Multi Objective Scheduling of Utility-scale Energy Storages and Demand Response Programs Portfolio for Grid Integration of Wind Power

Document Type : Research paper

Authors

1 Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran

2 Department of Electrical Engineering, Eyvanekey University, Tehran, Iran

Abstract

Increasing the penetration of variable wind generation in power systems has created some new challenges in the power system operation. In such a situation, the inclusion of flexible resources which have the potential of facilitating wind power integration is necessary. Demand response (DR) programs and emerging utility-scale energy storages (ESs) are known as two powerful flexible tools that can improve large-scale integration of intermittent wind power from technical and economic aspects. Under this perspective, this paper proposes a multi objective stochastic framework that schedules conventional generation units, bulk ESs, and DR resources simultaneously with the application to wind integration. The proposed formulation is a sophisticated problem which coordinates supply-side and demand-side resources in energy and up/down spinning reserve markets so that the cost, emission, and multi objective functions are minimized separately. In order to determine the most efficient DR program which can potentially coordinate with bulk ESs in the system with a significant amount of wind power, a comprehensive DR programs portfolio including time- and incentive-based programs is designed. Afterwards, strategy success index (SSI) is employed to prioritize DR programs from independent system operator (ISO) perspective. The IEEE-RTS is used to reveal the effectiveness of the proposed method.

Keywords

Main Subjects

References

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Journal of Operation and Automation in Power Engineering
Volume 4, Issue 2
December 2016
Pages 104-116

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History

  • Receive Date: 29 August 2015
  • Revise Date: 05 April 2016
  • Accept Date: 25 December 2016
  • First Publish Date: 25 December 2016

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