Stochastic Optimal Operation and Risk Analysis for Integrated Power and Gas Systems

Document Type : Research paper

Authors

1 ‎Department of Electrical Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran

2 Department of Electrical Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran

3 Department of Computer and Electrical Engineering, Tabriz University, Tabriz, Iran

4 Department of Energy Technology, Aalborg University, 9220, Aalborg East, Denmark

Abstract

The increment integration of renewable distributed energies means the desired operation of the electric power system will significantly depend on the performance of primary energy. In this order, an integrated approach for mutual interaction between the electricity and natural gas systems has been considered for the purpose of ensuring optimal energy exchanging between the electric power system and the natural gas network. We propose a scenario based optimal operation approach to optimize the operation of integrated power and gas systems (IPGS). Regarding the unpredictable nature of wind speed and solar radiation as well as uncertain load demand, random scenarios are generated by a normal probability density function. Then, Latin hypercube sampling is applied to realize the stochastic framework of IPGS operation. The proposed model minimizes the operation cost of conventional power system generators and gas wells over a 24 h operation horizon. In addition, the conditional value-at-risk is utilized to manage financial risks and uncertainties due to the operation cost-minimizing in the proposed IPGS optimal operation problem. The proposed integrated operating approach is applied to a 24-Bus power system with renewable resources of a photovoltaic, wind turbine, energy storage, with a 7-node natural gas network and two gas wells.

Keywords

References

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Journal of Operation and Automation in Power Engineering
Volume 9, Issue 1
April 2021
Pages 80-87

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History

  • Receive Date: 26 December 2019
  • Revise Date: 23 August 2020
  • Accept Date: 10 October 2020

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