Socially-and-Environmentally-Aware Power Management in a Residential Neighborhood under Heat Wave Events

Dindar, Amin; Choobineh, Moein; Mohagheghi, Salman

doi:10.1109/ias44978.2020.9334807

Cited by 1 publication

(4 citation statements)

References 16 publications

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“…Typically, during peak hours, the HEMS requires consumers to match their load demand with the expected load demand and minimize the difference between them at time h. This can be accomplished by adjusting the AC setpoint or shifting loads from peak hours to off-peak hours. Therefore, the load demand reduction objective function can be defined as [38]:…”

Section: ) Objective Functionsmentioning

confidence: 99%

“…( 42) and ( 43) state that appliances cannot start before the predetermined start time and must complete their processes before the predetermined end time. Moreover, if a demand shift is planned, the entire load will be shifted to the following time step [38]:…”

Section: ) Objective Functionsmentioning

confidence: 99%

“…• Forecasting and simulation were carried out for 12 hours (It is applicable for larger periods if needed). • For all uncertain parameters, a box of uncertainty was defined as 0.9 to 1.1 times their nominal value [38]. • Each time slot was taken to be one hour without loss of generality, which means power demand in kW is equivalent to energy consumption in kWh.…”

Section: A Assumption and Data Collectionmentioning

confidence: 99%

“…• Each time slot was taken to be one hour without loss of generality, which means power demand in kW is equivalent to energy consumption in kWh. • The financial fines and rewards are assumed to remain constant throughout all hours [38]. • It was assumed the house is demand responsive through HEMS.…”

Section: A Assumption and Data Collectionmentioning

confidence: 99%

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Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

Nematirad,

Ardehali,

Khorsandi

et al. 2024

IEEE Access

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Residential consumers can optimize their participation in demand response programs (DRPs) using home energy management systems (HEMS). By automatically adjusting air conditioning (AC) setpoints and shifting certain appliances to off-peak hours, HEMS can lead to significant cost reductions. While HEMS aims to adjust AC temperature setpoints, it is important to consider the occupants thermal comfort. This study aims to develop a multi-objective model for the application of DRPs in a smart residential house. The objectives of the model are to achieve (a) reduction in electrical load demand, (b) adjustment in thermal comfort temperature setpoints, and (c) minimization of consumer costs subject to the related constraints. Determining occupancy status through HEMS provides more economic benefits and thermal comfort for consumers. However, traditional methods such as direct occupancy monitoring are often costly, inaccurate, and can intrusively collect data on residents' activities, locations, and routines, compromising their privacy. To tackle these challenges, this study introduces advanced forecasting algorithms such as random forest, light gradient boosting machine, and multilayer perception artificial neural networks to predict occupancy by utilizing indirect data sources, such as energy consumption patterns. This approach enables the prediction of residential presence without direct monitoring. However, inherent uncertainty associated with predicted parameters can compromise the effectiveness of DRPs, and potentially lead to non-optimal energy savings, jeopardizing consumer comfort, and even system instability. To address these uncertainties, this study integrates robust counterpart optimization techniques, augmented with uncertainty budgets to control uncertainty variation making them less conservative. Simulations show uncertainty increases costs by 36% and reduces AC temperature setpoints. Further, implementing DRPs reduces demand by shifting some appliances to off-peak hours and lowers costs by 13.2%.

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Section: ) Objective Functionsmentioning

confidence: 99%

Section: ) Objective Functionsmentioning

confidence: 99%

Section: A Assumption and Data Collectionmentioning

confidence: 99%

Section: A Assumption and Data Collectionmentioning

confidence: 99%

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