Optimal capacity configuration of wind-photovoltaic-storage hybrid systems based on improved chaotic evolution...

Area of Science:

• Renewable Energy Systems Engineering
• Optimization Algorithms
• Grid Integration

Background:

• High renewable energy penetration presents challenges for grid stability and economic viability.
• Wind-photovoltaic-storage (WPS) systems offer a promising solution but require optimal capacity configuration.
• Existing planning models struggle with complex nonlinear constraints and economic requirements.

Purpose of the Study:

• To develop an optimal capacity configuration model for wind-photovoltaic-storage (WPS) systems.
• To address complex nonlinear constraints and economic factors in high-renewable energy grids.
• To enhance the cost-effectiveness and robustness of WPS capacity planning.

Main Methods:

• Developed a multi-energy collaborative capacity planning model.
• Formulated an energy management strategy capturing interdependencies between wind, PV, and storage.
• Proposed an improved chaotic evolution optimization algorithm (ICEO) with self-learning perturbation and adaptive local search.

Main Results:

• ICEO demonstrated superior solution quality and robustness compared to state-of-the-art meta-heuristics on benchmark functions.
• Simulations on a practical WPS case study validated the algorithm's effectiveness.
• The proposed method significantly improved cost-effectiveness in WPS capacity planning.

Conclusions:

• The developed ICEO algorithm effectively solves complex optimization problems in WPS capacity planning.
• The integrated approach enhances the economic viability and reliability of grids with high renewable energy shares.
• This research provides a robust framework for optimizing hybrid renewable energy systems.