Supervisory and Optimal Control of Building HVAC Systems: A Review

Wang, Shengwei; Ma, Zhenjun

doi:10.1080/10789669.2008.10390991

Cited by 388 publications

(163 citation statements)

References 59 publications

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“…in (18) or (19). The performance of this method of changing a set point depends entirely on the operation of the existing controller.…”

Section: Methods Of Changing a Set Pointmentioning

confidence: 99%

“…Different from (17), it is possible to apply (18) (19). This method of changing a set point is represented as a block diagram in Fig.…”

Section: Methods Of Changing a Set Pointmentioning

confidence: 99%

“…In other words, the function of the existing on-off controller corresponds to the regulatory control in the timescale of seconds and the response to the electricity prices for the reduction of electricity cost corresponds to the supervisory control in the timescale of minutes. It is true that DR capability can be effectively implemented in the TCL with the intelligent control methods such as adaptive control and evolutionary algorithm [19][20][21]. However, the traditional control method such as the on-off control is still a commonly used method in the TCL because of low cost [22,23].…”

Section: Introductionmentioning

confidence: 99%

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Methods for Adding Demand Response Capability to a Thermostatically Controlled Load with an Existing On-off Controller

Jin¹,

Yoon²

2015

Journal of Electrical Engineering and Technology

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-A thermostatically controlled load (TCL) can be one of the most appropriate resources for demand response (DR) in a smart grid environment. DR capability can be effectively implemented in a TCL with various intelligent control methods. However, because traditional on-off control is still a commonly used method in a TCL, it is useful to develop a method for adding DR capability to the TCL with an existing on-off controller. As a specific realization of supervisory control for implementing DR capability in the TCL, two methods are proposed -a method involving the changing of a set point and a method involving the paralleling of an identified system without delay. The proposed methods are analyzed through the simulations with an electric heater for different power consumption levels in the on-state. Considerable cost benefit can be achieved with the proposed methods when compared with the case without DR. In addition, the observations suggest that a medium power consumption level, instead of the maximum power, in the on-state should be used for consistently obtaining the cost benefit without severe temperature deviation from the specified temperature range for DR.

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“…in (18) or (19). The performance of this method of changing a set point depends entirely on the operation of the existing controller.…”

Section: Methods Of Changing a Set Pointmentioning

confidence: 99%

“…Different from (17), it is possible to apply (18) (19). This method of changing a set point is represented as a block diagram in Fig.…”

Section: Methods Of Changing a Set Pointmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Methods for Adding Demand Response Capability to a Thermostatically Controlled Load with an Existing On-off Controller

Jin¹,

Yoon²

2015

Journal of Electrical Engineering and Technology

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“…Few researchers have reported practical validation of supervisory and optimal control methods on real HVAC systems (West et al, 2014), especially large and complicated systems (Wang and Ma, 2008). Some researchers have presented computer models based on laboratory data.…”

Section: Introductionmentioning

confidence: 99%

Global optimal control of variable air volume air-conditioning system with iterative learning: an experimental case study

Meng

Yan

Ren

2015

J. Zhejiang Univ. Sci. A

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Abstract:The air-conditioning system in a large commercial or high-rise building is a complex multi-variable system influenced by many factors. The energy saving potential from the optimal operation and control of heating, ventilating, and airconditioning (HVAC) systems can be large, even when they are properly designed. The ultimate goal of optimization is to use the minimum amount of energy needed to improve system efficiency while meeting comfort requirements. In this study, a multizone variable air volume (VAV) and variable water volume (VWV) air-conditioning system is developed. The steady state modes and dynamic models of the HVAC subsystems are constructed. Optimal control based on large scale system theory for system-level energy-saving of HVAC is introduced. Control strategies such as proportional-integral-derivative (PID) controller (gearshift integral PID and self-tuning PID) and iterative learning control (ILC) are studied in the platform to improve the dynamic characteristics. The system performance is improved. An 18.2% energy saving is achieved with the integration of ILC and sequential quadratic programming based on a steady-state hierarchical optimization control scheme.

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“…However, appropriate energy storage technology, 8 that could facilitate the former is File", UK Depatment of Energy and Climate Change, available on-line at www.gov.uk/government/uploads/system/ uploads/attachment_data/file/274766/uk_housing_fact_file_2013.pdf (retrieved on 01/2016). 2 In this work, space heating control refers to supervisory control (typically applied manually via thermostats) and not to the low-level control used to maintain the indoor temperature close to set-point with minimum oscillations [Wang and Ma 2008]. 3 www.nest.com; www.honeywell.com; and www.hivehome.com 4 This is also supported by the ever falling cost of such installations [Sioshansi 2016] and the numerous governmental policies to promote respective investments employed around the world [Hawkey 2015;Sioshansi 2016].…”

Section: Introductionmentioning

confidence: 99%

Advanced Economic Control of Electricity-Based Space Heating Systems in Domestic Coalitions with Shared Intermittent Energy Resources

Panagopoulos

Maleki

Rogers

et al. 2017

ACM Trans. Intell. Syst. Technol.

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Over the past few years, domestic heating automation systems (DHASs) that optimize the domestic space heating control process with minimum user-input, utilizing appropriate occupancy prediction technology, have emerged as commercial products (e.g, the smart thermostats from Nest and Honeywell). At the same time, many houses are being equipped with, potentially grid-connected, intermittent energy resources (IERs), such as rooftop photovoltaic systems and/or small wind turbine generators. Now, in many regions of the world, such houses can sell energy to the grid but at a lower price than the price of buying it. In this context, and given the anticipated increase in electrification of heating, the next generation DHASs need to incorporate advanced economic control (AEC). Such AEC can exploit the energy buffer that heating loads provide, in order to shift the consumption of electricity-based heating systems to follow the intermittent energy generation of the house. By so doing, the energy imported from the grid can be minimized and considerable monetary gains for the household can be achieved, without affecting the occupants' schedule. These benefits can be amplified still further in domestic coalitions, where a number of houses come together and share their IER generation to minimize their cumulative grid energy import.Given the above, in this work we extend a state-of-the-art DHAS, to propose AdaHeat+, a practical DHAS, that, for the first time, incorporates AEC. Our work is applicable to both individual houses and domestic coalitions and comes complete with an allocation mechanism to share the coalition gains. Importantly, we propose an effective heuristic heating schedule planning approach for collective AEC which: (i) has a complexity that scales in a linear and parallelizable manner with the coalition size, and (ii) enables AdaHeat+ to handle the distinct preferences, in balancing heating cost and thermal discomfort, of the households. Our approach relies on stochastic IER power output predictions. In this context, we propose a simple and effective formulation for the site-specific calibration of such predictions based on adaptive Gaussian process modeling. Finally, we demonstrate the effectiveness of AdaHeat+ through real data evaluation, to show that collective AEC can improve heating cost-efficiency by up to 60%, compared to independent AEC (and even more when compared to no-AEC).

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Supervisory and Optimal Control of Building HVAC Systems: A Review

Cited by 388 publications

References 59 publications

Methods for Adding Demand Response Capability to a Thermostatically Controlled Load with an Existing On-off Controller

Methods for Adding Demand Response Capability to a Thermostatically Controlled Load with an Existing On-off Controller

Global optimal control of variable air volume air-conditioning system with iterative learning: an experimental case study

Advanced Economic Control of Electricity-Based Space Heating Systems in Domestic Coalitions with Shared Intermittent Energy Resources

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