The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges

By Bo Shen, G. Ghatikar, Zen Lei, Jinkai Li, G. Wikler, Phil Martin. 2014

184 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/00ef247618e4c9ae065c9ad1eb4fde2af2c33f0f

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In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

An overview of Demand Response: Key-elements and international experience (2017, background): “Shen et al. reviewed the role of regulatory reforms, market structure changes and technological developments in rendering DR more viable in the electric power system [16].”
A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems (2015, background): “In 2011, FERC Order 745 determined that DR resources should be compensated at the locational marginal price for their participation in wholesale markets, thus establishing an equal treatment between demandside resources and generation [76], [77].”
Challenges and barriers to demand response deployment and evaluation (2015, background): “Additionally, regulatory changes and electricity market reform have played a major part in allowing DR to become a more viable resource [6].”
Incentive-based demand response for smart grid with reinforcement learning and deep neural network (2019, background): “Incentive-based DR provides fixed or time-varying incentives to participating CUs for reducing their electricity usage [8].”
Microgrid to enable optimal distributed energy retail and end-user demand response (2018, background): “Demand response (DR) is becoming a cost-effective balancing resource in power systems [33].”
MOD-DR: Microgrid optimal dispatch with demand response (2017, background): “, peak clipping, valley filling, load shifting and flexible load shape) [41,43,42,67,6].”
A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach (2018, background): “The existing literature generally discusses two categories of DR: price-based and incentive-based [4].”