Open AccessAgent‐based system to control the air‐conditioner and EV charging for residents in smart cities
Author(s) -
Singh Shashank,
Namboodiri Aryesh,
Selvan Manickavasagam Parvathy
Publication year - 2019
Publication title -
iet smart cities
Language(s) - English
Resource type - Journals
ISSN - 2631-7680
DOI - 10.1049/iet-smc.2019.0060
Subject(s) - battery (electricity) , air conditioning , automotive engineering , energy consumption , controller (irrigation) , computer science , control (management) , node (physics) , state of charge , smart grid , energy (signal processing) , electrical engineering , engineering , power (physics) , mechanical engineering , agronomy , physics , structural engineering , quantum mechanics , artificial intelligence , biology , statistics , mathematics
Air‐conditioner (AC) accounts for a significant share of residential energy consumption. Considering the widespread rise in electric vehicle (EV) usage, its charging would also contribute a considerable percentage of consumer's total energy consumption. Consequently, the concurrent operation of AC and EV charging would result in peaky load curves. Hence, this study proposes a system of agents for AC and EV charging applications, which incorporates load‐management strategies to flatten the load curve. Thereby, the presented system includes two agents, namely: a smart load node for a thermostatically controlled load (SLN‐TCL) and a smart battery charge controller. Subsequently, a subagent, namely micro‐node, has been introduced to support SLN‐TCL and to implement the concept of distributed temperature sensing (DTS). The implementation of DTS subdues the conventional temperature sensing mechanism of AC and ensures a more flexible operation. This study includes the design, development, and features of agents and subagents for AC and EV applications. Furthermore, this study also demonstrates the agent‐based control actions for peak‐shaving under real conditions to showcase the performance of this system.