The power of analytics to master the modern grid

• 6 years ago (2018-02-07)
• Junior Isles

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By Dan Beasley, Director, Utilities, Cyient

The electric utility industry is undergoing a major transformation driven by new sources of energy generation (solar and wind power), consumer demand for faster and more affordable services and cyber security. The emerging modern grid demands accurate data and electric network information. As a result, managing data to harvest insights and forecast more accurately has huge potential to optimise operations. Utilities must therefore overcome current constraints and limitations to ensure high quality operational data is available.

Demanding high quality data

To become a top operator within the industry, utility organisations need high quality data to achieve maximum quartiles in System Average Interruption Duration Index (SAIDI) and operational costs. It enables utilities to understand network and asset behaviour, operating conditions, and their impact on customer service. Electric networks change routinely, meaning quality data must be timely. Ensuring correct inputs from system and operations data enhances the quality and cost efficiency of its operations.

Regulators are urging utilities to take advantage of rich data sets to improve grid operation, asset management and provide a better customer experience. For example, New York launched Reforming the Energy Vision (REV) in 2015 to improve customer choice and affordability of services.

Data limitations in the Modern Grid

The GIS network has long been utilities’ system of record to model network behaviour within operations systems. It offers the best tools and mechanisms to manage electric network connections. The platform can model the network behaviour to meet advancing market conditions, but many constraints limit GIS from meeting today’s modern grid requirements:

• GIS is built from a design and planning (as-built) perspective rather than an operations perspective
• Electric operations personnel require a version of the network reflecting the existing operating state of the model (as-operated)
• Electric operations personnel work closely with field personnel who require less, but more precise and timely data than GIS typically can provide
• Advanced Distribution Management Systems (ADMS) seek to make operations decisions without human interaction, requiring as-operated content along with extensive correlative asset data

GIS typically reflects the initial network construction but rarely incorporates essential operating characteristics. Constructing the network generates volumes of data to be stored in an asset management system. Additionally, electric utilities operate in a regulated environment and generate inspection data requiring organisation, storage and access. Finally, there is customer specific data including location, service experience, amount and quality of electric data. The new imperatives of the modern grid require harmonising data sources into a cohesive story to enable immediate decisions based on customer demand.

The Internet of Things (IoT) also creates multiple new data points that can pressure infrastructure. BI Intelligence estimates that the global installed base of smart meters will increase from 450 million in 2015 to 930 million in 2020 . Distributed Energy Resources (DER) and legacy IT systems also add to the volumes of information to interpret. A system is therefore required to gather and maintain multiple sources of data.

Realising the full data potential in the modern grid

Consolidating and aligning multiple platforms where data resides is essential. ADMS is at the core of the Modern Grid, requiring utilities to combine data from multiple business functions. This provides control centre personnel with insights to manage all aspects of the distribution system. By adopting this tool, utilities can improve their resilience and ability to withstand or recover from natural disasters quickly and accommodate larger quantities of DER, enabling them to offer more renewables.

The following three areas illustrate how utilities can improve data use and comply with the modern grid:

Optimise GIS for modern electric operations

Enabling GIS to support electric operations will require key modifications to create significant benefits:

• Improve the speed and timeliness of network updates
• Enable the accurate views which electric operations require
• Deploy machine learning algorithms to harmonise phase and transformer connectivity with actual network conditions

Machine learning (ML) provides the ability to leverage different types of data. It helps the GIS provide the most accurate information at the right time to contribute good decisions within the ADMS platform. Field operations personnel can use current and accurate data during a natural disaster, for example, to distribute different sources of energy during downtime.

By harnessing ML, asset behaviour can provide additional intelligence and create a virtual circle of data quality. While many utilities understand the need to harmonise processes, systems and data, legacy organisations and stand-alone data repositories make consolidation and aggregation difficult.

Master a data governance model

Electric utilities are made up of discrete organisations, each of which manage various programs, processes and systems. Typically, these organisations work separately, often duplicating, not sharing data. However, the modern grid is changing this paradigm. The increasing volume of data is exacerbating the problems associated with a lack of data governance. Today’s mandate is therefore to engage a governance model, assuring process, system and data alignment to meet modern grid demands.

Data governance enables the utility to aggregate data across multiple processes and systems, and requires blending accountability, agreed service levels and measurement. An IDMS must provide windows into service levels. Therefore, adopting a strong governance model will improve their approach to the data lifecycle.

Create a culture of data quality

Ultimately, the modern grid requires a culture that thrives on the generation and assimilation of high quality data. We can look to our objectives of creating a safety culture to help inform the models that we need to achieve this. This includes where data quality begins, building accountable teams, education and knowledge management, understanding what data quality means and ownership at the employee level.

One of the key initiatives in harnessing the new dynamic changes to the electric utility industry is to empower operations with a level of data quality and homogeneity not typically present. This will enable the utility to overcome current constraints and limitations to master the power of analytics.