The main objective of this project is to develop a stochastic portfolio optimization model to address the problem of renewable portfolio selection for a business entity with large real estate properties, and to develop algorithmic approaches for solving the underlying models. In addition to considering various renewable options, the model also includes battery storage since power storage can significantly increase the benefits of intermittent renewable sources. If the decisions of renewables are fixed, the remaining problem is a dynamic program with decisions of power distribution and how much power to store or extract from batteries in each hour. A further complicating factor resulting from a long time horizon is the requirement of battery replacement after reaching their lifespan. The overarching optimization problem minimizes the cost of installing renewable sources where the objective function is the value function of the aforementioned dynamic program, which depends on the installed renewable sources. Several algorithms will be designed for solving this unique optimization problem by exploiting the inherent structure.
Due to technological advancements and fierce manufacturing competition, it is anticipated that many firms will find a positive return on investment on renewable on-site sources. Such on-site installations will directly benefit from the proposed models and algorithms. First, the models will assist decision makers in selecting the best options at a single site or across several sites. Second, uncertainties inherent in the business - grid prices, renewable energy credits, technological advancements, to name only a few of them will be directly incorporated in the models, which will provide robust solutions. General Motors will provide test cases of their existing renewable generation projects and those under consideration. The modeling framework and methodologies do not include anything specific to the automotive industry and thus can easily adopt by any entity owing large properties.
