Digital Energy Innovation Hotspots
“The highest intensity of innovation is focused on leveraging IoT technology across the energy ecosystem”

GENERATION
In power generation, these digital innovations are making an impact in three main areas: optimising the performance and productivity of generating assets; supporting the integration of unpredictable renewable energy sources; and creating the backbone for sophisticated asset management services.

Machine learning and data analytics enable predictive generation and maintenance tools in critical areas such as scheduling outages optimally against demand and the provision of balancing energy to reserve markets.

Planned predictive maintenance (PPM) will become reality in the next two to three years as generators seek to significantly reduce reactive maintenance while retaining a network of assets at peak quality.

PPM will increasingly converge with condition monitoring techniques to predict faults or failures and prevent incidents by providing intelligence in advance to allow the right maintenance to be done at the right time. Not only will these tools improve the productivity of assets, but maintenance costs and expensive asset failures will be avoided.

Then comes the challenge of integrating the colossal 1,600 GW of renewable energy capacity that will be added globally between 2017 and 2025. IoT-based innovation will become ever more important to connect generating assets to grids, while also predicting demand and balancing supply. Advanced data analytics will be deployed for modelling weather data, energy demand and optimised pricing to ensure providers are fully flexible and responsive to change while optimising the profitability of assets.

TRADING
This growth in renewables is creating more price volatility in trading markets. For generators this means pricing and trading will become more complex, and price forecasting and real-time analysis become critical. Leveraging complex data from generating assets and flexibility in demand, AI-based analytics tools for trading optimisation and automation will converge with IoT-based power generation and demand management to become a crucial solution for the future.

From the largest utilities to the smallest prosumers, the market is becoming increasingly competitive due to markets liberalising and new entrants moving into the space. At the same time, we’re seeing the reduction or removal of subsidies as renewable energy matures and competes in its own right, meaning a more ‘real’ competition will emerge. Tools for the optimisation and automation of trading will become central and vital to give players the deep insight and analysis of the short-term market dynamics in real time.

In the distributed energy future, autonomous trading tools will have an even bigger role to play with the IoT connecting demand-side assets with complex generation portfolios and trading the VPP capacities on platforms and exchanges. The trading tools will be key to driving the transition from today’s VPPs—largely aggregated capacity resources to tomorrow’s solutions—which will be fully autonomous networks of assets with advanced analytics and trading capabilities.

At an even more micro level, the next level of decentralisation is seeing the emergence of peer-to-peer (P2P) trading models that will take hold beyond 2020. Blockchain technology is already being used in pilot projects in conjunction with IoT data infrastructure to provide the trust layers for P2P energy transactions over the internet for consumers with installed and connected PV, storage and electric vehicles. This will be a key enabler of the democratisation of energy within communities as consumers are empowered with more decision-making tools.

TRANSMISSION & DISTRIBUTION
For the past decade, smart grids have been the flagship application for digital technology in energy. While market shifts such as renewable integration, automated metering, grid decentralisation and prosumer growth have driven the need for smarter grids, technology innovation has been the enabler.

We’ve moved into the era of dynamic, multi-directional energy flow with the capability to create autonomous, flexible grids. Networks of connected sensors and controllers interact with grid infrastructure to deliver monitoring tools alongside optimisation software and grid automation solutions. Self-healing grids are becoming a reality as real-time distribution data is used to detect and isolate faults, and to reconfigure networks to minimize customer impact and downtime.

At the interface between user and grid, dynamic communication will move beyond automated metering to deliver better customer engagement, DR solutions and remote building control.

Utilities face the challenge of transforming from simply being owners and operators of assets to offering servicebased selling and promoting energy efficiency and clean energy. Data analytics will be used extensively across grid infrastructure to provide high-value, tailor-made services to customers for asset maintenance and outage management, load forecasting, microgrid management, VPPs and DR scheduling.

But it’s rarely about building new systems from scratch. In the more mature energy markets of Europe, Japan and North America, the critical challenge for grid operators is to transform existing infrastructure into resilient future-proof smart grids. The collaboration between advanced IT and the OT of the grid will be key to success.

RETAIL
There’s also a huge opportunity to use digital technology to offer flexibility and customisation to deliver optimised pricing and improved customer experience, not just in electricity retailing, but also in the creation of new revenue streams and value-adding services. We’re already beginning to see AI software that learns from customer behaviour and offers the best configuration for connected devices and systems, such as connected cars, energy management systems and smart home products like thermostats and lighting.

For the utility, digitalisation at the retail level has the potential to improve customer satisfaction while also reducing costs and increasing efficiency. The customer benefits from cost savings while the grid operator gains the ability to react to consumption fluctuations and encourage users to develop usage patterns that balance loads and flatten demand peaks.

Data analytics will increasingly be used to combine the prediction of demand peaks with personalised usage profiling to support tariff optimisation for individual customers. As utilities increasingly acknowledge the benefits, we’ll see customised tariffs based on individual usage and needs, demand response services, time-of-day usage billing and dynamic pricing becoming a reality by 2020.

Dynamic pricing and demand-side management tools will be critical innovation areas for utilities to stay connected to their customers in the more competitive future, while making sure that both parties reap the benefits of revitalised relationships and value-adding services.

We’ll also start to see AI and robotics used in the support and management of retail customers. We’ll be able to move from a world where customers are largely treated the same to one where AI-based predictive customer learning enables suppliers to offer a truly automated personal service to each client using robots. We’ll see customised adaptive value propositions based on technology to offer customers a range of services based on identified or predicted needs.

CONSUMPTION
Energy users—whether commercial, industrial or residential—are being empowered by the availability of digital tools and affordable energy technology to take more control over their energy. We’ve seen new market entrants, new business models and new revenue streams emerging.

Energy companies are being forced to innovate and adapt to reconsider their future value propositions and customer engagement models in the new energy future. Those that embrace the new future will thrive, but those that underestimate the pace of change will struggle to survive.

DR models are enabling real-time analysis of customer demand trends and allowing the energy infrastructure to react accordingly. The energy value chain will witness an investment of $10 billion in DR programs by 2020, which will incentivise consumers to actively participate for financial benefits while contributing to energy efficiency. New communication technologies and supportive government regulations are imperatives for its integration into the future energy system, but the benefits are clear—80 GW/year of capacity will be reduced in the US alone with the help of efficient DR programs by 2025.

Meanwhile, AI and machine learning are converging with energy analytics to create active energy management solutions to help customers visualise and manage their energy consumption. That covers everything from smart thermostats and IoT-enabled home energy management systems to advanced building energy management platforms for commercial and industrial users. We’re about to see a boom in data-driven energy optimisation delivered as a cloud-based service to customers, providing guaranteed savings and outcomes.

Energy Savings as a Service (ESaaS) combines IT with OT to deliver savings for customers while driving down upfront costs. The fundamental shift here is that the product or system becomes part of the service, rather than the historical model, where the service is part of the product. The customer pays for what they value the most—the outcome.

And then, at the heart of it all, we have the next big strategic question: How will organisations leverage and monetise the extensive data from meters, electric vehicles, energy management systems, thermostats and renewable energy sources to add value and create new revenue streams? This will undoubtedly create another new impetus for innovations in the next five years.