The request, which was made by South Australia distributor SA Power Networks, energy supplier Alinta Energy and metering provider Intellihub, calls for the implementation of a framework for a universal deployment of smart meters to all customers by 2030.

In addition, it calls for the implementation of a range of measures to better support customers though this accelerated rollout, including improving the information provided to them and applying new consumer protections when they receive a smart meter.

The proposed rule change was made in a letter dated 22 September 2023 following the AEMC’s completion of its review of the regulatory framework for metering services and mirrors the recommendations therein, the letter states.

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The only material difference is that the rule change request does not include the AEMC’s recommendations in relation to customer access to real-time data, as it was unlikely the changes could be implemented by 1 July 2025 – the commencement date for the acceleration of deployment.

“We have jointly proposed this rule change request because we support the recommendations in the AEMC’s final report and consider that they should be progressed as a matter of urgency,” state the signatories in the letter.

As the AEMC is unable to self-initiate a rule change request, the intent is to enable it to commence the process, they write.

Other proposed changes include improving the meter installation process by reducing barriers to installing smart meters and enabling more efficient and coordinated deployments and implementing a new regulatory framework for metering businesses to provide power quality data from smart meters to the distribution network service providers.

This would enable these providers to improve the visibility of their low voltage networks, better integrate consumer energy resources and improve safety for customers.

The requirements for undertaking tests and inspections of meters to avoid unnecessary costs also should be clarified and improved.

Smart meters status

The proposed rule change is set to have the most impact in New South Wales, the Australian Capital Territory, Queensland and South Australia where, at the time of the AEMC’s report, the average smart meter uptake was around 30%.

Tasmania has a programme in place to accelerate smart meter deployment by 2026, while Victoria has already achieved a near-universal uptake of smart meters.

While the number of smart meter deployments has been increasing each year it needs to increase substantially more from a little over 400,000 units in FY23 to over 700,000 units per year from FY26 to meet the 2030 universal rollout.

The proposed rule change based on a draft determination due on 4 April is open to comment until 30 May. The AEMC then expects to complete the process by 11 July.

In an exclusive interview with Stuart Thompson, President of ABB Electrification Service and Antonio Martinez Reiner, Utilities ＆ Renewables Global Leader at ABB Electrification Service, we unpack circularity as a solution and what the barriers to digital adoption are.

Watch the full video interview below.

This interview was filmed in November 2023 at Enlit Europe in Paris, France.

ABB helps customers deploy proactive, predictive maintenance using real-time data analytics to monitor and evaluate asset health, reducing downtime, total cost of ownership, extending asset lifespan and preserving finite resources.

Read ABB’s latest whitepapers on how circularity and digitalisation improve sustainability and drive new levels of operational efficiency:

• Tackling Throwaway Culture – your guide to smart services
• See the full potential of digital, faster – find ways to overcome your biggest IIoT barriers with this guide

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The initiative, part of the Tennessee Tech University led smart grid modelling and testing ‘Smart Grid Deployment Consortium’ project in the Appalachian region of the US, will focus on creating AI-driven generative models for customer load data.

These will then form inputs to the modelling services of the HILLTOP microgrid simulation platform for modelling and testing new smart grid technologies, in particular for the rural electric utilities that serve much of the region and for example for energy tech startups that are interested in scalability and interoperability.

“This project is a powerful example of how generative AI can transform a sector – in this case, the energy sector,” says Kalyan Veeramachaneni, principal research scientist and principal investigator at the LIDS.

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“In order to be useful, generative AI technologies and their development have to be closely integrated with domain expertise. I am thrilled to be collaborating with experts in grid modelling, and working alongside them to integrate the latest and greatest from my research group and push the boundaries of these technologies.”

The generative models are expected to have far-reaching applications in that when trained on existing data, they can create additional, realistic data that can augment or replace limited datasets.

For example, in this case generated data can predict the potential load on the grid if an additional 1,000 households were to adopt solar technologies and how that load might change throughout the day

The initiative has been awarded $1.37 million in funding from the Appalachian Regional Commission and will include other participants from across Ohio, Pennsylvania, West Virginia and Tennessee.

The legislation, proposed by Peter Welch of Vermont and Angus King of Maine in the Senate and Kathy Castor of Florida, Paul Tonko of New York and Scott Peters of California in the House of Representatives, requires the Federal Energy Regulatory Commission (FERC) to establish a shared savings incentive for GETs to encourage their deployment by July 2025.

Instead of the traditional fixed rate of return on a capital investment, a shared savings incentive would return to the developer a portion of the savings attributable to the investment in a GETs, with some of the savings also going to customers.

Additionally, the proposed Act includes an annual reporting requirement that directs transmission owners to report the costs associated with congestion to FERC and directs FERC to analyse and make this data publicly available.

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It also charges the Department of Energy with creating an application guide for implementing GETs projects. providing technical assistance to stakeholders interested in GETs and managing a clearinghouse with examples of implemented GETs projects.

Senator Welch commented: “We’re at a crucial turning point in our work to achieve a clean energy transition, and meeting this moment requires new investments in clean energy technologies that strengthen the capacity of our transmission system.

“The Advancing GETs Act will motivate grid operators and developers to bring new projects online that expand transmission capacity by guaranteeing returns for these targeted, cost saving investments. This legislation will be crucial to boosting transmission capacity and helping the United States achieve its clean energy electricity goals.”

The introduction of the Act follows a week after Senators Welch and King and Representatives Castor and Tonko urged the FERC in a letter to implement a cost saving incentive for GETs – a proposal first made to the organisation in 2020 by the industry associations the WATT Coalition and Advanced Energy United.

Julia Selker, Executive Director of the WATT Coalition, said since that time no alternative proposals to incentivise utilities to deploy these technologies have been made.

“GETs do not fit well into today’s utility business model. By designing an incentive based on the system benefits of deployments, consumer value and protection is built into the regulation. This policy would drive innovation that has been stalled for years and start to unlock capacity and flexibility on the existing and future transmission grid.”

Some individual states have started acting on GETs. In Illinois and New York, for example, studies are underway to evaluate their potential and legislation is being advanced in Minnesota and Virginia among others.

Grid enhancing technologies are hardware and/or software that dynamically increase the capacity, efficiency, reliability or safety of the existing grid and include dynamic line rating, advanced power flow control and topology optimisation.

Karman is built on a custom module that leverages the NVIDIA Jetson platform for AI in order to capture and analyse data to improve grid operations and manage distributed energy resources (DERs).

A first for the Karman platform, the integration should bring a new level of data insights to Aclara smart meter implementers, with up to a stated 100 times more processing power than traditional solutions.

Local AI models also will continuously learn to improve in areas such as grid planning, grid operations, load management, customer service and more.

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“As the grid becomes increasingly more complex and dynamic, utilities need more technology options to operate a clean and reliable grid,” comments Erik Christian, Division President, Grid Automation of Hubbell, of which Aclara is a division.

“By combining Utilidata’s extensive knowledge of distributed AI with Aclara’s decades of experience providing industry-leading hardware, we’re now able to bring new, innovative, interoperable solutions to our customers, starting with smart meters.”

A company statement reports that the partnership follows the trend of market momentum and federal funding driving the utility industry’s need to modernise and report its operational efficiencies, for which distributed AI holds vast potential.

In October 2023, the Department of Energy announced $3.5 billion in funding for grid modernisation projects, including awards to Portland General Electric, Duquesne Light Company and Commonwealth Edison Company to deploy over 100,000 Karman units to increase reliability and accelerate decarbonisation and electrification.

Elizabeth Cook, Vice President of Technical Strategy of the Association of Edison Illuminating Companies, said that utilities are seeking more options for new and innovative technologies, like distributed AI, for easily accessible and actionable data.

“Utilidata’s partnership with Aclara makes it easier for utilities to integrate this important technology as the industry continues to build a smart and adaptive grid that is resilient and reliable for customers.”

Josh Brumberger, CEO of Utilidata, points to a meter-embedded distributed AI platform as only a beginning.

“There are more opportunities beyond meters within the electric grid ecosystem that can benefit from having easy to access data and predictive analytics.”

The blueprint, which has been prepared by the Interoperability Network for the Energy Transition (int:net), is aimed to guide on transitioning the existing energy sector data infrastructures towards data space solutions and to define a general data space architecture that can enable an initial set of real-world business use cases.

In particular, the architecture is aimed at interconnecting the existing data infrastructures with federated data spaces, in which multiple datasets are mapped.

The concept of data spaces has been gathering momentum in various domains for sharing of data between multiple participants and the establishment of a common energy data space is one of the key actions set out in the EU’s energy sector digitalisation action plan.

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Foundational aspects that must be considered pertain to security and privacy, data quality and integrity and governance, while other aspects that need to be taken into account include the business model related to data exchange, legal and operational details and the technology, with a primary objective to ensure interoperability both internally and with other data spaces.

The blueprint document states that at the highest level, the CEEDS is foreseen as the common framework that federates different data spaces implemented at national, sub-national or international levels and allows the participation of single users.

Business use cases

The five representative business use cases, in which specific exchanges of data from diverse sources must occur among the involved actors, were defined as:

• Use case #1 – Collective self-consumption and optimised sharing for energy communities
• Use case #2 – Residential home energy management integrating distributed energy resources (DER) flexibility aggregation
• Use case #3 – TSO-DSO coordination for flexibility
• Use case #4 – Electromobility: services roaming, load forecasting and schedule planning
• Use case #5 – Renewables O&M optimisation and grid integration.

Based on these the proposed model corresponds to the creation of the energy data space as the combination of multiple ‘distributed data ecosystems’, i.e. the existing legacy data platforms, with an overarching layer defined as the ‘federated data space’ where the data is indexed and made discoverable and providing a ‘marketplace’ for sharing and possibly trading of data and data services.

Data space connector

The different data space participants are connected through a software component known as the ‘data space connector’, which realises the interconnection and data exchange.

This data space connector should be incorporated into the pre-existing platforms to enable identification, data harmonisation and brokerage towards data spaces, which can be useful for integrating data from different sources or for allowing multiple applications to access the same data without having to duplicate it in multiple places.

Moreover, in this model, the data space connector also enables the exchange of energy data and execution of services both among the existing legacy platforms and through the federated layer.

The document notes that to fully achieve the deployment of the CEEDS, starting from the federation of projects’ data space instances, detailed interoperability measures are necessary including technical interoperability, semantic interoperability and governance interoperability.

The document states that the presented blueprint underscores the critical need to adopt data space solutions within the energy domain, marking a pivotal moment for the transformation of the industry.

“The fundamental pillars of data spaces not only foster the active engagement of key stakeholders across the energy value chain but also promise mutual benefits, ranging from monetary compensations to an elevated quality of services.

“At this scope, the establishment of clear rules, policies and regulatory adaptations is a linchpin in facilitating fair data exchange, paving the way for an open market that fosters the participation of new actors, including data and service providers, as well as data consumers.”

The int:net initiative managed by the Fraunhofer FIT is an EU Horizon Europe-supported project to bring together stakeholders from across the European energy sector to jointly work on developing, testing and deploying interoperable energy services.

Key parties are the projects in the ‘energy data spaces cluster’, i.e. Omega-X, EDDIE, Enershare, Synergies and DATA CELLAR, whose findings have fed into the blueprint, while further inputs should come from the newly launched HEDGE-IoT, ODEON and TwinEU projects.

In the meantime, the blueprint will continue to be updated with version 2 due to be released in June 2024.

The project, implemented as part of Enea Operator’s activities to modernise the energy infrastructure and introduce new solutions to improve the quality and security of supply, is aimed to contribute to improving the management of the distribution network.

Its basis is the use of proprietary deep learning algorithms from Polish software developer Affexy, with a focus on the most relevant data enabling continuous improvements that can improve the precision of forecasts and their adaptation to dynamically changing network conditions.

The project is predicated on the need to better adapt the grid to the changing conditions of the electricity market with growing renewable energies and for example new energy efficiency regulations.

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It is focused on three areas, of which the main one is the data analysis to enable understanding of the load on the network and identification of factors that affect the variability of that load.

Prior to that is the processing of the acquired data so that it can be efficiently analysed by the advanced AI models.

In the final step, a report and recommendations will be made with the conclusions from the analysis and suggestion of actions to optimise the data collection process and the quality of the forecasts.

Enea Operator anticipates that benefits will include a better understanding of the network load and the patterns that drive it – aspects that are critical for effective network management.

The project also introduces and pilots the advanced AI techniques, thereby testing the effectiveness of the algorithms in real-world conditions.

With the results and experience obtained, Enea Operator anticipates the implementation of a full-scale system.

Alongside this project, Enea Operator has concluded a PLN1 billion ($255 million) investment loan agreement with the European Investment Bank for distribution works.

These are to include the modernisation and construction of almost 8,000km of medium and low voltage grids, installation of 2.4 million smart meters, connections of 140,000 new customers, upgrading transformers by 633MVA and the connection of 4.3GW of new renewable generation capacity.

If there’s one place where generative AI has the ability to make a tremendous impact, it’s the energy sector. AI could help streamline energy production and distribution, increasing efficiency and cutting carbon emissions from the vital processes that enable us to keep warm, travel and live our modern lives.

While businesses all over the world are investing in generative AI and the talent needed to implement it, the energy sector is facing its own talent issues when it comes to generative AI.

For example, data shows that 40% of businesses in the energy sector find it difficult to hire data scientists with the skills they need. Without the right combination of talent and data necessary to accurately and quickly utilise foundational models, many energy companies will not be able to leverage generative AI fully. They will therefore be at a disadvantage compared to competitors for the future of energy — one that entails a global transition that will likely occur at an increasingly rapid pace. 

So what do you do when there’s a talent shortage but your business needs to add more skills? You train the talent you already have. In 2024 and beyond, as energy demands grow on a global scale, the energy sector will find combining generative AI with proper upskilling to be extremely valuable.

Generative AI’s effect on enterprise talent

For many organisations, a successful approach to upskilling will begin with an understanding of how generative AI can affect almost any position within an organisation.

Some employees will see generative AI agents augment their current position and give them access to more relevant data. Other staff members may collaborate side-by-side with generative AI agents that sit either upstream or downstream of a human in a business workflow. Then there’s the software engineers, or those who will be charged with creating or fine-tuning generative AI agents.

Ultimately, each company will have to assess which new roles will be required (i.e. prompt engineers) and which ones will change materially (i.e., software developer). They’ll have to train each employee on how to use the technology — even if that training is at a basic level.

Generative AI and the energy sector

There are already growing use cases where generative AI is making an impact in energy and, as energy consumption increases, those use cases will expand.

For example, generative AI is currently playing a key role in safety procedures at various energy companies around the world. Historically, before any operator at an energy company begins a job, they’d receive a standard, but often generic, safety briefing. Traditionally, the safety analysis has been completed manually, which can leave out a more comprehensive view of all the necessary safety measures.

With a generative AI agent trained on the right safety data, operators will have access to insights on near misses, extensive safety records, weather conditions, etc. Operators can receive briefings that are specific to their role, job site and team.

Generative AI is also accelerating the rate at which energy employees can access data. With a generative-AI powered enterprise search, enterprises now allow their employees to find pertinent data as soon as they start a job. Through retrieval augmented generation, the generative-AI agent will learn which data is most relevant to the right staff members as they search. This will allow employees to access the right data such as log data, geographical data, information on local culture and much more.

For the energy sector to reap these benefits they’ll need to apply sound, repeatable upskilling practices at the right scale and to the right employees.

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Upskilling talent — Who? How? Why?

Like other industries, the energy sector is competing for talent that can match their plans for generative AI implementation. This is why external hiring of data scientists isn’t occurring at the rate energy executives would like to see. Also, the energy sector requires a specific set of expertise, which further commodifies generative AI-related roles at energy companies such as data scientists, prompt engineers and software developers.

Since finding data scientists and other roles with the right expertise hasn’t been easy, certain energy companies are instead looking to the experts already within their ranks. This translates to upskilling initiatives for as much talent as possible in generative AI, which helps close the gap in external hiring. Some of the upskilling will be for roles such as data scientists, enterprise architects and data engineers.

There are already examples of energy companies taking intentional approaches to upskilling. For example, as Duke Energy continues its cloud journey, it has built a framework that allows many of its employees to access relevant training content and engage in learning that aligns with Duke Energy’s cloud computing and clean energy goals.

Other avenues are available to energy companies who want to upskill their talent on generative AI. Some companies may choose to turn to cloud and foundation model providers to support internal employee training through formal class or online learning. This is another reason why it’s important to develop relationships with the partners who are developing the generative AI technology that companies are trying to use.

Other upskilling approaches include providing free sources of consumable training content through sites such as deeplearning.ai. Further, there are some enterprises that are creating opportunities for experiential learning through proof of concepts, hackathons and workshops. These experiential opportunities in particular are great ways to train talent on scenarios that extend past technical skills.

One energy company AWS worked with recently leaned into an experimental upskilling initiative to better leverage radio communication transcripts. The radio communications team and edge team worked together to develop a generative AI agent that combines radio communication transcripts with asset IoT data to produce daily job status reports. The radio communications and edge teams did not typically work together. However, joining forces to create the prototype helped them develop the required relationships to take generative AI to the next level.

Breaking silos and bringing different departments together — like the example above — is a major reason to invest in the right generative AI training. However, getting buy-in from boards of directors and participating in responsible AI practices are also good reasons to practice upskilling.

Upskilling promotes responsible AI

Despite some of the benefits generative AI brings, there are still many leaders, particularly in the energy sector, that have concerns about the introduction of generative AI into their companies. For example, many energy/utility companies have access to customer information and confidential internal data that can be introduced to foundational models. Board members and c-suite leaders want to be sure this information remains secure. From a financial standpoint, today’s enterprise leaders want to make sure investing in generative AI issues an adequate return on investment.

In each scenario, training all members of an organisation on responsible AI practices should be a part of any upskilling approach. This includes understanding responsible AI tenets such as fairness, explainability, robustness, privacy and security, governance, and transparency. With proper responsible AI training, enterprises can safely power innovation, mitigate stakeholder concerns and see continued ROI.

Keeping pace with innovation

The current hiring landscape suggests that energy companies won’t be able to hire generative AI talent at the expected (and necessary) pace of innovation.

This talent and recruitment landscape likely points to a world where energy companies will not only have to begin investment in upskilling their own talent, but possibly double and triple down on investment initiatives.

With energy consumption on the rise, organisations will have to place greater focus on empowering the talent they already have to train generative AI models, analyse subsequent data and leverage that data to create the solutions necessary for the future.

About the Author
Joseph Santamaria is director of WW Energy and Utilities Solution Architecture at AWS.

In his role, he works with the largest utilities, oil and gas and energy producers in the world to utilise the cloud to solve the most complex problems in the energy transition and operations.

The five-year project included the deployment of 50,000 smart meters and the connection of Itron’s distribution intelligence (DI) enabled platform with Coopesantos RL’s customer information system.

With this upgrade with grid edge intelligence Coopesantos RL now has visibility and control across its diverse rural service territory, which includes mountainous and hard-to-access areas.

“Being the first cooperative to deploy smart meters in Costa Rica and Central America marks a significant step toward the modernisation of the infrastructure and distribution of electrical energy in rural areas where we provide service to all homes, businesses and industries,” said Mario Patricio Solis Solis, General Manager of Coopesantos RL.

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“Our electrical distribution deployment has been strengthened with this investment in technology and devices from Itron, automating and optimising our operational processes. We’re thrilled to continue working together as we embark on creating a reliable smart grid to prepare for the future.”

The solution was delivered via local Itron channel partners Conelectricas RL and Itecna.

Benefits anticipated by Coopesantos RL include more rapid insight and response to power outages with Costa Rica prone to extreme weather events, a reduced carbon footprint with reduced need for meter reading and increased client satisfaction with detailed insight into energy use and early detection of technical irregularities such as theft.

Coopesantos RL, headquartered in the city of San Marcos de Tarrazú, is one of four electric cooperatives in Costa Rica.

The cooperative claims 100% clean energy generation from two hydro plants, the 13MW Los Santos wind farm and growing distributed solar PV, primarily from commercial users.

In addition to energy generation and supply the cooperative offers fibre Internet services.

ChatGrid, the brainchild of Pacific Northwest National Laboratory (PNNL) optimisation and grid modelling researcher Shrirang Abhyankar, was conceived to exploit the question-and-answer approach of generative AI tools to support grid operators in their decision making.

With this, the user can then ask a question such as: “What is the generation capacity of the top five wind power generators in the Western Interconnection?” with the response of a visualisation showing the desired information.

Users can ask questions about generation capacity, voltage, power flow and more, while customising the visualization to show different information layers.

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Abhyankar says the aim is to simplify the experience for grid operators who have to make many decisions as they monitor the grid in real-time.

“We’re envisioning a new way to look at data through questions. ChatGrid allows someone to query the data – in a literal sense – and get an instantaneous answer.”

ChatGrid runs on a publicly available large language model, with the input data the synthesised data from the Exascale Grid Optimisation (ExaGO) model, which was developed to take advantage of the power of supercomputing to simulate the nation’s power grid in real time.

Moreover, to further protect grid security, the model was not trained on the data itself.

Instead, the data was compiled in an internal database in columns with headings such as ‘capacity’ and ‘location’ of power plants and the model used to produce a ‘structured query language’ (SQL) that allows ChatGrid to search this database for its answers.

Work in progress

For Abhyankar, ChatGrid remains very much a work in progress.

He hopes that once grid operators start using ChatGrid and providing feedback, a better version can be built that they can then safely use in their control rooms with real-life data.

For that to work, ExaGO’s developers need the data to be useful on regular computers as well, however.

Further, while ChatGrid is available for download on GitHub, the process takes a few steps and once the feedback starts rolling in, the development of a one-step download process is anticipated.

Users also are invited to play around with the phrasing prompts and questions to assess how to produce the best answers.

The report, prepared with EY, points to the complexity of the e-mobility ecosystem with increasing EV adoption with multiple parties including drivers and players from the charging station and energy systems and the web of relationships between them.

Underlying all of these is data and the need for data exchange not only to assure the day-to-day driver experience but also to enable the integration of EVs into the broader energy system, of which ultimately they must be an integral part.

For the driver, some key issues are charge point accessibility and payments and roaming on a seamless basis.

For the energy sector challenges include the timely connection of charging infrastructure to the grid and the grid integration of the EVs both to avoid congestion on the one hand and to draw on the battery storage potential for flexibility on the other.

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To enable these standards and communication, protocols are essential for each interaction, while any use of data also invokes sharing, privacy and cybersecurity requirements.

e-mobility activities

The report states that data interoperability promotes collaboration and information sharing between stakeholders to enable services and facilities that support EV rollout.

For example, charging station optimisation can be achieved with strategically sited stations that provide EV users with easy access and convenience and thereby reduce range anxiety, enhance the driver experience and encourage wider adoption.

Seamless integration of EV charging with the energy grid enables energy optimisation and grid services, such as frequency regulation and voltage support.

With interoperability between data systems, drivers can benefit from real-time information on charging station availability, pricing and compatibility with their vehicles, they can initiate charging sessions remotely, manage payments and track their charging history via an app.

Industry opportunities

The report suggests that while electric vehicle uptake is advancing towards mainstream adoption, to maintain momentum and keep drivers onside, every interaction must be configured around the customer journey.

“From purchase or lease, through EV usage and management, to end-of-life decommissioning, we must deliver a hassle-free and green driver experience.”

The right structural and regulatory mechanisms need to be in place across each of the relevant areas.

Moreover, by getting the mechanics of data interoperability right, connections will form across conventional demarcation lines with players exploring new commercial opportunities, crossing into adjacent services and competing to win over the customer and capture value.

For example, automotive companies, such as Tesla, Volkswagen and Volvo are already crossing over into energy provision, battery services and solar solutions, while energy companies are building out charging infrastructure and e-mobility services.

Greater cross-over and innovation can be expected as the customer journey is redesigned and commercial lines are redrawn, suggests the report.

Building blocks for success

The report offers five building blocks considered for success in the e-mobility space, irrespective of industry segment.

These are:

• ‘Control tower’ to provide visibility and understanding across the commercial landscape.
• Customer proposition defined using segmentation and analytics to inform the product and service delivery.
• Dataspace acting as a centralised hub for curating, integrating and managing data from diverse sources.
• Data privacy and legal, commercial and regulatory diligence around areas such as customer consent and data sharing.
• Trust and cybersecurity taking into account the multiple connection points and extending to supply chains.

The report concludes by stating that progress is being made, but there’s a long road ahead.

“E-mobility is gaining pace in Europe. To accelerate EVs’ uptake in Europe all players across the value chain need to work together with open, interoperable and secure data to create a smooth, seamless experience for the customer,” comments Eurelectric Secretary General, Kristian Ruby.

Though the ‘edge’ has been talked about for years, it is increasingly taking central stage as more and more homes and businesses take up solar and battery systems, switch to electric vehicles ((EVs) and heat pumps and instal the smart appliances that are advertised as bringing more convenience to life.

In broad terms the edge is where the utility and customer meet and is effectively represented by the meter – the utility side in front of the meter and the customer side behind the meter.

With this growth of decentralised resources and the increasingly complex and unpredictable power flows, some of the risks include the likely emergence of hyper-local capacity constraints and that ageing infrastructure can be put at risk.

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But there has been what Itron has termed a ‘control gap’, with the challenge that whereas a typical SCADA manages approximately 1,000 assets per million customers and a typical advanced distribution management systems 10,000 assets, that to the point of service is a further two orders of magnitude greater at around 1 million points.

But that is changing, with the evolution of IT and other technologies opening the way for a variety of new products and services to provide visibility and control to address the challenges of the evolving grid.

Itron’s grid edge intelligence portfolio

A pioneer of edge intelligence in the energy sector, Itron has consolidated its offerings into a cloud-based edge intelligence portfolio combining connectivity, analytics and applications with intelligence for AMI operations and optimisers for the low and medium voltage grid, DERs and EVs, coupled to a central datahub.

To simplify the process the Itron Enterprise Edition has been made available in the Microsoft Azure marketplace, also opening the way for integrating the Azure OpenAI generative AI solution for users to expedite and improve visibility on data and operations.

Among the new solutions launched are Active Transformer Load and Voltage Monitoring (ATLM/ATVM) applications to enable visibility on transformer loading and voltage statistics in real-time along with configurable threshold-based alarms.

Key for broader uptake of the solutions is partnerships with other providers with these opening the way for Itron’s grid edge intelligence solutions to be integrated into Schneider Electric’s digital grid solutions and to GE Vernova’s new GridOS Data Fabric alongside the GridOS apps.

A further partnership is with the Mobility House as part of its Fast & Flexible Interconnect (FIX) programme for charging of EV fleets in constrained distribution systems.

Don Reeves, senior VP of Outcomes at Itron, reports that customers have advised that the company’s Grid Edge Intelligence portfolio can enhance grid capacity by approximately 20% through the optimisation of existing grid assets.

“Utilities are operating in a more complex environment than ever before and there is a real sense of urgency that change is needed to ensure grid reliability, resiliency and sustainability and improve the customer experience.”

Landis+Gyr and Span partnership

Landis+Gyr has announced a partnership with home electrification technology developer Span, with the first joint product combining their respective solutions to deliver a grid edge solution with circuit-level billing-grade metering, DER visibility and controls.

Describing the co-innovated solution as “a whole-home multi-asset virtual power plant (VPP)”, Werner Lieberherr, CEO of Landis+Gyr, says: “The partnership not only expands our flexibility management platform but also helps [utilities] reduce costly grid infrastructure investments required for electrification. We’re particularly excited to bring SPAN’s service upgrade avoidance capabilities and intuitive app experience … to drive energy efficiency and flexibility.”

While full details of the solution are still to be released, the companies promise to evaluate it in pilots with US utilities starting later in the year.

Siemens Gridscale X

Siemens has launched Gridscale X as a modular software to scale grid capacity and handle the complexity of DERs.

A key component of Gridscale X is DER Insights which is designed to unlock visibility over the distribution grid, with features including the location and behaviour of DERs, grid impact identification and digital grid mapping and modelling.

“With the electrification of everything and the exponential growth of DERs, there is an urgent need for increasing grid capacity fast,” says Sabine Erlinghagen, CEO Siemens Grid Software, pointing to the use of such software as enabling utilities to focus on critical infrastructure upgrades and reducing the impact and occurrence of grid equipment failure, outages and technical debt.

Users of these or similar softwares are invited to contact us with case studies.

Jonathan Spencer Jones

Specialist writer
Smart Energy International

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To support the decision-making process, Microsoft has launched a guide on the North American Electric Reliability Corporation’s Critical Infrastructure Protection (NERC CIP) standards, which play a crucial role in ensuring the security and reliability of the electric grid.

Updates to NERC CIP guidelines

As of January 1, 2024, significant changes have been implemented, allowing the storage of medium- and high-impact Bulk Cyber System Information (BCSI) in the cloud, subject to specific requirements.

By embracing cloud technologies, while adhering to NERC CIP guidelines, power and utilities leaders can enhance operational efficiency, promote sustainability, and ensure grid reliability.

As the energy sector evolves, proactive engagement with NERC CIP standards will be pivotal in shaping a resilient and interconnected future.

1. Cloud Adoption and Security
   • The recent changes permit power companies to leverage cloud infrastructure for storing BCSI. While this opens up new possibilities for scalability and efficiency, it also introduces security challenges.
   • Organizations must carefully evaluate cloud service providers, ensuring compliance with NERC CIP requirements. Robust encryption, access controls, and continuous monitoring are essential.
     
2. Benefits of Cloud Adoption
   • Cloud-based storage offers flexibility, enabling seamless data sharing across geographically dispersed teams. It promotes collaboration and accelerates decision-making.
   • Scalability allows utilities to handle increasing data volumes, especially with the proliferation of smart meters and IoT devices.
   • Cost savings result from reduced on-premises infrastructure maintenance and operational expenses.
     
3. Challenges and Mitigation Strategies
   • Security Concerns: Cloud adoption introduces potential vulnerabilities. Companies must implement robust authentication mechanisms, intrusion detection systems, and regular vulnerability assessments.
   • Compliance: Organizations must align cloud practices with NERC CIP requirements. Detailed documentation, audit trails, and incident response plans are critical.
   • Data Residency and Sovereignty: Address legal and regulatory aspects of data storage locations.
   • Third-Party Risk: Evaluate cloud providers’ security practices and contractual agreements.
     
4. Future Outlook
   • The evolving landscape of cybersecurity necessitates continuous adaptation. Companies should actively participate in shaping future NERC CIP standards.
   • Collaboration among industry stakeholders, regulators, and technology experts will drive innovation and resilience.

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The European Commission is making available €74 million (US$80 million) in funding towards the creation of the energy and other sectoral data spaces as part of an over €176 million ($191 million) package for the Digital Europe 2023-2024 work programme.

The sectoral data spaces, a key component of the EU’s data strategy, are intended to form repositories for pooling, accessing, sharing and processing data from within the respective sectors from across the EU.

Based on common data infrastructures and governance frameworks, their evolution is being driven by users within their respective sectors.

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Over time the goal is for them to be gradually interconnected, furthering the data sharing – for example, energy with mobility opening the energy sector to wider participation – and ultimately forming a single market for data that can assure Europe’s global competitiveness and data sovereignty.

Energy data space

With various initiatives well under way towards the energy data space, proposals for the 2023-2024 programme should foresee the deployment of the first version, building on these, in at least ten member states with piloting of at least five use cases in areas such as distributed energy resources management, provision of flexibility services for electricity grids or smart and bi-directional electric vehicle (EV) charging.

These should use a commonly agreed reference architecture with replicable and scalable building blocks, e.g. on data models and formats, data exchange APIs, data provenance and traceability, metadata, etc.

In particular regarding data interoperability arrangements, the data space should be based on agreed minimal interoperability mechanisms that will align energy-relevant key stakeholders on a set of minimal sufficient capabilities needed to achieve interoperability of data, systems and services between the key players of the energy value chains at all levels, i.e. European, national and local.

Another requirement is the consideration of a complete set of open standards, while other deliverables required are the definition of suitable business models that can ensure financial sustainability of the energy data space beyond the end of the project and the implementation of a governance system for overseeing its operations.

An amount of €8 million ($8.7 million) is allocated for this energy data space advancement, which is expected to take place over 36 months.

In addition to the support for the data spaces, the €176 million package includes funding to advance research and use of artificial intelligence, for projects on the cloud to edge infrastructure and for skills development.

Since the deployment began in October 2022, Vodafone Spain has already connected more than 250,000 meters to its NB-IoT network and the initial target is to complete connections to over 1 million water meters during the first 5 years of the contract.

Aqualia’s goal is to offer remote meter reading and other services to its over 3 million customers in Spain in the coming years.

Daniel Barallat, director of IoT at Vodafone Spain, says the control and management of water use in Spain is a constant and vitally important challenge today.

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“With this contract, we put our most cutting-edge technology at the service of Aqualia to actively contribute to the better conservation of natural resources and more efficient water management.”

The initiative is being undertaken as part of the government’s Strategic Project for Recovery and Economic Transformation (PERTE), which is aimed to modernise the urban water systems to improve efficiency, reduce losses in the distribution systems and improve the wastewater treatment infrastructure.

Vodafone’s Water Meter solution for homes, businesses and public institutions includes the digitalisation of the meters and the reading and analysis of the data.

In a pilot currently underway in the city of Vigo, Aqualia and Vodafone are investigating the use of the remote meter read data for detection of fraudulent activities such as tampering and reverse flow and for leak detection with hourly water balance analyses, with the aim to develop a comprehensive solution.

It is also intended to provide the data to customers to enable them to become aware of their consumption and to use the data for the prediction of consumption patterns to improve water management and broadly make the cycle more sustainable.

For example, with optimised pumping in turn the energy consumption is optimised and stresses on the network reduced, which also should reduce the risk of breakage and leaks and the loss of non-revenue water.

NB-IoT is the preferred communication for water meters in Spain as many are underground or in other outdoor locations.

The company has been piloting the software 1Streetworks, which provides traffic management plans, with the finding that the average time taken to connect new or altered power connections was reduced by a quarter across approximately 300 Surrey roadworks sites where it was used.

Based on this outcome UK Power Networks is now planning to roll out the technology in a larger one-year trial in Kent to test its use in other workstreams across the company.

“Planning streetworks to the high standards we expect takes time and few have tried to revolutionise the process during my 23 years in the sector,” says Paul Dooley, streetworks performance manager at UK Power Networks.

“So, I’m excited about the potential of 1Streetworks to streamline complex traffic management plans, enable better communication with customers and highway authorities and improve the speed and accuracy of streetworks plans.”

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Mapping the future of utility networks

Traditionally a streetwork design plan for a smaller power connection, overhead line or underground cable repair usually needs a surveyor to assess the site, take measurements then plot signage on a CAD system – a process that can take hours of work, with the need to maintain high levels of safety and accuracy and to meet industry regulations.

The cloud-based 1Streetworks from the Cambridge headquartered location and geospatial data specialist 1Spatial is stated able to produce similar plans on-site in under two minutes.

Once the user plots the location of the work on a map, the software instantaneously plans site-specific traffic management, integrating layers of maps with main roads, bus stops, lane rental and streetworks rules.

Claire Milverton, CEO of 1Spatial, said the pilot has been a key milestone for the company and a culmination of many years of work and investment.

“We are delighted that our innovative 1Streetworks application has delivered such fantastic results for UK Power Networks and its customers. We look forward to roll out the software further across the network over the coming months.”

UK Power Networks has 190,000km of cables and delivers thousands of streetworks every year across London, the southeast and east of England.

On average there are over 4 million low-speed road works undertaken in the UK each year.

1Streetworks was developed initially with GB gas network operator Northern Gas Networks for expediting works on its underground pipelines.

The increasing complexity of power grids with high levels of inverter-based variable renewables and unpredictable electric vehicle charging patterns has brought with it challenges for power grid operation and the need for real-time control as key for maintaining voltage stability.

Based on deep reinforcement learning – a subset of machine learning – the new algorithms are designed to solve this challenge by delivering intelligence to power converters in the grid utilising what the researchers describe as a novel data synchronisation strategy to optimise the large-scale coordination of energy sources safely under fast fluctuations without real-time communication.

“Centralised control is not cost-efficient or fast under continuous fluctuations of renewable energy and electric vehicles,” says Qianwen Xu, one of the researchers involved in the project.

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“Our purpose is to improve control strategies for power converters, by making them more adaptive and intelligent in order to stabilise complex and changing power grids.”

The research, which was demonstrated in KTH’s smart microgrid hardware platform and published in the IEEE Transactions on Sustainable Energy, proposes a projection-embedded deep reinforcement learning algorithm to achieve decentralised optimal control with guaranteed 100% safety.

This is intended to overcome the challenge of existing deep reinforcement learning methods in power system applications of not being able to achieve optimal performance and guarantee safe operation at the same time.

In essence, the approach of the researchers is to formulate the grid control problem as a deep reinforcement learning problem with hard physical constraints and then based on this to project a multi-agent algorithm onto a set of constraints characterised by the physics of the distribution system.

With this, the proposed method can achieve the optimal control of the distribution system in a decentralised manner without real-time communication while guaranteeing the physical constraints of the system all the time. As such, it is thus flexible for scalability and practical deployment.

The research formed part of KTH’s Digital Futures Centre which collaborates with researchers from the Universities of California, Berkeley and Stockholm University.

Deep reinforcement learning combines deep learning and reinforcement learning and has been developed for application in complex, unpredictable systems.

With the publication, the data, which has been aggregated to a minimum of five smart meters on an individual feeder, is now available for access by interested parties such as local authorities, flexibility providers or academia for modelling and planning.

With the publication also, SSEN has become the first of Britain’s network operators to make such data available, having pioneered the development of an ‘open data’ portal for distribution data in the country.

“These are exciting times for accessing and utilising smart meter data. SSEN is delighted to be the first DNO to unlock the full consumption datasets at such a granular level, and to make them available on our open data portal for interested parties to access,” says Paul Fitzgerald, Smart Energy Systems Manager at SSEN.

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“Smart meter data plays a key role in supporting a cost-effective and efficient transition to net zero. As we decarbonise heat and transport and move towards low carbon technologies, knowing current demand on the electricity network will help identify new opportunities for low carbon technologies, flexibility and reinforcement.”

SSEN has over 1.8 million smart meters – about 54% coverage and increasing daily – in its two distribution areas in the north of Scotland and across central southern England recording active and reactive half-hourly consumption.

This equates to over 170 million readings per day with the opportunity for insight into 84,000 LV street-level feeders and over 36,000 distribution substations.

The systems to process and aggregate this large volume of data for publication on a daily basis were developed with CGI as SSEN’s ‘DCC adapter’ – the intermediary enabling the supplier to connect and communicate with the Data Communications Company’s (DCC) smart meter network.

A key aspect was around the methodology for sharing the data in a consistent manner and in particular aggregation to protect individual consumer privacy.

In collaboration with the other network operators agreement was reached on an aggregation level of five or more smart meters from an individual circuit on the low voltage network, with any less than five meters or sensitive sites, following data triage, being excluded.

Other top initiatives are renewable energy integration, grid modernisation and microgrids and disaster response and recovery plans.

Furthermore, the utilities are adopting new approaches to improve outage recovery times through advanced networking, with predictive maintenance analytics topping the list, followed by smart grid technologies and enhanced communications, as well as the use of drones and robotics to inspect assets.

Phil Beecher, President and CEO of the Wi-SUN Alliance, comments that extreme weather events are fast evolving from a rare occurrence to something that should be built into the risk profile of any utility company.

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“The emergence of smart grids, microgrids and other technologies, like predictive maintenance and fault finding, offers a way of controlling costs while increasing resilience and stability to help mitigate the impact of outages.”

But, he adds, “technologies like this are only as good as the underlying communications network to provide reliable and secure delivery of the data needed to deliver a truly smart grid.”

The research was conducted among more than 250 senior professionals in the US utilities and power sectors and highlights the role of new tools and technologies to help improve resilience and outage recovery times as weather events and environmental disasters become commonplace.

According to US Department of Energy data cited by the Wi-SUN Alliance, extreme weather conditions – from heatwaves to Arctic vortexes – have doubled power outages in the US over the past 20 years.

The research also founds that utilities recognise opportunities to integrate artificial intelligence technologies to address resilience, with viable use cases including energy consumption forecasting, automated fault detection and grid optimisation.

Looking ahead to the next five years building infrastructure resilience remains among the top issues, with others including security enhancement, customer-centric services, renewable energy integration and IoT integration and data analytics.

The study was conducted for the Wi-SUN Alliance by Censuswide in February 2024.

The inertia measurement technology, GridMetrix, offers network operators continuous measurement and analytics data, including real-time measurement of inertia.

The collaboration, supported by the New York Independent System Operator (NYISO) and utilities across the state, will be showcased in a demonstration project to confirm the operational and grid planning benefits.

New York’s Climate Leadership and Community Protection Act mandates the quadrupling of offshore wind capacity to 9,000MW by 2035, doubling distributed solar deployment to 6,000MW by 2025, and deploying 3,000MW of energy storage by 2030.

“We are delighted to see NYSERDA and the New York State utilities exemplify climate leadership by supporting and adopting our technology,” said Marc Borrett, CEO of Reactive Technologies. “This award underscores New York’s dedication to fostering new technologies that advance a future powered by clean energy. We are eager to continue our collaboration with utilities across New York State, aiding them in achieving their net zero goals, safer and faster.”

Reactive’s GridMetrix technology has been deployed globally, serving utilities in Asia, Europe, and the Middle East.

“NYSERDA is moving forward strategically to support the demonstration of tools like Reactive Technologies GridMetrix through our Future Grid Challenge program,” said Doreen M. Harris, president and CEO, NYSERDA. “Investments in innovation are investments in a grid of the future that incorporates clean energy and allows for dynamic management and operation to ensure resilient and reliable transmission and distribution, even when factoring in the impacts of climate change.”

The Future Grid Challenge is part of NYSERDA’s Grid Modernization Program included in the State’s Clean Energy Fund (CEF), which is providing a total of $140 million through 2026 to further research, develop, and provide funding for solutions that support the advancement of a smart, modernised electric grid.

Originally published on power-grid.com

Understanding that the energy market is complex and costly due to many stakeholders, Nikolopoulos notes that artificial intelligence is the key to enabling a green transition for all stakeholders.

“Artificial intelligence together with machine learning, and advanced algorithms, come into play to simplify and orchestrate all these stakeholders towards a common goal.”

He adds that AI will accelerate the transition towards a green sustainable goal.

Avokado Energy AI goes beyond conventional energy efficiency offerings by providing a comprehensive portfolio of AI offerings that accelerate the green energy transition of cities, businesses, and households.

It combines key Energy AI ingredients, such as Machine Learning for Energy, Geolocation AI and Generative AI, into ready-to-run Energy AI offerings for energy optimisation.

Key offerings include:

• AVOS™, a universal Operating System inside energy storage batteries
• Avokado AI™, the Energy API suite that enables flexibility in markets
• AVOX™, the Digital Experience platform that combines Energy AI, Location AI and Internet of Energy

Avokado offerings use patented proprietary ML models and scientific algorithms.

Watch the full video interview below with Vassilis Nikolopoulos, co-founder and CTO of Avokado.

This interview was filmed in November 2023 at Enlit Europe in Paris, France.

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According to Hoffman, speaking at DISTRIBUTECH International, extreme weather events have brought to the fore an urgency for utilities to look at new ways of partnering on advanced analytics.

“A message that I want to get across to folks here at the forum is that we have an opportunity, we have a critical mandate and critical urgency,” she added, stating the need for power sector players to be responsible and plan for these events, that are no longer a ‘once-every-other-year’ occurrence.

Hoffman referenced bouts of funding made available by the US DOE’s Grid Resilience and Innovation Partnership (GRIP) programme.

The major funding programme, part of the Bipartisan Infrastructure Law and consisting of $10.5 billion, aims to enhance grid flexibility. Additionally, as emphasised by Hoffman, it recognises the need for a resilient power system to combat the growing threats of extreme weather and climate change.

Indeed, weather-induced outages are becoming ever more frequent; a common news headline calling for utilities to overhaul their grid planning. According to Hoffman, new collaborations will be crucial.

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“[This planning] requires a lot of collaboration. It requires building things at scale, looking at data at scale, looking at partnering so that we can do the analytics at scale.”

Referencing wildfires on the US west coast that called for public safety power shut-offs, Hoffman stated how the opportunity to mitigate the impacts of these events lies in the collection of multiple data streams.

“To better understand and predict the potential for wildfires, there’s a lot of data streams out there: LIDAR data; forest service data; fuel loadings; drought conditions; temperatures; heat zones.”

Hoffman explained how accounting for all of these data streams, pulling them together with transmission planning and mapping, will open new avenues for mitigation and prevention of their impact on the grid.

Considering key questions, she added, such as power safety shut off being coordinated in larger or narrower areas or how to pre-position or lighten the load in the system due to event conditions, would then work towards minimising the damage and effect on our power systems.

“That’s one example, to merge data streams, sensor information, and be able to capitalise on that as an industry and be able to provide feedback to the customer.

“That’s what it goes back to … this call from the customer for greater transparency, greater awareness, an expectation by the customer for us to do more to provide them more information.”

Generative AI appears to be attracting as much interest in the energy sector as it is concerned.

New data from IBM, unveiled at DISTRIBUTECH International, indicate that almost three-quarters of energy and utility companies surveyed have implemented or are exploring using AI in their operations.

Moreover, in an earlier survey two-thirds of energy and resource CEOs felt they are more likely than their global peers to expect to realise value in the next three years from generative AI and automation.

However, an almost similar percentage expressed concerns about the sources of data used in generative AI.

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As the latest iteration of AI, generative AI is attracting much interest in the energy sector, as elsewhere, with tentative steps being taken as utilities investigate use cases where it can add value.

Itron integrates Microsoft Azure OpenAI

So far few energy solution providers have incorporated generative AI into their offerings and among the frontrunners is Itron, which ahead of DISTRIBUTECH 2024 announced what should prove a significant innovation for the sector with its integration of the Microsoft Azure OpenAI service.

Initially, the integration of the OpenAI service – which is based on the same model as ChatGPT – is with the Itron Enterprise Edition Meter Data Management system, distributed intelligence applications and Itron’s cloud-based DataHub platform, which is intended as the common access point for all data from the company’s portfolio.

This includes data created by Itron as well as that from third-party DI applications, such as load disaggregation, transformer connectivity and loading data.

With the OpenAI solution users should be enabled to get answers to questions, perform tasks, access data across the MDM and on the DataHub and for example use plain language queries to generate charts and reports as well as receive suggestions and feedback to improve data quality, accuracy and analysis.

“Through this collaboration [with Microsoft], we will enable utilities and cities to leverage the power of generative AI to boost efficiency, unlock creativity and enhance data management,” said Don Reeves, Senior Vice President of Outcomes at Itron.

IBM’s watsonx governance

While IBM’s watsonx generative AI and data platform for utilities and other enterprises was announced in May 2023, the latest addition to it is the governance toolkit that is designed to allow users to direct, manage and monitor their AI.

In particular, it is aimed to strengthen a company’s ability to detect and mitigate risks, manage changing regulatory requirements and address ethical concerns for both generative AI and machine learning models.

Casey Werth, IBM Global Energy Industry General Manager, says that as energy and utility CEOs manage their ongoing transformation efforts, they can also capitalise on the opportunities of generative AI and foundation models.

“In doing so they need to remember to focus on their own data, how it is gathered, accessed and used within their workflows along with the governance that should be baked into their tools and processes.”

The dataset for 2019, chosen rather than the latest available COVID-impacted 2021 dataset, aims to provide insights into the usage patterns of different energy products in the EU and to investigate energy scenarios to 2050.

With a more detailed understanding of how energy is produced, traded and transformed across different regions, the tool should allow spatial analyses to identify patterns, bottlenecks and opportunities for optimising the energy infrastructure.

At a scale previously not impossible, the map is likely to provide a powerful tool for policymakers and infrastructure planners as well as for others such as energy service providers.

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“This concept is totally unique,” says Salla Saastamoinen, JRC Deputy Director-General.

“[Policymakers and infrastructure planners] will be able to see on a map what fuels are being used and where, in unprecedented geographical detail. It will help them gauge the future impact of energy policies in every community across Europe.”

The basic dataset is the Eurostat national energy balances, which are downscaled to the 1kmx1km cell size in a series of steps drawing on other data sets such as the Emissions Trading System and socioeconomic data and then mapped using land use and land cover data.

Some of the energy products with supply and demand mapped include electricity, natural gas, heat, renewables and biofuels, solid fossil fuels and oil and petroleum products.

Among the general findings highlighted is that natural gas is primarily consumed by power plants, industry and households, with high concentrations in densely populated areas.

Electricity consumption is also significant in urban areas, while other energy carriers, such as manufactured gases, oil shale and waste are consumed near their sources for industrial processes.

The same methodology used to break down national energy balances to the 1km square cells is applied to the energy scenarios.

An example highlighted is projected changes in natural gas demand from 2019 to 2050 showing a general shift towards lower consumption, indicating progress towards decarbonisation goals.

The energy atlas has been embedded in the Energy and Industry Geography Lab geospatial tool, which combines energy and industry data.

A key role considered for this tool is to support EU member states in identifying acceleration areas for the rapid deployment of wind and solar.

Combined with other datasets, the new energy atlas provides a boost to the EIGL’s spatial analysis capabilities.

As the Eurostat data is produced on a two-year basis and published two years after the reporting period, the next available dataset for 2023 is likely to become available only in 2025.

The rollout, launched in 2015, has seen a J$14 billion (US$90 million) investment as part of the initiative to modernise the electricity network and improve the customer experience.

Since late 2023 the project primarily focussed on St Mary parish, to the north of the capital, Kingston.

Other parishes on this eastern side of the island, including Portland and southern sections of Kingston and St. Andrew, are due to be completed later this year.

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Smart meter customers can track their consumption with the company’s MyJPS mobile app and also benefit from other services including remote connection/disconnection and account transfers and switching between postpaid and prepaid options.

“Our ongoing future-oriented approach, underscores our dedication to providing faster, more efficient service,” said Pia Baker, Senior VP of Customer Experience and Commercial at JPS.

“We are proud of the progress we have made and the positive impact it is having on our customers’ experiences.”

Under a 2018 agreement, Itron was contracted to extend the existing Gen 5 network to support the full 670,000 smart meter deployment, while the smart meters were being provided by Aclara.

In other news, JPSCo has been awarded a US$100 million financing package from IDB Invest for its 2024-2025 investment programme for the ongoing modernisation of its grid, including the smart meter deployment and transmission and distribution upgrades, and the expansion of access to new customers.

Additionally, the funding should support JPS’s continuing digital transformation through cloud-based solutions as well as the island’s electric vehicle (EV) infrastructure development, including 17 new EV charging stations.

These are expected to support a rapid rollout of renewable energies and storage technology.

Currently, approximately 14% of Jamaica’s energy supply is renewable, primarily hydro, wind and solar.

i-DE is testing the technology on its distribution network in Vizcaya province near Bilbao in the Basque region of Spain, where Iberdrola is headquartered, throughout 2024.

If the trial’s outcome shows improvement in i-DE’s processes, including supporting its vegetation management strategy, enhancing network reliability and aiding wildfire prevention, the technology could be further rolled across the company’s network areas.

The LiveEO Treeline satellite software solution is designed to improve vegetation risk assessments, including modelling and predicting vegetation growth as well auditing previous work.

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The AI-based package is designed to identify and evaluate all forms of vegetation risks based on satellite data that is processed on a near real-time basis, to enable identification and response to emerging risks as they occur.

LiveEO, regularly named among the top 100 geospatial companies, states its Treeline solution can deliver results including a 15% reduction in vegetation-related outages, a 20% reduction in contractor cost per km and a one-year return on investment.

Other utilities that have deployed the solution include Iberdrola subsidiary Avangrid, Dominion Energy and Liberty Utilities in the US and E.DIS in Germany.

Data from earth observation satellites is becoming an increasingly important element of utility datasets, with vegetation management being a key use case and others including planning and design of new renewables projects, while other space-based data is available for positioning and timing use cases.

The rollout of the Rometrix 2G smart meters was launched in October 2020, with the goal to replace the approximately 1.7 million meters in Rome and neighbouring municipality Formello by the end of 2025.

Since the end of January, as of the day of writing a further almost 21,000 smart meters have been deployed.

In a statement from Acea, the Rometrix 2G smart meters are stated to represent a significant turning point in the way energy consumption is managed and monitored, allowing greater transparency in consumption, as well as the possibility of optimising energy efficiency and reducing waste.

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Through the installation of these smart meters, Rome is moving towards becoming a sustainable and intelligent city, in line with the vision of the Acea Group which considers the use of energy in a responsible manner as essential.

The Rometrix 2G smart meters provide reads on a 15-minute basis, which are transmitted to Areti’s central system and from there delivered to suppliers for invoicing.

The primary communication is PLC in the Cenelec A band (9-95kHz).

Customers with smart meters are then able to activate the ‘Chain 2’ service to access their consumption data and start enabling home automation services.

Towards the delivery of new consumption models Areti was a participant in the PlatOne Horizon 2020 project, coordinating the Italian pilot to investigate blockchain-based flexibility mechanisms with smart meter customers.

Findings from that project have now been incorporated into the newly launched RomeFlex project also using a blockchain model focussed on the delivery of flexibility in congestion and voltage management.

Providing a comprehensive understanding of grid orchestration, its challenges, and the transformative potential it holds, this eBook focuses on:

1. Grid Orchestration: The art of harmonising diverse energy sources, demand patterns, and grid infrastructure.
   • Explore the role of artificial intelligence, machine learning, and predictive analytics in optimising grid operations.
     
2. Decentralisation: The shift toward decentralised energy production.
   • Decentralised grids empower local communities, enhance resilience, and reduce reliance on centralised power plants.
     
3. Smart Grids: Smart grid technologies, including advanced sensors, real-time data analytics, and demand-side management.
   • Smart grids enable efficient load balancing, fault detection, and self-healing capabilities.
     
4. Cybersecurity Challenges: Grid orchestration faces cybersecurity threats due to increased connectivity.
   • The importance of robust security measures to safeguard critical infrastructure.
     
5. Renewable Integration: Integrating renewable energy sources seamlessly into the grid.
   • Addressing challenges related to intermittency, storage, and grid stability.
     
6. Policy and Regulation: Policy frameworks and regulatory aspects influencing grid orchestration.
   • Balancing innovation with compliance is crucial for a sustainable energy future.

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“Orchestrating the Grid” eBook, serves as a roadmap for energy professionals, policymakers, and researchers. By embracing grid orchestration, together we can create a resilient, efficient, and sustainable energy future.

If you analyse the decisions being made in the energy sector globally, it becomes very clear that there is a strategic movement to not only have abundant and sustainable clean energy, but to also ensure that the systems at the heart of the infrastructure itself are well protected by the most state-of-the-art security measures available.

Since we live in an era that is dominated by digital advancements, and the sheer volume of sensitive information being transferred will continue to grow, the need for robust and trustworthy security measures becomes exponentially more important. An integral part of a well-defined data security system is how the encrypted data itself is managed, alongside the ‘encryption keys’ used at various stages of the process.

Centralized control and enhanced security:

Let’s picture a scenario for a moment: You wish to set up a smart-metering AMI that includes the option for prepayment, and one of the many considerations you’ll have to make is how you’re going to protect the transfer of credit from a POS into a meter.

The simple answer is to use cryptographic algorithms to securely encrypt/decrypt the data. This is only one part of it, however, as you still need to figure out how this entire system is managed. With that in mind, there are essentially two options when it comes to managing a system like this…

The first option is to design your own system from scratch, which leaves you at the mercy of your own design choices, but it also means that your system would be proprietary. When trying to source suppliers that would be compatible with your system, you ultimately end up with a situation of ‘supplier lock in’, which has many downsides to it.

This would negatively affect the growth of your infrastructure, as many suppliers / vendors would refuse to create “unique products” that only work when used with your proprietary system. This is very limiting and not an ideal situation for the long term.

The second option is to go with an existing, already globally accepted system. The KMC (Key Management Centre) provided by the STS is a centralized secure facility for managing cryptographic keys that are used across all STS certified systems globally.

It simplifies the Key Management process by being a fully ready ‘plug and play’ option that allows you to scale your infrastructure easily without becoming complicated. It achieves this seamlessly because it was designed to be fully interoperable at all levels, no matter the size of your infrastructure, or how many suppliers/vendors you wish to have.

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Mitigation of threats:

The STS subscribes to the best practices for Key-Management, Key-Security, and Key-Generation. This is then combined with a few simple, but very important features regarding the functionality of the keys themselves – Namely: KEY REFRESH, KEY EXPIRY and KEY REVOCATION. These form an important part of the latest STS Specification Suite, and when used correctly, can give each user/owner peace of mind by giving them full control of the lifespan and functionality of their keys.

These features also play a critical role in limiting the possibility of insider threats, and fraudulent behaviour such as ‘ghost vending’. If your keys are ever compromised, you can contact the KMC via a dedicated email gateway that is available 24/7 throughout the year to revoke your keys, giving you yet another layer of added security. In the unlikely event that the KMC itself is ever compromised, there is a fully functional ‘back-up’ KMC that is ready to step in, so there will be zero interruption to the services and functionality offered.

Conclusion:

In Conclusion, the energy landscape and the security requirements involved are constantly growing and evolving, and having a secure system to manage sensitive data is truly a fundamental component that cannot be overlooked. The Key Management Centre provided by the STS is one of those solutions, as it serves as a ‘fixed point’ that all Utilities, Electricity Vendors, and Prepayment System Manufacturers can rely on. No matter the size of your infrastructure, the STS and its associated KMC will provide a Simple, Trusted and Secure solution for your prepayment systems worldwide, and for the foreseeable future.

For more information about the STSA, please visit our website at www.sts.org.za

Written by STSA

The Standard Transfer Specification (STS) has become recognized as the only globally accepted open standard for prepayment systems, ensuring interoperability between system components from different manufacturers of prepayment systems.

GridOS Data Fabric, claimed a first for grid orchestration, uses data federation to access the decentralised data, enabling multiple datasets to be combined and then virtualised to create a centralised view across the grid ecosystem, from transmission to distribution and the edge and across information technology and operational technology applications.

With this grid operators should be able to discover, govern and utilise large volumes of highly distributed data from multiple sources for improved and more rapid decision making while orchestrating the grid in real time.

“Energy data plays a key role in delivering a more efficient grid. Utilities will need to connect energy data from across the grid ecosystem to effectively automate grid operations, orchestrating more intelligent, secure and resilient grid that is ready and built for future electrification needs,” says Mahesh Sudhakaran, General Manager at GE Vernova’s Grid Software business.

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“By leveraging energy data, AI and machine learning powered technologies, utilities can move at speed to meet demand while addressing renewables management and electrification challenges.”

The GridOS strategy is centred around a modular, composable, data-driven software portfolio designed to orchestrate a more sustainable energy grid and help utilities keep pace with the energy transition, a GE Vernova statement says.

The announcement of GridOS Data Fabric, which follows GE Vernova’s acquisition of Greenbird Integration Technology and is the first major addition since the launch of GridOS a year ago, also includes GridOS Connect.

GridOS Connect is an energy data integration engine as a key component for the federated grid data fabric that feeds continuously updated data sets into the system – information that traditionally has been difficult for utilities to compile and analyse.

Itron collaboration

With the release, GE Vernova also has announced a collaboration with Itron, which intends to leverage GridOS Data Fabric to connect grid operations and grid edge data from sources such as residential solar, electric vehicles and more.

Don Reeves, Senior Vice President of Outcomes at Itron, says that a key component of the GridOS Data Fabric software is its ability to connect and integrate data to make more accurate real-time decisions to enable a reliable and resilient grid.

“The data to be exchanged with Itron’s grid edge intelligence solutions can provide new insights to promote grid stability and to train and power AI and machine learning applications that help automate key aspects of the grid operation.”