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

Sicheng Liu, Bo Yang, Xu Yang, Xin Li, Zhaojian Wang, Xinping Guan

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.

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

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.

Paper Structure

This paper contains 35 sections, 2 theorems, 41 equations, 7 figures, 4 tables.

Table of Contents

  1. Indices and Sets
  2. Parameters
  3. Variables
  4. Introduction
  5. Problem Formultaion
  6. Deterministic E-HCNG Scheduling Formulation
  7. Objective Function
  8. Constraints of Power Flow
  9. Constraints of Hydrogen Station
  10. Constraints of HCNG Flow
  11. Affine Policy for the E-HCNG System
  12. Affine Policy for the Grid
  13. Affine Policy for Hydrogen Components
  14. Affine Policy for the HCNG Network
  15. Formulation of DRJCC E-HCNG Scheduling
  16. ...and 20 more sections

Key Result

Proposition 1

For chance constraints (21a) and (21b), the following equivalent relationship holds

Figures (7)

  • Figure 1: Configuration of E-HCNG network system.
  • Figure 2: Topology of the small-scale E-HCNG network with a 5-bus power grid and a 7-node HCNG network.
  • Figure 3: Profiles of electricity load and HCNG load converted to heat values.
  • Figure 4: Operations of the small-scale E-HCNG network: (a) Operations and power balance of the grid. (b) HCNG supply and HVF value in the HCNG network. (c) Operations and stored volume of hydrogen storage.
  • Figure 5: Operating costs under different HVF models.
  • ...and 2 more figures

Theorems & Definitions (6)

  • Proposition 1
  • proof
  • Remark 1
  • Proposition 2
  • proof
  • Remark 2