Demand Response Potential Day-Ahead Forecasting Approach Based on LSSA-BPNN Considering the Electricity-Carbon Coupling Incentive Effects-Reference-Cited by-同舟云学术

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Demand Response Potential Day-Ahead Forecasting Approach Based on LSSA-BPNN Considering the Electricity-Carbon Coupling Incentive Effects

Published:2024-05 Issue:3 Volume:60 Page:4505-4516
ISSN:0093-9994
Container-title:IEEE Transactions on Industry Applications
language:
Short-container-title:IEEE Trans. on Ind. Applicat.

Author:

Zhang Yiran¹,Ge Xinxin²^ORCID,Li Meiyi²^ORCID,Li Nan²^ORCID,Wang Fei²^ORCID,Wang Liyong³,Sun Qinfei³

Affiliation:

1. School of International Education (BAODING), North China Electric Power University, Baoding, China

2. Department of Electrical Engineering, North China Electric Power University, Baoding, China

3. State Grid Beijing Electric Power Research Institute, Beijing, China

Funder

Science and Technology Project of State Grid Corporation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/28/10535351/10460118.pdf?arnumber=10460118

Reference58 articles.

1. A Day-ahead Demand Response Potential Forecasting Approach Based on LSSA-BPNN Considering the Electricity-carbon Coupling Incentive Effects

2. Two-Stage Decoupled Estimation Approach of Aggregated Baseline Load Under High Penetration of Behind-the-Meter PV System

3. Fundamentals and business model for resource aggregator of demand response in electricity markets

4. Impact Analysis of Customized Feedback Interventions on Residential Electricity Load Consumption Behavior for Demand Response

5. Evolutionary Game-theoretic Modeling of Massive Distributed Renewable Energy Deployment Towards Low-carbon Distribution Networks

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1. Assessment of Low-Carbon Flexibility in Self-Organized Virtual Power Plants Using Multi-Agent Reinforcement Learning;Energies;2024-07-26

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