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What is Open Automated Demand Response (OpenADR)

Chapter 5
Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
Demand Response plays a vital role in grid stabilization during hot Summers, easing severely constrained electrical grids from coast-to-coast. Future energy crises caused by electricity demand exceeding system capacity can be postponed or even averted through Demand Response. The U.S. Federal Energy Regulatory Commission defines DR as “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.” The ability of DR to avert an energy crisis is so promising that one FERC Commissioner has identified DR as the “killer application” for the Smart Grid.
Published in Chapter:
Power Management in Microgrids: A Multi Agent Petri Nets Based Approach
Tarek Jellad (ENIS, Tunisia), Khaled Taouil (ENIS, Tunisia), and Zied Chtourou (ENIS, Tunisia)
Copyright: © 2014 |Pages: 24
DOI: 10.4018/978-1-4666-4450-2.ch005
Abstract
Conventional power generation stations used to be centralized and located far from customers. The transport and distribution infrastructure incur power losses that are mainly due to cabling resistance. Distributed power generation resources located close to customers are sought as a solution to minimize transport power losses. They are also good alternatives in situations where connection to the grid is not possible due to geographical or economical reasons. Furthermore, the adoption of renewable energies as alternatives for the scarce fossil energy sources paves the way to more distributed energy production. These Distributed energy resources, when located in a limited region, can be interconnected with loads and eventually storages to form a microgrid. A microgrid can operate in off-grid, on-grid, or alternate between these modes while optimizing power quality and cost. Multi-Agent Systems (MAS) and Petri nets could be put into contribution for high level planning of energy exchanges within a microgrid. This strategy has been validated on the basis of a dynamic model for the simulation and optimization of power exchanges between different DERs.
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