Optimal Distributed Energy Management for Local Energy Community: A Decision Regret Oriented Smart Predict and Optimize Approach

More accurate prediction does not necessarily lead to better decision. Distinguished from existing accuracy based "predict then optimize" methods, this work offers optimal distributed energy management strategies in modern power system operation utilizing a decision regret oriented "s...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on industrial informatics Ročník 21; číslo 12; s. 9690 - 9700
Hlavní autori: Ding, Yibo, Sun, Xianzhuo, Shi, Wenzhuo, Ruan, Jiaqi, Chen, Junyu, Zhao, Yuhong, Xu, Zhao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.12.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Costs
Decision making
Decision regret
Decision theory
distributed energy management
Energy consumption
Energy management
Generators
local energy community (LEC)
Machine learning
Optimization
peer-to-peer (P2P) energy sharing
Peer-to-peer computing
Power systems
Prediction models
Predictive models
Real time
Real-time systems
Renewable energy sources
Training
ISSN:1551-3203, 1941-0050
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:More accurate prediction does not necessarily lead to better decision. Distinguished from existing accuracy based "predict then optimize" methods, this work offers optimal distributed energy management strategies in modern power system operation utilizing a decision regret oriented "smart predict and optimize" approach as the first trial. A two-stage day-ahead scheduling and real-time redispatching problem in a local energy community (LEC) is specifically modeled, where peer-to-peer (P2P) energy sharing is considered. The asymmetric and nonmonotonic relationship between prediction error and decision regret in this problem is revealed. A centralized energy and auxiliary service joint market and a decentralized P2P energy market are introduced to ensure efficient local energy consumption and tackle with prediction errors, which are almost inevitable. Two computational efficient prediction models, linear regression, and extreme learning machine, are employed as market predictors. Considering the potential risk of communication failures among smart agents in practical applications, and to avoid being trapped into local optimum points, a novel censored communication robust distributed algorithm is proposed. Simulation for a highly distributed energy sources penetrated LEC statistically prove the economic benefits of the proposed strategies.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2025.3606875