Stand_and_Adapt_Bend: Power System Investment Planning Code with 15-Year Horizon

University of Edinburgh researcher Nicolò Mazzi provides Julia code for solving a power system investment planning problem over a 15-year time horizon. The code models twelve technologies, including six thermal units, three storage units, and three renewable generation units, and solves the problem using two Benders decomposition algorithms. The deterministic version has three decision nodes at present, 5 years, and 10 years, while the stochastic version models different future scenarios.

Use Cases

• Benchmarking Benders decomposition algorithms based on the described Stand_Bend and Adapt_Bend implementations.
• Modeling long-term power system capacity expansion based on the described 15-year horizon and 5-year construction time.
• Comparing deterministic and stochastic investment planning outcomes based on the described scenario modeling at 5 and 10-year nodes.
• Analyzing the cost of system operation for given installed capacity, as mentioned in the description for each decision node.

Strengths

• Explicitly models a 15-year planning horizon with three decision points.
• Includes a defined set of twelve generation and storage technologies.
• Provides two specific algorithms (Stand_Bend and Adapt_Bend) for solving the optimization problem.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Nicolò Mazzi, University of Edinburgh, via paperswithcode.

Collection Method

Code implementation for an optimization model, as described.

Time Range

Modeling horizon of 15 years.