Coordinating Dispatch of Distributed Energy Resources with Model Predictive Control and Q-Learning

Kowli, Anupama; Mayhorn, Ebony; Kalsi, Karanjit; Meyn, Sean P.

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https://hdl.handle.net/2142/90431 Copy

Description

Title

Coordinating Dispatch of Distributed Energy Resources with Model Predictive Control and Q-Learning

Author(s)

• Kowli, Anupama
• Mayhorn, Ebony
• Kalsi, Karanjit
• Meyn, Sean P.

Issue Date

2012-05

Keyword(s)

• Approximate dynamic programming
• Distributed energy resources
• Dynamic dispatch
• Energy storage
• Model predictive control
• Power grid
• Reinforcement learning
• Q-learning

Abstract

Distributed energy resources such as renewable generators (wind, solar), energy storage, and demand response can be used to complement fossil-fueled generators. The uncertainty and variability due to high penetration of renewable resources make power system operations and controls challenging. This work addresses the coordinated operation of these distributed resources to meet economic, reliability, and environmental objectives. Recent research proposes Model Predictive Control (MPC) to solve the problem. However, MPC may yield a poor performance if the terminal penalty function is not chosen correctly. In this work, a parameterized Q-learning algorithm is devised to approximate the optimal terminal penalty function. This approximate penalty function is then used in MPC, thus effectively combining the two techniques. It is argued that this combination approach would lead to the best solution in terms of computation, and adaptability to a changing environment. Simulation studies demonstrating the efficacy of the proposed methodology for power system dispatch problems are presented.

Publisher

Coordinated Science Laboratory, University of Illinois at Urbana-Champaign

Series/Report Name or Number

Coordinated Science Laboratory Report no. UILU-ENG-12-2204, DC-256

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/90431

Sponsor(s)/Grant Number(s)

• National Science Foundation / CPS-0931416
• Department of Energy / DE-OE0000097 and DE-SC0003879
• Pacific Northwest National Laboratory

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