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access icon openaccess Virtual energy storage capacity estimation using ANN-based kWh modelling of refrigerators

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Received 21/12/2017, Accepted 30/04/2018, Revised 27/02/2018, Published 01/05/2018

Prolific integration of renewable energy sources (RESs) such as solar photovoltaic systems into the distribution network will result in various issues associated with their intermittent nature. Energy storage is a vital component for overcoming issues associated with the intermittent nature of such RES. Though stationary battery systems are used as energy storage for such applications, smart energy storage (SES) systems are also becoming popular owing to various advantages and advent of smart grid systems. SES can be achieved by aggregating electric vehicles (EVs) or by using demand response management for loads with large time constants. Aggregated residential refrigerators are potential candidates for creating SES which has virtual storage capacity, unlike EVs. In this study, residential refrigerators are modelled analogously to energy capacity and self-discharge of electro-chemical batteries using the artificial neural network based kWh modelling. The model is further extended to estimate the virtual energy storage (VES) capacity with aggregated residential refrigerators; particularly in high-rise residential buildings. Simulation results are presented for scenarios covering the complete range of thermal capacity of typical refrigerators applicable in Singapore's climatic condition. Furthermore, a brief description of the possible applications for the estimated VES, pertaining to smart grid architecture and cyber-attack is also presented.

Inspec keywords: energy storage; distribution networks; neural nets; renewable energy sources; distributed power generation; photovoltaic power systems; electric vehicles; smart power grids; demand side management; building management systems; power grids

Other keywords: kWh modelling; SES; thermal capacity; electro-chemical batteries; ANN; high-rise residential buildings; virtual storage capacity; overcoming issues; solar photovoltaic systems; cyber-attack; smart grid architecture; artificial neural network; smart energy storage systems; stationary battery systems; EVs; intermittent nature; smart grid systems; virtual energy storage capacity estimation; prolific integration; renewable energy sources; distribution network; energy capacity; self-discharge; aggregated residential refrigerators; typical refrigerators

Subjects: Energy resources; Power system management, operation and economics; Neural computing techniques

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