Empirical analysis of real time pricing mechanisms for demand side management: contemporary review

Mahmud, Asm Ashraf; Sant, Paul; Tariq, Faisal; Jazani, David

doi:10.1109/fgct.2016.7605071

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…The algorithm finds the lowest possible load, and makes suggestions for the particular energy users' balancing the particular time slot of the whole Smart Grid (SG). The PSU would expect a response from the user, however, if the user is non-responsive, energy consumption would still be passed on to Price Control Unit (PCU) and stochastic price approximation algorithm calculates the price on a real-time The model [13] (modified)_ is shown in fig. 2 where minimising the cost function of demand load in the next upcoming state of the PSU from a user and algorithm developed in PSU would assist to reduce the peak load.…”

Section: Figure 2 Proposed Pricing Model Working Proceduresmentioning

confidence: 99%

“…Let us define an office user based total power load ≜ ∑ ∈ , the algorithm proposed in [13] and assume that there will be a maximum or minimum charge applied based on the office usages. Let us denote ℎ as the price parameters, which can be defined as…”

Section: Problem Formulation In Pcumentioning

confidence: 99%

“…The daily basis price suggestion presented [13], that considers the previous day only to calculate the price. This paper addresses the issue where energy users expect their monthly bill based on their monthly usages though monthly basis data dimension is significantly high.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Real-Time monthly DR Price system for the Smart Energy Grid

Mahmud¹,

Sant²,

Tariq³

et al. 2017

EAI Endorsed Transactions on Energy Web

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The smart grid is the next generation bidirectional modern grid. Energy users' are keen on reducing their bill and energy suppliers are also keen on reducing their industrial cost. Our demand response model would benefit them both. We have tested our model with the UK based traditional price value using a real-time basis. Energy users significantly reduced their bill and energy suppliers reduced their industrial cost due to load shifting. The Price Control Unit (PCU) and Price Suggestions Unit (PSU) utilise and embedded algorithms to vary price based upon demand. Our model makes suggestions based on energy threshold and makes use of stochastic approximation methods to produce prices. Our results shows that bill and peak load reductions benefit both the energy provider and users. This model also addresses users' preferences, if users are non-responsive, they can still reduce their bills.

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Section: Figure 2 Proposed Pricing Model Working Proceduresmentioning

confidence: 99%

Section: Problem Formulation In Pcumentioning

confidence: 99%