Coupon-Based Demand Response Considering Wind Power Uncertainty: A Strategic Bidding Model for Load Serving Entities

By Xin Fang, Qinran Hu, Fangxing Li, Beibei Wang, Yang Li. IEEE Transactions on Power Systems, 2016

190 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/956d967def6e541a260f1c9e122584e0ff93fca2

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. Renzhi Lu
2. Seung Ho Hong
3. Xin Fang
4. Linquan Bai
5. Fangxing Li
6. Tao Jiang
7. Yan Du
8. Haoyu Yuan

Citations in the corpus

• Mean-Variance Optimization-Based Energy Storage Scheduling Considering Day-Ahead and Real-Time LMP Uncertainties (2018, background): “R ECENTLY energy storage systems have gained significant interest in both academia and industry under the new paradigm of renewable energy and demand response [1], [2].”
• Security-constrained bi-level economic dispatch model for integrated natural gas and electricity systems considering wind power and power-to-gas process (2017, background): “(3) The linearity of the lower-level model in (25)–(27) transfers the bi-level model into a single level Mathematic Program With Equilibrium Constraints (MPEC) problem by recasting the lower level problem as its KKT optimality conditions [25,26], which can be added into the upper level problem as a ”
• Analytical Method to Aggregate Multi-Machine SFR Model With Applications in Power System Dynamic Studies (2018, methodology): “The ASFR model provides a fast technique for this study, which is useful in power planning and generation dispatch [17], [18], and [32].”
• Novel Linearized Power Flow and Linearized OPF Models for Active Distribution Networks With Application in Distribution LMP (2018, background): “This is reasonable when DGs are aggregated by electric vehicle aggregators [11] or loading serving entity aggregators [23] who are economically rational.”
• Intelligent Multi-Microgrid Energy Management Based on Deep Neural Network and Model-Free Reinforcement Learning (2020, background): “In recent years, emerging demand-side resources are playing an increasingly important role in maintaining the economy and security of bulk power system operation [2]–[4].”
• Incentive-based demand response for smart grid with reinforcement learning and deep neural network (2019, background): “In (2), λmax and λmin are the upper and lower bounds of incentive rate λn h , , derived by mutual agreement or regulatory requirement between the SP and CUs, maintaining incentive rates fair and protecting each profit [19,36].”