Supply Orchestration. Home battery systems and electric vehicles are growing in acceptance and their storage capacity will eventually become an important piece of infrastructure for time-shifting supply to match demand. The increasing build out of solar PV has created an oversupply in the middle of the day while the rising adoption of home air conditioning creates a spike in demand after working hours, resulting in the so-called Duck Curve (see Figure 2).
By predicting periods of potential supply shortfall, distributors can increase prices to a level attractive enough to prompt battery owners to sell excess electricity rather than store it. The complexity inherent in such a distributed system is only manageable with machine learning to constantly optimise pricing and supply orchestration to simultaneously prevent excessive degradation of battery performance. This is already available for large scale battery operators, e.g. using Tesla Autobidder, and will become accessible to networks of home and eventually vehicle owners.
Optimising Renewable Generation with AI
Renewable energy sources continue to make efficiency gains due to engineering improvements. However, advances in AI will increase generation even further. Solar PV and solar concentrators that rotate on dual-axis trackers to follow the path of the sun must each operate individually according to their own precise position and the time of day and year. This must be balanced for efficiency to reduce excessive movement, which consumes a portion of electricity output. Neural networks and fuzzy logic can be applied to optimise rotation to maximise production while reducing power consumption for operation. Input variables can include position, time, temperature, and even sky colour. Similarly, wind turbines can dynamically alter their positions to maximise wind flow across the entire fleet rather than at an individual level. The large streams of data must be processed in real-time as wind variables change to have an immediate effect on output.
Stabilising the Super Grid
To improve resiliency and lessen the effects of renewable intermittency, there is a growing push towards increasing the interconnectivity of national grids. This ensures supply even when regional generators go offline or if sudden local peaks in demand occur. Moreover, interconnected grids help even out supply from renewable sources using the philosophy that it is always windy or sunny somewhere. For example, the proposed European super grid would take advantage of higher wind generation in northern countries in winter and in North Africa in the summer. Additionally, hydroelectric plants in the north could be modified to become pumped storage facilities powered by solar thermal plants in the south to supply all of Europe.
Not only will a super grid require investment in new infrastructure, such as high voltage direct current (HVDC) for efficient long-distance transmission but also in intelligent systems to manage the new complexity. The retirement of fossil-fuel generators and greater variability of renewable sources will require rethinking grid inertia and frequency control between countries. Measurement solutions, such as GridMetrix by Reactive, have been deployed by AEMO in Australia and National Grid in the UK to better monitor how inertia fluctuates as renewable sources ebb and flow. Once real-time data becomes available for analysis, infrastructure such as synchronous condensers and quick-response batteries can be automatically utilised to regulate frequency.
A Positive Outlook
Countries such as China, India, the US, Germany, and Spain have shown that it is possible to add large amounts of solar and wind generation capacity at a pace. The next chapter in the renewable revolution will be ensuring that this can be done at scale without disrupting the grid and AI will be a key component in managing the transition.
Effective prescriptive maintenance only becomes possible after the accumulation and integration of multiple data sources over an extended period. Inference models should understand both normal and abnormal equipment performance in various conditions, such as extreme weather, during incorrect operation, or when adjacent parts are degraded. For many smaller organisations or those deploying new equipment, the necessary volume of data will not be available without the assistance of equipment manufacturers. Moreover, even manufacturers will not have sufficient data on interaction with complementary equipment. This provides an opportunity for large operators to sell their own inference models as a new revenue stream. For example, an electrical grid operator in North America can partner with a similar, but smaller organisation in Europe to provide operational data and maintenance recommendations. Similarly, telecom providers, regional transportation providers, logistics companies, and smart cities will find industry players in other geographies that they do not naturally compete with.
Employing multiple sensors. Baseline conditions and failure signatures are improved using machine learning based on feeds from multiple sensors, such as those that monitor vibration, sound, temperature, pressure, and humidity. The use of multiple sensors makes it possible to not only identify potential failure but also the reason for it and can therefore more accurately prescribe a solution to prevent an outage.
Data assessment and integration. Prescriptive maintenance is most effective when multiple data sources are unified as inputs. Identify the location of these sources, such as ERP systems, time series on site, environmental data provided externally, or even in emails or on paper. A data fabric should be considered to ensure insights can be extracted from data no matter the environment it resides in.
Automated action. Reduce the potential for human error or delay by automatically generating alerts and work orders for resource managers and service staff in the event of anomaly detection. Criticality measures should be adopted to help prioritise maintenance tasks and reduce alert noise.
Last year Microsoft’s industry updates showcased several IoT implementations across industries and their edge-based solutions portfolio, customers and partner ecosystem. The tech giant revealed nearly 150% YoY growth with customers such as Starbucks, Chevron, Walmart, Walgreens, BMW and Volkswagen added to the Azure platform, leveraging IoT services to accelerate their digital transformation journey. Microsoft also announced more than 70 partnerships with some of the big names in the IoT ecosystem, such as Universal Electronics, SAP, and Cradlepoint to extend solutions and support for the Microsoft IoT business.
Extending IoT Capabilities with Strategic Partnerships
There were several recent announcements which indicate that Microsoft is focused on strengthening their IoT and industry capabilities – and this is a timely move. Ecosystm Principal Advisor, Kaushik Ghatak says, “COVID-19 has brought to the fore the need for managing risks better. And the key to managing risks is to have better visibility and drive data-driven decisions; the sweet spot for IoT technologies. IoT is at the core of the Industry 4.0 story where deep domain expertise in industry verticals is a pre-requisite to success. It is heartening to see that Microsoft is taking the lead in building a powerful ecosystem by developing key partnerships with leading providers of Industry solutions.”
Last week, Microsoft and Hitachi announced a strategic alliance to accelerate the digital transformation of the Manufacturing and Logistics industries across Southeast Asia, Japan and North America. The first solutions are expected to be made available in Thailand as early as this month. Hitachi brings to the table their industry solutions, such as Lumada, and their IoT-ready industrial controllers HX Series. These solutions will be fully integrated with the Microsoft cloud platform, leveraging Azure, Dynamics 365 and Microsoft 365.
The three areas where the Hitachi solution is expected to bring strength to Microsoft’s industry offering are:
Process optimisation and increased manufacturing productivity. Hitachi Digital Supply Chain and Azure IoT leveraged to analyse 4M data collected from manufacturing sites for visualisation/ analysis of production processes
Logistics optimisation. Digital technologies such as Azure Maps and Hitachi Digital Solution for Logistics/Delivery Optimisation Service to analyse data on parameters such as traffic congestion, storage locations and delivery locations, to enabling smart routing
Predictive maintenance and remote assist. HoloLens 2, Dynamics 365 Remote Assist and other smart devices, to empower first-line workers
Ecosystm Principal Advisor, Niloy Mukherjee feels that with projections of 43 – 100 billion IoT connected devices in the next few years, IoT is obviously a hot space. “We can think of IoT as a stack with four layers – the devices/sensors, the connection layer, the cloud and computing layer and the business apps layer. With Azure, Microsoft is very well positioned in the cloud and compute layer and can grab a large chunk of this fast-growing market. Tying with players like Hitachi allows Microsoft to integrate with the business apps layer and perhaps also some devices. It is absolutely the right strategy and I would expect them to go for many more such alliances. With Microsoft’s strength in the enterprise market, IoT gives them a great opportunity to increase their share of cloud workloads with customers.”
Addressing the Challenges of IoT Adoption
Ecosystm research shows that the biggest challenges in IoT adoption are security and integration concerns (Figure 1).
In 2018, when Microsoft started actively focusing on IoT, they also launched the Azure Certified for IoT program to maintain consistency and enhanced interoperability across their device partner ecosystem. This addresses the integration challenges that organisations face when deploying IoT. Microsoft continues to grow their IoT ecosystem, ensuring faster IoT deployments, with hardware and software that has been pre-tested and verified to work with Microsoft Azure IoT services. Last week also saw Cyient joining Microsoft Azure as a certified partner for IoT. Cyient IoT Edge Gateway 5400, their flagship IoT gateway product is now Microsoft Azure Certified for IoT. This is expected to accelerate IoT deployment for Cyient customers and enable a seamless integration of edge devices to the cloud.
Ghatak says, “To scale up their IoT business, Microsoft would need to develop a substantially large ecosystem, beyond few key players such as Hitachi, who dominate at the large enterprise segment of the market. That is where partnerships with smaller and niche industry solutions providers such as Cyient fits in. More niche providers such as Cyient will increase Microsoft’s reach into medium and smaller segments of the enterprise market.”
Addressing the Increasing Threat Landscape
Recent cyber-attack trends and security breach statistics reveal a huge increase in cybercrime activities, in the wake of the COVID-19 pandemic. As the number of IoT sensors, devices and gateways increase, so does the risk of security breaches. As shown in figure 1, cybersecurity concerns are real and can act as a barrier to IoT adoption, despite the benefits that the technology brings. Automated vulnerability management capabilities, that allow risk assessment and patch installation where necessary will see an increase in IoT adoption.
To complement Microsoft Azure IoT security, Microsoft acquired IoT security specialist CyberX, last month. The acquisition will enable greater security for the IoT devices connected to the Microsoft network and will help their customers to gain visibility through a map of devices thus allowing them to gather information on security risks associated with thousands of sensors and connected devices. This will enhance smart grid, smart manufacturing and digital assets and profiles and reduce vulnerabilities across production and supply chain.
Mukherjee says, “The key concern for the expansion of IoT into more and more use cases in the next few years is really going to be security. New areas like VR and AR are emerging from futuristic fantasy to real-world reality. These will tempt many enterprises – but security will be the key concern to address. And so, Microsoft’s simultaneous push on security completely aligns with this. As the Ecosystm MSSP VendorScope results show Microsoft’s strategy on cybersecurity seems to be working.”
Talking about Microsoft’s go-to-market strategy, Mukherjee adds, “Microsoft is obviously spreading its net far and wide for all cloud applications including IoT, to go-to-market with partners. One of the key focus area here is the SME segment, which is forecast to be one of the hot growing segments for IoT in the next few years. The more offerings from the business apps layer that Microsoft integrates, the more they enable their partners to sell to their customers.”