Maximum demand limiter for reliable supply by reducing the power cuts to domestic loads

Babu, P. Ravi; Divya, V.

doi:10.1109/icpec.2013.6527716

Cited by 3 publications

(1 citation statement)

References 2 publications

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“….... [3] = P .... [4] The real power has an average value of P = VI and it represents the power utilized in Watts. Reactive power has a mean value of Q = VI and it is the power merely absorbed and returned in load due to its reactive properties, it is measured in Volt-Amps-Reactive(Var) [20].…”

Section: Mathematical Modellingmentioning

confidence: 99%

End-use Appliance Control Technique for Domestic Peak Load Management

Chipango¹,

Popoola²,

Munda³

2016

Environment and Water Resource Management / 837: Health Informatics / 838: Modelling and Simulation / 839: Power and Energy Sys

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Several peak load management strategies are being widely employed in the residential sector, due to increased energy consumption and peak demand. The residential peak demand is very prominent and largely depends on the interaction between occupant's energy consumption behaviour and appliance usage, hence the need to address end-use appliance usage based on consumer utilization pattern. This paper presents a domestic peak load management model using end-use appliance control technique, with focus mainly put on reducing demand and energy consumption whilst ensuring that the consumer's needs are met to a certain degree during peak periods. The proposed technique uses a bottom-up approach to capture the diversity and variability of various end-use appliance energy profiles in the household based on the consumer usage pattern. Empirical time of use (ToU), frequency of appliance usage and appliance power characteristics are the variables considered in the proposed methodology. Results obtained from simulation of proposed control model shows that the system is practically realizable using Matlab/Simulink and this is expected to contribute towards peak demand and energy usage reduction. This eventually will cumulate into reduced electricity costs for consumers and utility operational expenditure.

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Section: Mathematical Modellingmentioning

confidence: 99%