Sustainable Energy and Artificial Intelligence

Location of Distributed Wind Energy with Consideration Capacity Credit Using the Monte-Carlo Method for Probabilities Evaluation of Wind

Document Type : Original Article

Authors

Mohammadali Arash 1
Mohammad Khakroei 1
Ashkan Mirzaei Rajeooni 2

1 MAPNA Electric & Control Engineering & Manufacturing Company (MECO), Alborz, Iran

2 MAPNA Generator Engineering and Manufacturing Company (PARS), Alborz, Iran.

https://doi.org/10.61186/seai.2411-1022
Abstract
Significant advances have been made in electrical energy distribution networks in recent years. Distributed Generation (DG) technology is rapidly advancing, particularly in response to the needs of sensitive loads in the network that demand high reliability. This paper explores using distributed generation sources to increase capacity credit (CC) in Electrical energy distribution. This article focused on studying wind sources. The issue of planning DG in the distribution network is represented as a non-linear optimization problem. The goal is to make wind power more reliable, reduce losses, and improve capacity credit. The problem model includes the network's and DG's technical and economic constraints. Two methods, Monte Carlo and k-means, have been used to model uncertainties in network load and wind power generation during the planning process. The cut-set is used to assess the network's reliability. The IEEE 33-bus distribution network was studied using the teaching learning-based optimization algorithm in two scenarios to improve response efficiency. The article found that DG can provide up to 33% of the network load in capacity credit.

Highlights

  • Explore using distributed generation to raise capacity credit in energy distributed
  • Calculation and comparison of reliability index with and without wind sources
  • Using the TLBO algorithm to optimize planning, reduce losses, and enhance reliability
  • Using Monte-Carlo and k-means methods to model wind power variability uncertainty
  • Applying the IEEE 33-bus network to test DG planning under uncertainty scenarios

Keywords

Capacity Credit
Distributed Generation
Generation Planning
Load Planning
Optimization Algorithm
Subjects
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Sustainable Energy and Artificial Intelligence
Volume 1, Issue 1
Winter 2025
Pages 1-12

  • Receive Date 17 November 2024
  • Revise Date 03 December 2024
  • Accept Date 08 December 2024
  • First Publish Date 09 December 2024

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