Microgrid to enable optimal distributed energy retail and end-user demand response
By Ming Jin, W. Feng, Chris Marnay, Costas J. Spanos. 2018
175 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/b6d21223230ca4f53f86d8889997569f09a48567
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Renzhi Lu
- Chau Yuen
- Wayes Tushar
- Seung Ho Hong
- Tapan Kumar Saha
- H. Vincent Poor
- Malcolm Duncan McCulloch
- Thomas Morstyn
In Electrical and Electronic Engineering
- Chau Yuen
- H. Vincent Poor
- Renzhi Lu
- Wayes Tushar
- Tapan Kumar Saha
- Seung Ho Hong
- Malcolm Duncan McCulloch
- Thomas Morstyn
Citations in the corpus
- A Motivational Game-Theoretic Approach for Peer-to-Peer Energy Trading in the Smart Grid (2019, background): “As such, there has been a significant increase in adopting distributed energy resources (DERs) at the edge of the grid level [2], i.”
- Incentive-based demand response for smart grid with reinforcement learning and deep neural network (2019, background): “Nowadays, continued growth in energy demand imposes major burdens on power systems, and a mismatch between electricity demand and supply would cause high costs for both the service provider (SP) and customers (CUs) and may even cripple the whole grid [1,2].”
- A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach (2018, methodology): “5 [25] which is an acceptable value that considers both SP’s profit and CUs’ costs, thus the retail price bounds are [2.”