Multi-Objective Optimization for Day-Ahead HT-WP-PV-PSH with LS-EVs Systems Self-Scheduling Unit Commitment Using HHO-PSO Algorithm

Articles in Press

Document Type : Research paper

Authors

1 Khuzestan Regional Electric Company (KZREC), Ahvaz, Iran.

2 Department of Electrical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran.

3 Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak, Iran.

4 Research Institute of Renewable Energy, Arak University, Arak, Iran.

Abstract

A stochastic multi-objective structure is introduced for integrating hydro-thermal, wind power, photovoltaic (PV), pumped storage hydro (PSH), and large-scale electric vehicle (LS-EV) systems using a day-ahead self-scheduling mechanism. The paper incorporates an improved Harris Hawks Optimizer combined with Particle Swarm Optimization, termed HHO-PSO. Uncertain parameters of the problem, such as energy prices, spinning reserve, non-spinning reserve prices, and renewable output, are also considered. Additionally, the lattice Monte Carlo simulation and roulette wheel mechanism are utilized. By adopting an objective function that optimizes multiple goals, the paper proposes an approach to assist generation companies (GenCos) in maximizing profit (PFM) and minimizing emissions (EMM). However, to make the modeling of the multi/single-objective day-ahead hydro-thermal self-scheduling problem with WP, PV, PSH, and LS-EVs practical, additional factors must be considered in the problem formulation. According to the findings, the HHO-PSO algorithm provides satisfactory values for profit maximization and emission minimization in the day-ahead operation of power systems across all considered cases. The paper applies the proposed method to a 118-bus test network, demonstrating its accuracy and capability.

Keywords

Main Subjects

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Journal of Operation and Automation in Power Engineering

Articles in Press, Corrected Proof
Available Online from 13 February 2025

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  • Receive Date: 25 February 2023
  • Revise Date: 16 August 2024
  • Accept Date: 08 September 2024
  • First Publish Date: 13 February 2025

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