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Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning

Ren Xu¹, Fei Lin¹, Wenyi Shao¹

¹School of Information and Automation, Qilu University of Technology, Jinan 250353, China.

Entropy (Basel, Switzerland)

|April 26, 2024

Summary

This summary is machine-generated.

This study introduces a renewable energy scheduling strategy for the Bohai and Yellow Seas, integrating wave, wind, and solar power. The approach enhances grid stability by managing energy output fluctuations and reducing costs.

Keywords:

deep reinforcement learning energy forecast energy scheduling entropy value multi-energy complementarity

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Area of Science:

Marine Renewable Energy
Energy Systems Engineering
Artificial Intelligence in Power Systems

Background:

The Bohai and Yellow Seas offer significant marine energy potential, but wind and solar power generation face challenges due to seasonality and intra-day variability.
Integrating diverse renewable sources is crucial for grid stability, yet scheduling complementary energy generation presents a substantial challenge.

Purpose of the Study:

To propose a multi-time-scale rolling optimization scheduling strategy for complementary dispatch of wave, offshore photovoltaic, and wind power generation.
To address the seasonality and intra-day uncertainty of renewable energy sources in the Bohai and Yellow Sea regions.
To reduce system costs and ensure grid stability through intelligent energy management.

Main Methods:

Utilized actual meteorological data from the Shandong Peninsula region.
Employed a CNN-LSTM neural network for day-ahead (24h) and intra-day (1h) power generation and load demand forecasting.
Applied the DDPG algorithm for rolling optimization scheduling based on forecast data.

Main Results:

The proposed strategy effectively meets load demands by complementary scheduling of wave, wind, and photovoltaic power.
Demonstrated reduction in the negative impacts of energy source variability on the grid.
Achieved system cost reductions of 16.1% (winter) and 22% (summer) compared to day-ahead scheduling alone.

Conclusions:

The integrated wave, wind, and photovoltaic power generation strategy, coupled with rolling optimization, enhances grid stability and economic efficiency.
The developed scheduling approach effectively leverages the climatic characteristics of the Bohai and Yellow Sea regions for reliable renewable energy integration.
This study provides a robust framework for managing intermittent renewable energy sources in complex maritime environments.