KuppingerCole Report
Advisory Note
By Anne Bailey

Emerging Technologies Fostering Digital Business Innovation: Utilities & Energy

The energy and utilities sector will need to adapt to new industry trends of digitalization, decentralization, and the advent of a new type of consumer: the prosumer. Smart grids are a more efficient offering for energy management, and new business models beyond the prosumer will require the partnership of different actors in the industry. Emerging technologies like artificial intelligence (AI), blockchain, and IoT have can contribute to achieving these goals, but they are most impactful as supportive technologies that enable a deeper company transformation.
By Anne Bailey
aba@kuppingercole.com

Content of Figures

  1. Figure 1 Technology Trends in Utilities and Energy Industries
  2. Figure 2 The Changing Ecosystem
  3. Figure 3 Drivers Affecting Business Models in the Energy and Utilities Sector
  4. Figure 4 Comparison of AI Use Cases Based on Profit Orientation, Internal/External Orientation, and Maturity
  5. Figure 5 Data Inputs for an AI-Based Energy Consumption Analytics Use Case
  6. Figure 6 Data Inputs for an AI-Based Inspections and Maintenance Use Case
  7. Figure 7 Data Inputs for AI-Enhanced Digital Twin Use Case
  8. Figure 8 Data Inputs for AI-Based Predictive Analytics in Edge Devices Use Case
  9. Figure 9 Data Inputs for AI-Based Document Management
  10. Figure 10 Data Inputs for an AI-Based Chatbot Use Case
  11. Figure 11 Comparison of Blockchain Use Cases Based on Profit Orientation, Internal/External Orientation, and Maturity
  12. Figure 12 Process Improvements in Utilities Billing with Blockchain
  13. Figure 13 Process Changes to Microgrid Coordination with Blockchain
  14. Figure 14 Process Improvements to Purchasing Renewable Energy Certificates with Blockchain
  15. Figure 15 Process Improvements in Energy Provision to Underserved Markets with Blockchain
  16. Figure 16 Process Improvements for Dynamic Energy Pricing with Blockchain
  17. Figure 17 Comparison of IoT Use Cases Based on Profit-Orientation, Internal/External Orientation, and Maturity
  18. Figure 18 Examples of Smart Devices for Utilities System Maintenance
  19. Figure 19 Examples of Smart Devices in Decentralized Grid Management
  20. Figure 20 Smart Functions of Advanced Metering Devices
  21. Figure 21 Role of AI, Blockchain, and IoT in Delivering Digital Identity Use Cases
  22. Figure 22 IDoT Management and Emerging Technologies
  23. Figure 23 Decentralized Identity and Emerging Technologies
  24. Figure 24 Risk-Adaptive Multifactor Authentication and Emerging Technologies

1 Executive Summary

Utilities and energy companies are still relatively early on the path to digitization. Company structure for this industry has typically favored stability, particularly since there is a very low tolerance for maintenance downtime or outage. This mentality of stability is challenged by environmental pressures and public demand for alternative energy sources. Future energy systems will likely have three factors: digitalization, decentralization, and the advent of the prosumer. The combination of these trends will lead to solutions such as the smart grid which depends upon and leverages partnerships between multiple stakeholders, such as energy providers like Transmission System Operators and Distribution System Administrators, businesses, and consumers. Overhauling currently stable processes for uncertain gains is a difficult transition for the utilities sector since it contradicts this philosophy of stability.

Enter emerging technologies to this picture, and the way becomes more ambiguous. Are AI, blockchain, and IoT enough to meet changing expectations and needs from consumers in a stable, profitable way? New business models that rely on these technologies for the energy and utilities sector are gaining traction on the market. Among the top are the mobilization of the “prosumer”, provision of IoT-based efficiency services, monetizing Renewable Energy Certificates (RECs) and other global certificates, and the hypothetical entrance of utilities companies into a whole new role as identity verifiers in a Decentralized Identity scenario.

Figure 1: Technology Trends in Utilities and Energy Industries

There are many potential use cases for emerging technologies in this sector, but only a few stand out as being sufficiently stable for potential adoption. AI brings opportunities for data analysis such as resource consumption analysis and predictive maintenance, while also helping with administrative tasks like document management, conversational interfaces for user behavior analytics (UBA). Industrial and consumer IoT devices provide the data to achieve these ends. Blockchain has very limited uses for this sector; billing which reflects dynamic pricing could be automated, but blockchain isn’t the sole method to achieve this. This is the same with issuing RECs, or coordinating microgrids. Blockchain has yet to prove itself the leading solution for these tasks.

The energy and utilities sector should aim to integrate emerging technologies, not because they are cutting edge or promise huge cost savings, but because they are supportive tools to achieve a more impactful end: of stimulating a digitized unification between all departments to deliver synchronized, intelligent, and efficient processes. Developing a strategy to deliver granular control of synchronized operational and administrative tasks will do more to enable energy and utilities companies to keep up with sustainability and profitability trends than simply adopting new technologies. But these technologies are some of the essential tools that would bring such a strategy to success.

2 Highlights

  • Future energy and utility trends will likely be digitalization, decentralization, and migration to a prosumer model
  • Most utilities and energy companies will need a strategy change to a leaner, more synchronized approach to operational and administrative management in order to lower operational costs
  • Emerging technologies can support a strategy change, but will not be enough to deliver effective results without an intentional strategy
  • Artificial intelligence enables data analysis like granular resource consumption reports and predictive maintenance, as well as administrative tasks like document management and conversational interfaces for user behavior analytics (UBA)
  • Blockchain can be used in billing use cases with dynamic pricing, issuing renewable energy certificates (RECs), and coordinating decentralized microgrids
  • Industrial and consumer IoT devices provide the data to support general digitalization efforts as well as AI and blockchain tools
  • Digital identity is an important topic for secure IoT management and holds potential for consumer identity use cases

3 Competitive Landscape

The utilities and energy sector is under pressure from public sentiment, perceptions of sustainability, and the expectation to consistently deliver uninterrupted service. Emerging technologies provide some promising solutions for this sector, but require careful planning from each individual company as to how deploying a solution would fit into a larger strategy.

Utilities and energy companies are still relatively early on the path to digitization compared to other industries that have a more dynamic consumer-service provider relationship, for example the financial industry that has helped customers become accustomed to and now expect online banking as part of their experience. The utilities and energy industry only rarely utilize digital services; if so, then only recently to run smart meters or for prosumers producing and selling energy on an edge device. A major cause of the digitalization delay is that updating the operational technology (OT) used for energy production, distribution, and utilities management is a costly long-term journey. Cost and implementation barriers for simply updating the necessary OT may also prevent other change for the industry, such as the ability to embrace new business models, take rapid action on environmental concerns, and provide a dynamic, digital customer experience.

Business processes are still typically compartmentalized into operational processes and administrative/IT processes. The separation of these within an organization – especially when they are still managed manually – leads to duplication of business tasks and data management. If not careful, revamping these processes for a digital era would also happen in a compartmentalized manner: machinery is replaced to meet new efficiency standards and operational processes optimized independently from administrative/IT digitization efforts to redefine customer relations, billing, employee management, etc. Digitization alone is an insufficient guiding principle for reinventing utilities companies for current climate and consumption needs. Unified systems management – supported by tools for digitization – better captures the goal of delivering energy and utilities in the most efficient, reliable, sustainable, and intelligent way possible.

Similarly but with regard to network levels, Transmission System Operators (TSOs) and Distribution System Operators (DSOs) often do not coordinate their actions to maximize grid balance in a way that reduces costs. DSOs connect electricity generators and consumers to the electricity grid, and are connected to TSOs themselves. While conventional power plants are connected to TSOs, renewable energy generators are usually connected to DSOs. TSOs have a primary responsibility to ensure system stability through tendering energy trades to shift capacity to meet demand. The impact of DSOs on grid stability is increasing with the increase of renewable energy generators being added to the distribution grid. This influx of renewables causes an energy imbalance exacerbated by TSOs having limited access to DSO forecast data, and vice versa. This results in inefficient grid balancing actions like redispatching energy production schedules curtailing excess energy production with monetary compensation, often to renewable energies. Balancing energy, or offsetting the imbalances between production and consumption is generally managed between TSOs at a national level and between economic regions such as TSOs in EU member states part of the International Grid Control Cooperation (IGCC).

Although the roles of TSOs and DSOs remain fixed, their inherent interdependency should encourage them to approach digital transformation with the aim of balancing feed-in renewables on the grid to achieve the most stability with the highest levels of efficiency. Instead, DSOs and TSOs are often using digital transformation as a competitive edge to compete for the same types of business. Digital transformation almost always gives a competitive edge, but uniquely for the energy and utilities sector it could be a much more powerful as a tool for industry collaboration.

Environmental pressures, given voice by private citizens and national coalitions are changing expectations for the provision of energy and utilities. The loudest voices call for a fundamental paradigm shift in energy sourcing, generation, and consumption, but practically this transition must be smooth and well planned to avoid disruptions in service delivery. Because renewable energy generation is irregular and difficult to predict, it is incompatible with traditional grid management where production plants are continuously running and produce planned amounts to meet the minimum baseload demand. For example, nuclear and coal are only profitable and feasible when generating huge quantities of energy, again highlighting the difference between renewable resource planning dealing with unreliable quantities at irregular times.

Unified systems management – supported by tools for digitalization – better captures the goal of delivering energy and utilities in the most efficient, reliable, sustainable, and intelligent way possible.

The need for a reliable energy grid makes renewables unappealing because they carry the risk of not producing enough quality energy at the correct time to meet demand. And as renewable energy production increases, it reduces the economies of scale that non-renewable energy providers have, making it less and less worth it to keep large scale plants open. Reducing “mass produced” energy results in higher prices, more grid frequency fluctuations, and potentially less consistent access to power. This gives energy providers little incentive to include renewables in their energy portfolio. State-owned energy providers add an additional layer of complication; not only are they typically slower to adapt and change, the quasi-monopolized market is at times hostile to new actors. For countries that have state-owned energy providers, acquisition of new entrants is a way to adapt to new processes but continue to closely manage energy generation and distribution.

Figure 2: The Changing Ecosystem

To reflect the heightened sensitivity to consumption and resource sustainability, future energy systems will likely prioritize shifting to a more decentralized model, exploring prosumer business models, and pursuing digitization. These three factors – digitalization, decentralization, and the prosumer model – are the result of reverse engineering the method from the solution found in renewable energy resources. To solve the issue of fluctuating energy frequencies and production, decentralized grids that allow both the purchase and sale of energy from edge devices can balance grid frequency and demand. Digitalization facilitates decentralized grid management and allows more data to be collected and shared within organizations for process optimization.

The potential value that emerging technologies such as AI, blockchain, and IoT bring to the utilities sector is that they are supportive tools: these technologies will not bring a revolution to the utilities sector, but add key capabilities that favor a connected, intelligent system for managing energy generation, grid and system management, data analytics for efficiency benchmarking, and distributed participation to achieve sustainable goals.

The organizational structure of utilities companies has typically favored stability, as stability is expected in the delivery of such necessity services. These companies provide foundational services to consumers, where there is very low tolerance for maintenance downtime or outage. Overhauling currently stable processes for uncertain gains and transitioning to uncertain technologies is a difficult transition for the utilities sector since it contradicts this philosophy of stability. Use cases should reflect a maximum level of stability before they will be attractive enough to adopt.

New business models for the energy and utilities sector are entering the market. Among the top are the mobilization of the “prosumer”, provision of IoT-based efficiency services, monetizing Renewable Energy Certificates (RECs) and other global certificates, and the hypothetical entrance of utilities companies into a whole new role as identity verifiers in a Decentralized Identity scenario.

Figure 3: Drivers Affecting Business Models in the Energy and Utilities Sector

3.1 The Energy Prosumer

The future of the earth under increasingly extreme climate pressures is becoming a top concern of private citizens as well as regional and national governments. Citizens are searching for more and more active ways to meaningfully address climate change, including changing their energy and utility consumption habits. Renewable energy sources are a favorite option of consumers, many of whom choose active roles by installing energy generation devices (like photovoltaic panels) in their home. These devices offer their private owners access to renewable energy sources, but lack grid stability and efficient production and consumption without connection to a larger network. Production hours for solar and wind are variable, and often do not coincide with peak energy consumption hours, creating a mismatch of renewable generation to consumption.

The prosumer is a solution to this challenge. A prosumer is an energy consumer who also produces energy with a private device, made available for sale on a micro or national energy grid. These private devices are often referred to as edge devices because they participate on the periphery of an energy grid. This serves to incentivize renewable energy production, spur innovation in renewable energy storage, monetize any excess energy produced, and facilitate a change from centralized to localized systems.

3.2 IoT-Based Efficiency Services

The rise of IoT devices means a new age of efficiency, for utilities companies and their customers. Industrial IoT provides new insight into operational processes, like the addition of sensors to critical points in utility production or delivery can yield data to expand the analysis and control of those processes in supervisory control and data acquisition (SCADA) systems. Administrative processes can also benefit from operational smart sensor data to deploy work orders, impact scheduling, CRM, and billing. For example, a heat sensor at a critical point in an energy production plant may register a dangerous temperature which stimulates a work order to be filed in the administrative system.

The next step up from smart meters (internet connected meters that record and report utility usage to the utility provider, typically hourly or daily) are home efficiency apps. These visualize data from smart sensors for consumers to use energy efficiently and conveniently interact with utilities companies. Aside from the upfront costs to install smart sensors and related software, both industrial and consumer-oriented efficiency services are cost savers for a utilities company.

3.3 Smart Cities

In some ways, the smart city is the ultimate achievement for and of decentralized technologies like IoT-based efficiency services and smart grids. A city is one of the largest systems that can conceivably be linked by independently installed and managed sensors with data made available for widespread use. The vision of smart cities holds hope for efficiencies in almost every aspect, from being able to map cycling routes that have the cleanest air to balancing decentralized energy production, usage, and storage.

Smart cities are a strong driver for digitalization and connecting systems together for better data collection and analysis. Running projects such as the smart city initiative in Taipei, Taiwan focus on selected topics that have high impact for citizens’ lives: transportation, housing, healthcare, education, and payment systems. Their approach favors p ublic private partnerships (PPP), relying on private corporations’ digital transformation efforts. Barriers exist of course, including mitigating the concerns of the city-wide range of stakeholders, and balancing costs of innovation and updating existing systems.

3.4 Identity Verifier for Decentralized Identity

Decentralized Identity is a philosophical concept that allows the user to be the sole owner of their own personal data and reveal data to other parties only when necessary. This is a use case that lies outside the scope of energy and utilities, but this sector has a logical link. A consumer’s utility bill is often used as a form of identity verification when opening a new account, for example at a bank. The utility bill can confirm a consumer’s address or name as a supporting document to national identity cards.

In a Decentralized Identity ecosystem, the utility company can provide the same service with a monetized business model. A utilities company could consider issuing identity verification credentials to Decentralized Identity providers, as a service to individual consumers. A few vendors have already launched Decentralized Identity solutions on the market, but this is a relatively unexplored option for utilities companies.

4 AI in Utilities & Energy

Artificial Intelligence allows companies to automate repetitive tasks and leverage mathematical analysis. AI in the utilities and energy sector include energy consumption analytics, inspections and maintenance, enhanced digital twins, predictive analytics for edge devices, document management, and chatbots.

Artificial intelligence holds high potential for the utilities and energy sector. It is an industry that inherently produces and operates on data: capacity prediction, demand forecasting, and production optimization all can be improved with a closer analysis of the data on hand. A critical element is to digitize these processes and data; much is still done manually, so pairing AI systems with the installation of IoT devices will unlock even more efficiencies.

4.1 AI Use Case Suitability

This comparison matrix identifies several AI use cases for the utilities and energy sector. A strong AI use case leverages the availability of historical data, real-time data, and the ability to fulfill a repetitive task. The table below compares their contributions to an energy or utility company according to the potential to generate revenue, stimulate cost-savings, whether the solution is focused on internal processes or on external relations with customers, and the solution’s general maturity.

Figure 4: Comparison of AI Use Cases Based on Profit Orientation, Internal/External Orientation, and Maturity

4.2 AI Use Cases

Energy Consumption Analytics
Renewable energies like wind and solar are variable, dependent on meteorological influences. The variable supply creates challenges to effectively meeting demand. Artificial intelligence which integrates weather forecasts, predictions of variable renewable energy source (vRES) generation, and demand patterns helps manage the use of renewables on the electric grid. Such AI predictive analytics are used by renewable energy generators for more effective integration with established grids or decentralized grids.

Figure 5: Data Inputs for an AI-Based Energy Consumption Analytics Use Case

Grids supported by vRES experience regular drops in power generation, for example at night when no solar energy is collected. This is incongruent with current energy demand, which peaks in early morning and evenings. Smart energy storage helps to manage independent renewable energy systems as well as centralized and decentralized grids. The use of predictive analytics systemizes when and how renewable energy storage is used. An option to stabilize vRES grids is to supplement drops in supply with unused power in electric vehicles. A decentralized network of EV charging devices is facilitated by an AI-trained model that economically manages when EVS charge or sell energy to the grid. When energy supply is high and prices low – sunny or windy weather, or at low demand times – the device is instructed to charge the electric vehicle. When energy supply is low due to weather conditions or high demand, the device can sell energy stored in the electric vehicle’s battery to balance the needs of the grid. This use case is being implemented by governments and independent projects, and is supported by IoT connected devices.

Inspections and Maintenance
Image classification models can be used in performing machinery inspections and automatically flag issues for repair. Energy production and distribution systems are spread over huge geographical areas, and the machines themselves are often both large and in inaccessible locations. This makes manual inspection challenging and costly, and is in some cases being replaced by drones that stream real-time photos or video. This photo and video data is processed by an image classification model trained on datasets relevant to each machine, identifying parts that deviate from the norm. Depending on the type of learning model used, deviations may be further categorized by their type, i.e. damage, corrosion, or routine maintenance. These deviations are automatically flagged for human inspection for more in-depth investigation.

Figure 6: Data Inputs for an AI-Based Inspections and Maintenance Use Case

AI-Enhanced Digital Twins
Digital twin technology creates a virtual twin of a real-world object for its entire lifecycle, including mechanical designs, electrical, software, CAD models, representations of the object’s behavior during operation and maintenance, etc. This digital record of a product or asset’s lifecycle is compiled to create a virtual environment where the digital twin exists and interacts with other objects. Complex physical systems – like energy grids – can be simulated in this way. Simulating the grid under varying conditions delivers significant value to utilities companies by allowing them to prepare for outage, overload, or other situations. Digital twins are a typical application of industrial IoT, and can be enhanced with AI.

Figure 7: Data Inputs for AI-Enhanced Digital Twin Use Case

Digital twins produce simulated data and stream real-time data, making them a natural input source for AI models.

Digital twins produce simulated data and stream real-time data, making them a natural input source for AI models. This data can be used to train image classification models, harnessing neural networks to identify images of machine parts that are normal, in need of repair, etc. Once trained, an AI-enhanced digital twin can make these observations independently and inform the human team when repairs or closer inspection is needed, and use predictive analytics to recommend future courses of action. AI-enhanced digital twins are useful in repetitive tasks such as finding an optimal action in a scenario using trial and error. Reinforcement learning is also suited to virtual environments using digital twins, which can consume time without exhausting physical systems or resources. Or AI neural networks can be used in model order reduction to create a model for the digital twin to operate on, pre-selecting the options that have the highest chance of succeeding so that the number of simulations needed can be reduced.

Predictive Analytics in Edge Devices
Predictive analytics in edge devices: Distributed energy grids depend on information from edge devices, such as privately-owned solar panels or a privately-owned electric vehicle. The complexity of such energy grids is high because user behavior, weather patterns, production capacity, and storage capacity are outside the energy producer’s control, yet must be predicted and coordinated. Applying AI predictive analytics to energy grid edge devices determines load and helps to optimize distributed energy resources at the appliance level. Predictive systems can also be trained to forecast grid weaknesses, faults, and appropriate courses of action.

Figure 8: Data Inputs for AI-Based Predictive Analytics in Edge Devices Use Case

For example, electric vehicles can provide stabilizing energy to a renewable energy grid. When an electric vehicle is at a charging station but not in use, it can contribute energy to the grid at times of peak demand or voltage fluctuations. AI predictive analytics can assess a car user’s behavior, energy demands of the grid, and other conditions like energy price to suggest to edge-device owners whether to sell residual energy in their car to the grid.

Document Management
This use case is relevant for all industry verticals, but the utilities and energy sector is particularly affected by inefficient document management. Natural language processing (NLP) AI systems can be used for entity tagging, digital transcription or query-based searches to streamline. A trained AI model searches for relevant documents or datasets to autofill work request forms, flag document fields that are incorrect for human intervention, and tag entities – like product name, number, date, location – for quicker search access in the future.

Figure 9: Data Inputs for AI-Based Document Management

Chatbots
One of the closest AI enabled customer interfaces is with chatbots. Using NLP, chatbots can answer customer questions on demand, recommend products or services that the customer or user may be interested in, and give support to the customer’s experience. Chatbots are also helpful internally in enterprises to guide employee workflows. Chatbots are already found across all industries, including the utilities and energy sector.

Figure 10: Data Inputs for an AI-Based Chatbot Use Case

4.3 Implications of AI Deployment

AI deployment in this sector has the potential for significant improvements in efficiency. The accuracy of forecasts, coordination of edge devices for consumption and sale, more capabilities for digital twins, and the automation of other repetitive tasks are the types of use cases where AI would be most effective. This can lead to cost savings for an energy or utility provider as well as consumers.

5 Blockchain in Utilities & Energy

Blockchain has potential to improve transaction speed and reliability as well as energy grid coordination for the utilities sector. Applicable use cases include billing, microgrid coordination, renewable energy certificate distribution, energy provision to underserved markets, and dynamic energy pricing.

Blockchain is being used primarily in organizing distributed energy resources, but also brings improvements to existing processes. Because few of the blockchain use cases are exclusively transaction focused, they rely on IoT for data input. These use cases are focused on securing DIDs of real world objects, and issuing smart contracts for their coordinated deployment.

5.1 Blockchain Use Case Suitability

There are several use cases that incorporate blockchain into the utilities sector. These take advantage of blockchain’s strengths: its decentralized nature, the immutable ledger function, recording events in sequential order, and deploying smart contracts. These use cases are rated for the potential value they bring to the energy or utility organization based on the profit orientation of the solution, the internal or external orientation, and the general maturity.

Figure 11: Comparison of Blockchain Use Cases Based on Profit Orientation, Internal/External Orientation, and Maturity

5.2 Blockchain Use Cases

Billing
Energy billing can benefit from blockchain solutions when supported by IoT smart meters. Current methods for billing often require meters to be read manually, wasting valuable time and contributing to higher emissions when travelling to each site. When smart meters are installed in buildings and private residences, a decentralized system can automatically connect the smart meter with the appropriate consumer. Decentralized billing also offers the possibility of dynamic pricing, in which a blockchain ledger immutably records the time of energy usage and the associated price for peak or low volume times, and automatically bills the customer accordingly.

Figure 12: Process Improvements in Utilities Billing with Blockchain

Blockchain-supported billing solutions depend on the adoption of smart meters. This would be a costly investment either borne by the customer or by the energy provider. It would be a slow transition, which would allow the blockchain infrastructure to be tested on a small scale with a limited number of customers before needing to handle the whole network. The slow rollout of a blockchain billing system would reduce risk for an energy provider, but would add complication as billing tasks are done in duplication. This use case has high potential for cost savings and creating incentives for customers to change their consumption habits, but is likely to be adopted by startups or renewable energy segments that do not need to cope with high switching costs. Billing solutions would be semi-centralized, likely using private blockchains.

Microgrid Coordination
Decentralized, regionally localized energy grids that include multiple energy sources are known as microgrids. Microgrids are inherently difficult to organize since the energy generators are independently owned, geographically spread, and typically use a variety of technology to generate power from different sources such as solar photovoltaic panels or wind turbines. Coordinating the variable production of variable energy sources like wind with the inconsistent demand for energy which peaks in the morning and evening adds stability to microgrid participants, but is not feasible to manage manually.

Figure 13: Process Changes to Microgrid Coordination with Blockchain

An emerging solution is to connect microgrids via the blockchain. A blockchain solution capitalizes on its immutable ledger functions to receive data from smart sensors on distributed energy resource (DER) devices and deploy smart contracts to organize the sale of surplus energy from a “prosumer” to a consumer. The blockchain automatically aggregates the information from DER devices to create a marketplace for “prosumers” of energy to sell excess energy. The energy itself is distributed on main energy grids, but the certification of origin and payment transaction are facilitated via blockchain. An example of this is seen with the decentralized coordination of electric car charging stations, which allows electric vehicle (EV) drivers to access more charging stations. Private users or small business owners that have EV charging stations can make them available for public use and accept payment. The blockchain provides the framework to design a smart contract to charge appropriate prices, while also pinpointing the exact location for users to find using IoT decentralized identities (DIDs).

Renewable Energy Certificates
While a system for purchasing RECs already exists, there are some challenges that cannot be addressed without changing the process. The current process makes nearly impossible to digitally connect the renewable energy produced with the energy consumed by a customer. In this case RECs are a symbolic representation of renewable energy that exists on the grid, not what a customer has consumed. Existing methods are not completely transparent, being paper-based private contractual purchase agreements between companies, grid operators, and energy providers. Blockchain offers a different model for REC trading. When renewable energy generators have IoT components, data on the source, quantity, quality, and price can be immutably stored on the blockchain and tracked through the value chain. This specific data, cryptographically attached to unique units of energy, can be formatted into a renewable energy certificate (REC). This brings instantaneous delivery and settlement, enablement of micro-trades of less than 1MWh, and transparent tracking of renewable energy from generation to consumption.

Figure 14: Process Improvements to Purchasing Renewable Energy Certificates with Blockchain

Energy Provision to Underserved Markets
Energy grids have consistently been inadequate in serving rural and undeveloped areas. Extending a national grid to unreached areas can have prohibitive costs, especially in extremely remote areas such as the Australian or Alaskan wilderness or in the developing world where investment funds are limited. Blockchain-based microgrids are a solution to providing energy to remote areas. Installing infrastructure for microgrids can be more cost effective than extending a national grid. Blockchain-based marketplaces can issue discrete amounts of energy which allow impoverished or developing areas to purchase the amount of energy which is necessary.

Figure 15: Process Improvements in Energy Provision to Underserved Markets with Blockchain

Blockchain-based marketplaces also can facilitate microtransactions between peers which enable local energy production and trading for areas that are not served by major energy companies. Local, privately-owned solar farms could then sell energy directly to local consumers in a transparent, trustworthy format through smart contracts. Issues of bribes, reliability, and accessibility can be avoided.

Blockchain-based marketplaces can issue discrete amounts of energy and facilitate microtransactions between peers which enable local energy ecosystems to flourish.

Dynamic Energy Pricing
Energy demand fluctuates highly throughout the day. There are peak hours, when the vast majority of the population is using energy, putting stress on the grid with high loads. And there are low demand times, when most of the population is asleep and not consuming energy. Using blockchain, dynamic prices can be issued to reflect real-time demand. Smart contracts can be deployed to apply the relevant dynamic price to the time of energy consumption, and create a secure, transparent ledger of transactions. Instant settlement is also possible in blockchain-enabled trades and pricing.

Figure 16: Process Improvements for Dynamic Energy Pricing with Blockchain

5.3 Implications of Blockchain Deployment

For the energy and utilities sector, blockchain provides solutions to very specific problems which often are solved with other existing technologies. The unique features of blockchain, such as its immutable, transparent ledger, set it apart from other solutions to the above use cases. This may not be enough to launch blockchain into widespread usage in this sector.

6 IoT in Utilities & Energy

IoT in the utilities sector is essential for most AI and blockchain solutions. IoT use cases include inspections and maintenance of systems, decentralized grid coordination, and advanced metering infrastructure.

IoT is the foundation for most AI and blockchain solutions in the utilities sector. Access to real-time data for AI solutions, and creating digital identities to track objects in blockchain transactions require connected devices and sensors. National plans to incorporate Advanced Metering Infrastructure is already present in some regions, and this lays the groundwork for the intensive investment needed to install a smart meter system or equip machinery and devices with smart sensors.

6.1 IoT Use Case Suitability

Measuring the suitability of IoT use cases in the utilities sector depends on several factors: the real-world objects such as machinery, meters, etc. that host the sensor, provision of real-time data from the object, whether or not the data is standardized, if the device manufacture and distribution is internally or externally controlled, and the orientation towards B2B or B2C. The suitability of such use cases are assigned based on the profit orientation, the internal and/or external orientation, and maturity of the solutions.

Figure 17: Comparison of IoT Use Cases Based on Profit-Orientation, Internal/External Orientation, and Maturity

6.2 IoT Use Cases

Inspections and Maintenance
Smart infrastructure management for utilities providers yields insight to machinery operations, maintenance, and lifecycle. Individual machines or components are connected by smart sensors that collect real-time data on critical values such as temperature, pressure, capacity, weather patterns, etc. This data is streamed to a management system – sometimes supported by AI, as seen in previous use cases – providing a network overview of the energy grid or production. Insight into overall downtime, waves of repairs, or planned maintenance can be intelligently organized.

Figure 18: Examples of Smart Devices for Utilities System Maintenance

Smart infrastructure is a key component in multiple digital transformation topics in utilities:

  • Connecting operational technology and information technology. Operational technology and information technology have traditionally been separated in energy companies, leading to a disjointed approach to handling production and/or internal system issues. With the relevant IoT devices in place, a sensor that reads an abnormal temperature stimulates the automatic creation of a work order to find the issue.
  • Customer responsiveness: with real-time data processing, customer service channels can identify issues, provide real-time information on energy consumption or leaks, etc.
  • Digital Twin system simulation: IoT sensors on energy generation machinery and consumer smart meters are critical to providing real-time data for maintaining digital twins, enabling their use for simulations.

Decentralized Grid Management
Management of decentralized grids – not necessarily referring to blockchain solutions, but to multiple small-scale producers of renewable energy – is only feasible with IoT sensors. These track the volume and quality of energy, aggregating data from all participating energy generating units to stabilize the grid frequency.

Figure 19: Examples of Smart Devices in Decentralized Grid Management

Smart weather stations are critical to managing many renewable energy sources due to the obvious effects that cloud cover or weather patterns have on solar or wind energy production. Access to current weather conditions helps to predict and deal with irregular solar irradiance caused by cloud cover or dust on PV panels, which if unmanaged would yield energy of a much lower quality. Connected consumer energy storage, such as home solar batteries or electric vehicle batteries enable the “prosumer” to benefit by stabilizing an energy grid. Renewable energy sources cannot always reliably produce the quantity of energy required by consumers, and connected energy storage could be sold to the grid to balance high demand with low production when necessary. Sensors on wind turbines, solar panels, or other machinery provide data on production capacity. With an overview on all parts of the energy pipeline, decentralized grids can be optimized to a higher level of control.

Advanced Metering Infrastructure
Advanced metering infrastructure – or smart meters – allow for two-way communication between an energy provider and a meter in a home or office building. The two-way communication enables regular data to be sent to the energy provider as has been previously possible, but also for information from the energy provider like dynamic pricing or demand-response actions. Timestamped energy usage allows for accurate billing that incentivizes energy consumption when demand is low, while charging a premium during peak demand hours. More sophisticated communication between energy provider and consumer provides a range of options to stimulate demand-response actions from the consumer, such as switching to a solar energy source when demand is high. Anomaly detection is also possible with smart meters, able to detect leaks or other abnormal behavior.

Figure 20: Smart Functions of Advanced Metering Devices

Timestamped energy usage allows for accurate billing that incentivizes energy consumption when demand is low, while charging a premium during peak demand hours.

6.3 Implications of IoT Deployment

Implementation of IoT devices and sensors has already impacted the energy and utilities sector. Industrial implications are for significant cost savings, garnered from preventative maintenance and remote inspections. Consumers are impacted through the rising use of smart meters and other IoT-based services to promote and reward predictable consumption habits. Although IoT devices may be placed in consumers’ homes, they are typically issued and maintained by the energy or utilities company, increasing their control incoming data types and format.

7 Digital Identity in Utilities & Energy

The increased use of IoT devices requires robust, secure, and standard identity management for these devices. Managing the identity of things (IDoT) is the primary use case in the utilities and energy sector, but this sector could also have a role in up-and-coming decentralized identity ecosystems.

Convergence of operational technologies (OT) and IT requires cooperation in IAM, instead of managing identity and access in a fragmented manner across multiple departments. An IAM system for the energy sector to handle digital transformation should have a unified IAM system with the ability to provision physical, operational, and business system access. Achieving a unified IAM system will soon require identity management for IoT devices.

7.1 Role of Emerging Technologies in Delivering Digital Identity

Of the emerging technologies, IoT is the main driver for implementing digital identity. IoT devices are consumers of identity services, meaning that each device, sensor, and object require a unique identity that can be recorded digitally. To fully leverage the data from IoT devices, they have to be treated as an entity with access management, authentication, privilege restrictions, etc. The other emerging technologies – blockchain and AI – do not play a strong role here. Blockchain in very rare cases can help facilitate assigning or managing IDoT, and may be the underlying infrastructure in a Decentralized Identity ecosystem. AI does not significantly impact the adoption or development of digital identity use cases for the utilities sector.

Figure 21: Role of AI, Blockchain, and IoT in Delivering Digital Identity Use Cases

7.2 Digital Identity Use Cases

Figure 22: IDoT Management and Emerging Technologies

Identity of Things (IDoT) management is necessary for the digital transformation of the energy sector, and enables the convergence of OT and IT. Generating unique digital identities for all devices and machinery generates the data required for the main wins of a digital utilities enterprise such as insights into preventative maintenance and optimized grid management. Common connection and identification protocols for IoT devices include MAC, SIM, PKI, NFC, DRM, and RFID. While they are sufficient for identification and establishing connectivity, some of these protocols are not totally appropriate for authenticating and securing IoT devices since these devices often have reduced storage and computing power. Proprietary solutions are being developed to address security and authentication of smart devices. With the shift to security and privacy by design, the identity management of smart devices is being given higher priority by device manufacturers and implementors of IoT systems and standardization of IDoT management will improve.

Facilitating the exchange of digital identity credentials is one of blockchain’s strong use cases. Several solutions exist on the market that support a blockchain-based digital ID. The majority of solutions target human identities, but a few currently offer blockchain-based IDoT for credentialing and attestations. The benefit of a blockchain-based ID management is the distributed ledger which acts as an immutable transaction platform without passing through a third party. All identity information remains with the entity (a person) or object (an IoT device), which protects the identity data itself from falling into malicious hands. Blockchain identity solutions are described further in Decentralized Identity solutions.

Figure 23: Decentralized Identity and Emerging Technologies

Decentralized Identity a philosophical concept that allows the user to be the sole owner of their own personal data and reveal data to organizations only when necessary, instead of the current paradigm where organizations hold user data indefinitely. Blockchain is usually the critical technology in building Decentralized Identity ecosystems (but not always) because it provides a secure transaction platform on which to exchange digital credentials. A tamper-proof digital space where identity data can be shown but not given is currently lacking in digital identity management, and blockchain provides this with its immutable ledger and the rise of zero-knowledge proofs. Decentralized Identity ecosystems are able to provide validity to the identities they store because they are based in a real-world identity issued by governing authorities and verified by reliable partners like the postal service or utilities companies. Utilities companies would be able to generate a new stream of revenue by participating in Decentralized Identity ecosystems: the utilities company can simply verify digitally that a customer lives at XYZ address, something which users often do by bringing their utilities bill to a bank. With a digital identity, especially Decentralized Identity, providing a digital verification of address can be monetized. The energy and utilities sector can leverage a Decentralized Identity ecosystem to manage privileged access management (PAM) of their employees, provide a privacy-oriented means of interacting with customers, or become an identity verifier.

The energy and utilities sector can leverage a Decentralized Identity ecosystem as a new revenue stream by becoming an identity verifier.

IoT devices are a consumer of identity services, and have little application to Decentralized Identity other than perhaps benefiting from the more secure handling of device identity data. AI can support Decentralized Identity ecosystems by providing biometric services for fingerprint, voice, and facial-recognition.

Figure 24: Risk-Adaptive Multifactor Authentication and Emerging Technologies

Risk-adaptive multifactor authentication (MFA) is now a security standard, which has stimulated a rise in AI-based MFA options. However, MFA does not require AI and most MFA methods today are not AI-based. Blockchain does not contribute significantly to MFA solutions. IoT is a consumer of identity services, thus giving it a different relationship to digital identity than the other emerging technologies. Every smart device must be uniquely identifiable when they exchange information with each other or report data to a relying party. The successful implementation of IoT devices requires strong CIAM solutions for edge devices, which would include PKI certifications, FIDO authenticators and servers, UBA, and more. Each IoT device that interacts typically requires its own identity and must be secured before it can interact in a secure energy ecosystem or business environment. More IoT devices means a substantial increase in risk due to the numbers of potentially unsecured devices in the ecosystem.

7.3 Implications of Digital Identity in the Energy and Utilities Sector

Digital identity as it relates to IoT devices is crucial to the digital transformation of this sector. Developing a robust, secure, and standard method for digitally identifying real-world devices is essential to their efficient management, as well as unlocking potential for intelligent analysis. Artificial intelligence and blockchain are not critical technologies to IDoT, but would likely benefit from a more developed IoT system.

8 Recommendations and Conclusions

Emerging technologies have a lot to offer the energy and utilities sector. However, they are not the only means to matching new industry trends of decarbonization, decentralization, and digitalization. They are supportive technologies to enable a deeper company transformation.

The energy and utilities sector should aim to integrate emerging technologies, not because they are cutting edge or promise huge cost savings, but to achieve a unified systems management of operations and administration. Without fine-grain control over both administrative and operational actions – identifying overlap, eliminating redundancy, using OT information to streamline administrative actions and vice versa – energy and utilities companies will continue to live up to their stereotype as prohibitively rigid.

This stereotype is understandable, as this sector delivers services that underpin basic functions of society. There are many potential use cases for emerging technologies in this sector, but only a few stand out as being sufficiently stable for potential adoption. AI brings opportunities for data analysis such as resource consumption analysis and predictive maintenance, while also helping with administrative tasks like document management, conversational interfaces for user behavior analytics (UBA). Industrial and consumer IoT devices provide the data to achieve these ends. Blockchain has very limited uses for this sector; billing which reflects dynamic pricing could be automated, but blockchain isn’t the sole method to achieve this. This is the same with issuing RECs, or coordinating microgrids. Blockchain has yet to prove itself the leading solution for these tasks.

Future energy systems will likely shift to a more decentralized model, prioritize decarbonization of energy and processes, and pursue digitization. It is recommended that companies planning to adopt any of these emerging technologies first consider the company’s strategic goals. Any one of these technologies are not the solution to achieving decarbonization, decentralization, or digitalization, but they can support a company-wide effort to match these trends.

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