Welfare-maximizing transmission capacity expansion under uncertainty

Area of Science:

• Energy Systems Analysis
• Operations Research
• Mathematical Optimization

Background:

• Electricity markets involve complex strategic interactions between producers and consumers.
• Transmission capacity expansion is crucial for system reliability and economic efficiency.
• Stochastic modeling is essential to capture uncertainties in energy systems.

Purpose of the Study:

• To apply the JuDGE optimization package to a multistage stochastic leader-follower model for transmission capacity expansion.
• To maximize expected social welfare considering Cournot oligopolistic behavior.
• To compare the computational performance of JuDGE with traditional optimization methods.

Main Methods:

• Formulation of a large-scale mixed integer program.
• Application to a 5-bus test system with varying scenario tree sizes.
• Comparison of computational effort between JuDGE and a state-of-the-art integer programming package.

Main Results:

• The JuDGE package was successfully applied to the stochastic leader-follower model.
• Evaluated the computational efficiency of JuDGE for transmission expansion planning.
• Provided a benchmark for JuDGE's performance against deterministic equivalent methods.

Conclusions:

• The JuDGE package offers a viable approach for optimizing transmission capacity expansion in stochastic energy markets.
• The study highlights the trade-offs in computational effort for different optimization approaches.
• Findings contribute to the mathematics of energy systems, informing future planning strategies.