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Charlotte Marguerite, K. Siau, C. Beauthier, J. Blanchard, C. Verhelst, R. De Coninck
Contact: [email protected]
Optimization of flexible electricity loads of a building cluster using distributed model predictive control
SES Conference, 10-11 Sept. 2019
Doc. ref.: SES Conference-NS-001-00
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The BATTERIE project - Context
• For tertiary buildings, final electricity consumption ≥ 25% of the energy balance
• Increasing renewable electricity production need more flexibility of electricity demand
• Possible improvement through better controls.
SES Conference, 10-11 Sept. 2019
Lawrence-Berkeley National Lab case study of 60 buildings (2002)
© 2019 Cenaero – All rights reserved
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The BATTERIE project - Objective
• “Développement d'une interface pour les BATiments Tertiaires Efficaces intégrés au Réseau Electrique Intelligent”
• Development of an interface for smart control of tertiary buildings:– Reduction of building energy consumption through optimal control– Improvement of the flexibility of a group of buildings by collaborative control
SES Conference, 10-11 Sept. 2019
Optimal Control
Multiple objectivesConstraintsDynamics
Time-varying inputs
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Gas Boiler
Heat Pump TES
€/kWh
t
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The BATTERIE project
• Block diagram of the developed Interface:– Building monitoring, data mining– Identification of building model– Loads forecast and optimization
of consumption– Operation of flexible resources
through optimization of HVAC control (MPC)
– Aggregation of flexibility at building stock level
SES Conference, 10-11 Sept. 2019 © 2019 Cenaero – All rights reserved
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Study case - description
• Test case: 4 buildings with different heating systems and insulation levels: Radiators & K30, Radiators & K45, Floor heating & K30, Floor heating & K45.
• Objective function: minimizing thermal discomfort and energy costs for each building.
• Global constraints: – Scenario 1: Sum of the power of all buildings at each time step
cannot exceed a given threshold (5000 W) (to prevent grid congestion when N houses are connected to the same power line).
– Scenario 2: Minimum shared usage of PV.
• Modeled in Modelica and optimized via the NLP solver IPOPT (Interior Point OPTimizer).
SES Conference, 10-11 Sept. 2019 © 2019 Cenaero – All rights reserved
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MPC methods
SES Conference, 10-11 Sept. 2019
Global objective = min of individual objectives
Global constraint
𝑓𝑓𝑖𝑖 local objective 𝐸𝐸𝑖𝑖 local energy consumptionλ shadow price for the use of the shared resourcePcons total power consumptionPmax maximum load supported by a power grid line
Supplier
SupplierSupplier
1- Penalty function2- Local static constraints3- Local dynamic constraints
© 2019 Cenaero – All rights reserved
Dual decompositionCooperative MPC
Centralized MPC
Distributed MPC
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Scenario 1: Results Centralized vs. Distributed MPC methods
• Comparison of total power usage
SES Conference, 10-11 Sept. 2019
The Local Dynamic Constraints Method fits best with the centralized method.
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Scenario 1: Results Centralized vs. Distributed MPC methods
• Comparison of air zone temperature
SES Conference, 10-11 Sept. 2019 © 2019 Cenaero – All rights reserved
The Local Dynamic Constraints Method fits best with the centralized method.
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Scenario 1: Results Centralized vs. Distributed MPC methods
• Dual decomposition method vs Cooperative MPC (computational time for each time step)
SES Conference, 10-11 Sept. 2019 © 2019 Cenaero – All rights reserved
The Local Dynamic Constraints is faster
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Scenario 2: ResultsMPC methods comparison
• 4 buildings – minimum shared PV power usage: Temperature profile
SES Conference, 10-11 Sept. 2019
CooperativeCentralized Building n°1 (Floor heating K30) Building n°2 (Floor heating K45)
Building n°3 (Radiators K30) Building n°4 (Radiators K45)
Cooperative MPC matches centralized method
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Scenario 2: ResultsMPC methods comparison
• 4 buildings – minimum shared PV power usage: total power consumption
SES Conference, 10-11 Sept. 2019
Cooperative MPCCentralized MPCPV Production
Pow
er c
onsu
mpt
ion
(W)
© 2019 Cenaero – All rights reserved
Cooperative MPC matches centralized method
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Conclusions
• The aim of Distributed MPC is to decompose a largeoptimization problem into smaller, easier to solve andmanageable problems, leading to the same optimal solution.
• For this specific test case, the dynamic constraints methodperforms better than the dual decomposition method(accuracy and computational time).
• Different algorithms were implemented and tested ondifferent use cases– The algorithms are robust with respect to global constraints– The applied methodology is working well and scalable.
SES Conference, 10-11 Sept. 2019 © 2019 Cenaero – All rights reserved
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Thank you for your attention!
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Scenario 1 (extended to 50 buildings): Results Centralized vs. Distributed MPC methods
• Test case extended to 50 buildings to test the scaling capabilities and robustness of the algorithms.
• Total power consumption of the 50 buildings for Distributed MPC and centralized MPC:
SES Conference, 10-11 Sept. 2019 © 2019 Cenaero – All rights reserved
Cooperative MPC fits the best to the centralized method