AI orchestrates future of energy through EVs, heat and hydrogen

Artificial intelligence is quickly becoming a critical force in reshaping energy markets and integrating renewable sources into power systems. A new editorial study underscores how AI is enabling demand response, building consumer trust, and coordinating emerging flexibility pathways across electricity, transport, heating, and hydrogen sectors.

The research, titled “Artificial Intelligence and Energy” and published in Energies, argues that AI is not just a technological tool but a mediator between consumer engagement and system efficiency. The study explores the challenges of data security, transparency, and inclusivity while highlighting AI’s pivotal role in fostering a fair and human-centered energy transition.

How AI transforms consumer engagement in demand response

One of the study’s main concerns is the integration of consumers into demand response programs, which allow households, small businesses, and industrial players to adjust their energy use in response to grid signals. Historically, demand response has focused on large industrial consumers where flexibility is easier to deliver. Extending these programs to residential and small commercial participants presents new hurdles, as individual comfort, convenience, and trust become decisive factors.

AI-enabled platforms are presented as solutions to these challenges. By delivering real-time feedback on cost savings, environmental benefits, and grid stability contributions, AI can transform passive consumers into active participants. Gamification strategies and personalized recommendations further enhance motivation, while automation through smart devices, electric vehicle chargers, and thermostats reduces cognitive burden. The study emphasizes that widespread adoption cannot rely solely on altruism or environmental concern. Automation and tangible financial rewards must be combined to encourage sustained participation.

The authors stress that adaptability is key. Energy enthusiasts may want detailed dashboards and optimization controls, while less-engaged households may prefer automated systems with simple opt-out options. AI-driven multi-agent reinforcement learning can help tailor demand response programs to diverse consumer profiles, balancing individual comfort with system needs. Importantly, vulnerable households must be supported to avoid widening inequalities, ensuring that flexibility programs benefit all social groups.

Why security, privacy, and trust are non-negotiable

The paper identifies data security and privacy as central barriers to consumer trust. Demand response programs require high-resolution data from smart meters, IoT devices, and household appliances. Without strong safeguards, these data streams can expose sensitive information about daily routines, presence at home, or specific appliance usage.

To address this, the authors highlight privacy-preserving techniques such as federated learning, which enables AI models to be trained across distributed data without centralizing sensitive information. Coupled with differential privacy and secure aggregation, these methods ensure that raw consumer data remains protected. Blockchain-based platforms and smart contracts are also identified as promising solutions for creating auditable, tamper-proof records of energy transactions.

Transparency emerges as another vital condition. Consumers are unlikely to accept opaque optimization decisions if they cannot understand why their energy use was shifted or curtailed. Here, explainable AI plays a decisive role. By making automated actions interpretable for both end-users and regulators, explainable AI strengthens accountability, prevents bias, and ensures fairness across consumer groups.

The authors argue that security, privacy, and explainability together form the foundation of trustworthy AI in the energy sector. Without this triad, consumer participation risks collapsing, undermining the very programs that flexibility and renewable integration depend upon.

What new flexibility pathways mean for the energy transition

The study explores how electrification of transport and heating, combined with the emergence of hydrogen, expands opportunities for flexibility in power systems. Electric vehicles are highlighted as particularly valuable assets. Through vehicle-to-grid (V2G) technology, EVs can operate as mobile storage units, providing ancillary services such as peak shaving and frequency regulation. However, the study notes that consumer acceptance of V2G depends on addressing concerns around battery degradation, charging access, and fair compensation.

Power-to-heat and power-to-hydrogen technologies represent additional avenues to absorb renewable surpluses and avoid curtailment. Heat pumps and thermal storage are especially effective in colder climates, while hydrogen offers versatility for industry, mobility, and long-term storage. Prosumers with rooftop solar could use excess generation to produce hydrogen, store it, and later use it for heating or sell it back to the grid.

AI is positioned as the key enabler for coordinating these distributed resources. By aggregating flexibility across households, businesses, EVs, and hydrogen production facilities, AI enables accurate forecasting and real-time coordination. Regulatory models must evolve to recognize and reward availability, responsiveness, and sector coupling benefits, rather than focusing solely on energy volumes. 

Overall, cross-sector digital platforms with unified identity, settlement, and cybersecurity layers will be essential for managing the complexity of future flexibility markets.