Distributed Smart Grid Asset Control Strategies for Providing Ancillary Services

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With large-scale plans to integrate renewable generation driven mainly by state-level renewable portfolio requirements, more resources will be needed to compensate for the uncertainty and variability associated with intermittent generation resources. Distributed assets can be used to mitigate the concerns associated with renewable energy resources and to keep costs down. Under such conditions, performing primary frequency control using only supply-side resources becomes not only prohibitively expensive but also technically difficult. It is therefore important to explore how a sufficient proportion of the loads could assume a routine role in primary frequency control to maintain the stability of the system at ... continued below

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Kalsi, Karanjit; Zhang, Wei; Lian, Jianming; Marinovici, Laurentiu D.; Moya, Christian & Dagle, Jeffery E. October 30, 2013.

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With large-scale plans to integrate renewable generation driven mainly by state-level renewable portfolio requirements, more resources will be needed to compensate for the uncertainty and variability associated with intermittent generation resources. Distributed assets can be used to mitigate the concerns associated with renewable energy resources and to keep costs down. Under such conditions, performing primary frequency control using only supply-side resources becomes not only prohibitively expensive but also technically difficult. It is therefore important to explore how a sufficient proportion of the loads could assume a routine role in primary frequency control to maintain the stability of the system at an acceptable cost. The main objective of this project is to develop a novel hierarchical distributed framework for frequency based load control. The framework involves two decision layers. The top decision layer determines the optimal gain for aggregated loads for each load bus. The gains are computed using decentralized robust control methods, and will be broadcast to the corresponding participating loads every control period. The second layer consists of a large number of heterogeneous devices, which switch probabilistically during contingencies so that aggregated power change matches the desired amount according to the most recently received gains. The simulation results show great potential to enable systematic design of demand-side primary frequency control with stability guarantees on the overall power system. The proposed design systematically accounts for the interactions between the total load response and bulk power system frequency dynamics. It also guarantees frequency stability under a wide range of time varying operating conditions. The local device-level load response rules fully respect the device constraints (such as temperature setpoint, compressor time delays of HVACs, or arrival and departure of the deferrable loads), which are crucial for implementing real load control programs. The promise of autonomous, Grid Friendly™ response by smart appliances in the form of under-frequency load shedding was demonstrated in the GridWise Olympic Peninsula Demonstration in 2006. Each controller monitored the power grid voltage signal and requested that electrical load be shed by its appliance whenever electric power-grid frequency fell below 59.95 Hz. The controllers and their appliances responded reliably to each shallow under-frequency event, which was an average of one event per day and shed their loads for the durations of these events. Another objective of this project was to perform extensive simulation studies to investigate the impact of a population of Grid Friendly™ Appliances (GFAs) on the bulk power system frequency stability. The GFAs considered in this report are represented as demonstration units with water heaters individually modeled.

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  • Report No.: PNNL-22875
  • Grant Number: AC05-76RL01830
  • DOI: 10.2172/1117087 | External Link
  • Office of Scientific & Technical Information Report Number: 1117087
  • Archival Resource Key: ark:/67531/metadc867617

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Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • October 30, 2013

Added to The UNT Digital Library

  • Sept. 16, 2016, 12:32 a.m.

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  • Dec. 5, 2016, 2:24 p.m.

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Kalsi, Karanjit; Zhang, Wei; Lian, Jianming; Marinovici, Laurentiu D.; Moya, Christian & Dagle, Jeffery E. Distributed Smart Grid Asset Control Strategies for Providing Ancillary Services, report, October 30, 2013; Richland, Washington. (digital.library.unt.edu/ark:/67531/metadc867617/: accessed November 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.