Equivalent Electric and Heat-Pump Water Heater Models for Aggregated Community-Level Demand Response Virtual Power Plant Controls

Gong, Huangjie; Rooney, Tim; Akeyo, Oluwaseun M.; Branecky, Brian; Ionel, Dan M.

doi:10.1109/access.2021.3119581

Cited by 23 publications

(5 citation statements)

References 15 publications

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“…As a result, there is a lot of interest in, and momentum behind, the concept of digital twin. All physical entities, including humans, can have their functions monitored, understood, and optimised with the help of digital twins, which also provide constant feedback to enhance quality of life [30]. The ideal way to define a digital twin is as the seamless exchange of information between a real world machine and its digital counterpart.…”

Section: Literature Surveymentioning

confidence: 99%

Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network

Padmapriya¹,

Srivenkatesh²

2023

IJACSA

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Digital twin is one of the most important innovations in the Internet of Things (IoT) era and business disruption. Digital twins are a growing technology that bridges the gap between the real and the digital. Home automation in the IoT refers to the practice of automatically managing and monitoring smart home electronics by use of a variety of control system methods. The geysers, refrigerators, fans, lighting, fire alarms, kitchen timers, and other electrical and electronic items in the home can all be managed and monitored with the help of a variety of control methods. Digital twins replicate the physical machine in real time and produce data, such as asset degradation, product performance level that may be used by the predictive maintenance algorithm to identify the product functionality levels. The purpose of this research is to design the framework of Digital Twin using machine learning and state estimation algorithms model to assess and predict home appliances based on the probability rate of smart home system gadgets functionality. The main goal of this research is to create a digital twin for smart home gadgets that are used to monitor the health status of these devices for increasing the life time and to reduce maintenance costs. This research presents a Deep Convolution Neural Network based Logistic Regression Model with Digital Twins (DCNN-LR-DT) for accurate prediction of smart home gadget functionality levels and to predict the threats in advance. The proposed model is compared with the traditional models and the results represent that the proposed model performance is better than traditional models.

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Section: Literature Surveymentioning

confidence: 99%

Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network

Padmapriya¹,

Srivenkatesh²

2023

IJACSA

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“…Incorporating indoor temperature into VPP control schemes that leverage HVAC systems as DER is necessary to abide by occupant thermal comfort preferences. Improved control methods, for example, those utilizing the CTA-2045 protocol for DER demand response and GES operation through Energy Star definitions address the comfort issue by adopting energy storage capacity and equivalent state-of-charge (SOC) calculations [33,34]. The equivalent HVAC energy storage capacity and SOC at time t may be calculated following…”

Section: Power System and Der Operationmentioning

confidence: 99%

Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control

Jones

Alden

Gong³

et al. 2023

Sustainability

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Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%.

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“…The aggregate effect on human behavior is seen in large-scale experimental studies of community-level wide air-conditioning, lighting, dishwasher, and clothes washer/dryer loads utilized by U.S. Department of Energy Building America Program and the National Renewable Energy Laboratory (NREL) [33]. This known phenomenon was used by our group in [34] to produce equivalent aggregated water heater models for community-wide virtual power plant (VPP) control studies.…”

Section: Problem Formulation and Experimental Datamentioning

confidence: 99%

Forecast of Community Total Electric Load and HVAC Component Disaggregation through a New LSTM-Based Method

Gong

Alden

Patrick³

et al. 2022

Energies

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The forecast and estimation of total electric power demand of a residential community, its baseload, and its heating ventilation and air-conditioning (HVAC) power component, which represents a very large portion of a community electricity usage, are important enablers for optimal energy controls and utility planning. This paper proposes a method that employs machine learning in a multi-step integrated approach. An LSTM model for total electric power at the main circuit feeder is trained using historic multi-year hourly data, outdoor temperature, and solar irradiance. New key temperature indicators, TmHAVC, corresponding to the standby zero-power operation for HVAC systems for summer cooling and winter heating are introduced using a V-shaped hourly total load curve. The trained LTSM model is additionally run with TmHVAC and zero irradiance inputs yielding an estimated baseload, which is representative of typical occupancy patterns. The HVAC power component is disaggregated as the difference between total and baseload power. Total power forecasts of an aggregated residential community as seen by major distribution lines are experimentally validated with a satisfactory MAPE error below 10% based on a 4-year dataset from a representative suburban community with more than 1800 homes in Kentucky, U.S. Discussions regarding the validity of the separation method based on combined considerations of fundamental physics, statistics, and human behavior are also included.

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Equivalent Electric and Heat-Pump Water Heater Models for Aggregated Community-Level Demand Response Virtual Power Plant Controls

Cited by 23 publications

References 15 publications

Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network

Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network

Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control

Forecast of Community Total Electric Load and HVAC Component Disaggregation through a New LSTM-Based Method

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