Multi-objective day-ahead scheduling of microgrids using modified grey wolf optimizer algorithm

Mahshid Javidsharifi

doi:10.3233/JIFS-171688

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Multi-objective day-ahead scheduling of microgrids using modified grey wolf optimizer algorithm

2019-03-26

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Panagiotis Papadopoulos

DOI：10.3233/JIFS-171688

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Abstract

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Investigation of the environmental/economic optimal operation management of a microgrid (MG) as a case study for applying a novel modified multi-objective grey wolf optimizer (MMOGWO) algorithm is presented in this paper. MGs can be considered as a fundamental solution in order for distributed generators' (DGs) management in future smart grids. In the multi-objective problems, since the objective functions are conflict, the best compromised solution should be extracted through an efficient approach. Accordingly, a proper method is applied for exploring the best compromised solution. Additionally, a novel distance-based method is proposed to control the size of the repository within an aimed limit which leads to a fast and precise convergence along with a well-distributed Pareto optimal front. The proposed method is implemented in a typical grid-connected MG with non-dispatchable units including renewable energy sources (RESs), along with a hybrid power source (micro-turbine, fuel-cell and battery) as dispatchable units, to accumulate excess energy or to equalize power mismatch, by optimal scheduling of DGs and the power exchange between the utility grid and storage system. The efficiency of the suggested algorithm in satisfying the load and optimizing the objective functions is validated through comparison with different methods, including PSO and the original GWO.

Keywords:

Multi objective optimal operation management

pareto optimal solution

modified grey wolf optimizer

micro-grid

renewable energy sources

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