Fair and Balance Demand Response application in Distribution Networks

Al‐Kharsan, Ibrahim H.; Marhoon, Ali; Mahmood, Jawad

doi:10.37917/ijeee.sceeer.3rd.20

Cited by 1 publication

(2 citation statements)

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“…P Load (t ) and P BESS (t ) are the load demand power and the charging/discharging power of the BESS, respectively. The minimum and maximum power output from the PV, wind, and utility grid are shown in ( 27)- (29). P PV ,min and P PV ,max are the limits of the PV output power, P Wind ,min and P Wind ,max are the boundaries of the wind output power, and P grid ,max is the maximum power drawn from the utility grid in the case of gridconnected mode.…”

Section: The Binary Particle Swarm Optimization Algorithmmentioning

confidence: 99%

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A new Internet of Things based optimization scheme of residential demand side management system

Alhasnawi

Jasim

Mansoor

et al. 2022

IET Renewable Power Gen

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The steady increase in the energy demand and the growing carbon footprint has forced electricity-based utilities to shift from their use of non-renewable energy sources to renewable energy sources. Furthermore, there has been an increase in the integration of renewable energy sources in the electric grid. Hence, one needs to manage the energy consumption needs of the consumers, more effectively. Consumers can connect all the devices and houses to the internet by using Internet of Things (IoT) technology. In this study, the researchers have developed and proposed a novel 2-stage hybrid method that schedules the power consumption of the houses possessing a distributed energy generation and storage system. Stage 1 modeled the non-identical Home Energy Management Systems (HEMSs) that can contain the DGS like WT and PV. The HEMS organise the controllable appliances after taking into consideration the user preferences, electricity prices and the amount of energy produced /stored. The set of optimal consumption schedules for every HEMS was estimated using a BPSO and BSA. On the other hand, Stage 2 includes a Multi-Agent-System (MAS) based on the IoT. The system comprises two portions: software and hardware. The hardware comprises the Base Station Unit (BSU) and many Terminal Units (TUs).

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Section: The Binary Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…However, the data storage and processing using IoT layer the platform is not considered. In [28, 29] the authors introduced fair and balance demand response application in distribution networks. However, the HEMS operation based on BPSO and BSA and augmented with ToU, CPP, RTP, simultaneous regulation are not investigated.…”

Section: Introductionmentioning

confidence: 99%