Long-Term Optimal Planning for a Hydrogen-Based Industrial Energy System Considering the Randomness of Renewable Energy*

Abstract

The industrial sector is a major source of carbon emissions, and zero-carbon industrial parks with a high proportion of renewable energy integration have become an important pathway for industrial carbon reduction. However, wind and photovoltaic power exhibit significant long-term randomness, making it difficult for planning based on a single historical year to support long-term investment decisions. To address this issue, this paper proposes a long-term optimal planning method for a hydrogen-based industrial energy system considering renewable energy randomness. A multi-stage planning model is established with phased load growth and staged equipment construction. Meanwhile, low-output events of wind and photovoltaic power are extracted from historical data, and a non-homogeneous Poisson process combined with an Itô process is used to generate long-term renewable energy scenarios. Based on the simulated scenarios, pessimistic, moderate, and optimistic cases are selected for planning analysis. The results show that renewable randomness significantly affects long-term investment behavior. In the selected representative quantile-based scenarios, the pessimistic scenario leads to a 7.5% higher discounted investment cost and a 24.3% larger installed storage capacity than the optimistic scenario. In addition, pessimistic renewable conditions lead to more equipment investment in the early stages.