Today, utility engineers spend a significant portion of their time completing repetitive administration tasks. Some organizations estimate that upwards of 40 per cent of the time of highly trained engineers is spent on these mundane tasks.

The maturation of artificial intelligence techniques such as machine learning and natural language processing has made them increasingly viable for use in automating more complex and higher impact tasks. AI has tremendous potential to reduce the substantial repetitive tasks, enabling these engineers to concentrate more on engineering.

Recently, Ontario Power Generation (OPG) ” a generator with a diverse portfolio of over 16 GW of capacity, including 5.7 GW of in-service nuclear capacity ” demonstrated the potential of AI in one specific step in the nuclear unit outage planning process.

Planned maintenance outages of two to three months in duration are an annual occurrence within OPG nuclear facilities. Each outage requires the scheduling of around 20,000 to 25,000 individual tasks. The majority of these tasks are similar to previous outages, meaning a team of highly-skilled outage personnel are reviewing procedures as well as manually searching and populating tasks from past schedules.à‚ 

The current outage planning process at OPG consists of over 40 major milestones that start more than two years before a nuclear unit outage is set to begin. The outage milestones are based on industry best practices related to safety, reliability, scope and duration, and also take into consideration individual nuclear facility needs. Embedded throughout these milestones are the issuances of four revisions of the nuclear outage schedule, wherein each subsequent version is increasingly more detailed and comprehensive than the previous.

The Outage AI solution that OPG has deployed focuses on predicting logic ties for tasks that are to be included in the outage window, creating the very first version of the schedule with all tasks populated. This approach ensures reduces manual effort while sufficient oversight contingency is maintained throughout the development of each subsequent nuclear outage schedule revision to mitigate both risk and possible duration extensions.

Leveraging artificial intelligence

The Outage AI solution is a custom-built, cloud-hosted application that integrates seamlessly with the existing IT infrastructure at OPG and leverages elements of AI, machine learning, NLP and intelligent automation. The objective here is to predict the work breakdown structure of the 20,000 to 25,000 tasks, including their logical predecessors and successors (logic ties), and automatically schedule them within the upcoming nuclear unit outage Revision ‘B’ schedule.

The Outage AI solution currently uses eight-years’ worth of past outage data to create these predictions and has also been developed to ingest upcoming nuclear unit outage schedules into future preventative maintenance outages. This creates a robust solution that actively learns and gets more intelligent as more and more data is processed, delivered and consumed by its underlying algorithms.

The solution itself is composed of three foundational pillars that were developed to address a variety of specific functional and technical requirements provided by the outage team throughout the solution design: (i) the creation of dummy tasks to identify missing tasks from the current outage schedule that existed in historical ones; (ii) the automated scheduling of as-yet-unscheduled tasks; and (iii) the removal of logic ties that would otherwise cause loops in the schedule. The former two are centred around an NLP-based text-matching algorithm that compares work orders and tasks in the current outage to those existing in historical ones, whereas the third uses a loop detection algorithm to identify culprit logic ties and quality control the solution output.

To create dummy tasks, the text-matching algorithm compares an aggregate list of all historical tasks pertaining to a specific work order to the tasks of the corresponding work order in the current schedule. Those that exist in the historical list and are identified by the text-matching algorithm as missing from the current outage and brought into the current schedule as dummy tasks. These dummy tasks then serve as a quality assurance check for cases when the current list of tasks may have been incomplete, indicating to the work schedulers that it should be revisited for integrity. The dummy task creation process is repeated for all work order descriptions in the current nuclear unit outage schedule, referencing all historical outage schedules in the process.

Once the dummy tasks are created for all work orders in the current outage, the text-matching algorithm is used to facilitate the creation of logic ties between tasks in the current schedule. This process occurs by identifying the best-matching historical task for a given task in the current outage schedule, and inferring the correct predecessor and successor logic ties for the current task based on how the best-matching historical task was scheduled. The logic tie creation process is repeated for all unscheduled tasks in the current outage that are flagged to be scheduled by the Outage AI solution.

Lastly, the Outage AI solution performs a quality control check to ensure that any ties generated as a result of the logic tie creation pillar do not cause loops in the schedule. This process is relatively straightforward, wherein the loop detection algorithm traverses all predecessors of the current task of interest to determine whether it is indeed a predecessor of itself. If the current task is identified to be its own predecessor by means of an upstream logic tie, the culprit relationship is terminated and the loop is thereby removed.

While the three foundational pillars constitute the core of the Outage AI solution, they are also bookended by an on-demand data transfer mechanism that sends candidate nuclear unit outage schedules into (and out of) the cloud-hosted environment, directly from (and to) the original on-premises servers and software systems. This allows for seamless integration with the existing technical stack as well as minimal to no disruption to the outage work schedulers’ computer-aided scheduling duties.

Finally, throughout the development of this AI-focused initiative for OPG, the collective team has made sure to embed into the Outage AI solution three key elements that are critical to the long-term success of the project. First, the team has made sure the solution is valuable through improvements in human labour efficiency. Second, it has built the solution to be scalable within OPG to the planned maintenance of other power-generating sites and assets. And third, the team has developed a framework for OPG that is expandable in the future to account for scope changes and also include resource balancing, in an effort to further enhance the current solution’s capabilities.

AI, Machine Learning, NLP and Intelligent Automation are not single solutions. They each fall along a spectrum that is based on problem complexity and predictive power, and as such need to be identified and designed with the right combination of business knowledge and industry expertise to provide fruitful and meaningful results.

The endeavour to implement these new and emerging technologies into one specific step of the nuclear unit outage planning process at OPG allows outage staff to focus on higher value activities, not repetitive tasks related to creating the first version of the schedule. The Outage AI solution facilitates scheduling improvements and optimization by quickly processing large volumes of historical outage data that are slowly becoming far too vast and time-consuming for a human to thoroughly analyze. In turn, this enables reductions in human scheduling errors and oversights as well as more robust identification of missing tasks, leading to the potential for reduced nuclear unit downtime.

Throughout the course of the Outage AI project, additional AI opportunities have also been identified within the broader outage planning process, beyond just the generation of the first schedule. While many opportunities exist, certainly the most compelling to pursue is scaling the Outage AI solution to create draft schedules for planned maintenance outages at similar sites and assets.

An alternative but complementary opportunity for AI involves improved workload, resource and capacity balancing to promote process effectiveness and elucidate operational efficiencies in outage planning. There also exists a noteworthy opportunity in overlaying AI-based techniques onto the connected supply chain to drive performance and increase productivity by leveraging data in more of a prescriptive, rather than diagnostic or even predictive, manner. While this aforementioned list is by no means exhaustive, it offers a glimpse into the potential benefits that can be realized when AI is used to facilitate data-driven decision making and effectively complement the human workforce in the nuclear industry.

Maciej K. Hryniewicki is a Manager in the PricewaterhouseCoopers Artificial Intelligence Consulting Practice, based in Toronto, Ontario, Canada. James Strapp is a Partner in the PricewaterhouseCoopers Power & Utilities Consulting Practice, based in Toronto, Ontario, Canada.

Thisà‚ articleà‚ wasà‚ originallyà‚ publishedà‚ in the PEi section of Smart Energy International 5-2019.à‚ Read theà‚ full digimag hereà‚ orà‚ subscribe to receive a print copy here.

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The report ” Caught in the Crosshairs: Are Utilities Keeping Up with the Industrial Cyber Threat? ” details the utility industry’s vulnerability to cyber risk and its readiness to address future attacks, and provides solutions to help industry executives and managers better secure critical infrastructure.

Thisà‚ articleà‚ wasà‚ originallyà‚ publishedà‚ inà‚ Smart Energy International 5-2019.Read theà‚ full digimag hereà‚ orà‚ subscribe to receive a print copy here.

“The utility industry has woken up to the industrial cyber threat and is taking important steps to shore up defences, we hope this report helps utilities benchmark their readiness and leverage best practices to stay ahead of attackers,” says Leo Simonovich, Siemens VP & global head, industrial cyber & digital security.

The study surveyed 1,726 utility professionals responsible for securing or overseeing cyber risk in Operational Technology (OT) environments at electric utilities with gas, solar, wind assets, and water utilities across the globe.

It identified key vulnerabilities in energy infrastructure that malicious actors seek to exploit, including common security gaps that are created as utilities rely on digitalization to leverage data analytics, artificial intelligence, and balance the grid with intermittent renewable energy and distributed power generation.

In brief:

ࢀ¢ As utilities increasingly adopt business models that connect OT power generation, transmission, and distribution assets to Information Technology (IT) systems, critical infrastructure is more vulnerable to cyberattacks, according to the study.

ࢀ¢ The risk of cyberattacks on the utility industry may be worsening with 56%

of respondents reporting at least one shutdown or operational data loss per year, and 25% impacted by mega attacks, which are frequently aided with expertise developed by nation-state actors.

ࢀ¢ The vulnerability of critical infrastructure to cyberattacks has potential to cause severe financial, environmental and infrastructure damage, and according to all respondents, 64% say sophisticated attacks are a top challenge and 54% expect an attack on critical infrastructure in the next 12 months.

ࢀ¢ Cyber threats present a greater risk to critical infrastructure ” compared to IT systems ” and are concerned with unique industry challenges, including ensuring availability, reliability and safety of electricity delivery.

ࢀ¢ Industry-wide, readiness to address cyberattacks is uneven and has common blind spots, especially with regards to the unique cybersecurity requirements for OT, and the importance of distinguishing between security for OT and security for IT.

This remains a major challenge for many organizations across the industry. Only 42% rated their cyber readiness as high, and only 31% rated readiness to respond to or contain a breach as high.

“Increasing electrification across a range of sectors is a crucial piece in the decarbonization puzzle, but, as the Siemens and Ponemon Institute report documents, an increase in grid-connected infrastructure creates additional vulnerabilities to cyberattacks. A devastating attack would not only harm the economy, but it could also slow down the rate of electrification. This report provides recommendations to help utilities better address these risks. Getting this right is not only important for the security of our electricity system, but also for achieving our climate goals,” said Randy Bell, director of the Atlantic Council Global Energy Center.

The results of the report were released at a forum hosted by the Atlantic Council in Washington, D.C. focused on the growing national, economic, and energy security threat that cyberattacks pose to the utility industry.

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In an environment increasingly reliant on renewables, Martyn Williams, managing director of industrial software provider COPA-DATA UK, explains how energy managers can adopt new technologies to better manage and monitor these volatile energy generation sites.

Thisà‚ articleà‚ wasà‚ originallyà‚ publishedà‚ inà‚ Smart Energy International 5-2019.Read theà‚ full digimag hereà‚ orà‚ subscribe to receive a print copy here.

During 2018, coal was responsible for just 1% of Britain’s total energy generation. This resulted in the country’s coal-fired power stations remaining entirely unused for twelve days in June ࢀ” a longer period than in 2016 and 2017 combined.

With renewables taking an increasingly greater share of the country ‘s energy consumption, Britain is on course to meet its renewable targets of 30% renewable generation by 2020. However, transitioning to this form of power supply is not without challenges.

Transitioning to smart grids

Even today, the cost is one of the biggest barriers to the adoption of renewable energy generation resources. Developed countries like the UK have a mature fossil-fuel infrastructure that’s been around for over a hundred years.

Transitioning to a renewable alternative is, in many cases, more expensive and requires a much higher initial investment.

The same is true for developing countries where renewable technology is, even more, cost-prohibitive.

Britain’s energy infrastructure, originally designed to run on fossil fuels, has been forced to adapt. The grid is now used to distribute energy generated from both fossil-fuel and renewable sources.

This has seen the expansion of smart grids ࢀ” a grid which uses control and communication in a specific way ࢀ” to avoid the need for costly expansion of existing cable and wire infrastructures.

Over the next few decades, billions of pounds will be spent on Britain’s energy network. While some of this is required to maintain the existing system and replace ageing equipment, the creation of smart grids will also require investment in new technologies.

In fact, this requirement has encouraged a pledge by Britain’s leading electricity network operators, including SSE Networks and UK Power Networks, which promises to deliver à‚£17 billion of smart grid infrastructure by 2050.

Managing uncertainty

A key differentiator of smart grids is the integration of renewable energy generation resources.

Unlike fossil fuels, renewable sources do not generate energy at a pre-determined level. As a result, operators cannot accurately predict their output without technology investments.

Wind farms provide a useful demonstration of this uncertainty. Using historical data, an operator can estimate how often the farm will generate power consistently but making completely accurate predictions is almost impossible. In fact, energy output from a turbine can drop without warning ࢀ” and there is no proven method to precisely determine when output will improve.

The case is similar for photovoltaic (PV) solar farms. In fact, hydro-electric power is the only energy source that can be physically controlled. At these plants, water can be stored in reservoirs and released when required, thus simulating the natural generation of hydropower.

The volatility of renewable energy sources can also create problematic surplus situations. Let’s say wind speeds were to dramatically increase. If unprepared, the grid may not be able to handle the sudden surge in power from a wind power farm and this could cause power outages.

In fact, there have been instances of operators resorting to paying customers to use excess electricity to balance this unmanageable surplus.

Reserve power flow, or back feeding, is one method of managing this excess energy.

However, back feeding isn’t exclusively for renewable sources and occurs regularly on small-scale power grids, usually during the middle of the day when people are out of their homes. As residential energy demand during these periods is low, some of the generated electricity can be fed back to a transformer through the network.

Traditionally though, distributed systems did require back feeding. Most of the generation came from large-scale fossil-fuel sources, which were located on the main network; therefore, the power would flow predictably onto smaller systems.

However, increasing renewable energy sites, as well as microgeneration sites, means energy volatility is increasing, causing more occurrences of back feeding, as well as a new need for energy storage.

Forecasting generation

Improved forecasting could be used to alleviate this challenge. However, it is argued that forecasts are often provided solely as a comfort to decision-makers ࢀ” that’s network operators, end customers and investors in renewable energy. Forecasts, due to their limitations, are not used in the daily operations of energy sites.

Accurate forecasting requires a complicated cross-disciplinary approach, examining mathematics, statistics, meteorology and accumulation of historical data from the generation site. Even then, the shortcoming of a forecast is that it is simply an extrapolation of what has occurred, rather than certain knowledge about the future.

By employing more accurate forecasting, however, operators could better manage supply and demand. New technology is already beginning to improve the exactness of predictions by using advanced computer models. However, it is recommended that this is used in combination with real-time system monitoring.

Smart grid control software allows operators to actively manage grid behaviour in real-time. Therefore, the operator can react appropriately should the site begin to generate more or less power than expected.

This real-time insight doesn’t lessen the volatility of renewable generation, but it can improve awareness of a system’s conditions. For instance, the software could immediately alert an operator when wind speeds increase. By correlating this data with information from the wider network, the software could provide a warning that a surplus of energy will occur, and back feeding is required.

Back feeding isn’t always a feasible option, however. In some instances, excess energy must be stored. Research by market analyst Aurora Energy Research, suggests that to meet its renewable energy targets, Britain requires an additional 13GW of energy storage in order to successfully balance the grid.

Energy storage

Like many other forms of energy technology, a major challenge of implementing energy storage is related to cost. Aside from the water storing methods of hydro-electric plants, traditional electricity grids have little to no method of storing excess power. In fact, Aurora Energy Research’s paper states that deploying energy storage on Britain’s network will require a à‚£6 billion investment.

Energy storage plays an important role in creating a flexible grid. When there is more power supply than demand, excess energy needs to be stored safely to avoid wastages. Similarly, when demand is greater than supply, energy storage allows storage facilities to discharge this stored energy back to the grid.

With increasing reliance on renewables, energy storage facilities will be essential buffers for excess power. While there are copious research projects dedicated to the development of energy storage methods, including compressed air, thermal storage and battery storage, these technologies are still largely in their infancy.

Across the continent, there are several successful examples of using batteries to store excess renewable energy. This includes a BMW commissioned battery storage farm in Leipzig, Germany, which is housed on the grounds of its own wind generation site. The site operates using 700 second-life electric vehicle batteries, which are used to house excess wind power before it is fed back into the wider grid.

Without knowledge of when, where, and how much energy is required on the grid, however, battery storage is redundant.

Feeding this energy back to the network requires real-time insight into the state of the gird. Large scale installations, like BMW’s facility in Germany, will use intelligent software to constantly monitor and record demand for power and therefore supply appropriately.

Again, this requires investment from the facility itself but is essential for the creation of a truly smart energy grid.

Transitioning from a traditional energy network to a fully functioning smart grid is incredibly complex. Britain’s ageing infrastructure means that costs maintaining and repairing these facilities are essential, but investments in new technology cannot be overlooked.

Britain may be capable of generating 100% of its energy supply from clean sources, but until the nation has adopted technologies to efficiently manage, store and distribute this energy, this goal will not come to fruition.

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As with all facilities, regular maintenance is required to ensure operability and any repairs need to be carried out quickly and to a high standard.

Thisà‚ articleà‚ wasà‚ originallyà‚ publishedà‚ inà‚ Smart Energy International 5-2019.Read theà‚ full digimag hereà‚ orà‚ subscribe to receive a print copy here.

When the walls of the boiler in Lisahally Power Station, in Northern Ireland, had sustained erosion and corrosion damage, automated weld overlay technology was utilised to extend the service life of the boiler and ensure that the plant could continue to run at peak performance.

The fuels used to operate biomass power plants cause corrosion and erosive wear in boilers. In fact, biomass can release contaminants, such as alkali metals, chlorine, sulphur and other corrosive chemicals, when burned. As a result, regular maintenance is fundamental to maximize the lifespan and reliability of equipment in these power stations as well as ensure plant availability, performance and efficiency.

In an effort to increase the durability of its essential assets, maintenance planners at the power station wanted to repair the boiler walls with a long-lasting solution that could be completed with minimal disruption.

Fuelling plant efficiency and preserving equipment health The plant, located in Londonderry, Northern Ireland, was built and is operated by Burmeister & Wain Scandinavian Contractor A/S (BWSC), one of the world’s leading medium-sized commercial-scale power plant providers. The facility uses recycled wood as the main feedstock to generate six megawatts of heat (MWth) and 18.2 megawatts of power (MWe), 15.8 MWe of which is then used to power 30,000 local homes and businesses.

The key piece of equipment at Lisahally plant is the two-pass biomass grate boiler, which generates high-temperature, high-pressure steam by combusting the wood-based material fed to its furnace. The steam is then transferred to a turbine, coupled with a generator to produce electricity. Waste steam is condensed into water and fed back to the boiler via a closed-loop system.

As corrosion and erosion were affecting the integrity of Lisahally Power Station’s heat exchange system, i.e. boiler furnace and wall tubing, plant managers contacted Sulzer to help them conduct the necessary repair work.

The company’s Tower Field Service group offers specialised field services with over 40 years of experience in tower, drum and vessel maintenance, repair and revamps.

Weld overlay delivers quality and cost-efficient corrosion protection As an economical and sustainable alternative to replacing the existing structures within the boiler, it was decided to cover the damaged membrane walls with layers of Inconel 625 to repair and protect the membrane wall. This is an austenic nickel-chromium-based superalloy that is particularly resistant to corrosion and oxidation, even in harsh environments with elevated temperatures, such as in furnaces.

The additional Inconel layer would enable the boiler to efficiently maintain high steam pressure whilst protecting the surfaces from future corrosion and erosion.

The homogeneous deposition of Inconel onto the corroded and eroded surfaces was carried out onsite by automated weld overlay.

Using advanced automated technology, highly accurate welding operations were carried out for structures such as boilers, furnaces, vessels and towers.

CladFuse, the technology in question, uses a carriage that travels along a laser levelled track system fixed to the wall that needs repairing. On the carriage, a robotic index arm moves the welding torch and the oscillator in order to create weld Inconel beads with an overlap of approximately 50% between adjacent weld beads. All the weld overlay process parameters, e.g. carriage speed or bead thickness, are controlled by a programmable logic controller (PLC).

Operators can communicate with it and adjust the parameters manually using a human-machine interface (HMI).

In addition to offering a cost-effective and high-quality method to repair damaged metal surfaces, the solution is extremely fast. This was particularly important for managers and operators at Lisahally Power Station, who needed the welding to be completed by the end of a scheduled outage for regular maintenance activities.

Sean Hegarty comments: “Adhering to a strict timeline and coordinating this repair with all our operations was a must.

Whenever weld overlay is performed within the boiler, large volumes of different gasses are generated. Therefore, it is necessary to segregate the boiler, and it is not possible to carry out any other maintenance work in its immediate vicinity. As the power station needs to run continuously, extended shutdown and downtime would have greatly affected the plant. To ensure that this didn’t happen, Sulzer’s experts set up a number of measures and controls to ensure we could carry on conducting planned maintenance activities along the steam path.

“The project was challenging, and Sulzer’s knowledge and expertise provided a great and outstanding execution. We are extremely happy with the outcome.”

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By coupling electricity, heat and cooling ETES represents an opportunity to break the energy system from reliance on fossil fuels.

Thisà‚ articleà‚ wasà‚ originallyà‚ publishedà‚ inà‚ Smart Energy International 5-2019.Read theà‚ full digimag hereà‚ orà‚ subscribe to receive a print copy here.

There’s a compelling need to increase utility-scale energy storage capacity in response to the dramatic growth in intermittent renewable generating capacity like wind and solar. Although current technologies such as chemical batteries are rapidly improving, to date bulk power storage has been dominated by pumped storage hydropower. While pumped storage is low cost, efficient and typically offers huge capacities it is also strictly limited by geography.

Now though, new technology has emerged that offers bulk power storage at scale and with the potential for far lower costs than existing battery chemistries, such as lithium-ion. Perhaps even more significantly, Electro-Thermal Energy Storage (ETES) connects heating, cooling and electricity storage together. As a result, the system can meet multiple energy storage and supply needs simultaneously.

The ETES thermodynamic cycle

Based on a novel and reversible thermodynamic cycle, ETES is a scalable and efficient technology that supports sector coupling between the distinct energy needs of heating, cooling and electricity. With ETES, heating needed for food processing and district heating can meet cooling for applications like data centres, warehousing and large commercial buildings, as well as electricity storage capabilities to support grid balancing and renewable energy optimisation ” all in a single system.

By allowing industrial, commercial and domestic sectors to combine their needs, this offers a comprehensive and efficient solution to a host of energy system challenges while keeping capital and operational expenditures to a minimum.

Currently the only solution available that is capable of using, storing and distributing heat, cold and electricity simultaneously, the patented tri-generation energy-management system is based on the use of CO2 (R744) as the working fluid. At its core ETES allows the conversion of electrical energy into thermal energy in the form of hot water and ice and vice versa. The energy is stored in a series of thermally insulated water tanks, making the system low risk and very robust with high resilience. Similar to a domestic refrigeration unit, in ETES the closed CO2 cycle sees the working fluid compressed or expanded through turbo-machinery to store or extract energy. Depending on specific demands, energy stored as either heat or cold may be directly distributed or efficiently reconverted back to electrical energy as required.

During the charging cycle, electrical energy from any source ” such as excess renewable energy for example ” is used to power a turbo-compressor. The C02 working fluid is compressed to around 140 bar and 150à‚°C or more by this turbo-compressor. Passing through a heat exchanger, heat from the compressed CO2 is transferred to the hot storage tanks. There may be as many as four such tanks, for example three at atmospheric and one pressurised, depending on demand and application. Each tank is maintained at a separate temperature. The cooler but still pressurised CO2 then passes into an expander where the pressure drops, and it condenses to a liquid and cools down further. At this stage, the second set of heat exchangers chills the cold storage tank to produce ice.

In the reverse process, gaseous CO2 passes through the heat exchangers on the cold side. It condenses to a liquid while the temperature of the cold tank is increased. The now liquid CO2 has its pressure increased by a pump before being evaporated back to gas in the hot side heat exchangers. Now at supercritical conditions, the heated and pressurised CO2 passes through an expansion turbine where an attached generator is used to produce electricity as required.

Using water in simple insulated tanks, some of which may only require minimal insulation, along with standard turbomachinery equipment means the system has a low environmental impact and is reliant on well-proven and extensively deployed systems. As a result, overall capex is expected to be competitive with other commercially available energy storage systems.

Tried and tested technology

Scalable and site-independent, commercial development of ETES sprang from a cooperation agreement between MAN Energy Solutions Switzerland and ABB Switzerland that was announced in March 2018.

Among the core components of the ETES system is MAN’s hermetically sealed HOFIM turbo-compressor. Built for rugged extremes and used, for example, in subsea compression station applications, these units are multi-stage radial compressors. With casings designed for 220 bar, HOFIM compressors feature a 7-axes active magnetic bearing system and are arranged in a single shaft configuration together with a high-speed electric motor.

The turbo-compressor has no oil or sealing systems, which reduces complexity. Compared with traditional compressor designs HOFIM designs have a 60% smaller footprint and 30% less mass. The HOFIM compressor family, designed for subsea applications where reliability and service longevity are paramount, is currently available in a power range of 4 to 16MWe.

With a process based on solid, well-proven engineering and thermodynamic principles, ETES is essentially a simple reconfiguration of tried and tested technologies to produce efficient and sustainable energy storage and conversion system.

Market opportunities for heating, cooling and electricity storage

Modular and easily scalable, ETES features multiple thermal storage tanks, each at a different temperature. This quality makes it an ideal energy storage solution for mid- to large-scale thermal and electrical consumers and process industries. Hot side temperatures range from 20 to 120à‚°C while the cold side goes down to zero. This is well suited to many process industries as well as functions such as sterilisation, heating and cooling for large public buildings and chilling increasingly prevalent data centres.

Furthermore, although conceived with renewables in mind, any source of electricity may be used to energise ETES. Consequently, margins may be found in some markets by ‘time-shifting’ energy from low-cost nighttime power to peak load daytime markets, for example. Using ETES there are clearly multiple opportunities available from peaking markets and other power system functions such as grid balancing.

Uniquely tailored to each application, a typical system could feature an input of 5MWe, a six-hour charging cycle and storage capacities of 30MWhe, 110MWhth heat and 80MWhth of cold. Depending on factors such as usage mode as well as electricity purchase and sale prices, based on these figures a return on investment period of just five years or less is credible.

Certainly, there is no question over the huge surge in global demand for energy storage. A report from IHS Markit notes that a total of 4.3GWh of grid-connected energy storage is expected to be installed worldwide in 2019 alone. By 2025, annual installations are expected to reach more than 10GW.

Heating and cooling is a sector where renewable energies have made limited inroads, yet it accounts for about half of all global energy demand. By efficiently coupling heating, cooling and electricity, ETES offers an opportunity to maximise the renewable energy contribution to the energy sector as a whole and transform the bulk energy storage market at a stroke.

About the authors

Raymond C. Decorvetis responsible for business development for the MAN ETES technology at MAN Energy Solutions Switzerland Ltd. Based in Zu rich, he holds a degree in economics.

Mario Restelliis responsible for sales for the MAN ETES technology at MAN Energy Solutions Switzerland Ltd. Based in Zurich, he holds a degree in mechanical engineering.

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