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

• Published in: IET Smart Grid Vol. 1; no. 2; pp. 31 - 39
• Main Authors: Kumar, Kandasamy Nandha, Vijayakumar, Krishnasamy, Kalpesh, Chaudhari
• Format: Journal Article
• Language: English
• Published: Durham The Institution of Engineering and Technology 01.07.2018; John Wiley & Sons, Inc; Wiley
• Subjects: aggregated residential refrigerators; Algorithms; Alternative energy sources; ANN; artificial neural network; Artificial neural networks; B8110B Power system management, operation and economics; B8210 Energy resources; building management systems; C5290 Neural computing techniques; cyber-attack; Demand side management; distributed power generation; distribution network; distribution networks; Electric vehicles; electro-chemical batteries; energy capacity; Energy consumption; Energy distribution; Energy management; Energy storage; EVs; high-rise residential buildings; HVAC; intermittent nature; kWh modelling; Literature reviews; Modelling; neural nets; overcoming issues; photovoltaic power systems; power grids; prolific integration; Refrigerators; Renewable energy sources; Research Article; Residential buildings; Residential energy; self-discharge; SES; smart energy storage systems; Smart grid; smart grid architecture; smart grid systems; smart power grids; solar photovoltaic systems; stationary battery systems; Storage capacity; thermal capacity; Thermal energy; Thermal simulation; typical refrigerators; virtual energy storage capacity estimation; virtual storage capacity; Singapore; SES; artificial neural network; ANN; distribution networks; electric vehicles; typical refrigerators; smart power grids; distributed power generation; virtual storage capacity; overcoming issues; energy storage; prolific integration; virtual energy storage capacity estimation; power grids; building management systems; high-rise residential buildings; smart grid architecture; renewable energy sources; EVs; self-discharge; photovoltaic power systems; electro-chemical batteries; kWh modelling; distribution network; smart grid systems; cyber-attack; energy capacity; demand side management; aggregated residential refrigerators; solar photovoltaic systems; thermal capacity; neural nets; intermittent nature; stationary battery systems; smart energy storage systems
• ISSN: 2515-2947; 2515-2947
• DOI: 10.1049/iet-stg.2018.0001

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.