performed on two floors of our faculty building with corresponding
sensors wireless data acquisition, remote heating/cooling units and
central climate controller. Building walls are mathematically modeled
with corresponding material types, surface shapes and sizes. Models
are then exploited to predict thermal characteristics and changes in
different building zones. Exterior influences such as environmental
conditions and weather forecast, people behavior and comfort
demands are all taken into account for deriving price-optimal climate
control. Finally, a DC microgrid with photovoltaics, wind turbine,
supercapacitor, batteries and fuel cell stacks is added to make the
building a unit capable of active participation in a price-varying
energy market. Computational burden of applying model predictive
control on such a complex system is relaxed through a hierarchical
decomposition of the microgrid and climate control, where the
former is designed as higher hierarchical level with pre-calculated
price-optimal power flows control, and latter is designed as lower
level control responsible to ensure thermal comfort and exploit
the optimal supply conditions enabled by microgrid energy flows
management. Such an approach is expected to enable the inclusion
of more complex building subsystems into consideration in order to
further increase the energy efficiency.
[1] Renewable Energy Policy Network for the 21st Century (REN21),
Renewables 2015, Global Status Report, 2015.
[2] M. Erol-Kantarci, H. T. Mouftah, ”Energy-Efficient Information and
Communication Infrastructures in the Smart Grid: A Survey on
Interactions and Open Issues”, IEEE Communication Surveys & Tutorials,
vol. 17, no. 1, 2015.
[3] A.-H. Mohsenian-Rad, A. Leon-Garcia, Optimal Residential Load Control
With Price Prediction in Real-Time Electricity Pricing Environments,
IEEE Transactions on Smart Grid, vol. 1, no. 2, 2010.
[4] M. P. F. Hommelberg, C. J. Warmer, I. G. Kamphuis, J. K. Kok, G. J.
Schaeffer, Distributed Control Concepts using Multi-Agent technology
and Automatic Markets: an Indispensable Feature of Smart Power Grids,
Proceedings of the IEEE Power Engineering Society General Meeting,
pp. 7, 2007.
[5] M. Sechilariu, B. Wang, F. Locment, ”Building Integrated Photovoltaic
System With Energy Storage and Smart Grid Communication”, IEEE
Transactions on Industrial Electronics, vol. 60, no. 4, pp. 1607–1618,
2013.
[6] Y. Ma, F. Borrelli, B. Hencey, B. Coffey, S. Bengea, P. Haves, ”Model
Predictive Control for the Operation of Building Cooling Systems”, IEEE
Transactions on Control Systems Technology, vol.20, no.3, pp. 796–803,
2012.
[7] A. Schirrer, O. K¨onig, S. Ghaemi, F. Kupzog, M. Kozek, ”Hierarchical
Application of Model-predictive Control for Efficient Integration of
Active Buildings into Low Voltage Grids”, Proceedings of the 2013
Workshop on Modeling and Simulation of Cyber-Physical Energy
Systems, MSCPES, pp. 6, 2013.
[8] X. Zhang, G. Schildbach, D. Sturzenegger, M. Morari, ”Scenario-Based
MPC for Energy-Efficient Building Climate Control under Weather and
Occupancy Uncertainty”, Proceedings of European Control Conference,
ECC, pp. 1029–1034, 2013.
[9] Y. Ma, J. Matuˇsko, F. Borrelli: Stochastic Model Predictive Control
for Building HVAC Systems: Complexity and Conservatism, IEEE
Transactions on Control Systems Technology, vol. 23, no. 1, pp. 101–116,
2015.
[10] J. Matuˇsko, F. Borrelli, ”Scenario-based approach to stochastic linear
predictive control”, Proceedings of the IEEE Conference on Decision
and Control, CDC, pp. 5194–5199, 2012.
[11] F. Oldewurtel, A. Parisio, C. N. Jones, M. Morari, D. Gyalistras,
M. Gwerder, V. Stauch, B. Lehmann, K. Wirth, Energy Efficient
Building Climate Control using Stochastic Model Predictive Control and
Weather Predictions, Proceedings of the American Control Conference,
pp. 5100-5105, 2010.
[12] T. ˇ Zakula, P.R. Armstrong, L. Norford, ”Modeling Environment for
Model Predictive Control of Buildings”, Energy and Buildings, vol. 85,
pp. 549–559, 2014.
[13] M. Vaˇsak, A. Martinˇcevi´c, ”Optimal Control of a Family House Heating
System”, Proceedings of the International Convention on Information and
Communication Technology, Electronics and Microelectronics, MIPRO,
pp. 907–912, 2013.
[14] A. Martinˇcevi´c, A. Starˇci´c, M. Vaˇsak, ”Parameter Estimation for
Low-order Models of Complex Buildings”, Proceedings of the Innovative
Smart Grid Technologies Europe, ISGT EUROPE, pp. 6, 2014.
[15] F. Oldewurtel, A. Parisio, C. N. Jones, D. Gyalistras, M. Gwerder, V.
Stauch, B. Lehmann, M. Morari, ”Use of Model Predictive Control and
Weather Forecasts for Energy Efficient Building Climate Control”, Energy
and Buildings, vol. 45, pp. 15–27, 2012.
[16] J. Drgoˇna, M. Kvasnica, M. Klauˇco, M. Fikar, ”Explicit Stochastic
MPC Approach to Building Temperature Control”, Proceedings of IEEE
Conference on Decision and Control, CDC, pp. 6440–6445, 2013.
[17] D. Sturzenegger, D. Gyalistras, M. Gwerder, C. Sagerschnig, M. Morari,
R. S. Smith, ”Model Predictive Control of a Swiss Office Building”,
Proceedings of the 11th REHVA World Congress Clima 2013, 2013.
[18] A. Bejan, A. D. Kraus, Heat Transfer Handbook, New Jersey: John
Wiley & Sons, ISBN 0-471-39015-1, 2003.
[19] C. Lu, M. Zheng, W. H. Leong, Verification and Analysis of a TRNSYS
Model of a Demonstration House Equipped with a Solar Assisted Ground
Coupled Heat Pump System, Proceedings of the International Conference
on Consumer Electronics, Communications and Networks, CECNet, pp.
1887-1891, 2011.
[20] W. J. Cole, E. T. Hale, T. F. Edgar, ”Building Energy Model Reduction
for Model Predictive Control Using OpenStudio”, American Control
Conference, ACC, pp. 449–454, 2013.
[21] ”User Manual IDA Indoor Climate and Energy”, EQUA Simulation AB,
2013.
[22] International Building Performance Simulation Association (IBPSA).
(2015, Sep. 8). Building Energy Software Tools Directory. Available:
http://www.buildingenergysoftwaretools.com
[23] K. Deng, P. Barooah, P. G. Mehta, S. P. Meyn, ”Building Thermal Model
Reduction via Aggregation of States”, Proceedings of the American
Control Conference, ACC, pp. 5118-5123, 2010.
[24] American Society of Heating Refrigerating and Air-Conditioning
Engineers, ”2009 ASHRAE Handbook, Fundamentals”, 2009.
[25] A. C. Antoulas, ”An Overview of Model Reduction Methods and a New
Result”, Proceedings of the IEEE Conference on Decision and Control,
CDC, pp. 5357-5361, 2009. [26] K. Glover, ”All Optimal Hankel-norm Approximations of Linear
Multivariable Systems and Their L(infinity)-error Bounds”, International
Journal of Control, vol. 39, no. 6, pp. 1115-1193, 1984.
[27] M. G. Safonov, R. Y. Chiang, ”Schur Method for Balanced-truncation
Model Reduction”, IEEE Transactions on Automatic Control, vol. 34, no.
7, pp. 729-733, 1989.
[28] S. J. Julier and J. K. Uhlmann, ”Unscented Filtering and Nonlinear
Estimation”, Proceedings of the IEEE, vol. 92, no. 3, pp. 401-422, 2004.
[29] M. Maasoumy, B. Moridian, M. Razmara, M. Shahbakhti, A.
Sangiovanni-Vincentelli, ”Online simultaneous state estimation and
parameter adaptation for building predictive control”, ASME Conference
Proceedings, vol. 2013, 2013.
[30] J. Jang, ”System Design and House Dynamic Signature Identification
for Intelligent Energy Management in Residential Buildings”, Ph.D.
dissertation, Dept. Mech. Eng., Univ. California at Berkeley, Berkeley,
CA, USA, 2008.
[31] BSI, EN 15251:2007, Indoor Environmental Input Parameters for Design
and Assessment of Energy Performance of Buildings Addressing Indoor
Air Quality, Thermal Environment, Lighting and Acoustics.
[32] S. Goyal, H. A. Ingley, P. Barooah, ”Zone-Level Control Algorithms
Based on Occupancy Information for Energy Efficient Buildings”,
Proceedings of the American Control Conference, ACC, pp. 3063–3068,
2012.
[33] M. Gulin, M. Vaˇsak, M. Baoti´c, ”Analysis of Microgrid Power Flow
Optimization with Consideration of Residual Storages State”, Proceedings
of the 2015 European Control Conference, ECC, pp. 3131–3136, 2015.
[34] W. S. Parker, ”Predicting Weather and Climate: Uncertainty, Ensembles
and Probability”, Studies in History and Philosophy of Modern Physics
vol. 41, no. 3, pp. 263–272, 2010.
[35] R. H. Lasseter, Smart Distribution: Coupled Microgrids, Proceedings of
the IEEE, vol. 99, no. 6, pp. 1074-1082, 2011.
[36] A. Parisio, E. Rikos, L. Glielmo, ”A Model Predictive Control Approach
to Microgrid Operation Optimization”, IEEE Transactions on Control
Systems Technology, vol. 22, no. 5, pp. 1813-1827, 2014.
[37] P. O. Kriett, M. Salani, ”Optimal control of a residential microgrid”,
Energy, vol. 42, no. 1, pp. 321–330, 2012.
[38] G. Comodi, A. Giantomassi, M. Severini, S. Squartini, F. Ferracuti,
A. Fonti, D. Nardi Cesarini, M. Morodo, F. Polonara: Multi-apartment
Residential Microgrid with Electrical and Thermal Storage Devices:
Experimental Analysis and Simulation of Energy Management Strategies,
Applied Energy, vol. 137, pp. 854–866, 2015.
[39] F. Blaabjerg, D. M. Ionel, ”Renewable Energy Devices and Systems
State-of-the- Art Technology, Research and Development, Challenges and
Future Trends”, Electric Power Components and Systems, vol. 43, no. 12,
pp. 1319–1328, 2015.
[40] M. Gulin, M. Vaˇsak, T. Pavlovi´c, Dynamical Behaviour Analysis
of a DC Microgrid in Distributed and Centralized Voltage Control
Configurations, Proceedings of the 23rd International Symposium on
Industrial Electronics, ISIE, pp. 2365-2370, 2014.
[41] M. Gulin, M. Vaˇsak, N. Peri´c, ”Dynamical optimal positioning of a
photovoltaic panel in all weather conditions”, Applied Energy, vol. 108,
pp. 429–438, 2013.
[42] M. Gulin, M. Vaˇsak, T. Pavlovi´c, ”Model Identification of a Photovoltaic
System for a DC Microgrid Simulation”, Proceedings of the 16th
International Power Electronics and Motion Control Conference and
Exposition, PEMC, pp. 501–506, 2014.
[43] M. Gulin, J. Matuˇsko, M. Vaˇsak, ”Stochastic Model Predictive Control
for Optimal Economic Operation of a Residential DC Microgrid”,
Proceedings of the 2015 IEEE International Conference on Industrial
Technology, ICIT, pp. 505–510, 2015.
[44] M. Gulin, M. Vaˇsak, G. Banjac, T. Tomiˇsa, ”Load Forecast of
a University Building for Application in Microgrid Power Flow
Optimization”, Proceedings of the IEEE International Energy Conference,
EnergyCon, pp. 1284–1288, 2014.
[45] L. Y. Pao, K. E. Johnson, ”Control of Wind Turbines: Approaches,
Challenges, and Recent Developments”, IEEE Control Systems Magazine,
vol. 31, no. 2, pp. 44–62, 2011.
[46] S. Widergren, C. Marinovici, T. Berliner, A. Graves, ”Real-time Pricing
Demand Response in Operations”, Power and Energy Society General
Meeting, pp. 5, 2012.
[47] C. Eksin, H. Delic, A. Ribeiro, ”Real-Time Pricing with Uncertain
and Heterogeneous Consumer Preferences”, Proceedings of the American
Control Conference, ACC, pp. 5692–5699, 2015.
[48] R. Scattolini, ”Architectures for distributed and hierarchical Model
Predictive Control A review”, Journal of Process Control, vol. 19, no.
5, pp. 723–731, 2009.
[49] EPEX SPOT. (2015, Sep. 8). European Power Exchange Electricity
Index. Available: http://www.epexspot.com
[50] M. Herceg, M. Kvasnica, C.N. Jones, M. Morari, ”Multi-Parametric
Toolbox 3.0”, Proceedings of the European Control Conference, ECC,
pp. 502–510, 2013.
[1] Renewable Energy Policy Network for the 21st Century (REN21),
Renewables 2015, Global Status Report, 2015.
[2] M. Erol-Kantarci, H. T. Mouftah, ”Energy-Efficient Information and
Communication Infrastructures in the Smart Grid: A Survey on
Interactions and Open Issues”, IEEE Communication Surveys & Tutorials,
vol. 17, no. 1, 2015.
[3] A.-H. Mohsenian-Rad, A. Leon-Garcia, Optimal Residential Load Control
With Price Prediction in Real-Time Electricity Pricing Environments,
IEEE Transactions on Smart Grid, vol. 1, no. 2, 2010.
[4] M. P. F. Hommelberg, C. J. Warmer, I. G. Kamphuis, J. K. Kok, G. J.
Schaeffer, Distributed Control Concepts using Multi-Agent technology
and Automatic Markets: an Indispensable Feature of Smart Power Grids,
Proceedings of the IEEE Power Engineering Society General Meeting,
pp. 7, 2007.
[5] M. Sechilariu, B. Wang, F. Locment, ”Building Integrated Photovoltaic
System With Energy Storage and Smart Grid Communication”, IEEE
Transactions on Industrial Electronics, vol. 60, no. 4, pp. 1607–1618,
2013.
[6] Y. Ma, F. Borrelli, B. Hencey, B. Coffey, S. Bengea, P. Haves, ”Model
Predictive Control for the Operation of Building Cooling Systems”, IEEE
Transactions on Control Systems Technology, vol.20, no.3, pp. 796–803,
2012.
[7] A. Schirrer, O. K¨onig, S. Ghaemi, F. Kupzog, M. Kozek, ”Hierarchical
Application of Model-predictive Control for Efficient Integration of
Active Buildings into Low Voltage Grids”, Proceedings of the 2013
Workshop on Modeling and Simulation of Cyber-Physical Energy
Systems, MSCPES, pp. 6, 2013.
[8] X. Zhang, G. Schildbach, D. Sturzenegger, M. Morari, ”Scenario-Based
MPC for Energy-Efficient Building Climate Control under Weather and
Occupancy Uncertainty”, Proceedings of European Control Conference,
ECC, pp. 1029–1034, 2013.
[9] Y. Ma, J. Matuˇsko, F. Borrelli: Stochastic Model Predictive Control
for Building HVAC Systems: Complexity and Conservatism, IEEE
Transactions on Control Systems Technology, vol. 23, no. 1, pp. 101–116,
2015.
[10] J. Matuˇsko, F. Borrelli, ”Scenario-based approach to stochastic linear
predictive control”, Proceedings of the IEEE Conference on Decision
and Control, CDC, pp. 5194–5199, 2012.
[11] F. Oldewurtel, A. Parisio, C. N. Jones, M. Morari, D. Gyalistras,
M. Gwerder, V. Stauch, B. Lehmann, K. Wirth, Energy Efficient
Building Climate Control using Stochastic Model Predictive Control and
Weather Predictions, Proceedings of the American Control Conference,
pp. 5100-5105, 2010.
[12] T. ˇ Zakula, P.R. Armstrong, L. Norford, ”Modeling Environment for
Model Predictive Control of Buildings”, Energy and Buildings, vol. 85,
pp. 549–559, 2014.
[13] M. Vaˇsak, A. Martinˇcevi´c, ”Optimal Control of a Family House Heating
System”, Proceedings of the International Convention on Information and
Communication Technology, Electronics and Microelectronics, MIPRO,
pp. 907–912, 2013.
[14] A. Martinˇcevi´c, A. Starˇci´c, M. Vaˇsak, ”Parameter Estimation for
Low-order Models of Complex Buildings”, Proceedings of the Innovative
Smart Grid Technologies Europe, ISGT EUROPE, pp. 6, 2014.
[15] F. Oldewurtel, A. Parisio, C. N. Jones, D. Gyalistras, M. Gwerder, V.
Stauch, B. Lehmann, M. Morari, ”Use of Model Predictive Control and
Weather Forecasts for Energy Efficient Building Climate Control”, Energy
and Buildings, vol. 45, pp. 15–27, 2012.
[16] J. Drgoˇna, M. Kvasnica, M. Klauˇco, M. Fikar, ”Explicit Stochastic
MPC Approach to Building Temperature Control”, Proceedings of IEEE
Conference on Decision and Control, CDC, pp. 6440–6445, 2013.
[17] D. Sturzenegger, D. Gyalistras, M. Gwerder, C. Sagerschnig, M. Morari,
R. S. Smith, ”Model Predictive Control of a Swiss Office Building”,
Proceedings of the 11th REHVA World Congress Clima 2013, 2013.
[18] A. Bejan, A. D. Kraus, Heat Transfer Handbook, New Jersey: John
Wiley & Sons, ISBN 0-471-39015-1, 2003.
[19] C. Lu, M. Zheng, W. H. Leong, Verification and Analysis of a TRNSYS
Model of a Demonstration House Equipped with a Solar Assisted Ground
Coupled Heat Pump System, Proceedings of the International Conference
on Consumer Electronics, Communications and Networks, CECNet, pp.
1887-1891, 2011.
[20] W. J. Cole, E. T. Hale, T. F. Edgar, ”Building Energy Model Reduction
for Model Predictive Control Using OpenStudio”, American Control
Conference, ACC, pp. 449–454, 2013.
[21] ”User Manual IDA Indoor Climate and Energy”, EQUA Simulation AB,
2013.
[22] International Building Performance Simulation Association (IBPSA).
(2015, Sep. 8). Building Energy Software Tools Directory. Available:
http://www.buildingenergysoftwaretools.com
[23] K. Deng, P. Barooah, P. G. Mehta, S. P. Meyn, ”Building Thermal Model
Reduction via Aggregation of States”, Proceedings of the American
Control Conference, ACC, pp. 5118-5123, 2010.
[24] American Society of Heating Refrigerating and Air-Conditioning
Engineers, ”2009 ASHRAE Handbook, Fundamentals”, 2009.
[25] A. C. Antoulas, ”An Overview of Model Reduction Methods and a New
Result”, Proceedings of the IEEE Conference on Decision and Control,
CDC, pp. 5357-5361, 2009. [26] K. Glover, ”All Optimal Hankel-norm Approximations of Linear
Multivariable Systems and Their L(infinity)-error Bounds”, International
Journal of Control, vol. 39, no. 6, pp. 1115-1193, 1984.
[27] M. G. Safonov, R. Y. Chiang, ”Schur Method for Balanced-truncation
Model Reduction”, IEEE Transactions on Automatic Control, vol. 34, no.
7, pp. 729-733, 1989.
[28] S. J. Julier and J. K. Uhlmann, ”Unscented Filtering and Nonlinear
Estimation”, Proceedings of the IEEE, vol. 92, no. 3, pp. 401-422, 2004.
[29] M. Maasoumy, B. Moridian, M. Razmara, M. Shahbakhti, A.
Sangiovanni-Vincentelli, ”Online simultaneous state estimation and
parameter adaptation for building predictive control”, ASME Conference
Proceedings, vol. 2013, 2013.
[30] J. Jang, ”System Design and House Dynamic Signature Identification
for Intelligent Energy Management in Residential Buildings”, Ph.D.
dissertation, Dept. Mech. Eng., Univ. California at Berkeley, Berkeley,
CA, USA, 2008.
[31] BSI, EN 15251:2007, Indoor Environmental Input Parameters for Design
and Assessment of Energy Performance of Buildings Addressing Indoor
Air Quality, Thermal Environment, Lighting and Acoustics.
[32] S. Goyal, H. A. Ingley, P. Barooah, ”Zone-Level Control Algorithms
Based on Occupancy Information for Energy Efficient Buildings”,
Proceedings of the American Control Conference, ACC, pp. 3063–3068,
2012.
[33] M. Gulin, M. Vaˇsak, M. Baoti´c, ”Analysis of Microgrid Power Flow
Optimization with Consideration of Residual Storages State”, Proceedings
of the 2015 European Control Conference, ECC, pp. 3131–3136, 2015.
[34] W. S. Parker, ”Predicting Weather and Climate: Uncertainty, Ensembles
and Probability”, Studies in History and Philosophy of Modern Physics
vol. 41, no. 3, pp. 263–272, 2010.
[35] R. H. Lasseter, Smart Distribution: Coupled Microgrids, Proceedings of
the IEEE, vol. 99, no. 6, pp. 1074-1082, 2011.
[36] A. Parisio, E. Rikos, L. Glielmo, ”A Model Predictive Control Approach
to Microgrid Operation Optimization”, IEEE Transactions on Control
Systems Technology, vol. 22, no. 5, pp. 1813-1827, 2014.
[37] P. O. Kriett, M. Salani, ”Optimal control of a residential microgrid”,
Energy, vol. 42, no. 1, pp. 321–330, 2012.
[38] G. Comodi, A. Giantomassi, M. Severini, S. Squartini, F. Ferracuti,
A. Fonti, D. Nardi Cesarini, M. Morodo, F. Polonara: Multi-apartment
Residential Microgrid with Electrical and Thermal Storage Devices:
Experimental Analysis and Simulation of Energy Management Strategies,
Applied Energy, vol. 137, pp. 854–866, 2015.
[39] F. Blaabjerg, D. M. Ionel, ”Renewable Energy Devices and Systems
State-of-the- Art Technology, Research and Development, Challenges and
Future Trends”, Electric Power Components and Systems, vol. 43, no. 12,
pp. 1319–1328, 2015.
[40] M. Gulin, M. Vaˇsak, T. Pavlovi´c, Dynamical Behaviour Analysis
of a DC Microgrid in Distributed and Centralized Voltage Control
Configurations, Proceedings of the 23rd International Symposium on
Industrial Electronics, ISIE, pp. 2365-2370, 2014.
[41] M. Gulin, M. Vaˇsak, N. Peri´c, ”Dynamical optimal positioning of a
photovoltaic panel in all weather conditions”, Applied Energy, vol. 108,
pp. 429–438, 2013.
[42] M. Gulin, M. Vaˇsak, T. Pavlovi´c, ”Model Identification of a Photovoltaic
System for a DC Microgrid Simulation”, Proceedings of the 16th
International Power Electronics and Motion Control Conference and
Exposition, PEMC, pp. 501–506, 2014.
[43] M. Gulin, J. Matuˇsko, M. Vaˇsak, ”Stochastic Model Predictive Control
for Optimal Economic Operation of a Residential DC Microgrid”,
Proceedings of the 2015 IEEE International Conference on Industrial
Technology, ICIT, pp. 505–510, 2015.
[44] M. Gulin, M. Vaˇsak, G. Banjac, T. Tomiˇsa, ”Load Forecast of
a University Building for Application in Microgrid Power Flow
Optimization”, Proceedings of the IEEE International Energy Conference,
EnergyCon, pp. 1284–1288, 2014.
[45] L. Y. Pao, K. E. Johnson, ”Control of Wind Turbines: Approaches,
Challenges, and Recent Developments”, IEEE Control Systems Magazine,
vol. 31, no. 2, pp. 44–62, 2011.
[46] S. Widergren, C. Marinovici, T. Berliner, A. Graves, ”Real-time Pricing
Demand Response in Operations”, Power and Energy Society General
Meeting, pp. 5, 2012.
[47] C. Eksin, H. Delic, A. Ribeiro, ”Real-Time Pricing with Uncertain
and Heterogeneous Consumer Preferences”, Proceedings of the American
Control Conference, ACC, pp. 5692–5699, 2015.
[48] R. Scattolini, ”Architectures for distributed and hierarchical Model
Predictive Control A review”, Journal of Process Control, vol. 19, no.
5, pp. 723–731, 2009.
[49] EPEX SPOT. (2015, Sep. 8). European Power Exchange Electricity
Index. Available: http://www.epexspot.com
[50] M. Herceg, M. Kvasnica, C.N. Jones, M. Morari, ”Multi-Parametric
Toolbox 3.0”, Proceedings of the European Control Conference, ECC,
pp. 502–510, 2013.
@article{"International Journal of Information, Control and Computer Sciences:71887", author = "Vinko Lešić and Mario Vašak and Anita Martinčević and Marko Gulin and Antonio Starčić and Hrvoje Novak", title = "Computer-Assisted Management of Building Climate and Microgrid with Model Predictive Control", abstract = "With 40% of total world energy consumption,
building systems are developing into technically complex large
energy consumers suitable for application of sophisticated power
management approaches to largely increase the energy efficiency
and even make them active energy market participants. Centralized
control system of building heating and cooling managed by
economically-optimal model predictive control shows promising
results with estimated 30% of energy efficiency increase. The research
is focused on implementation of such a method on a case study
performed on two floors of our faculty building with corresponding
sensors wireless data acquisition, remote heating/cooling units and
central climate controller. Building walls are mathematically modeled
with corresponding material types, surface shapes and sizes. Models
are then exploited to predict thermal characteristics and changes in
different building zones. Exterior influences such as environmental
conditions and weather forecast, people behavior and comfort
demands are all taken into account for deriving price-optimal climate
control. Finally, a DC microgrid with photovoltaics, wind turbine,
supercapacitor, batteries and fuel cell stacks is added to make the
building a unit capable of active participation in a price-varying
energy market. Computational burden of applying model predictive
control on such a complex system is relaxed through a hierarchical
decomposition of the microgrid and climate control, where the
former is designed as higher hierarchical level with pre-calculated
price-optimal power flows control, and latter is designed as lower
level control responsible to ensure thermal comfort and exploit
the optimal supply conditions enabled by microgrid energy flows
management. Such an approach is expected to enable the inclusion
of more complex building subsystems into consideration in order to
further increase the energy efficiency.", keywords = "Energy-efficient buildings, Hierarchical model
predictive control, Microgrid power flow optimization, Price-optimal
building climate control.", volume = "9", number = "10", pages = "2262-12", }
