Flexible Operation of Electricity-HCNG Networks with Variable Hydrogen Fraction: A Distributionally Robust Joint Chance-Constrained Approach

TL;DR

This paper introduces a distributionally robust joint chance-constrained framework for flexible operation of electricity-HCNG networks with variable hydrogen fractions, enabling reliable real-time adjustments amid renewable energy volatility.

Contribution

It develops a novel affine policy and a distributionally robust chance constraint approach to enhance operational flexibility and reliability in E-HCNG networks with fluctuating renewable energy.

Findings

The proposed method effectively manages renewable energy volatility in E-HCNG networks.

The affine policy allows real-time re-dispatch based on renewable fluctuations.

Validation shows improved reliability and operational efficiency in experiments.

Abstract

Hydrogen-enriched compressed natural gas (HCNG) is a promising way to utilize surplus renewable energy through hydrogen electrolysis and blending it into natural gas. However, the optimal hydrogen volume fraction (HVF) of HCNG varies following the daily fluctuations of renewable energy. Besides, facing the rapid volatility of renewable energy, ensuring rapid and reliable real-time adjustments is challenging for electricity-HCNG (E-HCNG) coupling networks. To this end, this paper proposes a flexible operation framework for electricity-HCNG (E-HCNG) networks against the fluctuations and volatility of renewable energy. Based on operations with variable HVF, the framework developed an E-HCNG system-level affine policy, which allows real-time re-dispatch of operations according to the volatility. Meanwhile, to guarantee the operational reliability of the affine policy, a distributionally robust joint chance constraint (DRJCC) is introduced, which limits the violation probability of operational constraints under the uncertainties of renewable energy volatility. Furthermore, in the solving process, to mitigate the over-conservation in DRJCC decomposition, an improved risk allocation method is proposed, utilizing the correlations among violations under the affine policy. Moreover, to tackle the non-convexities arising from the variable HVF, customized approximations for HCNG flow formulations are developed. The problem is finally reformulated into a mix-integer second-order cone programming problem. The effectiveness of the proposed method is validated both in small-scale and large-scale experiments.