A Novel Multiagent Resource Sharing Algorithm for Control of Advanced Energy Systems

Journal Article · 18 July 2021 · Industrial and Engineering Chemistry Research

DOI: https://doi.org/10.1021/acs.iecr.1c00702 · OSTI ID:1817649

Pezzini, Paolo  ^[1]; Bryden, Kenneth M. ^[1]; Finzell, Peter ^[1]; Tucker, David ^[2]

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1. Ames Lab., Ames, IA (United States)
2. National Energy Technology Lab. (NETL), Morgantown, WV (United States)

This paper implements a novel resource sharing control strategy on a fuel cell–gas turbine hybrid power system at the National Energy Technology Laboratory’s Hybrid Performance Facility (Hyper). In a fuel cell–gas turbine hybrid power system, the simultaneous interaction of the gas turbine and the fuel cell creates a tightly coupled environment characterized by conflicting dynamics. In this paper, a model-free control approach is applied to solve the tightly coupled control problem posed by this challenging environment. Specifically, this control problem is presented as a resource sharing problem that can be solved using a resource sharing algorithm that is defined based on the distribution construction concept. This algorithm creates computational agents and solves the problem through the distribution and redistribution of shared resources defined as blocks. Furthermore, two agents were created; the first agent (agent 1) controls the gas turbine speed by adjusting the electric load, and the second agent (agent 2) controls the cathode mass flow through the fuel cell using the cold-air bypass valve. A parametric study was performed over the course of 15 experimental tests for both agents 1 and 2 through an evaluation of the responses based on setpoint changes. The algorithm was shown to have behavior comparable to a previously implemented multi-input multioutput state-space controller, which was designed through a model-based control approach. The resource sharing algorithm was able to find stable performance during run-time operations without any prior system knowledge identification on the power plant and without creating models used in traditional control strategies.

• Research Organization: Ames Laboratory (AMES), Ames, IA (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy (FE)
• Grant/Contract Number: AC02-07CH11358
• OSTI ID: 1817649
• Report Number(s): IS--J 10,559
• Journal Information: Industrial and Engineering Chemistry Research, Journal Name: Industrial and Engineering Chemistry Research Journal Issue: 30 Vol. 60; ISSN 0888-5885
• Publisher: American Chemical Society (ACS)Copyright Statement
• Country of Publication: United States
• Language: English