PSO-based optimization toward intelligent dynamic pricing schemes parameterization

Faza, Ayman; Al-Mousa, Amjed

doi:10.1016/j.scs.2019.101776

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…Faza et al [21] evaluated two intelligent dynamic pricing schemes, i.e., clipping and percentage reduction schemes, by employing a particle swarm optimization algorithm. They performed a large number of experiments with the real-time load demand data for Amman city, Jordan, where various DR strategies were implemented with these two pricing schemes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, to the best of our knowledge, none of the reviewed studies considered LECs while proposing their pricing tariffs. For example, [11], [12], [14]- [18], [20], [21] treated HECs and LECs at the same place without considering the fact that peak is only created due to LECs. Several previous studies [23], [24] proposed behavioral pricing strategy that can benefit consumers who actively participate in DR strategies even they cause peaks in different hours, and another study [25] proposed a pricing mechanism that only benefits utility companies in terms of peak alleviation.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users

et al. 2021

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Based on energy demand, consumers can be broadly categorized into low energy consumers (LECs) and high energy consumers (HECs). HECs use heavy load appliances, e.g., electric heaters and air conditioners, and LECs do not use heavy load appliances. Thus, HECs demand more energy compared to LECs. The usage of high energy consumption appliances by HECs leads to peak formation in various time intervals. Different pricing schemes, i.e., time of use (ToU), real time pricing (RTP), inclined block rate (IBR), and critical peak pricing (CPP), have been proposed previously. In ToU, an energy tariff is divided into three blocks, i.e., on-peak (high rates), off-peak (low rates), and mid-peak (between on-peak and offpeak rates) hours, and these rates are applied to all electricity users without distinction. The high energy demand by HECs causes the high peak formation; thus, higher rates should be applied to only HECs rather than all consumers, which is not the case in existing billing mechanisms. LECs are also charged higher rates in on-peak intervals and this billing mechanisms are unjustified. Thus, in this paper, a fair pricing scheme (FPS) based on power demand forecasting is developed to reduce extra bills of LECs. First, we developed a machine learning-based electricity load forecasting method, i.e., an extreme learning machine (ELM), in order to differentiate LECs and HECs. With the proposed FPS, electricity cost calculations for LECs and HECs are based on the actual energy consumption; thus, LECs do not subsidize HECs. Simulations were conducted for performance evaluation of our proposed FPS mechanism, and the results demonstrate LECs can reduce electricity cost up to 11.0075%, and HECs are charged relatively higher than previous pricing schemes as a penalty for their contribution to the on-peak formation. INDEX TERMS Smart grid; low energy consumers; pricing tariff; load forecasting; extreme learning machines; time of use; fair pricing scheme;

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users

et al. 2021

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“…Faza 42 utilized fuzzy logic model to simultaneously maximize profit while minimizing the generation cost using different dynamic pricing techniques that shaped the demand. PSO algorithm was utilized in the article to yield a solution for optimal grid operation.…”

Section: Economic Categorymentioning

confidence: 99%

A bibliometric review on the application of fuzzy optimization to sustainable energy technologies

Arriola

Ubando

Chen

2020

Intl J of Energy Research

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“…There are many optimization algorithms available for the optimization of the belief rule base. If the particle swarm optimization algorithm is used in the time complexity analysis, the calculations in a single iteration include the particle fitness calculation, optimal solution search, particle velocity updating, and particle position updating [17]. The swarm optimization algorithm complexity is related to the number of particles…”

Section: Time Complexity Analysismentioning

confidence: 99%

A Novel Construction and Inference Methodology of Belief Rule Base

et al. 2020

IEEE Access

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PSO-based optimization toward intelligent dynamic pricing schemes parameterization

Cited by 12 publications

References 17 publications

A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users

A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users

A bibliometric review on the application of fuzzy optimization to sustainable energy technologies

A Novel Construction and Inference Methodology of Belief Rule Base

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