The twin transition refers to the simultaneous shift towards a more sustainable and digital economy. This approach recognises the potential for technology and data to drive sustainability and decarbonisation. The twin transition is seen as a key strategy for achieving the goals of several global mandates, including the European Green Deal, the Inflation Reduction Act and the Chinese 14th 5-Year Plan, which all have an emphasis on green innovation as a key enabler of a carbon-neutral future. How can policymakers ensure alignment between digital technologies and the shift towards a sustainable net zero economy?

The interoperability of connected devices will be critical to enabling cross-sector resource sharing and accurate modelling of energy transition pathways. However, standards for device, platform, and system interoperability need to be established to ensure the seamless transfer of data across the energy, mobility, transportation and industrial value chains. How can policies and standards be improved to accelerate the development of ‘zero-trust’ cyber security infrastructure and drive quicker adoption of new technologies, faster innovation cycles and lower barriers to entry?

Web 3.0 is driving the move from a centralised internet to a decentralised one. Innovations in connectivity technologies such as 5G and 6G are fueling the convergence of AI, blockchain and edge computing, giving rise to a hyper-connected yet decentralised system. How will this decentralised model create opportunities for the energy sector to realise new solutions for the energy trilemma and enable new collaborations across traditional industry boundaries?

For many organisations, scope 3 emissions account for more than 70% of their carbon footprint. However, reducing this output is challenging, with the majority of emissions generated from activities and energy use outside businesses’ direct control. Tracking these emissions will require a level of collaboration not yet present across supply and value chains. How can digital technologies, such as blockchain, IIoT and edge computing, help track emissions effectively across value chains to provide unparalleled data insights and emissions tracking?

Blockchain has the potential to be a key enabler of a decentralised energy system by empowering users to share information in a zero-trust architecture that allows visibility and traceability of transactional data. However, blockchain platforms call for a horizontal collaborative form of governance in direct contrast to today’s traditional energy model. If the blockchain is to realise its potential as an enabler of a decentralised energy system, what must policymakers and industry do to rethink traditional forms of governance in order to align the digital domain with the requirements of a decentralised energy system?

Generative AI giving rise to new opportunities to optimise energy efficiencies and transform the way many critical functions of energy companies work today. Technologies like generative adversarial networks (GANs) and variational autoencoders (VAEs) can be used to generate data for simulations and modelling, which can improve the accuracy of algorithms for energy generation, maintenance, distribution, demand forecasting, load balancing, and grid optimisation. How can generative AI help the energy industry to improve performance and efficiencies and reduce its carbon footprint?

Digitalisation is creating new energy retail models that leverage advanced technologies and analytics such as energy service aggregators, peer-to-peer energy trading, virtual power plants and dynamic pricing and demand response. However, the adoption of these new energy business models depends on supportive regulatory frameworks, enabling market structures, and consumer preferences. As energy storage technologies reach commercialisation, how will digital technologies act as both a catalyst and a tool for the creation of new energy business models and new revenue streams, and how can they be used to inform pricing mechanisms and regulations that support decentralisation, demand side flexibility and consumer-driven energy models?

The majority of oil and gas operations are still being driven manually. However, manual operations create data siloes that prevent value creation, business model innovation and efficiencies. Intelligent automation systems can break down these siloes by connecting disparate databases, applications, and devices to create a unified data environment, allowing for a holistic view of operations. How can this holistic view of operations automate emissions monitoring and identify emissions hotspots in the value chain, to provide support for decarbonisation strategies and facilitate the integration of clean energy technologies into oil and gas processes?

Oil and gas companies are under increasing scrutiny to decarbonise and digital technologies have a critical role to play in driving productivity and efficiency enhancements across upstream operations. However, in order to achieve actionable data insights, greater alignment between business operations and IT departments is needed to ensure digital architecture aligns with the needs of the business, including the long-term objective to decarbonise. Understanding how data-driven technologies, such as AI, can be used to drive efficiency and decarbonisation efforts in oil and gas is critical to realising alignment between IT and operational teams and maximising the value of existing projects.

There exists an opportunity for energy companies, particularly utilities, to capitalise on the rise in demand for EV charging infrastructure. However, the EV charging market is diverse, with multiple customer segments each demanding different types of charging infrastructure. Though electrification is the driver of rising EV demand, the business model will be driven by digitalisation and customer data insights supported by advanced digital technologies and platforms. With each market having a differing interpretation of value, how will the ability to generate new commercial models at scale be impacted by the ability of traditional energy players to leverage data insights and align them to business objectives?

The deployment of the Industrial Internet of Things (IIoT), edge computing and AI, supported by 5G connectivity is set to improve the efficiency and resilience of smart grids. But what is the potential for smart grid infrastructure connected to a distributed AI platform and installed alongside electric metres and battery storage systems, to enable the integration of distributed energy resources -- including solar, energy storage, and EVs -- to realise security, affordability, efficiency and optionality in energy sources?

Carbon markets are fragmented and expanding. Countries and regions have their own accounting and pricing methods and registries. Such complexity across the market is making buyers reluctant to buy carbon credits from the other end of the world due to lack of transparency and potential issues with the double registration. Digital Ledger Technologies can help creating transparency, traceability, and trust. The session will deep dive into actual use case that is being developed and deployed to help facilitate global trade in the carbon market.

Open-source interfaces have the capability to facilitate collaboration between stakeholders across value chains, to innovate at pace and deliver agile, collaborative solutions to accelerate the energy transition. Which open-source code and software defined architecture solutions are contributing to solving the energy trilemma and how?

Transportation electrification  and renewable integration are pivotal for carbon footprint reduction. Smart grids play a crucial role by efficiently managing the demand for electricity and optimizing the charging of electric vehicles, ensuring that energy from renewable sources, are harnessed at its fullest potential. This synergy creates a harmonious ecosystem where transportation, energy generation, and distribution work in unison to reduce our carbon footprint and pave the way for a greener and more sustainable world.

The rise in distributed energy sources is enabling retail energy consumers in a handful of markets to partake in energy trading. This rise in electrification, coupled with a greater input of energy from decentralised renewable sources, is also creating new opportunities for consumers to participate as producers. In the context of today’s traditional energy system, peer-to-peer energy trading models act as a replacement for the energy retailer, enabling consumers to buy and sell energy and manage energy pricing risk themselves, with Balance Responsible Parties managing the aggregate supply and demand. In the future, how could peer-to-peer models allow for better alignment between local generation and demand and drive a greater proportion of renewable electricity to the grid?

Make money, save the planet. Understand how community solar can be used to get people involved in the fight against climate change through community solar and decentralised environment friendly solutions.

6G wireless connectivity has the ability to fuse the physical and cyber worlds, enabling a truly connected and intelligent era. With smart grids enabled by 6G connectivity architecture able to use edge computing technology and AI to accurately predict energy supply and demand through smart energy management systems, how will this impact future energy systems and clean energy technologies, such as hydrogen?

With the rise in edge computing technology and distributed devices and sensors, network perimeters have become more fluid, calling for security protocols to be embedded into network architectures rather than added as ‘bolt-ons’. To allow for interoperability and more fluid networks,zero trust architectures will need to deploy a range of complimentary security measures and solutions. Which zero trust solutions will create the greatest opportunities to accelerate smart grid development and interoperability?

Digital twins can support the energy sector’s transition to decentralisation by allowing for real-time modelling of energy policies, strategies and plans. This can enable policymakers and energy producers to test new network and grid architectures in order to predict energy supply and demand. How can digital twin technology help identify energy supply gaps and support multi-criteria decision-making and policymaking to ensure the security of energy supplies?

The hydrogen economy requires a dramatic scale-up of technologies and infrastructure, as well as collaboration across value chains, in order for hydrogen to contribute 20% of the total abatement needed to reach net zero by 2050. What impact will advances in digital technology, that can model design, operations and workflows, have on driving cross-sector collaboration and scaling technologies at the rate needed to accelerate the hydrogen economy?

Grid modernisation technologies such as AI platforms, smart metering and edge computing are offering new opportunities for utilities providers to better manage energy costs by offering demand side response programmes. Combined with smart customer systems such as in-home displays or home-area-networks, consumers can now be incentivised to change their energy consumption behaviours by benefiting from off-peak cost savings. In addition, demand response programmes, enabled by smart grid technologies and AI platforms, offer new grid balancing opportunities for energy providers, helping to drive down costs in the energy wholesale market through reductions in peak demand. Which digital technologies are being deployed by utilities and energy providers to provide more efficient, demand responsive energy to consumers?

Organisations looking to implement drone inspection programmes must ensure they deploy an integrated visual data management and integrated AI solution to store and analyse large quantities of visual data from drone technology. What are the latest developments in drone technology and how can the energy industry utilise them to identify, detect and address issues as they occur, as well as predict the potential future state of equipment, and reduce risk?