Distributed flexibility characterization and resource allocation for multi-zone commercial buildings in the smart grid

He, Hao; Lian, Jianming; Kalsi, Karanjit; Stoustrup, Jakob

doi:10.1109/cdc.2015.7402693

Cited by 20 publications

(18 citation statements)

References 19 publications

(30 reference statements)

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“…where C i is the thermal capacitance, T i is the indoor temperature, T o is the outdoor temperature, R i is the thermal resistance of the wall and window separating zone i and outside, R ij is the thermal resistance of the wall separating zones i and j, c a is the specific heat of the air, m i is the flow rate of the supply air, T s is the supply air temperature which is usually a constant [17], and Q i ≥ 0 is the heat gain from exogenous sources (e.g., user activity, solar radiation and device operation). If R i , R ij , C i are not known from design specification, they can be obtained via model identification [22], [23].…”

Section: B Thermal Dynamic Modelmentioning

confidence: 99%

See 1 more Smart Citation

Decentralized temperature control via HVAC systems in energy efficient buildings: An approximate solution procedure

Zhang

Shi

et al. 2016

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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In this paper, we design real-time decentralized and distributed control schemes for Heating Ventilation and Air Conditioning (HVAC) systems in energy efficient buildings. The control schemes balance user comfort and energy saving, and are implemented without measuring or predicting exogenous disturbances. Firstly, we introduce a thermal dynamic model of building systems and formulate a steady-state resource allocation problem, which aims to minimize the aggregate deviation between zone temperatures and their set points, as well as the building energy consumption, subject to practical operating constraints, by adjusting zone flow rates. Because this problem is nonconvex, we propose two methods to (approximately) solve it and to design the real-time control. In the first method, we present a convex relaxation approach to solve an approximate version of the steady-state optimization problem, where the heat transfer between neighboring zones is ignored. We prove the tightness of the relaxation and develop a real-time decentralized algorithm to regulate the zone flow rate. In the second method, we introduce a mild assumption under which the original optimization problem becomes convex, and then a real-time distributed algorithm is developed to regulate the zone flow rate. In both cases, the thermal dynamics can be driven to equilibria which are optimal solutions to those associated steady-state optimization problems. Finally, numerical examples are provided to illustrate the effectiveness of the designed control schemes.

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Section: B Thermal Dynamic Modelmentioning

confidence: 99%

“…Though system (1) is a 1st-order RC model, using higher order RC models does not affect the formulation of (2) since it is a steady-state optimization problem. For example, for the 2nd-order model in [17], [21]…”

Section: The Optimization Problemmentioning

confidence: 99%

Decentralized temperature control via HVAC systems in energy efficient buildings: An approximate solution procedure

Zhang

Shi

et al. 2016

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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“…For an HVAC system whose baseline fan power is difficult to predict, the two methods may have poor performance tracking a dispatch signal. Some examples in the literature use model predictive control to schedule a base operating point for the HVAC fan or chiller (Vrettos, Kara, et al, Experimental Demonstration of Frequency Regulation by Commercial Buildings-Part I: Modeling and Hierarchical Control Design 2016), (Vrettos, Kara, et al, Experimental Demonstration of Frequency Regulation by Commercial Buildings-Part II: Results and Performance Evaluation 2016), (Mai and Chung 2015), (Hao, Lian, et al 2015), (Lin, Barooah and Mathieu, Ancillary services to the grid from commercial buildings through demand scheduling and control 2015). However, these methods need to vastly change the existing building control loop and require accurate zone thermal models and load models, which are not desirable for building operators.…”

Section: 5mentioning

confidence: 99%

Coordination and Control of Flexible Building Loads for Renewable Integration; Demonstrations using VOLTTRON

Hao

Liu

Huang

et al. 2016

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Renewable energy resources such as wind and solar power have a high degree of uncertainty. Large-scale integration of these variable generation sources into the grid is a big challenge for power system operators. Buildings, in which we live and work, consume about 75% of the total electricity in the United States. They also have a large capacity of power flexibility due to their massive thermal capacitance. Therefore, they present a great opportunity to help the grid to manage power balance. In this report, we study coordination and control of flexible building loads for renewable integration. We first present the motivation and background, and conduct a literature review on building-to-grid integration. We also compile a catalog of flexible building loads that have great potential for renewable integration, and discuss their characteristics. We next collect solar generation data from a photovoltaic panel on Pacific Northwest National Laboratory campus, and conduct data analysis to study their characteristics. We find that solar generation output has a strong uncertainty, and the uncertainty occurs at almost all time scales. Additional data from other sources are also used to verify our study. We propose two transactive coordination strategies to manage flexible building loads for renewable integration. We prove the theories that support the two transactive coordination strategies and discuss their pros and cons. In this report, we select three types of flexible building loads-air-handling unit, rooftop unit, and a population of water heaters (WHs)-for which we demonstrate control of the flexible load to track a dispatch signal (e.g., renewable generation fluctuation) using experiment, simulation, or hardware-in-the-loop study. More specifically, we present the system description, model identification, controller design, test bed setup, and experiment results for each demonstration. We show that coordination and control of flexible loads has a great potential to integrate variable generation sources. The flexible loads can successfully track a power dispatch signal from the coordinator, while having little impact on the quality of service to the end-users. v Summary Decarbonizing the global energy system is one of the most important endeavors of our time. It is widely accepted that a sustainable energy future will increasingly rely on renewable energy sources such as wind and solar. However, these variable generations are volatile, intermittent, and uncontrollable. The vast integration of variable generation sources brings a significant amount of uncertainty to the power grid, and presents a daunting challenge to current power system operation and control. Maintaining the power balance with uncertain renewables is one of the most important problems in smart grid research. Traditionally, the uncertainty of renewables has been handled through supply-side (generation) reserves. However, recent studies show that deep penetration of renewables will substantially increase the procurement of additional backup generation...

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“…Clearly, in this case, the number of decision variables increases dramatically, which leads to the curse of dimensionality [5][6][7]. In addition, the thermal dynamic model of the HVAC systems in a building is complex, which may further increase the dimension of optimization problem and the computational complexity [8][9][10][11][12]. References [13,14] proposed a two-timescale stochastic optimization model for control and scheduling the appliances to satisfy user's thermal comfort requirement under the peak power and cost constraints.…”

Section: Introductionmentioning

confidence: 99%

“…In this mode, U B [k] and U C[k] indicate two different kinds of control effect. By solving the slow timescale model, the control variable is obtained with a set of u nk decomposition, i.e.,(10). Thus, the decision is realizable in the fast timescale, in other words, there is a set of fast timescale state transfer as X[k] = x nk .…”

mentioning

confidence: 99%

A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

2021

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Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

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Distributed flexibility characterization and resource allocation for multi-zone commercial buildings in the smart grid

Cited by 20 publications

References 19 publications

Decentralized temperature control via HVAC systems in energy efficient buildings: An approximate solution procedure

Decentralized temperature control via HVAC systems in energy efficient buildings: An approximate solution procedure

Coordination and Control of Flexible Building Loads for Renewable Integration; Demonstrations using VOLTTRON

A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

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