4.6 Article

Modular Hierarchical Model Predictive Control for Coordinated and Holistic Energy Management of Buildings

Journal

IEEE TRANSACTIONS ON ENERGY CONVERSION
Volume 36, Issue 4, Pages 2670-2682

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEC.2021.3116153

Keywords

Buildings; HVAC; Costs; Microgrids; Thermal energy; Optimization; Control systems; Building energy management system; central medium conditioning; zone comfort control; energy efficiency; hierarchical coordination; microgrid energy management; model predictive control; price-optimal control

Funding

  1. European Union Regional Development Fund via Operational Programme Competitiveness and Cohesion for Croatia through the Project PC-ATE Buildings - Development of a System for Predictive Control and Autonomous Trading of Energy in Buildings [KK.01.2.1.01.0069]

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The paper proposes a modular building energy management strategy based on a three-level hierarchical model predictive control, where building zones, central medium conditioning, and microgrid subsystems are controlled independently and then integrated for a holistic energy management strategy. Detailed simulations demonstrate significant cost reduction in building operation for typical summer days, showcasing the effectiveness of the proposed strategy.
Modular building energy management strategy based on a three-level hierarchical model predictive control is proposed in the paper. Building zones, central medium conditioning and microgrid subsystems are controlled independently by individual linear and nonlinear model predictive controllers, and further integrated together as levels of hierarchical coordination control structure based on price-consumption information exchange. The three-level coordination provides a holistic energy management strategy and enables significant demand response ancillary services for buildings as prosumers, while retaining the independence of required expertise in very different building subsystems. The approach is applied for daily operation scheduling of a full-scale building consisting of 248 offices. Models of building subsystems are obtained by identification procedures on measurement data. Compared to rule-based control, detailed realistic simulations show that the overall building operation cost for typical days in summer is reduced by 9-12% for level-by-level energy-optimal and by 15-24% for price-optimal, coordinated operation. The application of predictive control in the proposed way also improves the indoor comfort substantially.

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