- The paper introduces a multistage planning model that integrates HVDC, P2G clusters, and hydrogen pipelines to overcome renewable energy spatial mismatches.
- It employs detailed P2G cluster modeling with an equal-split rule and a Distributionally Robust Chance-Constrained optimization framework to handle variability and uncertainty.
- Case study results from China show a 34% boost in renewable energy utilization and a reduction in LCOE from 0.33 to 0.26 RMB/kWh, underscoring significant economic benefits.
This paper investigates the technoeconomic feasibility and benefits of integrating Power-to-Gas (P2G) clusters with hydrogen pipelines (HP) as a complement to High-Voltage Direct Current (HVDC) systems for utilizing large-scale renewable energy (RE). The core problem addressed is the spatial mismatch between RE generation potential and demand centers, and the limitations of relying solely on HVDC transmission, which struggles with RE variability and faces declining economic advantage as RE generation costs decrease.
The paper proposes a multistage coordinated planning model that includes renewable energy generators (wind and solar), HVDC transmission lines, hydrogen pipelines, and P2G clusters. The planning horizon is divided into multiple epochs, allowing the model to capture the evolving economics and technology maturity over time.
A key contribution is the detailed modeling of P2G clusters. Unlike simpler models that treat P2G as a basic energy conversion unit, this work considers the operational complexities of a cluster composed of multiple P2G facilities. The model accounts for farm-based planning units and facility-based operation, including three operational statuses for each facility: ON, BOOTING, and OFF. Operational constraints like minimum/maximum power, ramping limits, and the power consumption during the BOOTING phase are incorporated. To manage the computational complexity introduced by modeling individual facilities within a cluster, the paper verifies and applies an "equal-split" rule, which simplifies the operational constraints at the cluster level while maintaining the impact of unit commitment behavior.
To handle the inherent uncertainty and variability of renewable energy, the model utilizes a Distributionally Robust Chance-Constrained (DRCC) optimization framework. Variability over different times and conditions is captured through scenarios derived from historical data (e.g., using K-means clustering), each associated with a probability. Uncertainty within these scenarios (forecast errors for wind/solar generation) is modeled such that the system planning and operation are robust against a family of possible probability distributions, given known first and second moments (mean and covariance) of the uncertainty. To make the resulting complex problem computationally tractable (a Mixed-Integer Second-Order Cone Programming - MISOCP problem), the paper employs Linear Decision Rules (LDR) for continuous operation variables and uses Cantelli's inequality to convert the chance constraints into deterministic second-order cone constraints.
The objective of the planning model is to minimize the total net present value of costs over the entire planning horizon, including capital expenditures and fixed operation costs for all planned facilities (RE, HVDC, HP, P2G), variable operation costs (P2G booting costs), minus the revenue generated from supplying electricity and hydrogen to demand sectors (chemical, transportation, heating).
The model is applied to a real-world case paper based on the Inner Mongolia (source region with high RE potential) and Shandong Province (demand region) system in China. The paper spans multiple planning epochs, simulating development over future decades.
Practical results from the case paper demonstrate the technoeconomic supplement provided by P2G and HP:
- Planning and Temporal Complementarity: The planning results show that HVDC is typically established earlier (in the first epoch), while P2G clusters and hydrogen pipelines are planned in later epochs. This reflects a temporal complementarity: HVDC is initially crucial for transmitting RE as electricity, while P2G/HP become increasingly important over time as RE capacity grows, RE costs decrease, and hydrogen demand, particularly in high-value sectors like transportation, increases.
- Operational Flexibility: P2G clusters, with their ability to adjust power input and utilize the buffering capacity of hydrogen pipelines, provide essential operational flexibility. This flexibility helps absorb intraday and interday fluctuations in RE generation, cooperating with HVDC to improve the overall RE utilization rate compared to an HVDC-only system. The paper shows P2G facilitates consuming an extra 34% of renewable energy in the case paper.
- Economic Advantage: The combined HVDC+P2G+HP system enables the economic exploration of significantly more renewable energy capacity (an extra 24 GW in the case paper). While the additional levelized cost of P2G (0.04 RMB/kWh) is approximately twice that of HVDC (0.02 RMB/kWh) based on the paper's assumptions, the ability to utilize more low-cost RE and supply increasing high-value hydrogen demand leads to a substantial increase in total profit over the planning horizon compared to an HVDC-only system. The LCOE of the system decreases from 0.33 RMB/kWh (HVDC-only) to 0.26 RMB/kWh (HVDC+P2G).
Sensitivity analysis highlights key factors influencing the relative advantage of P2G:
- Variability: Higher RE variability favors P2G planning and operation (increasing the ratio of energy directed to hydrogen production), demonstrating P2G's technical advantage in handling fluctuations.
- Uncertainty: Increased uncertainty makes P2G planning more conservative and favors electricity transmission via HVDC. This is attributed to the higher additional cost of P2G, making investments more sensitive to potential underutilization under uncertain conditions compared to the larger-capacity HVDC.
- Economic Factors: Decreasing RE generation costs make P2G more competitive. The growth of hydrogen demand, particularly in the transportation sector, is critical for P2G profitability and drives increased P2G and HP planning. If hydrogen demand growth is pessimistic, P2G and HP planning significantly reduces, and electricity transmission via HVDC dominates.
For practical implementation, this research suggests the need for:
- A robust optimization modeling framework capable of handling mixed-integer and second-order cone constraints (MISOCP). Solvers like CPLEX or Gurobi would be necessary.
- Detailed data on RE potential profiles (including statistical moments for uncertainty), load profiles, P2G operational characteristics (min/max power, ramping, booting), cost parameters (CAPEX, OPEX, fuel/energy prices), and future demand forecasts for electricity and various hydrogen sectors.
- Careful consideration of the P2G cluster modeling approach, including the unit commitment aspects and the use of simplification techniques like the equal-split rule and LDR to manage computational burden for large-scale systems.
- Strategic planning that considers the distinct roles and temporal evolution of HVDC and P2G technologies based on technical factors (variability, uncertainty) and economic factors (cost trends, demand growth). P2G's role is likely to increase over time as RE costs fall and hydrogen markets develop, complementing the foundational grid infrastructure provided by HVDC.