Multi-timescale dispatch technology for islanded energy system in the Gobi Desert

Abstract

The Gobi Desert has vast wilderness to utilize, and its renewable energy capacity experiencing rapid growth. To better allocate regulation resources for maintaining power balance and frequency regulation capacity, an islanded grid optimization model considering multi-timescale dispatch optimization is constructed for integrating chemical parks with thermal power units, energy storage, and green hydrogen production. In the day-ahead optimization stage, to improve the level of renewable energy consumption, the ladder-type regulation performance of thermal power units involved in deep peak load with frequency regulation capacity is derived and established. Moving to the real-time frequency regulation stage, a novel graph neural network-based technique with infeasible region modification is proposed to rapidly acquire the operational power scheme. The input features are the time series of total power command, regulation capacity, past operating power, and past mileage command. And the output features are the mileage commands received by the resources. Training data are generated through offline optimization with the genetic algorithm, which utilizes renewable energy generation and load data with a scale of three months in the Gobi Desert. In the one-month simulation test, the proposed method demonstrated a reduction in power deviation by approximately 32.7 % and an improvement in accuracy by roughly 16 %.