Multiagent Control System for Residential Energy Management under Real Time Pricing Environment

Rasheed, Muhammad Babar; Javaid, Nadeem; Hussain, Sardar Mehboob; Akbar, Mariam; Khan, Zahoor Ali

doi:10.1109/aina.2017.156

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…Due to this, the maximum value lies at 0.25 kWh during 7 to 9 pm and decreases further between 9 to 11pm. Average percentage reduction in energy consumption of LIH using proposed SEM technique as compared to [33] is 50.63%. Figure 9b compares energy consumption patterns of AIH with and without the proposed SEM technique.…”

Section: B Energy Consumption Using Sem (Kwh)mentioning

confidence: 98%

“…Valley filling is employed to adjust the curve to a maximum value of 1.4 kWh during 7 to 11 pm, 2 kWh during 4 to 6 am, and 1 kWh from 9 to 10 am and 4 to 5 am. Average percentage reduction in energy consumption of AIH using the proposed SEM technique as compared to [33] is 32.9%. Figure 9c illustrates the energy consumption patterns of HIH.…”

Section: B Energy Consumption Using Sem (Kwh)mentioning

confidence: 98%

“…The purpose of this limit is to keep a check on the load and turn on the load only when the price is less than threshold value. The threshold value is represented by the following equation [33]:…”

Section: G Threshold Valuementioning

confidence: 99%

“…Techniques of valley filling, load shifting, and strategic conservation are employed whereas consumption is kept minimum. The kWh values of [33] are compared with the proposed technique. It is observed that the curve of consumption without SEM has higher energy consumption values and a greater peak.…”

Section: B Energy Consumption Using Sem (Kwh)mentioning

confidence: 99%

“…Figure 9a depicts energy consumption patterns of LIH throughout the day. Values taken from [33] show a consumption of 1 kWh during 8 to 10 pm whereas it drops below 0.4 kWh various times a day. This is mitigated using peak clipping, which turns OFF unnecessary load during peak hours and shifts it towards 5 to 8 am.…”

Section: B Energy Consumption Using Sem (Kwh)mentioning

confidence: 99%

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Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach

et al. 2022

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A virtual power plant (VPP) is a cloud based distributed power plant that aggregates the capacities of diverse distributed energy resources (DERs) for the purpose of enhancing power generation as well as trading or selling power on the electricity market. The main issue faced while working on VPPs is energy management. Smart energy management of a VPP is a complex problem due to the coordinated operation of diverse energy resources and their associated uncertainties. This research paper proposes a realtime (RT) smart energy management model for a VPP using a multi-objective, multi-level optimizationbased approach. The VPP consists of a solar, wind and thermal power unit, along with an energy storage unit and some flexible demands. The term multi-level refers to three different energy levels depicted as three homes comprising of different amounts of loads. RT operation of a VPP is enabled by exploiting the bidirectional communication infrastructure. Multi-objective RT smart energy management is implemented on a community-based dwelling system using three alternative algorithms i.e., hybrid optimal stopping rule (H-OSR), hybrid particle swarm optimization (H-PSO) and advanced multi-objective grey wolf optimization (AMO-GWO). The proposed technique focuses on achieving the objectives of optimal load scheduling, real-time pricing, efficient energy consumption, emission reduction, cost minimization and maximization of customer comfort altogether. A comparative analysis is performed among the three algorithms in which the calculated real-time prices are compared with each other. It is observed that on average H-PSO performs 7.86 % better than H-OSR whereas AMO-GWO performs 10.49% better than H-OSR and 5.7% better than H-P-SO. This paper concludes that AMO-GWO is the briskest, most economical, and efficient optimization algorithm for RT smart energy management of a VPP.INDEX TERMS Hybrid optimal stopping rule, hybrid particle swarm optimization, JADE, multi-agent system, multi-objective grey wolf optimization, smart energy management.

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Section: B Energy Consumption Using Sem (Kwh)mentioning

confidence: 98%

Section: B Energy Consumption Using Sem (Kwh)mentioning

confidence: 98%

Section: G Threshold Valuementioning

confidence: 99%

Section: B Energy Consumption Using Sem (Kwh)mentioning

confidence: 99%

Section: B Energy Consumption Using Sem (Kwh)mentioning

confidence: 99%

See 3 more Smart Citations

Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach

et al. 2022

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Demand side management through home area network systems

Bazydło

Wermiński

2018

International Journal of Electrical Power & Energy Systems

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Internet of Things Technologies for Smart Grid

Dhaou

Kondoro

Kakakhel

et al. 2022

Research Anthology on Smart Grid and Microgrid Development

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Smart grid is a new revolution in the energy sector in which the aging utility grid will be replaced with a grid that supports two-way communication between customers and the utility company. There are two popular smart-grid reference architectures. NIST (National Institute for Standards and Technology) has drafted a reference architecture in which seven domains and actors have been identified. The second reference architecture is elaborated by ETSI (European Telecommunications Standards Institute), which is an extension of the NIST model where a new domain named distributed energy resources has been added. This chapter aims at identifying the use of IoT and IoT-enabled technologies in the design of a secure smart grid using the ETSI reference model. Based on the discussion and analysis in the chapter, the authors offer two collaborative and development frameworks. One framework draws parallels' between IoT and smart grids and the second one between smart grids and edge computing. These frameworks can be used to broaden collaboration between the stakeholders and identify research gaps.

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Multiagent Control System for Residential Energy Management under Real Time Pricing Environment

Cited by 4 publications

References 9 publications

Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach

Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach

Demand side management through home area network systems

Internet of Things Technologies for Smart Grid

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