Peak load minimization in smart grid by optimal coordinated ON–OFF scheduling of air conditioning compressors

Uddin, Md. Forkan; Hassan, Kanza; Biswas, Soumav

doi:10.1016/j.segan.2021.100545

“…This paper proposes an approach for reducing peak power demand in residential HVAC systems by integrating HVAC management and optimal scheduling of smart appliances. The focus is on implementing a smart thermostat scheduling technique to achieve community-wide peak load reduction [41]. The proposed method is based on a mixed-integer linear programming model which accommodates the users' preferences, electricity pricing and the thermal dynamics of the building.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids

Saleem

¹

,

Shakir

²

,

Usman

³

et al. 2023

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The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers’ energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings.

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“…Ramin et al (2018) presented a scheduling model for a multiline process under a demand-side management program to minimize the energy cost and maximise the reserve provision. Uddin et al (2021) proposed optimal peak load minimization by coordinating on-off scheduling of compressors in air conditioners. Similar studies on modelling of compressed air system for demand response is done by Diekerhof et al (2016) to observe flexibility of the system in terms of energy shifting with storage size when dealing with variable electricity prices.…”

Section: Fig 1 Demand Response Categoriesmentioning

confidence: 99%

A demand response strategy for air compressors network with optimal production and energy utilisation

Zulkafli

¹

,

Jelas

²

,

Sulaima

³

et al. 2022

Preprint

1

0

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The approach of integrating demand response strategy with operational scheduling is used to investigate the flexibility in the operational performance under time-sensitive electricity tariffs and to provide cost benefits analysis to the industrial consumers. The proposed approach focused on implementing a demand response strategy in a case study of scheduling for an industrial network of compressors for Time of Use and Enhanced Time of Use tariffs for heavy industries. The optimisation framework was developed as a mixed integer linear programming model. The percentage of total cost reduction is around 5% to 13% by implementing a demand response program. A considerable percentage of cost saving is possible to achieve for the Enhanced Time of use tariff since it offers an extensive off-peak time zone during the night hours and on the weekend. It is concluded that the study highlights the operational performance goal of achieving cost savings by implementing a demand response strategy for energy industries.

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“…Ramin et al [23] presented an energy model for a multi-line process under a demand-side management program to minimize the energy cost and maximise the reserve provision. Uddin et al [24] proposed optimal peak load minimization by coordinating the on-off scheduling of compressors in air conditioners. Similar studies on the modelling of compressed air system for demand response is done by Diekerhof et al [25] to observe the flexibility of the system in terms of energy shifting with storage size when dealing with variable electricity prices.…”

Section: Fig 1 Demand Response Categoriesmentioning

confidence: 99%

A demand response strategy for air compressors network with optimal production and energy utilisation

Zulkafli

¹

,

Jelas

²

,

Sulaima

³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

The approach of integrating demand response strategy with operational scheduling is used to investigate the flexibility in the operational performance under time-sensitive electricity tariffs and to provide cost benefits analysis to the industrial consumers. The proposed approach focused on implementing a demand response strategy in a case study of scheduling for an industrial network of compressors for Time of Use and Enhanced Time of Use tariffs for heavy industries. The optimisation framework was developed as a mixed integer linear programming model. The percentage of total cost reduction is around 5% to 13% by implementing a demand response program. A considerable percentage of cost saving is possible to achieve for the Enhanced Time of use tariff since it offers an extensive off-peak time zone during the night hours and on the weekend. It is concluded that the study highlights the operational performance goal of achieving cost savings by implementing a demand response strategy for energy industries.

show abstract

Peak load minimization in smart grid by optimal coordinated ON–OFF scheduling of air conditioning compressors

Cited by 4 publications

References 17 publications

Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids

Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids

A demand response strategy for air compressors network with optimal production and energy utilisation

A demand response strategy for air compressors network with optimal production and energy utilisation

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