Abstract

It is no doubt that the optimal power flow (OPF) has great importance in electric power systems. It aims at assigning the adequate operating levels in order to meet the required demands with the objective of minimizing combined economic and environmental concerns. Integration of emerged technologies of voltage source converter (VSC) stations in AC meshed power systems changes foremost their corresponding operation and control features. The VSC stations are usually connected with each other through HVDC lines and consequently a multi-terminal direct current (MDC) system is established. This paper presents an improved manta ray foraging optimizer (IMRFO) for solving the OPF in electric power systems with and without emerged technologies of VSC stations. The proposed IMRFO aims at minimizing the total fuel costs, the total environmental emissions, and the total electrical losses. The MRFO simulates the foraging behaviors of the manta rays. MRFO is improved to handle multi-objectives by incorporating an outward store for the non-dominated Pareto individuals. The form of the fitness function is adaptively varied by iteratively changing their weights. Furthermore, a technique for order preference by similarity to ideal solution (TOPSIS) is applied to extract a suitable operating point among the resulted Pareto set. Several applications of the proposed IMRFO are presented for conventional IEEE 30-bus system, as an AC meshed power system, and modified IEEE 30-bus with emerged VSC stations, as a hybrid AC/MDC meshed power system. Simulation results declare that the proposed algorithm has great effectiveness and robustness features compared to the others. Also, various well-distributed Pareto solutions are obtained based on the proposed algorithm with adequate techno-economic-environmental characteristics.

Highlights

  • The optimal power flow (OPF) problem is considered one of the old and modern studies

  • The proposed algorithm has great outperformance compared with artificial bee colony (ABC) [42], teaching-learning algorithm [43], differential evolution (DE) [41], symbiotic organisms search (SOS) [44], moth swarm algorithm (MSA) [45], developed grey wolf algorithm (DGWA) [46], improved moth flame algorithm (IMFA) [47], grasshopper optimizer (GO) [48], adaptive grasshopper optimizer (AGO) [48]

  • 1) RESULTS OF SINGLE OBJECTIVE OPF In this subsection, the proposed improved manta ray foraging optimizer (IMRFO) and several competitive techniques are applied for optimal operation of hybrid AC/multi-terminal direct current (MDC) system with a single objective of fuel generation costs (FGC), TLL and total environmental emissions (TEE) minimization for Cases 1-3, respectively

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Summary

INTRODUCTION

The optimal power flow (OPF) problem is considered one of the old and modern studies. OPF searches for the combined economic and environmental operation of electrical power systems by finding the effective operating points of the power systems, which can overcome previous problems satisfactorily This optimal condition is distinct with respect to objective functions reducing generation costs, increasing system loadability, reducing transmission losses, reducing environmental emissions, improving voltage performance, and improving system reliability and security while maintaining different equality and inequality constraints [2]. OPF searches for the combined economic and environmental operation of the electrical power systems by searching for the effective operating points of the power systems, which can overcome previous problems satisfactorily This optimal condition is distinct with respect to objective functions reducing generation costs, increasing system loadability, reducing transmission losses, reducing environmental emissions, improving voltage performance, and improving system reliability and security while maintaining the different equality and inequality constraints [2], [15].

OPF CONSTRAINTS IN AC POWER SYSTEMS
Pdc-Vc constant control
SIMULATION RESULTS
CONCLUSION
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