Optimal allocation of distributed generation and energy storage system in microgrids

This study presents a new approach for optimal allocation of distributed generation (DG) and energy storage system (ESS) in microgrids (MGs). The practical optimal allocation problems have non-smooth cost functions with equality and inequality constraints that make the problem of finding the global...

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Veröffentlicht in:IET renewable power generation Jg. 8; H. 6; S. 581 - 589
Hauptverfasser: Chen, Changsong, Duan, Shanxu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Stevenage The Institution of Engineering and Technology 01.08.2014
The Institution of Engineering & Technology
Schlagworte:
Algorithms
Allocations
Cost engineering
Cost function
customer demand
Distributed generation
distributed power generation
dynamic capacity adjustment algorithm
Dynamics
Energy storage
energy storage equality constraint
energy storage system
genetic algorithms
investment cost minimisation
Marketing
matrix real coded genetic algorithm framework
microgrids
minimisation
nonsmooth cost functions
operation cost minimisation
optimal allocation
Optimization
system security
distributed power generation
genetic algorithms
operation cost minimisation
system security
distributed generation
energy storage system
energy storage
microgrids
optimal allocation
nonsmooth cost functions
dynamic capacity adjustment algorithm
energy storage equality constraint
customer demand
minimisation
investment cost minimisation
matrix real coded genetic algorithm framework
ISSN:1752-1416, 1752-1424, 1752-1424
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Beschreibung
Zusammenfassung:This study presents a new approach for optimal allocation of distributed generation (DG) and energy storage system (ESS) in microgrids (MGs). The practical optimal allocation problems have non-smooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. A dynamic capacity adjustment algorithm is incorporated in the matrix real-coded genetic algorithm (MRCGA) framework to deal with the non-smooth cost functions. The proposed cost function takes into consideration operation cost minimisation as well as investment cost minimisation at the same time for the MG. Moreover, an energy storage equality constraint is applied to manage the state of charge of EES in MGs. The MRCGA is used to minimise the cost function of the system while constraining it to meet the customer demand and security of the system. For each studied case, sets of optimal capacities and economic operation strategies of ESS and DG sources are determined. The computational simulation results are presented to verify the effectiveness of the proposed method.
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ISSN:1752-1416
1752-1424
1752-1424
DOI:10.1049/iet-rpg.2013.0193