Gas Flow Models and Computationally Efficient Methods for Energy Network Optimization.

Area of Science:

• Energy Systems Engineering
• Computational Optimization
• Gas Dynamics

Background:

• Gas flow dynamics present significant computational challenges for energy network optimization.
• Existing models struggle with hourly resolution over yearly decision horizons.

Purpose of the Study:

• To propose an efficient solution procedure for tractable energy network optimization.
• To enable hourly resolved yearly decision-making in gas networks.

Main Methods:

• Alternating between linear and second-order cone gas flow models based on pipe dimensions.
• Addressing bidirectional flow complexity by fixing integer variables from a static flow approximation.
• Aggregating parallel and serial pipes to enhance computational efficiency.

Main Results:

• Achieved up to 3.1% cost savings compared to static models.
• Improved the success rate of network optimization by at least 96%.
• Increased computational efficiency by up to three orders of magnitude over full dynamic models.

Conclusions:

• The proposed procedure offers a computationally efficient and accurate solution for gas network optimization.
• The method enhances cost savings and reliability in energy network management.
• This approach enables tractability for complex, long-term energy network planning.