Generation Scheduling of Active Distribution Network with Renewable ‎Energy Resources Considering Demand Response Management ‎

Document Type : Research paper

Authors

1 Department of Electrical Engineering, Lorestan University, Khorramabad, Iran

2 Department of Electrical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran‎

Abstract

The scheduling of electricity distribution networks has changed dramatically by integrating renewable energy sources (RES) as well as energy storage systems (ESS). The sizing and placement of these resources have significant technical and economic impacts on the network. Whereas the utilization of these resources in the active distribution network (ADN) has several advantages, accordingly, the undesirable effects of these resources on ADN need to be analyzed and recovered. In this paper, a hybrid ADN, including wind, PV, and ESS, is investigated in 33 buses IEEE standard system. First of all, optimal energy management and sizing of the RES and ESS are the purposes. Secondly, as demand response (DR) is another substantial option in ADNs for regulating production and demand, an incentive-based DR program is applied for peak shaving. Forasmuch as this method has uncertainty, due to its dependence on customer consumption patterns, the use of inappropriate incentives will not be able to stimulate customers to reduce their consumption at peak times. Accordingly, the climatic condition uncertainty, which is another factor of variability on the production side, is minimized in this paper by relying on the Monte Carlo estimation method. Besides, the optimization problem, which is formulated as optimal programming, is solved to calculate the optimal size and place of each RESs and ESS conditions regarding power loss, voltage profile, and cost optimization. Furthermore, a geometric, energy source and network capacity, and cost constraints, are considered. The results confirm the effectiveness of proposed energy management and cost reduction in the studied test system.

Keywords

References

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Journal of Operation and Automation in Power Engineering
Volume 9, Issue 2
August 2021
Pages 132-143

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History

  • Receive Date: 07 December 2019
  • Revise Date: 06 October 2020
  • Accept Date: 06 November 2020

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