Power and Hydrogen Hybrid Transmission for Renewable Energy Systems: An Integrated Expansion Planning Strategy (2312.10823v1)

Abstract: The increasing interest in hydrogen as a clean energy source has led to extensive research into its transmission, storage, and integration with bulk power systems. With the evolution of hydrogen technologies towards greater efficiency, and cost-effectiveness, it becomes essential to examine the operation and expansion of grids that include both electric power and hydrogen facilities. This paper introduces an expansion strategy for electric power and hydrogen transmission systems, tailored for future renewable energy-enriched grids. Our proposed transmission expansion planning with hydrogen facilities (TEP-H) model integrates daily operations of both electric power and hydrogen transmissions. The fuel cells and electrolyzers are used for electrical-hydrogen energy conversion, and related constraints are considered in TEP-H. We applied TEP-H to the Texas 123-bus backbone transmission grid (TX-123BT), for various renewable penetration levels and hydrogen technology development assumptions. It gave us insights on the scenarios that hydrogen transmission become feasible and economically beneficial. We also compared the performance of TX-123BT system with the hybrid transmission investment and the pure electrical transmission investment obtained by a traditional transmission expansion planning (TEP-T) model. The numerical results indicate that future renewable grids can have lower total cost with TEP-H if future electrical-hydrogen energy conversion efficiency is high.

• The paper presents an MILP model that integrates electric and hydrogen transmission planning to lower system costs.
• It details technical constraints and investment strategies, emphasizing conversion efficiencies and optimal scheduling for hydrogen facilities.
• Case studies show that hybrid transmission investments become economically viable above 60% renewable penetration, outperforming conventional approaches.

This paper (Power and Hydrogen Hybrid Transmission for Renewable Energy Systems: An Integrated Expansion Planning Strategy, 2023) introduces a Transmission Expansion Planning model that integrates both electric power and hydrogen transmission facilities, referred to as TEP-H. The increasing penetration of renewable energy sources necessitates significant investment in transmission capacity and energy storage to manage their intermittency and geographical distribution. Hydrogen, with its potential for energy conversion, transmission via pipelines, and storage, is explored as a complementary solution to purely electrical transmission expansion.

The core challenge addressed is how to optimally plan and invest in a hybrid electric and hydrogen transmission infrastructure for future renewable-rich grids, considering both capital costs and operational expenses over multiple future periods. Current grid planning methods primarily focus on electric transmission and do not account for the unique aspects and potential synergies of hydrogen integration. Practical challenges for hydrogen include energy conversion inefficiencies (electrolysis and fuel cells), capital costs of associated facilities, leakage risks, and material compatibility for pipelines.

The proposed TEP-H model formulates this planning problem as a mixed-integer linear programming (MILP) problem. The objective is to minimize the total system cost over multiple planning periods, which includes:

• Capital and maintenance costs of new electric transmission lines.
• Capital and maintenance costs of new hydrogen facilities (pipelines, electrolyzers, fuel cells, compressors).
• Operational costs of conventional (thermal) generators.
• A penalty for load shedding, representing the cost of unserved energy.

The model incorporates detailed constraints for both electric and hydrogen systems across multiple time intervals within typical days over several planning periods:

• Electric System Constraints: Nodal power balance, thermal generation limits, renewable generation limits and curtailment, existing and new transmission line power flow limits (based on reactances and phase angles), and dynamic line ratings.
• Hydrogen System Constraints: Hydrogen flow balance, capacities of hydrogen pipelines, power consumption of electrolyzers and compressors, power generation of fuel cells, and critically, the energy conversion efficiencies of electrolyzers and fuel cells.
• Investment Constraints: Binary variables determine when candidate electric lines and hydrogen routes (including pipelines, electrolyzers, and fuel cells) are constructed in a given period. Once constructed, they are operational for all subsequent periods within the planning horizon.

Practical Implementation Details:

Implementing TEP-H requires significant data and computational resources.

1. Data Acquisition: Detailed future profiles are needed, including:
   • Hourly or sub-hourly load forecasts for each bus.
   • Hourly or sub-hourly renewable generation profiles (wind, solar) at different locations, considering their variability.
   • Dynamic thermal ratings for existing and potential new electric transmission lines.
   • Candidate electric transmission line routes, costs, and electrical characteristics.
   • Candidate hydrogen transmission routes, including potential pipeline paths (possibly leveraging existing natural gas infrastructure), costs of pipelines, electrolyzers, fuel cells, and compressors associated with each route.
   • Technical parameters for hydrogen facilities, especially conversion efficiencies ($\eta_E$ for electrolyzers, $\eta_F$ for fuel cells) and capacity limits.
   • Operational costs for thermal generators.
   • Economic parameters like interest rates (implicitly in maintenance ratios), discount rates (if costs are discounted across periods), and penalty costs for load shedding.
2. Model Formulation: The MILP model needs to be translated into a mathematical programming language or framework. The paper uses Python with the Pyomo package, which allows defining optimization variables, constraints, and the objective function in a structured way.
3. Solver: Solving the MILP requires a powerful commercial or open-source solver capable of handling large mixed-integer problems, such as Gurobi (used in the paper), CPLEX, or high-performance open-source options like HiGHS or GLPK (though large-scale performance might differ). The model size grows with the number of buses, candidate lines/hydrogen routes, typical days, time intervals per day, and planning periods. Setting an optimality gap (e.g., 0.1% in the paper) is a practical approach to manage computational time for large problems, accepting a near-optimal solution.
4. Computational Resources: Solving large MILP problems can be computationally intensive, requiring substantial RAM and CPU power, especially for detailed models with fine time resolution and many periods.

Real-World Applications and Insights:

The case studies on the TX-123BT system provide practical insights into when and where hydrogen transmission becomes economically viable:

• Renewable Penetration Threshold: Hydrogen transmission investment is shown to become feasible and beneficial at higher levels of renewable penetration (around 60% in the paper). This suggests hydrogen is less likely to be cost-effective in grids dominated by conventional generation or with low renewable integration.
• Conversion Efficiency is Key: The round-trip efficiency of electric-to-hydrogen-to-electric conversion (product of electrolyzer and fuel cell efficiencies) is identified as the most critical factor influencing hydrogen investment. Significant investment occurs only when round-trip efficiency reaches higher levels (70-80% in the paper). This highlights the importance of technological advancements in electrolyzers and fuel cells for enabling hydrogen transmission.
• Cost Reduction Impact: While efficiency is dominant at lower levels, cost reduction of hydrogen facilities becomes increasingly influential on investment decisions as efficiency improves to high levels (70-80%).
• Hybrid Benefits: Compared to purely electrical expansion (TEP-T), TEP-H results in a lower total system cost by enabling strategic investment in hydrogen facilities alongside electric lines. This suggests hybrid planning can lead to more economic outcomes, potentially by better utilizing curtailed renewable energy and providing an alternative or supplementary transmission pathway.
• Operational Strategy: The model also provides operational schedules for hydrogen facilities (electrolyzer consumption, fuel cell generation, compressor usage, and hydrogen flow) on a daily basis, demonstrating how these assets would be dispatched in a future hybrid grid. This operational insight is crucial for grid operators planning to manage such systems.

Implementation Considerations and Limitations:

• Forecasting Uncertainty: The model heavily relies on long-term forecasts of load, renewable generation, dynamic line ratings, and hydrogen technology parameters. Accurately predicting these factors over decades is challenging. Practical implementations might need to incorporate uncertainty analysis or robustness considerations.
• Hydrogen Specifics: The model captures core aspects but simplified assumptions may be made regarding hydrogen storage (not explicitly modeled as a distinct asset beyond implicitly using pipeline capacity), hydrogen demand (only conversion back to electricity is considered), compressor power consumption model, and detailed pipeline physics (flow dynamics, pressure constraints). More complex models exist for these aspects but would increase computational burden.
• Safety and Regulatory Aspects: Real-world hydrogen transmission planning must consider safety standards, material degradation (hydrogen embrittlement), leakage detection, public acceptance, and regulatory frameworks, which are not explicitly modeled here but are critical for deployment.
• Market Design: The paper focuses on centralized planning. Integration into competitive electricity markets and potential hydrogen markets would involve complex market design considerations (e.g., how hydrogen assets participate in energy and ancillary service markets) which are not covered.

In summary, the TEP-H model provides a foundational MILP framework for integrated long-term planning of electric and hydrogen transmission grids. Its practical application requires extensive data and computational resources but offers valuable insights into the techno-economic feasibility of hydrogen transmission under future renewable scenarios, highlighting the critical role of conversion efficiency and the potential for cost savings through hybrid infrastructure development.