Work

Extensions of the Dynamic Programming Framework: Battery Scheduling, Demand Charges, and Renewable Integration

Year: 2020

Type: article

Abstract: We consider a general class of dynamic programming (DP) problems with nonseparable objective functions. We show that for any problem in this class, there exists an augmented-state DP problem that satisfies the principle of optimality and the solutions to which yield solutions to the original problem. Furthermore, we identify a subclass of DP problems with naturally forward separable objective functions for which this state-augmentation scheme is tractable. We extend this framework to stochastic DP problems, proposing a suitable definition of the principle of optimality. We then apply the resulting algorithms to the problem of optimal battery scheduling with demand charges using a data-based stochastic model for electricity usage and solar generation by the consumer.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2020.3002235

Authors: Morgan Jones, Matthew M. Peet

Institution: Arizona State University

Language: English

Topic: Smart Grid Energy Management

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

Funder: National Science Foundation