Comparative evaluation of model predictive control strategies for a building HVAC system

Putta, Vamsi; Zhu, Guangwei; Kim, Donghun; Hu, Jianghai; Braun, James E.

doi:10.1109/acc.2013.6580365

Cited by 8 publications

(7 citation statements)

References 2 publications

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“…There are research efforts on minimizing the operating costs of BACS based on the predictions of electricity price while respecting thermal comfort constraints [45][46][47][48]. Nonetheless, the attentions of these proposals are only based on static electricity price.…”

Section: Electricity Price Policy For Energy Trading In Microgridsmentioning

confidence: 99%

A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids

2018

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Abstract:With the penetration of grid-connected renewable energy generation, microgrids are facing stability and power quality problems caused by renewable intermittency. To alleviate such problems, demand side management (DSM) of responsive loads, such as building air-conditioning system (BACS), has been proposed and studied. In recent years, numerous control approaches have been published for proper management of single BACS. The majority of these approaches focus on either the control of BACS for attenuating power fluctuations in the grid or the operating cost minimization on behalf of the residents. These two control objectives are paramount for BACS control in microgrids and can be conflicting. As such, they should be considered together in control design. As individual buildings may have different owners/residents, it is natural to control different BACSs in an autonomous and self-interested manner to minimize the operational costs for the owners/residents. Unfortunately, such "selfish" operation can result in abrupt and large power fluctuations at the point of common coupling (PCC) of the microgrid due to lack of coordination. Consequently, the original objective of mitigating power fluctuations generated by renewable intermittency cannot be achieved. To minimize the operating costs of individual BACSs and simultaneously ensure desirable overall power flow at PCC, this paper proposes a novel distributed control framework based on the dissipativity theory. The proposed method achieves the objective of renewable intermittency mitigation through proper coordination of distributed BACS controllers and is scalable and computationally efficient. Simulation studies are carried out to illustrate the efficacy of the proposed control framework.

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Section: Electricity Price Policy For Energy Trading In Microgridsmentioning

confidence: 99%

A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids

2018

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“…These objectives are clearly conflicting and an appropriate compromise should be set when designing the control policy. Differently from [18], where both objectives are incorporated in the cost function by expressing the discomfort in terms of productivity loss and attributing to such a loss some monetary value, e.g., in terms of average salary of the occupants, here, the discomfort is evaluated in terms of integral of the difference between the actual building temperature and some comfort value and is accounted for in the design through some constraint.…”

Section: Introductionmentioning

confidence: 99%

Optimal building climate control: a solution based on nested dynamic programming and randomized optimization

Deori

Giulioni

Prandini

2014

53rd IEEE Conference on Decision and Control

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We consider the problem of minimizing the electric energy consumption in the climate control of a building. More specifically, we suppose that the reference temperature in the building can be modulated to some extent during the day, so as to reduce the power request while limiting the introduced level of discomfort. To account for the uncertain building occupancy, we formulate the optimal energy management problem as a chance constrained optimization problem where one seeks for a state feedback policy that is robust over all occupancy realizations except for a set of predefined (small) probability. By exploiting the different time scales of the control and disturbance inputs, an algorithmic solution based on nested dynamic programming and randomized optimization is put forward and its parallel GPU implementation is discussed. A numerical example shows the efficacy of the approach

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“…In our previous work[10] we have shown that the simplified metric gives a good approximation of the PPD and PMV within a certain range of the thermal conditions…”

mentioning

confidence: 99%

A switched dynamic programming approach towards optimal control of multiple rooftop units

Putta

Kim

Cai

et al. 2015

2015 American Control Conference (ACC)

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Buildings served by multiple rooftop units (RTUs) are widely prevalent in retail and food industries. Till date, most of such buildings utilize plain feedback or heuristic strategies to determine the RTU operations. We present a switched dynamic programming based approach for scheduling rooftop unit operations in an optimal manner. Using a mid-size restaurant served by multiple RTUs as an example, a switched affine dynamic system model is first presented. The RTU coordination control problem is then formulated as a switched optimal control problem with nontrivial switching cost. A dynamical programming based method is proposed for the solution of the switched optimal control problem, which has reasonable computational complexity and can be implemented as a model predictive control algorithm. The proposed method is evaluated through simulations, and its strengths and limitations are discussed.

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Comparative evaluation of model predictive control strategies for a building HVAC system

Cited by 8 publications

References 2 publications

A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids

A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids

Optimal building climate control: a solution based on nested dynamic programming and randomized optimization

A switched dynamic programming approach towards optimal control of multiple rooftop units

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