Multi-Objective Scheduling Optimization Based on a Modified Non-Dominated Sorting Genetic Algorithm-II in Voltage Source Converter−Multi-Terminal High Voltage DC Grid-Connected Offshore Wind Farms with Battery Energy Storage Systems

• Main Authors: Ho-Young Kim, Mun-Kyeom Kim, San Kim
• Format: Article
• Language: English
• Published: MDPI AG 2017-07-01
• Series: Energies
• Subjects: battery energy storage system; Benders’ decomposition; hybrid network station; voltage source converter multi-terminal high voltage direct current; optimal power flow; modified non-dominated sorting genetic algorithm-II
• Online Access: https://www.mdpi.com/1996-1073/10/7/986

Improving the performance of power systems has become a challenging task for system operators in an open access environment. This paper presents an optimization approach for solving the multi-objective scheduling problem using a modified non-dominated sorting genetic algorithm in a hybrid network of meshed alternating current (AC)/wind farm grids. This approach considers voltage and power control modes based on multi-terminal voltage source converter high-voltage direct current (MTDC) and battery energy storage systems (BESS). To enhance the hybrid network station performance, we implement an optimal process based on the battery energy storage system operational strategy for multi-objective scheduling over a 24 h demand profile. Furthermore, the proposed approach is formulated as a master problem and a set of sub-problems associated with the hybrid network station to improve the overall computational efficiency using Benders’ decomposition. Based on the results of the simulations conducted on modified institute of electrical and electronics engineers (IEEE-14) bus and IEEE-118 bus test systems, we demonstrate and confirm the applicability, effectiveness and validity of the proposed approach.