Multi-objective coordinated optimization of power system with wind power accommodation

Abstract

To decrease wind curtailment rate and increase wind power accommodation, power purchase area should increase wind power purchase. However, the current electricity price of wind power is higher than that of thermal power, which makes wind power accommodation difficult to quantify. It is not practical to optimize these two objectives of the wind power accommodation and power purchase cost. Based on this contradiction, this paper describes this contradiction by constructing a multi-objective optimization model (MOOM) that considers the above two objectives. Firstly, the model is resolved by hybrid particle swarm optimization and gravity search algorithm (HPSO-GSA) to obtain Pareto optimal solution set. Then, according to fuzzy satisfaction function, Pareto optimal solution is selected from Pareto optimal frontier. Finally, the typical daily load and wind farm output value in spring of a certain region are taken as examples for simulation verification. It is verified that the proposed method can indeed provide a scientific power purchase scheme for regional power purchase.

Keywords

• Wind power
• Electricity purchasing cost
• Multi-objective optimization
• HPSO-GSA