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  • David Brent Grantham

Clockspeed: Hybrid Intelligence (HI) for Adaptive Regional Hydrogen Hubs

Updated: Jun 4, 2023


March 14, 2023


The hydrogen decade began with a frenzy of planned global investment and public discourse regarding the proper formation of emerging global hydrogen markets. New policies and economic incentives have led to a vigorous debate over the best uses cases for hydrogen molecules and electrons in regional hubs across six continents. Despite the abundance of hydrogen in the universe, the calculus for determining its viability as a tradeable commodity remains exceedingly complex. Thermodynamic losses and conversion costs are often prohibitive and must be factored into a rigorous techno-economic analysis that responds to the grand trilemma - reliability, affordability, and sustainability - of the cleantech transition. This triple threat is the epic challenge of our age and requires a complex adaptive systems (CAS) approach that deploys the hybrid intelligence (HI) of human and machine for lucid action amid ongoing and anticipated disruptions in energy value chains and environmental support systems.


“Clockspeed” or the rate at which new generations of key elements in low-emission electricity and low-emission hydrogen technologies, project designs, production methods, and business models progress through their evolutionary life cycles is critical for closing the gap in the race toward more robust and resilient energy and power systems. As fate would have it, digital innovation is in hyperdrive as artificial intelligence (AI) emerges as a fundamental growth opportunity for the tech industry as it evolves through its current cycle of adaptive change. Much like hydrogen, AI technologies have been in the R&D and early operational phase for many years and are quickly moving toward mainstream application. QuantumBlack AI by McKinsey offers a view of an integrated architecture that can harness the power of HI across the full spectrum of cleantech value chains. QuantumBlack Labs features a code-sharing platform that improves collaboration between data scientists, data engineers and machine-learning engineers and shares its knowledge through a Python framework and an open-source Python library.


“Adaptive computation can encode the complexities of regional hydrogen hubs with HI acquired knowledge and AI integration techniques that optimize hydrogen assets for broadly beneficial outcomes in interconnected and interdependent commodity markets”


2023 will be a pivotal moment for adaptive computation as advanced techniques such as artificial intelligence (AI), machine learning (ML) and deep learning (DL) become mainstream. In general terms, AI is the ability of a computer or machine to perform tasks that would normally require human-level intelligence. ML is a subfield of AI that involves the development of algorithms that can learn from data without being explicitly programmed and DL can learn to recognize patterns and make decisions based on the data they’re trained on. In brief, AI is the broader field of which ML and DL are subfields. ML is based on algorithms while DL involves the use of artificial neural networks that are inspired by the structure and function of the human brain. Generative AI describes algorithms (such as ChatGPT) that can be used to create new content including audio, code, images, texts, simulations, and videos which have the potential to dramatically increase the speed at which technologies across the low-emission value chain become cleaner, more efficient, less costly, and easier to scale:


IEA – Key elements for each step in selected clean energy and technology supply chains



IEA (2023), Energy Technology Perspectives 2023, IEA, Paris https://www.iea.org/reports/energy-technology-perspectives-2023, License: CC BY 4.0


The clockspeed of HI will determine the ability of hydrogen hub project planners and asset owners to offset hydrogen’s inherent physical challenges through market simulations that identify the best use cases for hydrogen in a multi-commodity trading platform. The economic incentives in the U.S. Bipartisan Infrastructure Law (BIL) and the Inflation Reduction Act (IRA) have created bankable investment opportunities across energy and power value chains while the CHIPS and Science Act creates funding for semiconductor supply chains, AI, quantum computing and a STEM workforce that fosters innovation across the country. The U.S. Department of Energy (DOE), the Office of Science, and the Office of Technology Transitions have taken that support a step further by issuing a Request for Information (RFI) for place-based innovation ecosystems. They’re seeking input from stakeholders on key research, development, demonstration, and deployment (RDD&D) programs and funding opportunities for the industry and technology sectors of the future including advanced manufacturing and supply chains. AI models will permeate every aspect of the cleantech energy transition and can be especially valuable for analyzing the future natural resource demands and technology mixes of regional hydrogen hubs as they work through their RDD&D phases.


Natural language learning engines like ChatGPT are currently capable of processing and analyzing large amounts of energy consumption and emissions data, supply chain performance, predictive maintenance, energy management and CO2e footprint calculations. HI will accelerate the optimization of product design, integrated resource planning (IRP), waste management, and supply chain efficiencies and can play a key role in determining the optimal technology mix for various production, distribution, storage and end-uses. Adaptive computation can boost the value of electricity market complex adaptive systems (EMCAS) simulations that are used for market restructuring issues in the U.S., Europe and Asia through the following actions:

  • Combined Engineering Techniques with Quantitative Market Analysis: DC load flow models allow you to simulate the actual operation of the physical system configuration. Generators and transmission nodes are represented at the individual bus-level; transmission lines are represented as individual branches.

  • Decentralized Decision Making: represents multiple market participants and agents with decentralized, individual company-level decision-making capabilities. Each agent seeks to maximize its own utility that is derived from a “corporate utility function” that can combine multiple objectives. Each company agent has a set of corporate objectives, such as profit, risk exposure, market share, etc.

  • Alternative Company Strategies and Risk Profiles: simulates alternative company strategies as it provides the agents with access to a wide range of market strategies to fit their different risk preferences (from risk-averse to risk-prone).

  • Learning and Adaptation: incorporates agent learning and adaptation based on past performance and changing conditions.

  • Different Markets with Different Rules: explicitly models energy spot markets, bilateral contract markets, and ancillary services markets. EMCAS allows you to change market rules to test market operation and analyze the behavior of both individual agents and the system under different market configurations.

  • Transient Market Conditions and Emerging Behavior: As most energy markets are rarely in equilibrium, EMCAS allows you to study transient market conditions that receive increasing attention. The overall market behavior emerges from agent interactions. EMCAS allows you to identify contributors to system problems.

Supercomputers with AI capabilities that can deliver 2 exaflops - 2 billion billion calculations per second - of computing power will enable science that is impossible today. The Aurora Exascale Supercomputer will have the ability to seamlessly integrate data analysis, modeling, simulation, and artificial intelligence. Aurora will allow researchers around the world to construct infinitely more accurate models in a diversity of scientific domains critical to the sustained viability of hydrogen hubs like climate, materials science, long-duration energy storage, and carbon capture, utilization and storage (CCUS). IBM’s Vela supercomputer is an AI-optimized cloud-native system based on foundation model training that makes use of open-source technologies like Kubernetes, PyTorch and Ray. Supercomputers like Aurora and Vela and AI labs like QuantumBlack will provide complex content analysis at speeds that can correct previous missteps in the cleantech transition through common language that encompasses a more holistic view of our energy and power systems.


The decade of supercomputers, HI and the cleantech transition have come into alignment at a crucial moment for the grand trilemma. This new age of advanced technologies can support adaptive growth and development in ways that combine intergenerational collaboration and aspiration with acquired knowledge and expertise. Robust product and market-based decision-making simulations for regional hydrogen hubs based on core competencies and quick response times across cleantech innovation ecosystems are critical. The clockspeed of HI that de-risks the value chains in the energy and power sectors will be the prime indicator of our increased adaptive capacity in response to and anticipation of changes in our environmental support systems.


David B. Grantham is the founder of RedwoodAdaptive, a networked innovation and adaptive growth consultancy based in Norman, Oklahoma. David has worked in the energy and environmental space for over 30 years with formal training in energy resources policy & analysis and sustainability leadership for software design in industrial ecology. His work with RedwoodAI includes concept and content creation for generative AI platforms for cleantech value chains that feature Economic and Earth Science Indicators (EESIs) for primary energy feedstocks and technologies.


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