PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

Liu, Xuebo; Wu, Yingying; Wu, Hongyu

doi:10.3390/su132413826

Cited by 9 publications

(4 citation statements)

References 37 publications

(61 reference statements)

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“…Meanwhile, the risk brought by the uncertainty of a high proportion of renewable energy output is an important factor in the optimal scheduling. To deal with the uncertainty of renewable DG, stochastic programming [19][20][21] and robust optimization [22,23] are the regular methods. The stochastic programming based on the ellipsoid set is used to deal with the uncertainty in [24].…”

Section: Introductionmentioning

confidence: 99%

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

Hao,

Huang,

et al. 2023

Sustainability

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With increasing penetration of distributed generators (DG), the uncertainty and intermittence of renewable energy has brought new challenges to the economic dispatch and promotion of environment sustainability of microgrids. Active loads, especially in electric vehicles (EVs), are thought to be an efficient way to deal with the uncertainty and intermittence of renewable energy. One of the most important features of EVs is that their demand will vary in response to the electricity price. How to determine the real-time charging price to guide the orderly charging of EVs and operate with an uncertain renewable energy output represents an important topic for the microgrid operator (MGO). To this end, this paper formulates the optimal pricing and robust dispatch problem of the MGO as a Stackelberg game, in which the upper level minimizes the MGO’s cost, while the lower level minimizes the charging cost of each EV. In the problem, the approximate linear relationship between the node voltage and equivalent load is modeled, and the approximate linear expression of the node voltage security constraint is derived. Using dual optimization theory, the robust optimal dispatch model is transformed into a linear programming model without uncertain variables. Then, the Stackelberg game model is transformed into a mixed integer linear program by using the duality theorem of linear programming. Finally, the effectiveness of the proposed method is proved by simulation within the modified IEEE33-bus system.

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Section: Introductionmentioning

confidence: 99%

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

Hao,

Huang,

et al. 2023

Sustainability

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“…In past decades, ML techniques have been utilized to solve the classification and regression problems [7], leading to many noteworthy applications such as wind speed prediction [8], temperature forecasting [9], solar irradiance forecasting [10], train arriving time prediction [11], construction strength prediction [12], speech recognition [13],…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enabled 3D Body Measurement Estimation Using Hybrid Feature Selection and Bayesian Search

Liu

2022

Applied Sciences

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The 3D body scan technology has recently innovated the way of measuring human bodies and generated a large volume of body measurements. However, one inherent issue that plagues the use of the resultant database is the missing data usually caused by using automatic data extractions from the 3D body scans. Tedious extra efforts have to be made to manually fill the missing data for various applications. To tackle this problem, this paper proposes a machine learning (ML)-based approach for 3D body measurement estimation while considering the measurement (feature) importance. The proposed approach selects the most critical features to reduce the algorithm input and to improve the ML method performance. In addition, a Bayesian search is further used in fine-tuning the hyperparameters to minimize the mean square error. Two distinct ML methods, i.e., Random Forest and XGBoost, are used and tested on a real-world dataset that contains 3D body scans of 212 participants in the Kansas-Missouri area of the United States. The results show the effectiveness of the proposed methods with roughly 3% of Mean Absolute Percentage Errors in estimating the missing data. The two ML methods with the proposed hybrid feature selection and the Baysian search are comprehensively compared. The comparative results suggest that the Random Forest method performs better than the XGBoost counterpart in filling missing 3D body measurements.

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“…This SG component draws interest in modern power systems as a source of elastic demand. In [ 23 ], the authors presented some models and solutions to ensure the proper running of SG electric vehicles. A stochastic adaptive dynamic programming (ADP) approach to the HEMS model with SPV and EVs was provided.…”

Section: Introductionmentioning

confidence: 99%

Model for Managing the Integration of a Vehicle-to-Home Unit into an Intelligent Home Energy Management System

Almughram

Sami

Zafar

2022

Sensors

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Integration of vehicle-to-home (V2H) centralized photovoltaic (HCPV) systems is a requested and potentially fruitful research topic for both industry and academia. Renewable energy sources, such as wind turbines and solar photovoltaic panels, alleviate energy deficits. Furthermore, energy storage technologies, such as batteries, thermal, and electric vehicles, are indispensable. Consequently, in this article, we examine the impact of solar photovoltaic (SPV), microgrid (MG) storage, and an electric vehicle (EV) on maximum sun radiation hours. As a result, an HCPV scheduling algorithm is developed and applied to maximize energy sustainability in a smart home (SH). The suggested algorithm can manage energy demand between the MG and SPV systems, as well as the EV as a mobile storage system. The model is based on several limitations to meet households’ electrical needs during sunny and cloudy weather. A multi-agent system (MAS) is undertaken to ensure proper system operation and meet the power requirements of various devices. An experimental database for weather and appliances is deployed to evaluate and control energy consumption and production cost parameters. The obtained results illustrate the benefits of V2H technology as a prospective unit storage solution.

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PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

Cited by 9 publications

References 37 publications

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

Machine Learning Enabled 3D Body Measurement Estimation Using Hybrid Feature Selection and Bayesian Search

Model for Managing the Integration of a Vehicle-to-Home Unit into an Intelligent Home Energy Management System

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