Micro-scale smart grid optimization

The adoption of smart grid technologies will allow for more distributed generation of energy and for residential and commercial users of electricity to make intelligent decisions about energy usage. In previous research by Livengood and Larsen, a stochastic dynamic programming problem is formulated for a micro-scale smart grid system. A mathematical model of energy usage is developed where the goal is to optimize a finite horizon cost function reflecting both the cost of electricity and comfort/lifestyle. This paper extends this work by assuming key models and forecasts are unknown and implicitly learned via the softmax algorithm with neighborhood updating. The algorithm implements approximate dynamic programming with a goal of reducing dependancies on models and forecasting while achieving good performance. Simulations are conducted using the softmax algorithm showing that the solution approaches the optimal dynamic programming algorithm solution.