Operation Scheduling Optimization for Microgrids Considering Coordination of Their Components

Operation scheduling is one of the most practical optimization problems to efficiently manage the electric power supply and demand in microgrids. Although various microgrid-related techniques have been developed, there has been no established solution to the problem until now. This is because the fo...

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Veröffentlicht in:Future internet Jg. 11; H. 11; S. 223
Hauptverfasser: Takano, Hirotaka, Goto, Ryota, Soe, Thin Zar, Tuyen, Nguyen Duc, Asano, Hiroshi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.11.2019
Schlagworte:
Algorithms
Alternative energy sources
binary particle swarm optimization (bpso)
Computer simulation
Consumption
Coordination
Distributed generation
economic load dispatch (eld)
Efficiency
Electric power demand
Electric power grids
Electric power supplies
Electricity
Electricity distribution
Expected values
Integer programming
Internet
microgrids
Operation scheduling
Optimization
Particle swarm optimization
Power supply
power supply-demand management
Quadratic programming
quadratic programming (qp)
Renewable energy sources
Schedules
Scheduling
Solution space
Supply & demand
uncertainty
unit commitment (uc)
Variables
ISSN:1999-5903, 1999-5903
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Zusammenfassung:Operation scheduling is one of the most practical optimization problems to efficiently manage the electric power supply and demand in microgrids. Although various microgrid-related techniques have been developed, there has been no established solution to the problem until now. This is because the formulated problem becomes a complicated mixed-integer programming problem having multiple optimization variables. The authors present a framework for this problem and its effective solution to obtain an operation schedule of the microgrid components considering their coordination. In the framework, trading electricity with traditional main power grids is included in the optimization target, and uncertainty originating from variable renewable energy sources is considered. In the solution, the formulated problem is reformulated to reduce the dimensions of its solution space, and, as a result, a combined algorithm of binary particle swarm optimization and quadratic programming is applicable. Through numerical simulations and discussions of their results, the validity of the authors’ proposal is verified.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:1999-5903
1999-5903
DOI:10.3390/fi11110223