Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment
Integrating intermittent renewable energy sources has renders the power network operator task of balancing electricity generation and consumption increasingly challenging. Aside from heavily investing in additional storage capacities, an interesting solution might be the use predictive control metho...
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doaj-c42facb58af8420caf32713f71ef1fc12020-11-24T23:22:21ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2018-05-01610.3389/fenrg.2018.00022355321Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built EnvironmentPaul Stadler0Luc Girardin1Araz Ashouri2François Maréchal3École Polytechnique fédérale de Lausanne (EPFL), Sion, SwitzerlandÉcole Polytechnique fédérale de Lausanne (EPFL), Sion, SwitzerlandNational Research Council of Canada, Ottawa, ON, CanadaÉcole Polytechnique fédérale de Lausanne (EPFL), Sion, SwitzerlandIntegrating intermittent renewable energy sources has renders the power network operator task of balancing electricity generation and consumption increasingly challenging. Aside from heavily investing in additional storage capacities, an interesting solution might be the use predictive control methods to shift controllable loads toward production periods. Therefore, this article introduces a systematic approach to provide a preliminary evaluation of the thermoeconomic impact of model predictive control (MPC) when being applied to modern and complex building energy systems (BES). The proposed method applies an ϵ-constraint multi-objective optimization to generate a large panel of different BES configurations and their respective operating strategies. The problem formulation relies on a holistic BES framework to satisfy the different building service requirements using a mixed-integer linear programming technique. To illustrate the contribution of MPC, different applications on the single- and multi-dwelling level are presented and analyzed. The results suggest that MPC can facilitate the integration of renewable energy sources within the built environment by adjusting the heating and cooling demand to the fluctuating renewable generation, increasing the share of self-consumption by up to 27% while decreasing the operating expenses by up to 3% on the single-building level. Finally, a preliminary assessment of the national-wide potential is performed by means of an extended implementation on the Swiss building stock.http://journal.frontiersin.org/article/10.3389/fenrg.2018.00022/fullrenewable energyMILPmulti-objective optimisationdistributed energy systemsmodel predictive controlself-consumption |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Paul Stadler Luc Girardin Araz Ashouri François Maréchal |
spellingShingle |
Paul Stadler Luc Girardin Araz Ashouri François Maréchal Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment Frontiers in Energy Research renewable energy MILP multi-objective optimisation distributed energy systems model predictive control self-consumption |
author_facet |
Paul Stadler Luc Girardin Araz Ashouri François Maréchal |
author_sort |
Paul Stadler |
title |
Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment |
title_short |
Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment |
title_full |
Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment |
title_fullStr |
Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment |
title_full_unstemmed |
Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment |
title_sort |
contribution of model predictive control in the integration of renewable energy sources within the built environment |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Energy Research |
issn |
2296-598X |
publishDate |
2018-05-01 |
description |
Integrating intermittent renewable energy sources has renders the power network operator task of balancing electricity generation and consumption increasingly challenging. Aside from heavily investing in additional storage capacities, an interesting solution might be the use predictive control methods to shift controllable loads toward production periods. Therefore, this article introduces a systematic approach to provide a preliminary evaluation of the thermoeconomic impact of model predictive control (MPC) when being applied to modern and complex building energy systems (BES). The proposed method applies an ϵ-constraint multi-objective optimization to generate a large panel of different BES configurations and their respective operating strategies. The problem formulation relies on a holistic BES framework to satisfy the different building service requirements using a mixed-integer linear programming technique. To illustrate the contribution of MPC, different applications on the single- and multi-dwelling level are presented and analyzed. The results suggest that MPC can facilitate the integration of renewable energy sources within the built environment by adjusting the heating and cooling demand to the fluctuating renewable generation, increasing the share of self-consumption by up to 27% while decreasing the operating expenses by up to 3% on the single-building level. Finally, a preliminary assessment of the national-wide potential is performed by means of an extended implementation on the Swiss building stock. |
topic |
renewable energy MILP multi-objective optimisation distributed energy systems model predictive control self-consumption |
url |
http://journal.frontiersin.org/article/10.3389/fenrg.2018.00022/full |
work_keys_str_mv |
AT paulstadler contributionofmodelpredictivecontrolintheintegrationofrenewableenergysourceswithinthebuiltenvironment AT lucgirardin contributionofmodelpredictivecontrolintheintegrationofrenewableenergysourceswithinthebuiltenvironment AT arazashouri contributionofmodelpredictivecontrolintheintegrationofrenewableenergysourceswithinthebuiltenvironment AT francoismarechal contributionofmodelpredictivecontrolintheintegrationofrenewableenergysourceswithinthebuiltenvironment |
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