An adaptive modified Newton-Raphson-based optimizer for efficient optimal power flow performance under multiple...

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Renewable Energy Systems

Background:

• The optimal power flow (OPF) problem is critical for efficient and secure power system operation.
• Integrating renewable energy sources like photovoltaics presents new challenges for OPF.
• Existing optimization methods may struggle with the complexity and uncertainty introduced by renewables.

Purpose of the Study:

• To develop a novel optimization algorithm, the Modified Newton-Raphson-Based Optimizer (MNRBO), for solving the OPF problem.
• To enhance the MNRBO with adaptive mechanisms for improved performance and robustness.
• To evaluate the MNRBO's effectiveness in power systems with integrated photovoltaic generation under uncertainty.

Main Methods:

• The Modified Newton-Raphson-Based Optimizer (MNRBO) integrates gradient-inspired search with adaptive crossover and sigmoid decay.
• The algorithm was tested on the IEEE 30-bus system across various scenarios including fuel cost minimization and loss reduction.
• A probabilistic OPF framework using the Point Estimate Method (PEM) was employed to handle photovoltaic generation uncertainty.

Main Results:

• MNRBO consistently outperformed the original NRBO and other state-of-the-art algorithms in accuracy, convergence speed, and solution consistency.
• The adaptive components (ACM and sigmoid decay) significantly improved convergence stability and robustness.
• The probabilistic framework demonstrated the MNRBO's applicability under variable renewable energy generation.

Conclusions:

• The proposed MNRBO offers a robust and accurate solution for the complex OPF problem, especially with renewable energy integration.
• The adaptive strategies are crucial for enhancing the optimizer's performance and reliability.
• MNRBO provides a dependable tool for real-world power system operation and planning.