A day-ahead electricity pricing model based on smart metering and demand-side management

By M. Doostizadeh, H. Ghasemi. 2012

198 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/b49a08e49827f40fe525af52130b81080698910c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. M. Sheikh‐El‐Eslami
2. Ming Jin
3. Costas J. Spanos
4. Nizar Zorba
5. Christos V. Verikoukis
6. W. Feng
7. Chris Marnay
8. John S. Vardakas

In Renewable Energy, Sustainability and the Environment

1. Ming Jin
2. Costas J. Spanos
3. W. Feng
4. Chris Marnay

Citations in the corpus

• Risk Assessment of Virtual Power Plants Offering in Energy and Reserve Markets (2016, background): “contract pricing [2], nodal pricing [3] and locational marginal price calculation [4] for dispersed generations, and also day-ahead pricing [5] and new programs [6] for demand response resources.”
• Microgrid to enable optimal distributed energy retail and end-user demand response (2018, background): “load conditions typically correspond to amin 1⁄4 0:85 and amax 1⁄4 1:1 [42], while load reduction requires amin < a t max < 1.”
• A Real-Time Demand-Response Algorithm for Smart Grids: A Stackelberg Game Approach (2016, unknown): “, for the next 24 h) [10]–[12].”
• A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms (2015, background): “flatter demand curve, lower losses, lower peak-to-peak distance and higher load factor [98].”