The fundamentals of the energy industry are transforming, driven by a host of societal, policy and technological changes. Energy generation is becoming increasingly decentralised. The grid is rapidly ‘digitising’, leveraging a host of IT/OT tools and solutions such as AI to incorporate renewable energy resources and Distributed Energy Resources (DER), whilst all the time striving to improve customer expectations.
Information flows across the energy value chain are increasingly complex, with many parties accessing shared datasets. DER such as solar PV, but also Electric Vehicles (EV) and new smart -devices are impacting not only traditional one-way energy flows but also the related information. Furthermore, how customers engage with their energy has increased in frequency and type through in-home devices, social networks, and intelligent customer apps. The volume of data produced and consumed has increased creating challenges and unprecedented opportunities.
MHC believes that the role of data-driven, decentralised architectures is critical. Complex ecosystems of different devices – smart meters, electric vehicles, sensors, communication technologies, new players like aggregators, virtual power plants, device manufacturers, installers and customers – require a common vocabulary and seamless integration to communicate effectively.
Data-driven architectures support a more effective management of heterogenous data. They provide for semantically enhanced and interlinked cross domain repositories, visualization, querying and exploration tools along with configurable analytical models enriched with machine learning and deep learning algorithms to pave the way for interoperability and create value from data.
In simple terms, such architectures enable reliable and effective decision making, as well as support the creation of innovative applications through the utilisation of a wide variety of data for the safe and effective integration of DER and better customer service.
One model for decentralised architecture, is where data is stored locally at the “device-edge” and is transferred to a secure central cloud hosted gateway. Then AI-enabled analytics at this layer can facilitate real time decision making and data sharing in a secure and privacy-aware manner.
Key to realising the value from a decentralised architecture is the application of an effective data framework that outlines the approach and policy:
- Data Governance – effective governance for data collection, meta data creation, data quality management, distributed storage, data security and data access.
- Data Integration – intelligent API-based IT, OT and IoT integration for secure data sharing
- Data Processing – machine learning enabled data cleansing, formatting, value substitutions and managing data inconsistencies in real time and batch data processing with dedicated analytical environments for machine learning algorithms.
- Data analytics – deeper insights and smart analytical tools.
- Data Access – multiple digital channels for data access.
MHC believes a distributed, scalable data management framework facilitates data sharing and maximises the value of deep data insights. Energy and water utilities can leverage their investments in smart data architecture to create insights to support several use cases like:
- Workforce Management
- Fleet Management
- Preventative Asset Maintenance
- Predictive Asset Management
- Inventory Management
- Invoice Management
- Compliance Management
- Risk Management
- Contractor Onboarding
- License and accreditation management
Utilities are capturing increasingly large volumes of heterogeneous data from devices and for the most part are failing to realise value from it.
To exploit and drive value, organisations must look at de-centralised architectures to manage the device ecosystem enabled by data exchange standards and effective data management strategies.