Model-Based and Data-Driven HVAC Control Strategies for Residential Demand Response

<p>The substantial penetration of wind power introduces increased flexibility requirements on the power system and puts increased pressure on the instantaneous reserve levels required. Instantaneous reserves are a security product that ensures that electricity demand can continue to be met in the event of unplanned generation or transmission interruptions. This reserve must be available to respond very quickly to generation-demand variability. While this is an integral component of the power system, providing instantaneous reserve increases the production cost of power. More calls from energy researchers and stakeholders ask for loads to play an increasingly important role in balancing the short timescale fluctuations in generated wind power. The purpose of this study is to assess the current level of demand responsiveness among domestic refrigerators, freezers, and water heaters and their potential to contribute towards instantaneous reserve and balance the fluctuation of wind. Refrigerators, freezers, and water heaters can generally store energy due to their thermal mass. Interrupting these domestic loads for short time by employing direct load control strategies makes it possible to control these appliances by turning them on or off before their reach their maximum or minimum temperatures or by slightly modifying their temperature set point. Using this strategy helps to ensure that the overall satisfaction of consumers should not be affected. This study first modelled the load profiles of the participated residential appliances and statistically assessed the potential of controlling these residential loads using direct load control strategies to contribute towards instantaneous reserves to mitigate and balance the fluctuation of wind power in the years: 2014, 2020 and 2030. In the second section, it demonstrated the capabilities of the assessed residential responsive loads within Wellington Region network to compensate for and balance the fluctuation of wind power generated from the West Wind Farm in seven selected days in 2013-2014 as a showcase. Such technology can enable a power system operator to remove the burden of both providing instantaneous reserve from conventional sources, and instead maintain such capacity from available residential demand response. The study ends with recommendations to engage residential loads in fast timescale demand response and suggests directions for future research.</p>

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The Potential of Utilising Residential Demand Response to Balance the Fluctuation of Wind Power in New Zealand

10.26686/wgtn.17009765 ◽

2021 ◽

Author(s):

◽

Hatem I. Alzaanin

Keyword(s):

Power System ◽

Wind Power ◽

Demand Response ◽

Wind Farm ◽

Control Strategies ◽

Load Control ◽

Future Research ◽

Water Heaters ◽

Direct Load Control ◽

Residential Demand

<p>The substantial penetration of wind power introduces increased flexibility requirements on the power system and puts increased pressure on the instantaneous reserve levels required. Instantaneous reserves are a security product that ensures that electricity demand can continue to be met in the event of unplanned generation or transmission interruptions. This reserve must be available to respond very quickly to generation-demand variability. While this is an integral component of the power system, providing instantaneous reserve increases the production cost of power. More calls from energy researchers and stakeholders ask for loads to play an increasingly important role in balancing the short timescale fluctuations in generated wind power. The purpose of this study is to assess the current level of demand responsiveness among domestic refrigerators, freezers, and water heaters and their potential to contribute towards instantaneous reserve and balance the fluctuation of wind. Refrigerators, freezers, and water heaters can generally store energy due to their thermal mass. Interrupting these domestic loads for short time by employing direct load control strategies makes it possible to control these appliances by turning them on or off before their reach their maximum or minimum temperatures or by slightly modifying their temperature set point. Using this strategy helps to ensure that the overall satisfaction of consumers should not be affected. This study first modelled the load profiles of the participated residential appliances and statistically assessed the potential of controlling these residential loads using direct load control strategies to contribute towards instantaneous reserves to mitigate and balance the fluctuation of wind power in the years: 2014, 2020 and 2030. In the second section, it demonstrated the capabilities of the assessed residential responsive loads within Wellington Region network to compensate for and balance the fluctuation of wind power generated from the West Wind Farm in seven selected days in 2013-2014 as a showcase. Such technology can enable a power system operator to remove the burden of both providing instantaneous reserve from conventional sources, and instead maintain such capacity from available residential demand response. The study ends with recommendations to engage residential loads in fast timescale demand response and suggests directions for future research.</p>

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The potential and limits of residential demand response control strategies

2011 IEEE Power and Energy Society General Meeting ◽

10.1109/pes.2011.6039719 ◽

2011 ◽

Cited By ~ 13

Author(s):

D. P. Chassin ◽

P. Du ◽

J. C. Fuller

Keyword(s):

Demand Response ◽

Control Strategies ◽

Response Control ◽

Residential Demand

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Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

Energies ◽

10.3390/en10010067 ◽

2017 ◽

Vol 10 (1) ◽

pp. 67 ◽

Cited By ~ 4

Author(s):

Cihan Turhan ◽

Silvio Simani ◽

Ivan Zajic ◽

Gulden Gokcen Akkurt

Keyword(s):

Performance Analysis ◽

Control Strategies ◽

Data Driven ◽

Thermal Unit ◽

Model Based Control ◽

Model Based ◽

Unit Model

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Model-Based Evaluation of a Data-Driven Control Strategy: Application to Ibuprofen Crystallization

Processes ◽

10.3390/pr9040653 ◽

2021 ◽

Vol 9 (4) ◽

pp. 653

Author(s):

Frederico C. C. Montes ◽

Merve Öner ◽

Krist V. Gernaey ◽

Gürkan Sin

Keyword(s):

Predictive Control ◽

Crystallization Process ◽

Control Strategies ◽

Soft Sensor ◽

Data Driven ◽

Training Methods ◽

Moving Window ◽

Model Based ◽

Sensor Control ◽

Treatment Data

This work presents a methodology that relies on the application of the radial basis functions network (RBF)-based feedback control algorithms to a pharmaceutical crystallization process. Within the scope of the model-based evaluation of the proposed strategy, firstly strategies for the data treatment, data structure and the training methods reflecting the possible scenarios in the industry (Moving Window, Growing Window and Golden Batch strategies) were introduced. This was followed by the incorporation of such RBF strategies within a soft sensor application and a nonlinear predictive data-driven control application. The performance of the RBF control strategies was tested for the undisturbed cases as well as in the presence of disturbances in the process. The promising results from both RBF soft sensor control and the RBF predictive control demonstrated great potential of these techniques for the control of the crystallization process. In particular, both Moving Window and Golden Batch strategies performed the best results for an RBF soft sensor, and the Growing Window outperformed the remaining methodologies for predictive control.

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Residential demand response considering distributed PV consumption: A model based on China's PV policy

Energy ◽

10.1016/j.energy.2019.01.097 ◽

2019 ◽

Vol 172 ◽

pp. 443-456 ◽

Cited By ~ 12

Author(s):

Qing Lu ◽

Hao Yu ◽

Kangli Zhao ◽

Yajun Leng ◽

Jianchao Hou ◽

...

Keyword(s):

Demand Response ◽

Model Based ◽

Residential Demand

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Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads

Applied Energy ◽

10.1016/j.apenergy.2021.118017 ◽

2021 ◽

pp. 118017

Author(s):

Zhaofang Song ◽

Jing Shi ◽

Shujian Li ◽

Zexu Chen ◽

Fengshun Jiao ◽

...

Keyword(s):

Physical Model ◽

Demand Response ◽

Air Conditioning ◽

Evaluation Method ◽

Data Driven ◽

Model Based ◽

Residential Consumers

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The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽

10.3390/s21062085 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2085

Author(s):

Xue-Bo Jin ◽

Ruben Jonhson Robert RobertJeremiah ◽

Ting-Li Su ◽

Yu-Ting Bai ◽

Jian-Lei Kong

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Future Development ◽

Data Driven ◽

Estimation Methods ◽

Multiple Model ◽

Development Trends ◽

Research Results ◽

Model Driven ◽

Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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