Javed's Blog

Digitization of power sector

Introduction

The digital revolution is affecting the power industry. If the last decade was about developing hardware capable of generating cheap, plentiful renewable energy, the next one will be about making energy generation systems smarter. A set of technological and macroeconomic forces is converging to trigger a deep transformation of the energy industry. The world needs more power to extend electricity access to over one billion people and to support stable growth and rising living standards for billions more. This requires developing new energy supplies, while building and upgrading grid infrastructure.

Three trends are impacting the global energy sector at an unprecedented pace: decarbonization, digitization, and decentralization. Energy policy and regulations have to adopt quickly to respond to these technology-driven trends that are enabling empowered energy consumers and transforming demand and supply. Get the policy framework right and distributed generation offers new opportunities for countries to provide secure, affordable, and environmentally sustainable energy. Policy frameworks that do not integrate distributed energy resources will face investment challenges and may slip down the Energy Trilemma Index.

Technology drivers

Macroeconomic forces are now coming together with a new wave of major technological innovations that have begun to challenge the traditional setup of the energy sector. These innovations are driven by a convergence of digital and a physical technology that is starting to unfold across all industries it is likely to be as transformational and powerful as the industrial revolution. These factors needs three mutually reinforcing trends: The Industrial Internet merges big data and cloud-based analytics with industrial machinery, resulting in greater efficiency and increased operating performance. This is already making significant impact in the grid (smart meters), Next up is digitization of central generation and power consumption including distributed generation; Advanced Manufacturing weaves together design, product engineering, manufacturing, supply chain, distribution and servicing into one cohesive intelligent system, delivering greater speed and flexibility at lower cost; this is transforming how capital intensive critical equipment such as gas or wind turbines are being manufactured; and The Global Brain integrates the collective intelligence of human beings across the globe through digital communication, resulting in crowd sourcing, open collaboration, and a much faster pace of innovation; this trend in the power industry will have the most impact from a developer oriented software platform and its constituent application economy development.

The Future of Work is changing the nature of economies of scale, and blurring the lines between manufacturing and services—and it is beginning to deliver substantial gains in productivity and efficiency for individual companies and entire industries. A 2012 estimate suggested that just a 1% efficiency improvement would yield over $60 billion in efficiency gains in the power industry over a 15-year period, some $90 billion savings in the Oil and Gas industry $60 billion in the healthcare industry, and about $30 billion each in aviation and in rail transport. The Industrial Internet technologies that we have been developing since then are yielding efficiency improvements well in excess of those original lower-bound estimates. We are now seeing double-digit performance gains across our industries of operation.

In the power sector, the most recent estimates indicate that digitizing central generation could yield value of up to $100 million for new wind farms (up to 20% higher MW), up to $230 million for a new combined cycle gas power plant, and up to $50 million for an existing combined cycle gas powered plant. Across the whole power industry, this equates to up to $75 billion of impact for new combined cycle gas turbine and wind turbine orders, with additional value for upgrades to existing assets.

Digital innovations have already begun to transform the energy industry. They help open the way for multi-directional energy flows in the grid, for real-time demand adjustment in response to supply conditions, for a smarter combination of different energy supply sources and more. Together, these changes brought about through digital innovation will increase the efficiency, affordability, reliability, and sustainability of the system

Need for New Power Structure

In several emerging countries or regions, there is a massive need to expand access to electricity. Today, about 1.3 billion people in the world lack access to electricity; just in India, 250 million people do not have access. Large areas of Sub-Saharan Africa face the same challenge; the gap with time is not shirking

Reliable access to power is essential to improve the living conditions of populations across the world. It is also essential to enable the development of stronger manufacturing industries that can fuel economic growth and support the rise of a middle class. In short, access to power is absolutely necessary to allow emerging market countries to continue their economic convergence process.

As populations in large emerging markets populations improve their standards of living towards those currently enjoyed by advanced economies, natural resources consumption will grow at a fast pace. Sustainability is already a global priority—its importance will only grow in the coming decades. Countries will have to develop new strategies and solutions to make energy consumption more efficient, and improve the effectiveness and resiliency of energy distribution networks.

Developed countries, where electrification rates are already at or close to 100%, will also need to reduce emissions from power plants and improve energy efficiency as their economies continue to grow. Moreover, aging infrastructure in both generation and grid, as well as, an aging workforce, represent significant challenges.

Meanwhile, there is still significant scope to further reduce inefficiencies and losses in generation, transmission and distribution, to help ensure sufficient electricity provision across the global economy in a sustainable manner.

Impact of changes on Power system

The energy value chain will become more complex, encompassing a multiplicity of moving parts with different priorities and incentives, as well as a wider mix of supply sources. Ensuring that power can be delivered reliably, without outages or unforeseen changes in quality will require a commensurately sophisticated effort of monitoring and control.'

Exponential growth in low-cost processing power advances in big data management and growing cloud capabilities, coupled with the acceleration in analytics and machine-learning, have the potential to transform the way we produce, buy and sell electricity. The advent of distributed generation, solar and wind options, roof tops and two way metering has made it mandatory to adopt digitization, in order to control run and use the power system whist maintaining its integrity. Smart metering and time of use meters plus consumer devices that can respond to signals from the system all make digitization essential to achieve cost reductions in supply and provision of energy. At the same time, the convergence of digital and physical innovations, together with advances in energy technologies, has begun to impact the industry.

These advances help open the way for bi-directional energy flows in the grid, for real-time demand adjustment, for a smarter combination of energy supply sources and to deliver higher electricity output from existing assets, as well as enhanced performance from future infrastructure investment.

These challenges also present unprecedented opportunities. The future of the power sector is a new value chain augmented and interconnected by digital technologies—one where both power and information flow in multiple directions; all actors add value; and the overall efficiency, cost-effectiveness, resilience, and sustainability of the system are enhanced through information sharing, openness, collaboration, and coordination between stakeholders through the right set of incentives.

New Power and Energy System

It will encompass three key elements:

(1) A digital centralized generation pool, relying on a mix of fossil fuel and renewable sources; (2) a digital grid, connecting generation and consumption, and enabling the multidirectional flows of energy and information; and (3) a digital consumption setup, improving consumption patterns along with distributed generation and storage capacity.

Energy providers will join a new breed of digital-industrial companies. This will require changing their business models to take full advantage of new digital capabilities: balancing the fuel mix through big data analytics, accelerating the adoption of natural gas and renewable; optimizing plant operation by using analytics to reduce cost and emissions while maximizing economic output; and developing new ways to interact with customers. The power grid will realize its potential as a platform, accelerating innovation and efficiency gains.

This transformation will not be easy. It will require investing in infrastructure and new technologies; changing mindsets, public policies, and business models; investing in people through education and on-the-job skills upgrading; and developing open standards and ensuring interoperability. It will require the highest degree of cyber security against potential data privacy and system security risks.

The convergence of digital and physical technologies provides opportunity of unprecedented magnitude. It could provide a future of energy that realizes the goal of ubiquitous access to clean, reliable, sustainable and secure electricity, while fostering economic growth through the creation of new energy ecosystems.

Benefits of digitalization in the power industry

As electricity markets evolve for integrating large amounts of renewable energies and for improving energy efficiency of different sectors, energy and utility companies find it difficult to keep their margins and integrating new technologies they didn’t know before. Today, hundreds of startups and big companies provide technologies and platforms that allow power producers, utilities and other companies in the energy sector to reduce their operating costs, increase revenues and exploit new business opportunities. Digitalization of Energy Systems, a report by Bloomberg New Energy Finance (BNEF), predicts significant shifts in the intelligence of digital technologies used in energy from today to 2025, and a big change in the sectors of the energy system that most benefit from these technologies. The same study shows that investments in digital energy are growing at 20% rate every year, reaching $55 billion globally last year. These are shared among smart meters, energy management systems and automated demand response or micro grid load scheduling. The benefits of digitization include:

An optimized maintenance – the transition from preventive to predictive reduces the costs of ownership by maintaining the reliability and availability of an asset. Preventive and conditions-based maintenance allow asset owners to reduce their O&M costs, by the enhanced planning of the maintenance activities, in this way unnecessary work is avoided. Maintenance on a running machine which is in good condition puts the assets in jeopardy rather than improving them, plus reduced downtime period is ensured by identifying the point of failure quickly.

The ability to cut down asset management costs by freeing up asset managers’ significant time so that they can focus on the responsibilities that really matter. According to a McKinsey Global Institute study, only 39% of an employee’s time is spent on role-specific tasks. The other 61% is spent on slogging through email, trying to find a missing file, or syncing with co-workers. Many asset management activities, such as managing contracts, tasks and expenses, can be digitalized, reducing the time spent by people doing the job. Also, invoices, bills and reports can be greatly automated, thus bringing huge efficiency gains to the whole organization.

By managing electricity loads in response to price signals, these players are able to participate in the market and benefit from lower power costs. Accurate energy forecasting for variable renewable energies allows avoiding imbalance costs which are increasing in many countries. When these assets are coupled with flexibility sources or controllable consumption loads, forecasting energy and electricity prices becomes crucial to optimize load scheduling and participation to different energy and ancillary markets. Technologies today allow optimizing the behavior of complex systems made of different production units, energy storage systems and energy consumers.

Digital energy is the path to unlock new business opportunities Innovative business models unlock new revenue and profit sources by offering innovative services. Digital technologies allow asset managers to become virtual power plant managers, utilities to implement new business models based on distributed generation and solar installers to build energy communities.

Digital tools give companies in the energy sector new ways to interact with their customers, making them more loyal. Many utilities and other energy service organizations are seeking to transition from electricity provider to trusted energy advisor. Whether they are residential or industrial consumers or even owners of power plants, digital services allow enhancing the interaction with the customer. Custom web and mobile apps are helping energy & utility companies to enhance the overall perceived satisfaction with the brand. Advanced reporting tools allow O&M contractors and asset managers to improve the transparency with the owner and to effectively deliver the information when and how it’s needed.

New Power Structure

It will encompass three key elements: a digital centralized generation pillar, relying on a mix of fossil fuel and renewable sources; a digital grid, connecting generation and consumption, enabling the multidirectional flows of energy and information; and a digital consumption pillar, which will play an important role not just in improving consumption patterns, but in adding generation and storage capacity. Centralized power generation will remain critical even with the rise of distributed energy resources. It will provide the majority of the power supply, and ensure the continuity and reliability of electricity provision. The longstanding goal of ensuring reliable, affordable and safe access to electricity remains unchanged in the future of energy, and can only be guaranteed by a strong centralized power generation system.

Planning, Generation and Transmission

Digital technologies will transform power generation from the very earliest stages, starting with the design and siting of power plants, and continuing through the operations and maintenance phases. The planning process will utilize comprehensive big-data analysis of the energy network. Data from distribution grid assets such as advanced meters, intelligent feeder monitoring and distributed resources can be combined with transmission data from phasor measurement units and other monitoring devices to develop predicted sub-hourly scenarios. These analyses will enable designers to simulate the load demand on a power plant. They will also estimate the financial viability of the plant under different alternative configurations, through a better understanding of the plant’s complex interactions with all other resources in the energy system. This can also help utilities balance their generation portfolio.

Renewable power plants are particularly impacted by location. Shading or vegetation can affect how much light reaches a solar plant; wind farms are dependent on wind patterns A Digital Wind Farm, instead of settling for the least common denominator model, will allow to customize every turbine to its unique location on the farm. This can only be done by integrating the advances in digital infrastructure (cloud computation, advanced load and weather simulation algorithms, satellite topology images, etc.) and hardware technologies (modular turbines that allow different configurations such as optimum hub height, blade length and generator rating). The location of a new centralized power generation facility will also need to take into account the necessary Transmission and Distribution (T&D) infrastructure. T&D siting often involves a lengthy process of engaging multiple stakeholders to devise the appropriate pathway between power plants and load centers.

Today, new data tools such as advanced geospatial platforms and power flow modeling can evaluate the best grid layout and determine appropriate capacity requirements. Assessing sub-hourly interval data can help develop detailed scenarios to understand the tradeoff between installing or upgrading distribution lines and adding distributed energy resources, or how those resources may impact power flow. These digital capabilities are even more important when the environment becomes subject to faster, more frequent and more complex changes because of new revenue streams and system requirements such as frequency response, and new markets such as ancillary and capacity markets. These require more real time decisions and an improved transparency between plant capabilities and market dispatch.

Operational Factors

Power generation will be able to rely on an increasingly diverse and flexible range of supply sources: centralized generation through fossil fuels, nuclear or renewables, distributed generation, and stored energy. Balancing the supply mix on a real time basis will be essential to maximize the energy output and cost-effectiveness of the whole system.

Once the plant/farm is in operation, digital tools can enhance its performance and profitability. Today’s power plant and wind farm do not use integrated data and plant or fleet level software applications to run their complex operations efficiently. A typical power plant is a complex system that requires constant optimization across various tradeoffs between availability, output, efficiency, maintainability, wear & tear and flexibility. If these trade-offs are not tackled by using all available data and the right software applications, the plant is operating at a lower-efficiency profile, resulting in lower returns on the capital investments already made. Use of various digitization tools can significantly increase the operating profile of an existing power plant.

Software can provide power producers with both “inside the fence” and system-wide views, enabling them to maximize the plant’s operational efficiency while responding optimally to changing conditions on the grid and in the overall power market.

What power plants need today are “Software Defined Operations”. Which are : Deeper insights into a power plant or wind farm operations to help with more forward looking decisions; breaking down the barriers between silos in turn providing inputs for how to manage the market side of the power generation business; and predictive or condition-based maintenance is a defining benefit of the Industrial internet.

Digital tools will track and maintain historical performance baselines for individual assets as well as the whole plant, comparing it to real time performance monitored on a continuous basis. Any variance from “expected behavior” derived from these baselines or expected operation will trigger an alert. Advanced analytics will then determine whether the variance signals a potential future malfunction, its root cause and the likely timeframe over which the malfunction will occur. Analytics will also provide a cost-benefit analysis of how much longer the unit can perform, and at what load, before the issue must be addressed. This will allow the power producer to address issues proactively, reorganize workflow around planned maintenance and avoid outages. The resulting increase in uptime will improve a plant’s economic value.

Digital technologies can also assess a machine’s expected performance characteristics given the prevailing ambient conditions. This combines a digital assessment of the machine’s “state of readiness” based on sensor readings with temperature, humidity, and other data that can impact its output. These data points can be compared with the historical baseline and expected performance curve to detect any variance or anomalies.Software that understands a machine’s physical capabilities relative to its theoretical potential can do more than simply detect variance; it can adjust operating parameters in real time to maximize efficiency and minimize cost.

Aggregating and analyzing data across assets “within the fence” can establish operating levels for individual assets that optimize the entire plant system. In turn, as the plant operates more efficiency, its overall emissions can decline, improving the plant’s environmental footprint. Without these analytics, each asset would be individually configured, which might result in a suboptimal performance for the total plant. For example, digital wind farms that use analytics to adjust individual turbine performance to maximize system benefit can improve total plant output by up to 20%

A plant’s economic evaluation combines an output assessment with system or market data. In deregulated markets, this entails analyzing energy and/or ancillary product pricing for real-time or advanced delivery commitments. In regulated contexts, this may include assessing zonal demand, available transmission capacity, and the cost of dispatching other available plants. Once digital tools have determined the machine’s output capability, they can perform an optimization algorithm to provide suggested operating levels to maximize economic value. This may include recommendations on how to bid a plant’s output among various energy or ancillary markets, or at what time a plant should be dispatched.

Digital technologies also hold huge potential to improve fuel procurement and storage, which can account for up to 90% of a plant’s operating cost. Digital tools can improve fuel procurement by identifying the most cost-effective fuel suppliers, and tracking fuel transport and storage. Online platforms can run reverse auctions to enable multiple fuel suppliers to compete.

Once a supplier has been identified, digital tools can ensure that the power producer can track fuel delivery and storage, such as monitoring the location of LNG or coal deliveries and ensuring that appropriate quantities are transferred to reserves. Digital technologies can also help drive transparency around commodity pricing, enabling power producers to determine how they can adjust their hedging strategies to manage fuel cost volatility.

What power plants need today are “Software Defined Operations”. Deeper insights into a power plant or wind farm operations to help with more forward looking decisions, and breaking down the barriers between silos in turn providing inputs for how to manage the market side of the power generation business

Predictive or condition-based maintenance is a defining benefit of the Industrial internet. Digital tools will track and maintain historical performance baselines for individual assets as well as the whole plant, comparing it to real time performance monitored on a continuous basis. Any variance from “expected behavior” derived from these baselines or expected operation will trigger an alert. Advanced analytics will then determine whether the variance signals a potential future malfunction, its root cause and the likely timeframe over which the malfunction will occur. Analytics will also provide a cost-benefit analysis of how much longer the unit can perform, and at what load, before the issue must be addressed. This will allow the power producer to address issues proactively, reorganize workflow around planned maintenance and avoid outages. The resulting increase in uptime will improve a plant’s economic value.

Software that understands a machine’s physical capabilities relative to its theoretical potential can do more than simply detect variance; it can adjust operating parameters in real time to maximize efficiency and minimize cost.

Digitalisation comes with up-front costs, but technology’s ability to improve productivity can pay dividends. Digital technology can help to solve some of these challenges. For instance, intelligent data systems are enabling network operators to handle large volumes of intermittent wind, solar and other renewable power, and to accommodate more distributed power producers. For plant operators, building a database that forms a digital twin version of the physical plant can lead to the development of analytics that can trigger service and maintenance actions, even before problems occur.

Power companies perceive that digital technology will support the core fundamentals of their businesses with cost management as a major focus and also as a means of optimizing processes. Gains can also be made in operations and maintenance, alongside improvements in production capacity, safety and information management.

Digitalization has the potential to boost the production capacity of their facility by at least 10 % although 40% savings are also expected by some. . Companies think digitalization will reduce operating costs by anywhere from 10 per cent to 50 per cent, with an average of just over 27 per cent.

Economics and technical advantages will drive digitisation in the energy system, but major challenges remain. Power companies are awash with data – a modern gas-fired power plant is equipped with more than 10,000 sensors – but most have a long way to go before they can use this effectively to trim costs, increase sales, and boost efficiency and reliability not one uses more than half the data they collect. The average is 27 per cent. Establishing an intelligent engineering master data management system can be key to helping organisations improve on these numbers , data analysis would be easier with digital information management and it would enable them to make more informed decisions.

Digitisation comes with up-front costs, but technology’s ability to improve productivity can pay dividends it is because of the enormous capital expenditures required by the industry that it becomes so important to wring every last drop of productivity from power assets, and software is indispensable in this effort. And digitisation will bring great rewards. But it will also bring change and uncertainty to what is a relatively conservative, risk-averse industry

More than 50 percent of energy leaders said they expect a rapid increase in distributed generation — to a share of 15 percent or higher — of the installed generation capacity in their country by 2025. Indeed, Oliver Wyman estimates that every two minutes a home or business in Europe and North America goes solar. This pace of change is expected to accelerate with technological developments in storage options that can support distributed generation. As one energy leader we interviewed noted, "The pace of innovation on batteries and re-charging has increased at a faster pace than expected."

New Possibilities resulting from Digitization

Benchmarking across plants will now become possible, as data is transmitted back from assets “inside the fence” to be aggregated and analyzed. Operational anomalies can be identified and corrected, and plants that perform better than expected can be rewarded. Operational platforms that identify maintenance issues can proactively transfer data to IT systems that manage maintenance personnel and supply chain vendors, ensuring that the necessary parts are replaced by the right personnel at the most optimal time. Through the use of analytics and a holistic IT/OT perspective, power producers can optimize their plant performance and improve their portfolio’s sustainability while minimizing maintenance costs.

The black box behind applications that help digitize a power plant or a wind farm is a concept called “Digital Twin.” “Digital Twin” is a collection of physics-based methods and technologies that are used to model the present state of every asset in a Digital Power Plant or a Digital Wind Farm. The models start by providing guidance on “design limits” of a power generation unit at the commissioning stage or infer- ring the design limit for an existing plant/farm by matching the equipment to thousands of other similar equipment in the database.

These models are then continuously updated, and learn to accurately represent the “present” state of a plant or farm during its lifetime. The models accurately represent the plant or farm under a large number of variations related to operation—fuel mix, ambient temperature, air quality, moisture, load, weather forecast models, and market pricing. Using these digital twin models and state-of-the-art techniques of optimization, control, and forecasting, the applications can more accurately predict outcomes along different axes of availability, performance, reliability, wear & tear, flexibility, and maintainability. The models in conjunction with the sensor data give us the ability to predict the plant’s performance, evaluate different scenarios, understand tradeoffs, and enhance efficiency

The technology needed to run the complex algorithms described above is different from the traditional IT architecture prevalent in the industrial world currently. What is required is a scalable framework to run industrial analytics, It needs to have a seamless integration between the data layer (using a data lake and cloud computing paradigm), a model layer and the application layer. What the users will see is the experience captured in the application layer

Impacts of Digitization

The World Economic Forum estimates that $1.3 trillion could be generated by digitalizing electricity generation worldwide between 2016 and 2025. It lists five initiatives in particular – better management of asset performance, real-time platforms data, integration of energy storage and customer-centric solutions – that it believes will individually unlock at least $100 billion of value over the ten-year period.

The digital grid will underpin the future energy network: it will enable bi-directional flows of electricity, transmit information and price signals, and ensure optimal balance of supply and demand. Together, this will enhance grid reliability, reduce losses, and integrate distributed resources which can help decarbonize the system. Digital tools will play a significant role in achieving these outcomes. Utilities today manage their T&D operations through several technology systems, including outage management, customer information, maintenance management and meter data management among others. However, using digital tools and intelligent monitoring devices, T&D operators will be able to utilize an integrated digital system that will enable them to identify reliability issues, address customer problems, and ensure optimal electricity delivery in a more efficient manner.

Today, T&D operators often rely on manual, reactive intervention to ensure reliable grid operation. For example, without the installation of advanced meters and other intelligent devices, utility operators must piece together customer phone calls and other input to detect a power quality issue or an outage, prolonging the time between identifying the root cause of a delivery problem and deploying a solution.. Digital tools can detect and locate an outage, identify the root cause, and rapidly restore power. For instance, advanced meters can indicate an outage; geospatial data platforms can help navigate personnel to the right geographic area, and advanced distribution systems can dynamically reroute electricity. As more power is generated using distributed resources, micro grids can provide enhanced delivery and resilience. Digital tools can provide algorithms to island these resources from the grid, ensuring that critical load demand continues to be satisfied until main grid operation is restored.

Distributed energy resource adoption is growing, driven by favorable economics and technical advances. Onsite generation has become increasingly cost-efficient due to greater gas availability and rapid cost declines in solar and storage technologies. Intelligent load devices that reduce or shift energy use are becoming main stream through user-friendly mobile interfaces, turning consumers into "producers-consumers."

These technologies can bring benefits to the power system, including addressing demand more efficiently, deferring T&D investment, improving power quality, and increasing grid resiliency. However, distributed resources also present operational challenges. They may cause rapid voltage fluctuation, create bi-directional flows on radial distribution lines, or adversely affect transformer and other grid asset lifetimes.

Software can help monitor and control these distributed assets, as well as ensure that both consumers and the grid can benefit from their installation. It can analyze sensor data from substations, feeders, and connected devices to identify grid areas that may be at capacity, or experiencing volatile demand or power quality issues. Data from smart inverters or other devices can be analysed and merged with operational grid data to provide utilities with a single view to monitor and control distributed resources.

As distributed resources gain traction, they can operate as a network, and in effect become a “virtual” power plant. For distributed resources where output can be dispatched or controlled, software can utilise local demand projections, relevant market pricing, and output potential to ramp distributed generation or curtail demand in a coordinated manner. Utility systems can monitor and control distribution grid devices to ensure stability when distributed resources are in operation.

In reshaping the digital grid, it will be important to build in a high degree of flexibility to allow for experimentation and to adapt to future technological breakthroughs. Grid flexibility will also be instrumental in ensuring high reliability in a context characterized by more flexible and variable consumer demand and intermittent renewable energy sources

T&D operators must also address both technical and non-technical losses. To reduce technical loss due to natural power dissipation, software that processes voltage, current, real and reactive power, and phase can make real-time adjustments. To address non-technical loss, data from smart meters, intelligent devices, and distribution grid sensors can identify where there may be theft or other power loss.

Consumption end innovations

The transformation of the energy system has arguably been accelerated by changes on the consumption side. Advances in solar and in energy storage are allowing traditional energy consumers to become energy producers. Advanced meters, connected devices, and energy management systems are providing consumers with greater transparency and control over their energy use. Together, these trends will help consumers lower their energy spend while enabling them to provide overall benefits to the system in the form of lower peak demand and more economic energy and ancillary services.

With solar panels on the roof and an electric vehicle (EV) in the garage, a traditional consumer can start to play a much more active and multi-dimensional role in the energy system. When the customer’s photovoltaic (PV) energy generation falls short of consumption needs, customer will supplement it with energy supply from the digital grid; when generation exceeds needs can turn into a supplier, feeding energy into the digital grid. The battery of her electrical vehicle can act as a distributed storage system and help manage the demand and supply fluctuations in the household and potentially in the energy system as a whole.

Digital energy management can interface between a consumer’s equipment and the grid, regulating both energy consumption and the mix of supply sources utilized based on the price signals provided by the grid itself. At times when the grid faces peak demand, its prices will rise, and the home energy management system will respond by reducing consumption and/or switching from grid-provided power to locally-sited generation or battery-stored power, where those are available.

Consumers can gain better visibility into when and how they consume electricity, thanks to the continued rollout of smart meters and other connected devices, enabling advanced analytics to deliver data-driven insights through intuitive user interfaces on computers and mobile devices.This is a big shift from a situation where most consumers still limit their interaction with the energy provider to payin their bills and reporting outages. Now consumers can better understand their energy consumption and change their

strategies accordingly: residential customers can benchmark themselves against similar households and identify a menu of possible actions to reduce their spending; commercial and industrial customers can shift some of their consumption to off-peak periods to benefit from lower tariffs, or they can identify operational inefficiencies that can be addressed to lower consumption.

Through the digital grid, this process will generate a continuous flow of data on consumption behaviors, load fluctuations, adaptation to price signals and supply responses—data that will help raise the efficiency of the entire system. To maximize the potential benefits of greater consumer engagement—where consumers are seen in their new and greater role of co-suppliers—the energy system of the future will need to satisfy two conditions: Provide a system of price incentives designed to reward contributions to the system and to nudge consumers towards a behavior that can maximize system-level efficiency; Offer a monitoring and control system flexible enough to accommodate different degrees of customer engagement according to individual preferences.

Some consumers will embrace the opportunity to actively reshape their consumption strategy, whether driven by concern for the environment or a desire to minimize costs; these consumers should have the opportunity to closely monitor and modify their consumption with the high frequency they desire. Other consumers will be content with setting basic goals, parameters and constraints without having to spend too much time understanding load fluctuations and pricing schemes. These consumers should be able to rely on a smart energy system that will maximize efficiency subject to the consumer’s choice of basic settings.

Pricing schemes should reward consumption and distributed production behaviours that contribute to maximising system efficiency; they should also recognize the value of the information that different patterns of behaviour provide, while similarly assessing the value that consumers and other distributed actors derive from access to the grid’s infrastructure

New Power Sector Business Model

Digital technologies are set to completely transform the entire power sector value chain. This will require utilities and other traditional entities to adapt and adopt new business models, while emerging players introduce new services and capabilities. It will also require a set of enabling conditions for new investments, policies and regulations.

Energy providers will join a new breed of digital-industrial companies.This will require changing their business model to fully take advantage of new digital capabilities. A first priority will be to use the insights provided by big data analytics in order to balance the fuel mix. Conventional thermal generation will remain a vital component of the energy mix for decades to come,9 but new technologies will accelerate the adoption of natural gas and renewables, requiring software to manage and optimise the generation portfolio. Mind shift In general, management and operations of power producers will have to adopt a “data-first” mentality, always thinking in terms of the potential insights that can be gleaned from data and analytics to improve the value of the service.

Digital tools can also give energy providers new ways to interact with consumers. Many utilities and other energy service organizations are seeking to transition from electricity provider to trusted energy advisor. This requires utilities to work with customers in new ways to identify and tailor solutions. For example, detailed interval data as well as information from connected devices can help utilities and other service providers to develop onsite power solutions to increase reliability or efficiency retrofits to reduce spend. Social media can help utilities communicate with customers regarding outage restoration or peak demand events, or simply engage customers in a discussion around energy services or conservation

Progress to present

Transformation of the industry is already underway, it is happening now; plant digitalization is a rapidly growing focus across the industry with most organizations working on their strategy and initiatives. An overhaul of the power industry is long overdue. Power generation networks have become much more complex in recent years, thanks mainly to the rise in renewable energy power generation and the growing number of small, distributed power producers. Demand for power is also increasing in many countries, yet grid infrastructure is often old and creaking. Equipment is both difficult and costly to maintain, but tighter regulations are driving the industry to be more efficient and cleaner than ever.

Platforms have quickly emerged as a defining characteristic of the digital economy. Their role and value in consumer sectors is by now recognized: they are both an enabler of efficiencies and a key avenue of value monetization. Platform business models have revolutionized the way that value is created, delivered and monetized across a set of interdependent providers, users and intermediaries. As a concept, platform- driven businesses are not new. They have existed in the physical realm for centuries: a “bazaar” or “market” is a platform that brings sellers and consumers together in some central location for a trade, enabling faster diffusion of information (through physical co-location) and more efficient transactions.

Digital-age platforms do a lot more, however. They unlock the potential of under-utilized capacity. They enable instantaneous and universal access to information through digital apps on mobile devices; they turn data into analytical insights that can dramatically increase efficiency by accelerating the feedback loop between price changes, and supply and demand responses. They also accelerate innovation and value creation. Common operating systems that enable the rapid and wide deployment of new digital consumer apps have reached the point where “there is an app for everything”. A similar proliferation will take place in the industrial world.

The electric grid embodies many of the platform characteristics. It connects multiple users of a network, enabling the exchange of products, services and information. Traditionally however, the platform potential of the electric grid has been limited by the very specific nature of the sector.

Economies of scale, the exclusive role of centralized generation and the lack of data collection and response mechanisms dictated a very simple hub-and-spokes model, with centralized power producers supplying electricity and charging bulk tariffs regulated to allow them to cover investment and operational costs while ensuring affordable safe and reliable power access to consumers.

With the rise of distributed generation and demand management set to complement centralized power, the electric grid has for the first time, an opportunity to become a true platform, enabling a symbiotic relationship between central and distributed resources, utilizing a wide range of data such as weather and vegetation changes, granular load projections by neighborhoods, central generation and distributed resource output capabilities, demand response capability, and grid asset health to find the optimal resource mix and power flow path to maximize grid reliability and minimize delivered electricity cost.

These digital platforms will enable central and distributed resource providers to determine location prices for energy and ancillary services, and consumers to determine how best and when to use energy. In the event that a reliability issue such as an outage must be handled, the digital platform will autonomously communicate with grid devices to reroute power flow, island critical loads using micro grids, and ensure that the proper steps are taken to restore the grid to proper operation safely and quickly.

With a long track record of using SCADA and ICS platforms to drive business and operational efficiency, oil and gas firms are most eager to embrace IoT, with 88 per cent considering enablement as a priority.Utilities are not far behind, while over three-quarters of firms investing in IoT say it is a top priority for the coming year. One reason for this growing interest in IoT technologies is the fact that it plays into several other key areas such as IT automation,

Power providers see it as a great way not only to improve safety and quality of life for citizens but also to improve internal efficiencies and service reliability. These include companies like Oklahoma Gas & Electric, which has deployed a robust IoT network, helping to reduce operational costs, lower emissions, minimize the number of service vehicles on the roads and empower consumers to manage their own energy supply. The beauty of the firm’s expandable IoT network is that it had also been used to connect 250,000 LED street lights — improving service levels, reducing energy consumption and accelerating resolution of outages.

Florida Power and Light (FPL) used advanced metering infrastructure (AMI) and automated feeder switches (AFS) to boost resilience in the face of frequent storm-related outages. It worked: the firm claimed it avoided 118,000 outages and restored power within two days to 99 per cent of others affected during last year’s Hurricane Matthew.

Other utilities players including Baltimore Gas & Electric, ComEd, BC Hydro and Pepco Holdings are doing similarly innovative things with smart grids. These initiatives aren’t just confined to North America, either: smart lighting projects, for example, can be found all over the world, in places like Glasgow, Paris, Copenhagen and London.

While the benefits are potentially huge, there are also major IoT challenges potentially holding up deployments there have been problems

Network performance was also high up on the priority list, illustrating the continued importance of latency and bandwidth to effective IoT systems. Next come standards — a key requirement for over half of IT bosses. In fact, 45 per cent demanded open standards for smart city solutions, while a similar number said the same for utilities projects. Open standards are vital to keeping costs down and choice of products high for IT leaders. They promote performance, security and reliability, while backwards compatibility helps ensure that legacy assets don’t get stranded.

US firms appeared to be most mature in their approach to IoT, with two-thirds (65 per cent) claiming to have a fully implemented IoT strategy in place, versus just 47 per cent in the UK, 44 per cent in Sweden and a quarter (24 per cent) in Denmark. It’s clear, therefore, that the rate of IoT adoption will vary by geography, as it does by industry. UK firms seem to have the most trouble at present, with just 3 per cent claiming their projects were completely challenge-free.

Ultimately, the most important thing to remember is the end user. Power suppliers and utilities firms looking to utilize IoT technologies to drive internal and external benefits will only achieve success if they get the consumer on board. This makes awareness raising and education programs vitally important to sell your customers the benefits of any new IoT project. Your eye-catching new solution might have the potential to transform the customer experience, lower costs, improve efficiencies and drive greater business agility, but if your customers aren’t with you, all that planning and investment may be in vain.

We’ve already well and truly entered the IoT age. But unlocking value from these technologies requires more than blind investments in technology. Those organisations most likely to pull ahead of their competitors will be the ones that focus on security, performance and standards to drive success.

Enabling conditions

While there will be tremendous benefit gained from further digitizing the energy sector, challenges beyond technology and policy remain. In particular, a grid with seamless interaction between central and distributed resources will require open standards and interoperability. Any platform that manages assets as critical as the energy infrastructure will need to be secure physically, but especially digitally. Finally, a new generation of personnel will be required to facilitate the industry’s transition to a digital future as a significant portion of the current workforce retires.

Open standards and interoperability: As with any ecosystem that includes a multitude of technologies, products and stakeholders, a common set of standards is necessary for industry development and continued innovation. This need is magnified in the case of the Industrial Internet—a convergence of multiple technologies with advanced connectivity across devices and systems. To maximize the potential value of Industrial Internet innovations, it is essential that different systems and assets be able to communicate with each other, share data and respond to common monitoring and control systems. Moreover, as these connected devices interact with utility and other personnel, data standards can help drive consistent analytical views to aid in decision making

While progress has been made, more will need to be done to involve the emerging technology and service providers, along with the existing set of utility and OEM stakeholders. Even in the pre-digital age, the efficiency and value of power grids has often been held back by a tendency to “balkanize” the grid along state lines; in the digital age, artificial and arbitrary barriers to interconnectedness and interoperability would carry a much larger opportunity cost.

Cyber security: The digitization of energy opens a new form of vulnerability, exposing network participants to potential data privacy and system security risks. Reducing these risks to a minimum is a top priority.

There has been progress on multiple fronts. Vendors are developing and deploying solutions that include assessment services, firewall and security infrastructure. The U.S. Department of Energy and National Institute of Standards and Technology have put forth comprehensive programs to develop frameworks that address the risk to the electric grid, as well implementation guidelines and maturity models for cyber security. As government and private enterprises invest in digital technologies, they must continue to implement a robust cyber security infrastructure.

Education and skills development

Population ageing in advanced economies is mirrored by the ageing of the workforce across a number of industries—and the power industry is no exception. The prospective simultaneous retirement of large cohorts of experienced workers is set to create a problematic skills shortage just as the industry faces a challenging transformation. While younger generations of workers will bring new skills to the industry, it is crucial that the knowledge and experience accumulated by more senior workers is captured and embodied in the companies’ institutional memory, accessible to the new workforce. Digital innovations that facilitate communication and collaboration as well as the creation of a digital memory capturing the experience of the workforce should be used to this purpose.

Energy companies need people with software and analytical skills to reap the benefits of the digital transformation. They must invest in employee education and training, distribute and encourage the use of mobile devices, and partner with universities and other vocational training institutions to build data science capabilities.

Attracting new talent and workers with digital skills is becoming increasingly important as utilities also face a retiring workforce. Digital knowledge management systems can be used to capture processes, institutional knowledge, and other relevant operational data from the current workforce. Through the use of blogs, social media, conferencing, and other real-time knowledge-transfer platforms, retiring specialists can ensure that their experienced perspective can be captured and disseminated to newer employees in a fast and seamless way.

Power generating plant Digitization

The power sector has somewhat conflicting objectives: How do you lower the cost of electricity; achieve environmental; and business goals. The challenges facing the power industry are many and span from pairing with renewables to competing with newer plants to changing fuel economics. Power plants must adapt and respond to these challenges to remain viable. Power plants generate an overwhelming amount of data. The challenge is to know what to do with the data generated and also about how the data would be of use in achieving objectives.

A digital power plant applies sensor data and software to do more than human operators can do alone. This is especially relevant as competition continues to drive plant headcount and operating cost reduction while experienced experts retire and retention of even existing knowledge is a challenge There are various options for building the software infrastructure that is at the heart of a digital power plant. The first decision to make is whether to develop software internally invest in a comprehensive software overhaul or purchase application solutions that build on your existing software investments. All of which come with their own set of costs and outcomes. The second decision is to prioritize the challenges that can be cost-effectively addressed and avoid the danger of trying to do too much at once.

Each plant faces different challenges. Some are based on environmental factors or operating modes unique to that plant. Therefore, the experience of each plant adds valuable feedback to validate designs and improve O&M practices from fleet-wide learning. The path to digitization lies in being clear about: end goal in creating a digital power plant; and determining priorities and timeline.

Conclusions

The power industry has begun an exciting digital journey, one that will bring a deep transformation of the entire value chain. A set of macroeconomic and technological forces have catalysed this transformation, creating new challenges but also new opportunities for the industry.

Access to electricity across developed and emerging markets is critical to global growth. Today with over a billion people without electricity access and growing energy demand from rising living standards of billions more, the industry faces a formidable challenge. Supplying secure and reliable power in a sustainable manner will require investment in new generation and transmission-distribution infrastructure, making the existing system more energy efficient as well as diversifying the fuel mix.

Advances in distributed energy technologies, energy storage, and connected devices are making it possible for consumers to also play a role in the generation and distribution of energy, opening the way for bi-directional energy flows and optimizing peak demand. Utilities are deploying digital technologies to integrate distributed technologies, manage fluctuating demand and quickly resolve outages to realize industry goals. The penetration of digital technology adoption however is limited. Operational challenges of sustained profitability, data deluge and an aging workforce still remain significant.

The convergence of digital and physical technologies that is unfolding across industry can turn these challenges into unprecedented opportunities. The power sector needs a digital strategy that enables a new value chain augmented and interconnected by digital technologies. The new structure links digital generation and digital consumption by a new digital energy grid that can also serve as an intelligent technology platform and a marketplace for new revenue sources, pricing schemes that incentivize innovation for existing and new players in the energy ecosystem. A digitized value chain will yield a system with greater reliability, affordability and sustainability. In the energy sector, machines will merge with data analytics at a scale like never before. This will result in substantial value gains starting from the planning and siting of power generation plants to their operations. Moreover it will enable a more dynamic management of central and distributed power. Meanwhile, digital consumption will become more efficient, participative and responsive to demand and power supply conditions.

Power producers and utilities are embarking on a journey to digitize their processes. This will require not just investment in new technologies, but also a shift in mindset and business models—and the shift will need to be faster than ever before. Digital innovations rely on openness and collaboration to realize their full value. Therefore, power producers and utilities will need to break down barriers separating their organizational silos. To do so, their CEOs, CIOs and COOs need to select the right technology partners that can help them bridge the IT and OT domain expertise. Internal and external collaboration will be mutually reinforcing. As this new wave of innovations brings together very different areas of expertise at an accelerated pace, partnerships are essential to succeed.

This transformation will need high coordination among stakeholders. Energy providers will join a new breed of digital-industrial companies, by investing in new technologies and finding new ways to provide tailored solutions to customers. It will need development of open standards and interoperability between products, the nurturing of a new generation of personnel, and the highest level of cyber security.

Given its paramount importance for economic growth, energy will be a national security priority for every country. In developing new energy supply sources and infrastructure, ensuring the highest level of efficiency, security and resilience will be of the utmost importance.

This will include an optimal degree of supply diversification, including for imported energy, to reduce the risk of disruptions that may be caused by natural disasters or geopolitical shocks. It will also include a high standard of security safeguards around the energy infrastructure. As digital technology becomes pervasive, cyber security will become one of the most important pillars of energy security

In order to achieve both of the above objectives, system operators will need to be able to monitor—in real time— the state and performance of all assets linked to the network. This will enable them to continuously assess demand and supply expressed by all elements on the system, as well as their responsiveness to price signals. Identifying new revenue sources/correctly valuing and allocating the cost of investments and other efforts that add value to the system.

The traditional model that compensated utilities with volumetric tariffs is becoming suboptimal. The energy system of the future will need to develop a set of incentives that induces all players to add value through actions and information provision, ensuring adequate compensation for investment and incentivizing sufficient risk-taking for innovation and experimentation.

The management of industrial technology has traditionally been split between two separate fields: information technology (IT) and operations technology (OT). IT worked from the top down, deploying and maintaining data-driven infrastructure largely to the management side of business. OT built from the ground up, starting with machinery, equipment, and assets and moving up to monitoring and control systems. With smart machines, big data, and the Industrial Internet, the worlds of IT and OT suddenly collided. Data, once the purview of IT, is now ubiquitous on the operations floor. In order to fulfill the promise of using data to enhance productivity, IT and OT, developed separately with independent systems architectures, need to come together and find common ground to develop a new information-driven infrastructure

Each of these challenges represents a powerful opportunity. The convergence of digital and physical innovation can dramatically accelerate progress across all the critical dimensions listed above. As is becoming evident in other industries, as digital intelligence becomes embodied in industrial equipment it opens up an entirely new dimension for efficiency improvements. Digitally enabled interconnected devices can perform a much wider range of functions, benefit from faster performance improvements, and deliver disproportionately greater value to their users than their traditional versions.

The first step of the Industrial Internet maturity is to connect all critical assets in the energy value chain. This is not a trivial task as we are referring to nearly hundreds of discrete components with at least a dozen different communications and networking protocols. These devices will generate 24 Exabyte’s of data by 2020. We estimate at least 50% of this data will be stored and analyzed in various forms of clouds (public, private or hybrid).

In many cases, utilities have already embarked on this digital journey. They are deploying operational technologies such as generation and distribution asset smart sensors & controls, substation and distribution automation and smart meters. The industry has begun to develop a vision for a digitized future through various smart grid initiatives such as improved grid resilience and energy efficiency. However many assets still lack the capacity to collect and transmit data. Those that are so enabled transmit volumes of data into local repositories that are not accessible remotely; the few that are connected do not have the cloud capabilities to generate actionable insights in real time.

Digital maturity will be achieved by equipping energy assets across the generation, transmission, distribution, and consumption value chain with sensors and cloud-based analytics. This is becoming increasingly possible as sensor, bandwidth, and computing costs continue to decline. Moreover, as information and operational technologies continue to converge, emerging digital platforms will create a more connected and intelligent power system. These platforms will do so by improving planning, operations and maintenance procedures for each asset in the energy value chain .

The future of energy is a new value chain augmented and interconnected by digital technologies, where both power and information flow in multiple directions, all actors add value to the system, and the overall efficiency and resilience of the system hinge on information sharing, openness, collaboration, coordination, and the right set of incentives. The end result will be a system that provides electricity in the most reliable, sustainable, and economic manner. The new structure wil comprise; a digital centralized generation pillar, relying on a mix of fossil fuel and renewable sources; a digital grid, connecting generation and consumption, enabling the multidirectional flows of energy and information; and Centralized power generation will remain critical even with the rise of distributed energy resources. It will provide the majority of the power supply, and ensure the continuity and reliability of electricity provision. The longstanding goal of ensuring reliable, affordable and safe access to electricity remains unchanged in the future of energy, and can only be guaranteed by a strong centralized power generation system. Digital technologies will transform power generation from the very earliest stages, starting with the design and siting of power plants, and continuing through the operations and maintenance phases.

Power Sector outlook in 2019

The three Ds of energy: decarbonization; decentralization; and digitalization. Decarbonization of the power sector still seems fairly clear – the options haven’t changed so much; energy efficiency, renewables, nuclear and CCS. Although, the costs and policies have changed enormously over time and renewables have made great advances. For 2019 we can expect to see further advances and more geographies around the world in which renewables can out-compete traditional forms of generation and face the market more and more. This means renewables developers and investors getting comfortable with more market and less regulatory risk.

The step-change that we see is two-fold: on the one-hand, computing power increasing and enabling greater levels of AI and machine learning; and secondly, the amount of data available for those computers to analyze. These two combine to mean that better decisions are made and are also automated. These are the key areas we expect to see further progress in 2019 in digitalization in energy:

Fault prediction and dynamic maintenance: This is one of the clearest uses of AI which enables operators to predict equipment failures by using sensor data from various units to significantly reduce their costs of downtime and maintenance. Pöyry has an offering for this called KRTI4.0. On the retail side, a startup Verv is offering a meter device which identifies individual home appliances and tries to predict faults or a device being accidentally left on by building up individual profiles from the meter data.

Investment optimisation: BP’s venture arm invested in an AI startup called Beyond Limits to dig through seismic images and geological models to increase the chances of success when drilling wells. Another example of longer-term investment decisions is the US Department of Energy project where machine learning is being used on satellite imagery and operation data to prioritise reinforcement at vulnerable points of the grid to improve resiliency.

Energy efficiency: Deepmind, which is a part of Google, has championed the use of Reinforcement Learning to reduce energy use in its data centres by a claimed 15 per cent. The model learnt by looking at years of operational data and then issued changes to individual units within the operating constraints of the plant.

Better prediction: Deepmind is also currently in talks with National Grid of the UK to better forecast demand of the system with the stated goal of reducing the entire country’s energy usage by 10 per cent. Another example is improved prediction of wind power production to reduce imbalance costs by 50 per cent which was achieved by a company called Swhere.

Trading: According to the FT, systematic and algorithmic trading now account for nearly 60 per cent of the traded volume on just the CME energy product group – highest level of any commodity group. Anecdotal evidence from mid-2018 is that over 50 per cent of trades on the EPEX Spot intraday market are algo-trades (although the total volumes are still smaller than trades executed by human). Sophisticated machine learning models are also being deployed by speculators which are relying on large streams of diverse data to respond to the market changes quickly.

Retail: retailers are using machine learning to understand patterns of customer behavior, to attract and retain customers and even to predict bill (non)-payment. Customer call centers are being fronted by algorithms which chat to customers (verbally or online) and deal with queries.

Customers: For customers, AI solutions are also gaining traction, and many retailers are offering these systems as part of an integrated package. Devices such as Amazon’s Alexa enable the customer to seamlessly interact with their thermostat (such as Centrica’s Hive). This increasing customer interaction with the device leads to the development of a more personalized usage profile, which reduces bills for the consumer and also helps the energy provider to accurately forecast demand.

It would seem then that the digitalization opportunities in energy are large. It will be a vital enabler of decarbonization is some areas in the future such as flexible demand shifting to meet supply. The opportunities available rely heavily though on sufficient volumes of good quality data being available. So, expect more sensors and more data acquisition throughout the energy sector in 2019. And in time with growing autonomy expect the focus to switch to the appropriate monitoring, alerts and controls. “AI is the new electricity – enabling us to do more.”

Digitalization has become a worldwide focus and the race to become the global leader in Artificial Intelligence (AI) is becoming increasingly competitive, with many countries, including Canada, China, and the UK releasing strategies in the last twenty months to promote the use and development of AI.

Wind energy is one of the most innovative, forward-looking and fast-paced industry sectors. With the U.S. wind energy cost continuing to fall below the cost of coal and gas, wind energy asset owners and operators are redefining their approach to operations and maintenance (O&M) through adopting technologies that introduce greater efficiencies and streamline processes. Many have invested, and are continuing to invest, in predictive maintenance solutions, which combine improved SCADA data analytics, CMS systems, and oil monitoring with AI and machine learning.

AI platforms work by identifying deviations between expected component behaviors and actual behaviors, which are then flagged allowing an inspection team to identify the fault and fix it in advance of the fault causing a catastrophic failure or significant down time. This, as a result, extends the lifetime of the asset in question.

Ideally, such a solution should serve to not only flag to an asset owner that a failure is occurring, but also the nature of the issue, the rate at which the failure is occurring and the remaining useful life of the asset. This is where real-world engineering expertise plays a key role in shaping the AI solution.

Though AI, machine learning and digital twin are current buzzwords, they have the potential to create significant confusion in our industry. Below, we will examine six key market trends in order to cut through some of the “chatter” around these terms, to determine with greater clarity how to make digitalization work for the wind energy industry, rather than the other way around.

Bigger Is Not Always Better

Small, agile companies may have the advantage over large industrials when bringing innovative digital products into the market. Independent players that are closely connected to the marketplace, thus ascertaining a clear sense of market needs, have an advantage over bigger, more generalist companies. The ‘top-down’ approach employed by larger industrials can come with a significant price tag attached; often proposed solutions are abstract in practice and constrained by their legacy systems, creating a challenge for the asset owners and operators when it comes to implementing the solutions. The digital products offered by larger industrials have typically been developed around their own equipment; hence, the analytics are optimized for their equipment. Oftentimes, independent, agile companies can offer more flexibility by the way of pricing, customized solutions and equipment-agnostics analytics. It is interesting to note that it is often these bigger players that appear to struggle in the wind energy market.

Digitalization Focused on Value, Not Tools

Numerous multi-national corporations have embarked upon a digitalization journey by investing heavily in digital technologies and tools only to have these investments fail to materialize in business value. To avoid repeating these costly mistakes, a clear understanding of the business value delivered by digital tools needs to be established as the first step.

After that, an investment strategy to develop, partner or acquire the relevant digital technologies should follow. This will lead to a more “value-focused” digital transformation, which should, in time, result in a clear demonstration of the value that digital technologies bring to the industry.

Digitization Before Digitalization

Despite the importance of data and information to unleash the full power of AI and machine learning, some critical wind energy data falls into the category of not being “digitized” or stored in an “AI ready” or “IIoT” format. Furthermore, O&M teams currently spend 80 percent of their time organizing inspection data, which means they have less time to focus on utilizing their core skills to address engineering issues.

Tools that enable the O&M teams to “digitize” their inspection and maintenance data enable them to spend an increased amount of time tracking failures, responding to developing technical and safety issues and determining where the maintenance budget should be focused.

A fully digitalized and connected predictive maintenance approach is becoming increasingly important sector-wide; asset owners require the right data to be in place in order to maximize the investment of AI/machine learning. Asset and operations managers have cited a need for higher-quality data to improve the reliability of their organizations’ assets. The challenge lies in ensuring data is stored in a unified format and easily accessible. Cloud-based mobile inspection and maintenance software transforms inspection and maintenance reporting practices, creating substantial safety and efficiency gains for the organization, and generates good quality data, increasing the efficiency also of Digitalization processes.

Overcoming “False Positives” And “False Negatives”

In its enthusiasm to adopt innovative digital solutions, the industry may be in danger of undermining the progress made in predictive maintenance to date, by driving an increase in “false positives” that, in turn, will result in increased OPEX costs for operators. Clearly, there are only so many times that maintenance personnel can be sent out to respond to identified failures — which fail to materialize into an actual fault — before confidence is lost in the technology concerned.

Equally, “false negatives” are bad news to the industry too. A cautionary tale of “false negatives” in AI involves researchers training a Neural Network (NN) to detect camouflaged tanks in photography for the US Army, succeeding, only to realize the NN had learned to distinguish cloudy days from sunny days, instead of distinguishing camouflaged tanks from empty forest. These issues with AI and machine learning must be carefully managed to avoid the industry experiencing a turn from enthusiasm to disappointment whilst AI is still in its infancy stage in wind energy.

The AI technology currently available in the market is narrow AI, which means that it is able to handle just one particular task. It is still impossible for AI technology to fully complete the work of an all-round engineer solving complex engineering problems. Development of Industrial AI is even more challenging than the AI we use in our daily life through the likes of cloud-based smart home hubs, due to the lack of good-quality training datasets.

U.S. operators are finding that AI needs to be informed by real-world engineering expertise. A focus on engineering-led approaches — from agile companies which have grown up from an engineering base with a strong industry track record — is best positioned to harness all the improvements promised by AI.

It is therefore imperative that the two approaches, AI and engineering-led predictive maintenance, are not seen as opposites, and that operators’ use of emerging technologies is underpinned by deep engineering knowledge.

Data Access

During this digital transformation journey, turbine owners are increasingly running into obstacles relating to limited data access from their own assets. Without full access to data, owners cannot fully understand the health of their asset and manage it in an optimal way. Ultimately, results from AI/machine learning are only as good as the data available. It is possible for asset owners to come up against a barrier in a number of different ways, from insufficient data acquisition systems or infrastructure, meaning data isn’t collected in the first place, to a contractual limit that means owners and operators aren’t able to freely access all the data collected about their turbine performance.

That said, it is possible for the industry to surmount challenges in the areas of data collection, data handling and data access, so that turbine performance can be optimized, and the U.S. wind market remains competitive. For the former, innovative digital hardware and IIoT sensors are becoming increasingly cost-effective, making it easy to justify investment in improving data acquisition and infrastructure systems. With the latter, if U.S. wind farm owners collectively start holding their suppliers to account when it comes to data access, greater volumes of data will become increasingly available, ensuring a smoother procurement process.

DIY or Do-It-For-Me?

These factors naturally lead to one final consideration in evaluating your own predictive maintenance approaches: whether to carry out all assessment and monitoring in-house or outsource it to a predictive maintenance provider. The former, DIY approach may lend itself well to organizations whose personnel possess a broad and in-depth O&M experience in industries with a long track record in applying predictive analytics, and who are well placed to advise on the implementation of predictive maintenance approaches for wind assets. Equally, you may already be using some form of condition monitoring technology, and this familiarity could serve as the basis for a more fully developed predictive maintenance solution. Over time, after working with and alongside experts from your predictive maintenance supplier, you will develop levels of expertise that require less hand-holding and make some level of DIY feasible.

Alternatively, it may prove more resource- and cost-effective for your predictive maintenance partner to continue to use their existing expertise to monitor, detect and predict failures remotely and advise on the most suitable course of action, at least in the short-term. In the long run, and as your on-site team become more familiar with predictive maintenance technology and techniques, you could consider moving from this Do-It-For-Me approach, to a semi-DIY set-up before taking full control. Of course, for this to be an option, you’ll need to be working with a provider that can deliver this level of flexibility and a tailored approach.

If an asset owner or operator decides to go with the DIY approach eventually, it is still advisable to invest in a powerful digital platform, which could increase the efficiency up to ten-fold. After all, the core competence of asset owners and operators usually falls on energy production instead of AI/machine learning and digital product development. To have a strong technology partner in this area will enable a pain-free implementation of the DIY strategy in the short term as well as the longer-term maintenance of the most up-to-date digital technology in the current competitive and fast-evolving market.

Understanding these myths, challenges and potential approaches is a key first step in taking advantage of the opportunities on offer. When combined with real-world engineering expertise, AI and machine learning have the potential to transform decision making for wind energy asset owners. With benefits on offer including the ability to assess turbine condition, optimize maintenance programs and bring down the levelized cost of energy, asset owners would do well to take the lead in adopting approaches that combine AI and engineering expertise.

Javed's Blog

Friday, October 19, 2018

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