TY - JOUR
T1 - Multi-objective day-ahead scheduling of microgrids using modified grey Wolf optimizer algorithm
AU - Javidsharifi, Mahshid
AU - Niknam, Taher
AU - Aghaei, Jamshid
AU - Mokryani, Geev
AU - Papadopoulos, Panagiotis
N1 - Publisher Copyright:
© 2019 - IOS Press and the authors.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - micro-grid
KW - modified grey Wolf optimizer
KW - Multi objective optimal operation management
KW - pareto optimal solution
KW - renewable energy sources
UR - http://www.scopus.com/inward/record.url?scp=85063499931&partnerID=8YFLogxK
U2 - 10.3233/JIFS-171688
DO - 10.3233/JIFS-171688
M3 - Journal article
AN - SCOPUS:85063499931
SN - 1064-1246
VL - 36
SP - 2857
EP - 2870
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
IS - 3
ER -