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A Multi-Stage Planning Method for Distribution Networks Based on ARIMA with Error Gradient Sampling for Source-Load

Sheng Yan1, Minqiang Hu1

  • 1School of Electrical Engineering, Southeast University, Nanjing 210096, China.

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|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study proposes a multi-stage planning method for smart distribution networks. It uses source-load prediction to manage the integration of renewable energy and changing demands, ensuring grid stability.

Keywords:
ARIMAdata acquisitiondistribution networkerror gradient samplingmulti-stage planning methodprecise controlsensory feedbacksource–load prediction

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Area of Science:

  • Electrical Engineering
  • Energy Systems
  • Grid Planning

Background:

  • The expansion of distributed renewable energy (wind, photovoltaic) and dynamic load demands challenge smart distribution grid operations.
  • Grid data acquisition and control feedback face significant hurdles due to the flexibility and uncertainty of these resources.

Purpose of the Study:

  • To develop a precise control and feedback strategy for smart distribution network equipment with high renewable energy penetration.
  • To ensure the safe and stable operation of power systems by studying the evolution of future distribution grids.

Main Methods:

  • A multi-stage planning method for distribution networks based on source-load prediction.
  • Utilizing the Autoregressive Integrated Moving Average (ARIMA) model and error gradient sampling for source-load prediction and scenario generation.
  • Employing K-means clustering for scenario reduction to analyze multiple operating scenarios and derive forecast intervals for unit output and load demand.

Main Results:

  • Developed a source-load prediction method incorporating ARIMA and error gradient sampling to generate realistic scenarios.
  • Derived unit output forecast intervals and load demand for typical Chinese regions from 2021 to 2030 using rolling forecasts.
  • Constructed a multi-stage planning model to analyze future distribution grid evolution forms based on load cross-sections.

Conclusions:

  • The proposed method provides a development path reference for the future construction of distribution grids in China.
  • Accurate source-load prediction is crucial for managing the complexities of future smart distribution networks.
  • The study addresses the urgent need for advanced planning methods to accommodate high renewable energy integration.