This folder contains only the final outputs of a research project I've been working on for the last few months.
This research project is a very preliminary study on the optimal behaviour of regulated firms in SREC systems. In particular, we pose the optimal behaviour problem in the context of a continuous time stochastic optimal control problem and derive theoretical optimality conditions, as well as a partial differential equation that the solution to this problem must satisfy. We also solve this problem numerically in a simple discrete time setting, conducting a detailed numerical analysis on the results, including sample paths, comparison of strategies, and parameter sensitivity.
These preliminary results are the focus of a talk I am giving at the Workshop on Real Options and Energy, held at the Fields Institute from Feb 11-13, 2019 in Toronto. Further extensions to these results are possible - in particular, multi-period and mult-agent settings would be desirable to solve, as would refining the multi-agent setting to account for partial information on the part of the various agents. The optimality conditions presented in this paper can also potentially be numerically solved by Least Squares Monte Carlo methods, or machine learning methods.
Specifically, this folder includes a current version of a pre-print detailing the work that has been done, the code to run the dynamic programming algorithm described in the pre-print (though not produce the various figures in the pre-print), and various .mat files containing the results for different parameter sets.