Modelling the grid for decentralised energy
Vonnie Smith, vice president of utilities asset performance at Bentley Systems, examines the trends that have driven the acceleration of grid decentralisation and what technologies - such as Internet of Things (IoT) and digital twins - still need to be applied for smooth integration.
29th May 2018 by Networks
Utilities are facing massive changes that affect all aspects of their business, from planning through to operations. Once an industry characterised as technology-risk averse, utilities have been shifting to more agile approaches with a higher tolerance for risk. Modelling the grid to accommodate these changes requires new approaches and closer relationships with trusted technology partners. It’s time to examine what methodologies have driven the acceleration of grid decentralisation and what technologies still need to be applied for smooth integration.
Certain trends have led to an overworked grid
Growth in generation at grid edge, also referred to as distributed energy resources (DER), has inarguably reached critical mass. Although T&D networks with centralised generation sources have traditionally been modelled using geographic information systems (GIS) and other network management capabilities, managing assets over their complete lifecycles is a foundational component of modelling for a decentralised grid. As DER supplements or even replaces coal and nuclear generation, the management of utility assets becomes a larger issue: there are more assets to manage and maintain and reliability and predictability become more challenging.
Regulation uncertainty will continue as regulators who share the same technology issues as utilities struggle with how to represent to their constituencies. It is difficult to answer questions about which approaches to incentivse and how to balance consumer issues with using utilities to recover costs, especially when renewable sources clouds the definition of customer and supplier.
Ageing assets remain a problem. Replacing assets, notably transmission and generation assets that are operating past their design life, have been delayed due to the uncertainty around cost recovery and replacement choices. As a result, managing ageing assets increases the importance of full documentation of an assets’ operational history to understand and support predictive and preventative maintenance.
Investment deficit has been a past problem that must be addressed in emerging economies to support societal and economic growth. Rapid development of infrastructure and greenfield projects that incorporate DER and renewable energy sources are essential.
Common connectivity is the pathway to adaptation
Utility systems must adapt to share information and interact. Managing assets is an area where interoperability is crucial and federating information through a connected data environment (CDE) must be part of the solution. The Internet of Things (IoT) has already become a key part of many industries and increasing DER into the grid drives the need for the digitalisation of information. “Smart” instrumentation and control devices produce a substantial amount of data, much of which is important to understanding asset health. Existing and evolving standards all work to provide a standards-based architecture to facilitate exchange of information between utility systems to strengthen and improve the benefits of the CDE.
Renewable generation goals are being met ahead of schedule with some reports of 100 per cent renewable generation now considered possible, according to EUCI. Consisting of wind, solar, and more, renewable energy resources are being integrated with storage technology to improve the availability of power despite wind and sunlight variability, further driving the change to a decentralised grid. Utilities network models must change to meet those challenges.
Distributed energy sources have different operating models than traditional power generation. As a foundation for simulation and predictive analysis, utilities systems are expanding their capabilities to include support for DER.
Due to the nature of DERs, utilities are not always able to control how or when these resources are connected. Organisations are implementing microgrids to meet their own requirements for high availability rather than relying on the utility.
Digitalisation of the operation
Network management systems have become more sophisticated, enabling data tracking, advanced decision support, and operational analytics. Using high volumes of digital data from many devices and integrating information technologies (IT) and operational technologies (OT) with engineering systems provides the basis to create a digital view of a utility. Combined with reality data from laser scans and high-resolution photography, this “digital twin” utilises powerful capabilities for reliability analysis and design optimisation. These capabilities are critical whether in response to unplanned external events or the need to evaluate design options for future system changes in both greenfield and brownfield situations.
As DERs grow in usage, the grid is concurrently becoming more populated with sensing and actuating devices as part of the process of digitalisation, or the process of moving to a digital business. A digital representation of the grid is visible at its fullest extension and will provide massive volumes of data that can be used to better understand grid performance currently, previously, and potentially in the future. By combining algorithms and simulation capabilities with the digital representation, a “digital twin” is created. Though digital twins have been around for a long time, IoT has ensured that digital twin implementation is now cost-effective.
Big data must contain an information model that documents the grid assets and maintains their status at any point in time. Asset lifecycle management documents when and where the asset was installed, its maintenance and service record, and other key information required for comprehensive asset stewardship.
Speaking a common language
To make digitalisation worthwhile, systems must be able to share information seamlessly. Therefore, systems should interoperate through a CDE. Reliability analysis, compliance and safety reporting, and operational analytics are core functions needed for asset stewardship, and they involve multiple database components and systems. Through enterprise interoperability based on industry standards, data can be instantly available.
The IEC Common Information Model (CIM) is important to interoperability. CIM derives from the standards for distribution, transmission, and energy markets. It provides a common language for communication between utility systems within the industry and with external entities.
Proprietary interfaces that have been standardised and made public are just as important for interoperability because they are able to export and import data from other systems and are transparent. In a network reliability study, network models from a GIS, combined with asset lifecycle information, were used by a planning engineer to examine unreliable networks and improve maintenance. Furthermore, this approach can improve network modelling by determining where new DER will be most effective. Interoperability with the CDE can help identify optimum grid connection points.
CDE critical for efficient management of DER
Using asset lifecycle information to plan an asset’s reliability bolsters the value of network data because it is relied on from the engineering design phase to the analysis domain. This use of asset lifecycle information also demonstrates why interoperability with the CDE is critical for efficient management of a decentralised grid.
A utility will have regulatory and related internal standards for network design, including operating criteria, load conditions, failure modes, and digital catalogs of approved construction components that define and guide how planning and design is performed and what parts are usable. Without a CDE, implementation and management of the many interfaces become challenging IT problems when facing a decentralised grid.
Simulation uses digitalisation to model the current grid or show the grid with proposed grid changes. After identifying a connection point, the analyst needs to understand how the new DER will affect the network. A model of the DER is connected to a digital representation of the network and algorithms are applied to simulate network operation under varied conditions. This is done in the design stage.
Predictive analytics is another capability that is essential in a network model that supports DER. The goal of predictive analysis is to find problems before they occur at a reasonable cost. The historical and real-time data available through a CDE can make seeing trends and understanding anomalies difficult without the necessary capabilities to track, analyse, and report important events, especially in a decentralised grid populated with large numbers of DERs and IoT devices. Analytics can provide perspectives of the event that help determine whether a change is a symptom or a cause.
As utilities face huge changes and work to adjust all aspects of their business with the integration of DERs, agile approaches to technology and information management will assist in goal achievement. To embrace the digitalisation process, leaders need to emphasise the importance of the CDE, the need for integrated engineering and analysis, and the value of predictive analytics. In addition, new approaches to network modelling must be adopted to handle the new world of distributed generation. As costs reduce and DERs grow in popularity, it is important to the grid that DER integration, and not just connection, is carried out smoothly and reliably to the benefit of the utility and the customer. Incorporating DERs into the digital twin is one way of easing them into the grid at the design phase. Once implemented, DERs can be monitored in the same way as the rest of the grid to assess reliability and performance. Then, DERs become truly beneficial because they ease the burden of the central grid.
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