IGDT-Based Wind–Storage–EVs Hybrid System Robust Optimization Scheduling Model
Abstract
Wind power has features of uncertainty. When wind power producers (WPPs) bid in the day-ahead electricity market, how to deal with the deviation between forecasting output and actual output is one of the important topics in the design of electricity market with WPPs. This paper makes use of a non-probabilistic approach—Information gap decision theory (IGDT)—to model the uncertainty of wind power, and builds a robust optimization scheduling model for wind−storage−electric vehicles(EVs) hybrid system with EV participations, which can make the scheduling plan meet the requirements within the range of wind power fluctuations. The proposed IGDT robust optimization model first transforms the deterministic hybrid system optimization scheduling model into a robust optimization model that can achieve the minimum recovery requirement within the range of wind power output fluctuation, and comprehensively considers each constraint. The results show that the wind−storage−EVs hybrid system has greater operational profits and less impact on the safe and stable operation of power grids when considering the uncertainty of wind power. In addition, the proposed method can provide corresponding robust wind power fluctuation under different expected profits of the decision-maker to the wind−storage−EVs hybrid system.
Keywords
Technology (General)
Information technology
Technology
Science
environmental sciences
general works
nutrition. foods and food supply
nutritional diseases. deficiency diseases
agriculture
social sciences
urban groups. the city. urban sociology
regional planning
electronic computers. computer science
home economics
geography. anthropology. recreation
recreation. leisure
human ecology. anthropogeography
communities. classes. races
consciousness. cognition
nuclear and particle physics. atomic energy. radioactivity
Access
Citation
ID:
79630
References
Blockchain Verification
Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
79630
Unique Identifier:
79bd443e54e3cef94a5d9bed5221bc1b
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
Creates 1,000,000 NFT tokens for this article
Token Features:
- ERC-1155 Standard NFT
- 1 Million Supply per Article
- Transferable via MetaMask
- Permanent Blockchain Record
Scan with Saymatik Web3.0 Wallet