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A hybrid prediction-based microgrid energy management strategy considering demand-side response and data interruption
- Source :
- International Journal of Electrical Power & Energy Systems. 113:139-153
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Data interruption may cause the centralized energy management system of a microgrid (MG) to collapse. To solve this problem, a hybrid prediction-based energy management strategy is proposed in this paper to predict interrupted data for the centralized dispatching process of an MG. This hybrid prediction method is designed following a combination of model predictive control and extreme learning machine techniques. Based on the predicted data of distributed energy resources and three types of loads from demand-side response, an optimization model is formulated to minimize the operational cost. If some predicted data are interrupted during transmission, then a prospect vulnerability assessment method is applied to select a neighboring device to predict the interrupted data. In the end, an improved particle swarm optimization algorithm is proposed with the help of genetic algorithms to accelerate convergence to global optimal solutions for the proposed MG energy management problem. The effectiveness of the proposed models and solution methods is also verified by a case study.
- Subjects :
- Mathematical optimization
Energy
business.industry
Computer science
Energy management
020209 energy
020208 electrical & electronic engineering
Energy Engineering and Power Technology
Particle swarm optimization
02 engineering and technology
Energy management system
Model predictive control
Distributed generation
Convergence (routing)
0202 electrical engineering, electronic engineering, information engineering
Microgrid
Electrical and Electronic Engineering
business
Extreme learning machine
Subjects
Details
- ISSN :
- 01420615
- Volume :
- 113
- Database :
- OpenAIRE
- Journal :
- International Journal of Electrical Power & Energy Systems
- Accession number :
- edsair.doi.dedup.....687c46a7b53fbf85aefaa2fb82c92abe