Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

Abstract

A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed. The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems (IESs) in the operation scheduling problem of integrated energy production units (IEPUs). First, to solve the problem of inaccurate prediction of renewable energy output, an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction. Subsequently, to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs, a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established. The system considers the further utilization of energy using hydrogen energy coupling equipment (such as hydrogen storage devices and fuel cells) and the comprehensive demand response of load-side schedulable resources. The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions, improve the source-load interaction of the IES, realize the efficient use of hydrogen energy, and improve system robustness.