We address the problem of modeling energy resource allocation, including dispatch, storage, and the longterm investments in new technologies, capturing different sources of uncertainty such as energy from wind, demands, prices, and rainfall. We also wish to model long-term investment decisions in the presence of uncertainty. Accurately modeling the value of all investments, such as wind turbines and solar panels, requires handling fine-grained temporal variability and uncertainty in wind and solar in the presence of storage. We propose a modeling and algorithmic strategy based on the framework of approximate dynamic programming (ADP) that can model these problems at hourly time increments over an entire year or several decades. We demonstrate the methodology using both spatially aggregate and disaggregate representations of energy supply and demand. This paper describes the initial proof of concept experiments for an ADP-based model called SMART; we describe the modeling and algorithmic strategy and provide comparisons against a deterministic benchmark as well as initial experiments on stochastic data sets.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Management Science and Operations Research
- Analysis of algorithms
- Artificial intelligence
- Statistical analysis