Solution of optimal power flow with wind power uncertainty using hybridized self-adaptive differential evolution

Area of Science:

• Electrical Engineering
• Power Systems Analysis
• Renewable Energy Integration

Background:

• Optimal Power Flow (OPF) is crucial for efficient grid operation.
• Integrating wind power presents challenges due to its stochastic nature.
• Accurate wind power cost modeling, including reserve and penalty costs, is essential.

Purpose of the Study:

• To develop an advanced OPF solution for electrical systems with integrated wind power.
• To propose an enhanced self-adaptive differential evolution method with mixed crossover (ESDE-MC).
• To accurately model wind power costs considering wind speed variability and reserve/penalty factors.

Main Methods:

• Utilized a Wound Rotor Induction Generator (WRIG) model for wind power generation.
• Developed a WRIG reactive power (Q) - voltage (V) formulation integrated into power flow.
• Applied the ESDE-MC method to solve the OPF problem, minimizing generation costs.

Main Results:

• The ESDE-MC method achieved superior cost minimization for IEEE 57-bus and IEEE 30-bus systems.
• For the IEEE 57-bus system, ESDE-MC yielded a cost of $2788.69/h, outperforming existing methods.
• The method demonstrated reduced computational time and improved optimal values compared to literature.

Conclusions:

• The proposed ESDE-MC method is highly effective for OPF with wind power integration.
• The approach successfully minimizes generation costs while accounting for wind energy's uncertainties.
• Kruskal-Wallis test confirmed the statistical significance and effectiveness of the ESDE-MC method.