4.6 Article

An Optimal Primary Frequency Control Based on Adaptive Dynamic Programming for Islanded Modernized Microgrids

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2020.2996160

Keywords

Frequency control; Power system dynamics; Microgrids; Generators; Engines; Power system stability; Dynamic programming; Adaptive dynamic programming (ADP); coupled dynamics; engine delay; hardware-in-the-loop (HIL) islanded mode of modernized microgrids (MMGs); nonminimum phase zero dynamics; output-feedback control; primary frequency control; smart modernized grids; uncertain

Funding

  1. U.S. National Science Foundation (NSF) awards through the Core Program of Energy, Power, Control, and Networks in the Division of Electrical, Communications and Cyber Systems (ECCS) [1808279, 1902787, 1903781]
  2. Directorate For Engineering
  3. Div Of Electrical, Commun & Cyber Sys [1903781] Funding Source: National Science Foundation
  4. Div Of Electrical, Commun & Cyber Sys
  5. Directorate For Engineering [1902787] Funding Source: National Science Foundation

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This article discusses the primary frequency controls in modernized microgrids using engine generators, proposing an adaptive optimal output-feedback control approach based on adaptive dynamic programming. The method addresses challenges like uncertain disturbances and nonminimum phase dynamics, and its effectiveness is validated through hardware-in-the-loop experiments.
In many pilot research and development (R&D) microgrid projects, engine-based generators are employed in their power systems, either generating electrical energy or being mixed with the heat and power technology. One of the critical tasks of such engine-based generation units is the frequency regulation in the islanded mode of modernized microgrid (MMG) operation; MMGs are microgrids equipped with advanced controls to address more emerging scenarios in smart grids. For having a stable and reliable MMG, we need to synthesize an optimal, robust, primary frequency controller for the islanded mode of MMG of the future. This task is challenging because of unknown mechanical parameters, occurrence of uncertain disturbances, uncertainty of loads, operating point variations, and the appearance of engine delays, and hence nonminimum phase dynamics. This article presents an innovative primary frequency control for the engine generators regulating the frequency of an islanded MMG in the context of smart grids. The proposed approach is based on an adaptive optimal output-feedback control algorithm using adaptive dynamic programming (ADP). The convergence of algorithms, along with the stability analysis of the closed-loop system, is also shown in this article. Finally, as experimental validation, hardware-in-the-loop (HIL) test results are provided in order to examine the effectiveness of the proposed methodology practically. Note to Practitioners-This article was motivated by the problem of primary frequency controls in modernized microgrids (MMGs) using engine generators, which are still one of the prime sources of regulating frequency in pilot research and development (R&D) microgrid projects. Although MMGs will be integral parts of the smart grid of the future, their primary controls in the islanded mode are not advanced enough and not considering existing theoretical challenges scientifically. Existing approaches to regulate frequency using industrially accepted methods are highly model-based and not optimal. Besides, they are not considering the nonminimum phase dynamics. These dynamics are mainly associated with the engine delays-an inherent issue of mechanical parts-for islanded microgrids. This article suggests a new adaptive optimal output-feedback control approach based on the adaptive dynamic programming (ADP) to the abovementioned problem under consideration. By using the proposed methodology, MMGs can deal with the issues mentioned earlier, which are challenging. The proposed approach is optimally rejecting uncertain disturbances (considering the load uncertainty and operating point variations) and reducing the impacts of nonminimum phase dynamics caused by the engine delay. Based on our currently available hardware-in-the-loop (HIL) device's capability of modeling power systems' components in real time, our HIL-based experiments demonstrate that this approach is feasible.

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