Integration of OpenADR with Node-RED for Demand Response Load Control Using Internet of Things Approach

2017-01-1702

03/28/2017

Features
Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
The increased market share of electric vehicles and renewable energy resources have raised concerns about their impact on the current electrical distribution grid. To achieve sustainable and stable power distribution, a lot of effort has been made to implement smart grids. This paper addresses Demand Response (DR) load control in a smart grid using Internet of Things (IoT) technology. A smart grid is a networked electrical grid which includes a variety of components and sub-systems, including renewable energy resources, controllable loads, smart meters, and automation devices. An IoT approach is a good fit for the control and energy management of smart grids. Although there are various commercial systems available for smart grid control, the systems based on open sources are limited. In this study, we adopt an open source development platform named Node-RED to integrate DR capabilities in a smart grid for DR load control. The DR system employs the OpenADR standard. To enable an OpenADR Virtual Top Node (VTN) to communicate with Node-RED nodes, an OpenADR Node-RED node has been developed to translate and pass information between an OpenADR VTN and Node-RED nodes. The implementation is validated for the use cases involving charging control of plug-in electric vehicles in response to real-time pricing from a utility. It has been observed that the data transfer is taking place in accordance with the standard and the Plug-in Electric Vehicle (PEV) charging is coordinated with real-time pricing and desired target State of Charge (SOC).
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-1702
Pages
10
Citation
Aggarwal, P., Chen, B., and Harper, J., "Integration of OpenADR with Node-RED for Demand Response Load Control Using Internet of Things Approach," SAE Technical Paper 2017-01-1702, 2017, https://doi.org/10.4271/2017-01-1702.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-1702
Content Type
Technical Paper
Language
English