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Smart grid energy scheduling based on improved dynamic programming algorithm and LSTM.

Xiaoyu Huang1, Yubin Lin1, Xiaofei Ruan1

  • 1Department of Evaluation Center, Economic and Technological Research Institute of State Grid Fujian Electric Power Co., Ltd, Fuzhou, Fujian, China.

Peerj. Computer Science
|August 7, 2023
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Summary
This summary is machine-generated.

This study introduces an enhanced dynamic programming algorithm (DPA) and a long short-term memory (LSTM) model for smart grid energy scheduling. The new method significantly reduces energy consumption and emissions in smart grids.

Keywords:
DPALSTMDeep learningEnergy schedulingSmart grid

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Optimal energy scheduling is vital for smart grid efficiency and substantial energy savings in large-scale grids.
  • Existing methods may not fully leverage the dynamic states of energy storage systems or environmental factors for precise scheduling.

Purpose of the Study:

  • To develop an enhanced dynamic programming algorithm (DPA) for optimal smart grid energy scheduling.
  • To integrate a long short-term memory (LSTM) model for predicting grid power consumption based on environmental factors.
  • To improve overall smart grid energy efficiency and reduce emissions.

Main Methods:

  • An enhanced dynamic programming algorithm (DPA) was developed, incorporating two state variables to optimize power supply schedules.
  • The DPA accounts for the dynamic states of batteries and supercapacitors within the power supply system.
  • A long short-term memory (LSTM) deep learning model was utilized to predict grid power consumption by integrating environmental data (temperature, humidity, wind, precipitation).

Main Results:

  • Simulation experiments demonstrated that the proposed method significantly reduces overall energy consumption in smart grids.
  • The enhanced DPA, combined with LSTM-based predictions, outperformed existing grid energy consumption scheduling algorithms.
  • The integrated approach proved effective in optimizing energy usage and contributing to emission reduction.

Conclusions:

  • The proposed enhanced DPA and LSTM integration offers a superior approach to smart grid energy scheduling.
  • This method is critical for the efficient establishment and operation of smart grids.
  • The findings highlight the potential for significant energy savings and reduced environmental impact through intelligent grid management.