A collaborative energy management among plug-in electric vehicle, smart homes and neighbors’ interaction for residential power load profile smoothing

Khemakhem, Siwar; Rekik, Mouna; Krichen, Lotfi

doi:10.1016/j.jobe.2019.100976

Cited by 30 publications

(16 citation statements)

References 44 publications

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“…For example, in Figure 6, agent 2 supplies requested current by agent 1. Bit 2 1 shows voltage control mode and when its value is 1, it means agent is involved in controlling the voltage of DC bus. For instance, agent 1 in Figure 6 is in voltage control mode.…”

Section: Digital Messagementioning

confidence: 99%

“…However, intermittent generation from RES and unpredictable changes in demand profiles cause concerns regarding supply-demand balance and the stability and reliability of power systems. For example, in a building with engaged renewable energy resources, voltage stability and reliability of system could be significant as well as supporting supply-demand balance [1,2]. Energy Storage systems (ESSs) could be used in such system to guarantee these features [3].…”

Section: Introductionmentioning

confidence: 99%

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Autonomous energy management system with self-healing capabilities for green buildings (microgrids)

Jonban

Romeral

Akbarimajd

et al. 2021

Journal of Building Engineering

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Nowadays, distributed energy resources are widely used to supply demand in micro grids specially in green buildings. These resources are usually connected by using power electronic converters, which act as actuators, to the system and make it possible to inject desired active and reactive power, as determined by smart controllers.The overall performance of a converter in such system depends on the stability and robustness of the control techniques. This paper presents a smart control and energy management of a DC microgrid that split the demand among several generators. In this research, an energy management system (EMS) based on multi-agent system (MAS) controllers is developed to manage energy, control the voltage and create balance between supply and demand in the system with the aim of supporting the reliability characteristic. In the proposed approach, a reconfigurated hierarchical algorithm is implemented to control interaction of agents, where a CAN bus is used to provide communication among them. This framework has ability to control system, even if a failure appears into decision unit. Theoretical analysis and simulation results for a practical model demonstrate that the proposed technique provides a

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Section: Digital Messagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous energy management system with self-healing capabilities for green buildings (microgrids)

Jonban

Romeral

Akbarimajd

et al. 2021

Journal of Building Engineering

View full text Add to dashboard Cite

show abstract

“…These devices communicate via the Internet with a cloud-centered server serving as cloud computing, which is used for data science analytics to optimize electricity demands based on demand response (DR) prices. Most of the existing solutions in [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] have featured the viewpoints of HEMSs. However, the authors of [ 6 , 7 , 8 , 9 , 11 , 13 , 18 ] implemented the HEMSs based on cloud computing.…”

Section: Introductionmentioning

confidence: 99%

“…The studies in [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] have featured HEMSs. However, the HEMSs that were implemented in [ 6 , 7 , 8 , 9 , 11 , 13 , 18 ] are based on cloud computing for residential DSM. Additionally, in [ 10 ], proposing a new framework of HEMSs based on both centralized energy disaggregation and distributed appliance controls, no practical evaluation is demonstrated for the evaluation of the feasibility of the framework as a whole.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, in [ 10 ], proposing a new framework of HEMSs based on both centralized energy disaggregation and distributed appliance controls, no practical evaluation is demonstrated for the evaluation of the feasibility of the framework as a whole. Moreover, the HEMSs that were implemented in [ 7 , 8 , 9 ] monitor relevant electrical appliances in an intrusive way. NIALM approaches that monitor relevant electrical appliances in a non-intrusive fashion have been developed significantly in [ 18 , 25 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

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A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

Chen

Chang³

et al. 2021

Sensors

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Electricity is a vital resource for various human activities, supporting customers’ lifestyles in today’s modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium’s Niagara Framework® has been established over fog (edge)-cloud computing, where NIALM as an IoT application in energy management has also been investigated in the framework. The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive deployment of plug-load power meters (smart plugs), where a two-stage NIALM approach is completed. The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart building for residential DSM. As demonstrated and reported in this study, the established SHEMS prototype using the investigated two-stage NIALM approach is feasible and usable.

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Integrating renewable energy in electric V2G : Improved optimization assisting dispatch model

Shobana¹,

Praghash

Ramya

et al. 2022

Intl J of Energy Research

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Summary “Electric vehicles (EVs) are one of the most promising technologies to green the transportation systems. However, high penetration of EVs brings heavy electricity demand to the power grid.” Due to the intermittent character of renewable energy sources (RESs), it turns out to be very challenging to manage EV charging with other renewable generation and grid load. This paper aims to introduce a dispatch strategy assisted with the optimization concept for enhancing the economy of the microgrid system. The major objective is to minimize the cost of system operation and environmental control when meeting system load requirements. The output constraints related to the distributed power supply, such as power limits, are subjected to optimization. To solve this optimization issue, a new “Fitness Sorted Moth Search algorithm (FS‐MSA)” is introduced. Finally, the proposed work is compared and validated with other existing works with respect to various measures. The enhanced outcomes prove the efficacy of the implemented FS‐MSA model. This work concentrates on EV adoption integrated with RES for sustainable mobility. On observing the result, it can be noticed that the adopted scheme was 81.97%, 82.03%, 82%, and 81.97%, better than existing genetic algorithm, particle swarm optimization (PSO), moth search algorithm, and lagrange multiplier optimization (LMO) models for mean case scenario.

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A collaborative energy management among plug-in electric vehicle, smart homes and neighbors’ interaction for residential power load profile smoothing

Cited by 30 publications

References 44 publications

Autonomous energy management system with self-healing capabilities for green buildings (microgrids)

Autonomous energy management system with self-healing capabilities for green buildings (microgrids)

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

Integrating renewable energy in electric V2G : Improved optimization assisting dispatch model

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