A New Pricing Scheme for Intra-Microgrid and Inter-Microgrid Local Energy Trading

Islam, Shama Naz

doi:10.3390/electronics8080898

Cited by 13 publications

(8 citation statements)

References 37 publications

(57 reference statements)

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“…This has been validated in a lab environment. Considering a smart home architecture, IoT and sensor data optimization based peer-to-peer energy trading architecture is proposed in [85,56] Complex Event Processing (CEP) is used for reactive applications to provide a distributed but scalable solution within an IoT environment [8]. The combination of CEP and historical data has been used to develop a prediction algorithm using machine learning algorithms in [8].…”

Section: Iot Data Analytics In Energy Systemsmentioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

168

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The Internet of Things (IoT) applications has grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e. cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data is processed for machine learning analysis, and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications, and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

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Section: Iot Data Analytics In Energy Systemsmentioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

168

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“…The maximization of social welfare in the PEST can use auction-based mechanism [16,17]. The author from [18] uses an optimum pricing scheme for local electricity trading in µGs considering four particular priorities. In other words, the prosumers become the new actors in local electricity power market, considered as µM [19,20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, the consumer-driven scenario is S5-minimum price for consumers. It should be mentioned that many papers are the same with the References [7,[11][12][13][14][15][16][17][18][20][21][22][23][25][26][27][28][29][30][31][32][33] presented in Table 1. Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A comparative state of the art between our method and the literature. [7,17] no no no no yes yes [11,12,25] yes no no no no yes [13] no no yes yes no yes [14,15] no no yes yes yes yes [16,23] yes no no yes no yes [18] no no yes no no no [20,26] no no yes no no yes [21,22,30] no no no no yes no [27] no no no yes yes no [28] no no yes no yes yes [29] no yes no no no yes [31] no yes no yes no no [32,33] no no no no yes yes Proposed approach yes yes yes yes yes yes A previous work of the authors, in [34], proposes only at principle level a particular approach for prosumers energy trading in µGs as an efficient P2P exchange based on the blockchain technology. Specifically, the algorithm solves a mathematical model for the latest challenges regarding both the ADN and the newest type of electricity market participants (prosumers) using virtual or crypto price as the transaction currency.…”

Section: Literature Reviewmentioning

confidence: 99%

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A New Vision on the Prosumers Energy Surplus Trading Considering Smart Peer-to-Peer Contracts

et al. 2020

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A growing number of households benefit from government subsidies to install renewable generation facilities such as PV panels, used to gain independence from the grid and provide cheap energy. In the Romanian electricity market, these prosumers can sell their generation surplus only at regulated prices, back to the grid. A way to increase the number of prosumers is to allow them to make higher profit by selling this surplus back into the local network. This would also be an advantage for the consumers, who could pay less for electricity exempt from network tariffs and benefit from lower prices resulting from the competition between prosumers. One way of enabling this type of trade is to use peer-to-peer contracts traded in local markets, run at microgrid (μG) level. This paper presents a new trading platform based on smart peer-to-peer (P2P) contracts for prosumers energy surplus trading in a real local microgrid. Several trading scenarios are proposed, which give the possibility to perform trading based on participants’ locations, instantaneous active power demand, maximum daily energy demand, and the principle of first come first served implemented in an anonymous blockchain trading ledger. The developed scheme is tested on a low-voltage (LV) microgrid model to check its feasibility of deployment in a real network. A comparative analysis between the proposed scenarios, regarding traded quatities and financial benefits is performed.

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“…The advancement of Internet of Things (IoT) technologies has allowed the integration of intelligent algorithms for demand management applications in smart homes. Residential demand management tools, such as home energy management systems (HEMS), can assist in significantly reducing peak load demand by integrating optimum operational strategies for energy consumption in smart homes [ 6 ]. Thus, effective demand management schemes need to be integrated in residential HEMSs to reduce the energy consumption of HVAC systems [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home

Philip

Islam

Phillips³

et al. 2022

Sensors

Self Cite

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This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012−2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case.

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A New Pricing Scheme for Intra-Microgrid and Inter-Microgrid Local Energy Trading

Cited by 13 publications

References 37 publications

Machine learning and data analytics for the IoT

Machine learning and data analytics for the IoT

A New Vision on the Prosumers Energy Surplus Trading Considering Smart Peer-to-Peer Contracts

Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home

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