Pareto Optimal Solution and Multiobjective Optimal Power Flow under Uncertain Renewable Power Generation

Renewable energy is one of the five innovative industries promoted by the government. Therefore, renewable power generation will be one of the most important sectors in Taiwan’s electric power development. However, high penetration of renewable power generation will bring up the challenge to the operation of electric power system owing to the uncertainty of renewable power generation. Due to the uncertain renewable power generation, optimal non-renewable power generation and the risk of violating security constraints form two conflicting factors. In other words, to reduce the non-renewable power generation cost, the risk of violating security constraints will increase, and vice versa. Therefore, how to find a balance point between these two factors is an important research subject. Different system operators may have different emphasis on the non-renewable power generation cost and the risk of violating security constraints. Therefore, in this project, we will study the pareto optimal solution between these two factors under uncertain renewable power generation and a multiobjective optimal power flow, whose objective function consists of the two conflicting terms. We will use the method developed in our current project to build the pareto optimal solution curve, and we will develop a two-level golden section method to solve the multiobjective optimal power flow problem. In the meantime, we will use the IEEE 118 bus system and the Taiwan power system as the test systems.

Project ID:PB10608-2423
External Project ID:MOST106-2221-E182-039