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Journal of Renewable and Sustainable Energy / Volume 4 / Issue 1 / REGULAR ARTICLES

J. Renewable Sustainable Energy 4, 013120 (2012); http://dx.doi.org/10.1063/1.3683529 (20 pages)

A unit commitment study of the application of energy storage toward the integration of renewable generation

Chioke Harris1, Jeremy P. Meyers1,2, and Michael E. Webber1,3

1Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA
2Center for Electrochemistry, The University of Texas at Austin, Austin, Texas 78712, USA
3Center for International Energy and Environmental Policy, The University of Texas at Austin, Austin, Texas 78712, USA

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(Received 12 January 2011; accepted 5 January 2012; published online 27 February 2012)

To examine the potential benefits of energy storage in the electric grid, a generalized unit commitment model of thermal generating units and energy storage facilities is developed. Three different storage scenarios were tested—two without limits to total storage assignment and one with a constrained maximum storage portfolio. Given a generation fleet based on the City of Austin’s renewable energy deployment plans, results from the unlimited energy storage deployment scenarios studied show that if capital costs are ignored, large quantities of seasonal storage are preferred. This operational approach enables storage of plentiful wind generation during winter months that can then be dispatched during high cost peak periods in the summer. These two scenarios yielded $70 million and $94 million in yearly operational cost savings but would cost hundreds of billions to implement. Conversely, yearly cost reductions of $40 million can be achieved with one compressed air energy storage facility and a small set of electrochemical storage devices totaling 13 GWh of capacity. Similarly sized storage fleets with capital costs, service lifetimes, and financing consistent with these operational cost savings can yield significant operational benefit by avoiding dispatch of expensive peaking generators and improving utilization of renewable generation throughout the year. Further study using a modified unit commitment model can help to clarify optimal storage portfolios, reveal appropriate market participation approaches, and determine the optimal siting of storage within the grid.

© 2012 American Institute of Physics

Article Outline

  1. INTRODUCTION
  2. METHODOLOGY
    1. Objective function and costs
    2. Thermal generator constraints
    3. Modeling future scenarios
    4. Energy storage
  3. RESULTS AND DISCUSSION
  4. CONCLUSIONS

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KEYWORDS, PACS, and IPC

Keywords

energy storage, power generation dispatch, power generation scheduling, power grids, renewable energy sources

PACS

  • 84.70.+p

    High-current and high-voltage technology: power systems; power transmission lines and cables

  • 88.05.-b

    Energy analysis

  • 84.60.-h

    Direct energy conversion and storage

International Patent Classification (IPC)

  • H02J15/00

    Systems for storing electric energy

ARTICLE DATA

Digital Object Identifier
http://dx.doi.org/10.1063/1.3683529

PUBLICATION DATA

ISSN

1941-7012 (online)

Publisher

$abstract.society.value is a Member of CrossRef American Institute of Physics

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    References


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Figures (5) Tables (14)

Figures (click on thumbnails to view enlargements)

FIG.1
Energy storage flattens demand significantly throughout the year, and as shown in the histogram in the right panel, storage thus reduces the number of hours of peak generation and the magnitude of peak requirements while also increasing demand during the lowest few hours of the year. Average load and standard deviation for each of these cases are summarized in Table 6.

FIG.1 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.2
With the presence of CAES, the discrete scenario results show not only a concentration of load levels to be served, as in Figure 1, but also a small overall reduction in load.

FIG.2 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.3
With limited storage available, minimal reshaping of demand occurs, using storage to shift only the most expensive hours of the year, maximizing the benefit of what storage is available.

FIG.3 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.4
As in earlier results, the availability of energy storage improves dispatch of inexpensive generators by shaping renewables availability.

FIG.4 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.5
While there is no clear bias towards storage in any one period when energy storage is limited, when quantities are unlimited, storage is concentrated primarily in the winter and spring months, when stored energy is the cheapest. It is likely that the difference between generic and discrete storage behavior in the final months of the year is a consequence of limiting constraints in the discrete storage scenario.

FIG.5 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

Tables

Table I. Estimated capital costs for selected storage devices.16

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Table II. For the purposes of this study, a small subset of storage types has been selected based on their suitability for daily storage.

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Table III. Marginal costs for energy storage are also included in the discrete storage scenarios.

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Table IV. With low limits set for all available energy storage types, the optimal outcome still appears to be the maximum allowable storage.

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Table V. Comparing capital costs to annual savings for each of the storage scenarios suggests the limited storage portfolio provides the best economic basis for implementation.

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Table VI. Comparing the effects of storage availability reveals that even limited storage can manage the highest cost hours of the year, though large quantities of seasonal storage has dramatic effects on dispatch throughout the year.

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Table VII. If possible, large quantities of energy storage will be allocated by the model, even when operating costs are included.

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Table VIII. GAMS models are structured around controlling indices called “sets.”

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Table IX. Model parameters define the operating constraints of all generators in Table 13 as well as time-dependent functions.

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Table X. Model variables are combined with parameters to form the objective function and constraint equations.

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Table XI. For the discrete storage scenarios, additional parameters are required to enable constraints on their assignment and operation.

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Table XII. Additional variables must be defined to constrain the selection and operation of energy storage in the discrete storage scenarios.

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Table XIII. Austin Energy’s projected generating fleet in 2020 is comprised of a variety of thermal generating units as well as several types of renewables (PWR—pressurized water reactor; CC—combined-cycle; GT—gas turbine; NG—natural gas; and Pk—peaking).

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Table XIV. Startup and marginal costs.

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