Assessing the benefits of residential demand response in a real time distribution energy market

By Pierluigi Siano, D. Sarno. 2016

274 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/fdff67cc9865886b0a9596d16fe83741358595ed

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

1. João P. S. Catalão
2. Fei Wang
3. Pierluigi Siano
4. Fangxing Li
5. Rui Bo

In Electrical and Electronic Engineering

1. M. Shafie‐khah
2. Ming Zhou
3. João P. S. Catalão
4. Fei Wang
5. Vincenzo Loia
6. Pierluigi Siano
7. Yonghua Song
8. Gengyin Li

Citations in the corpus

• A Survey and Evaluation of the Potentials of Distributed Ledger Technology for Peer-to-Peer Transactive Energy Exchanges in Local Energy Markets (2019, unknown): “System architecture adopted for TCRs’ operation [33].”
• The values of market-based demand response on improving power system reliability under extreme circumstances (2017, background): “Meanwhile, paper [16] assess 37 the benefits of residential DR programs in real-time energy market from the distribution level.”
• Optimal residential community demand response scheduling in smart grid (2018, background): “Among loads that can participate in DR, residential load has great potentiality, and can effectively ameliorate the demand-side load curve [1,2].”
• A Review of Smart Cities Based on the Internet of Things Concept (2017, background): “To permit the house controllers to cooperate with aggregators, who are responsible for gathering information from lots of houses, a home gateway is introduced in [44].”
• Iot-based smart cities: A survey (2016, background): “In [26] a home gateway is introduced in order to allow the home controller to cooperate with the aggregator who is responsible to collect data from many homes.”
• Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network (2019, unknown): “The parameters of each appliance are listed in Table III, derived from [8], [24], [25], [39].”
• 5G network-based Internet of Things for demand response in smart grid: A survey on application potential (2020, background): “For example, it has been proved in [26] that appropriate DR is beneficial for decreasing both power system’s operation costs and users’ costs.”