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Multi-Objective Optimization for DC Microgrid Using Combination of NSGA-II Algorithm and Linear Search Method
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DOI：10.1109/JETCAS.2023.3284061.png)
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Abstract
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With the penetration of renewable sources, the DC microgrid is much more efficient and flexible to link renewable power generators and DC loads. Due to the uncertainties in both sources and loads, it is hard to maintain the economic and optimal operation simultaneously in DC microgrids. To solve it, this paper builds a multi-objective optimization model including the operation cost, power loss, and load expectation ratio to satisfy the overall optimal management and system economy requirement. To fasten the optimization, a novel hybrid algorithm that combines the non-dominated sorting genetic algorithm-II (NSGA-II) and linear search method (LSM) is proposed as NSGA-LSM, which uses the fast global searching capacity of the LSM to accelerate the iteration process of NSGA-II. Therefore, it is superior to optimize more than two objectives, and then suitable for the microgrid with different kinds of renewable sources. Finally, the simulation in a DC microgrid with distributed photovoltaics (PVs) as an example verifies the above analysis well.
Keywords:
Microgrids
Optimization
Batteries
Costs
State of charge
Renewable energy sources
Genetic algorithms
DC microgrid
multi-objective optimization
non-dominated sorting genetic algorithm-II
linear search method
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3.8
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1.4K
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2.8K
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