TY - JOUR
T1 - Distributed energy resource allocation using multi-objective grasshopper optimization algorithm
AU - Ahmadi, Bahman
AU - Ceylan, Oguzhan
AU - Ozdemir, Aydogan
N1 - Funding Information:
This research is funded as a part of “117E773 Advanced Evolutionary Computation for Smart Grid and Smart Community” project under the framework of 1001 Project organized by “The Scientific and Technological Research Council of Turkey TUBITAK”.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/12
Y1 - 2021/12
N2 - The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices.
AB - The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices.
KW - Battery energy storage system (BESS)
KW - Distribution network planning
KW - Grasshopper optimization algorithm
KW - Optimal planning
KW - Photovoltaic generation
KW - Wind energy generation
KW - n/a OA procedure
UR - http://www.scopus.com/inward/record.url?scp=85114477846&partnerID=8YFLogxK
U2 - 10.1016/j.epsr.2021.107564
DO - 10.1016/j.epsr.2021.107564
M3 - Article
SN - 0378-7796
VL - 201
JO - Electric power systems research
JF - Electric power systems research
M1 - 107564
ER -
