The electric utility industry is undergoing a major transformation that is being driven by new sources of energy generation (solar and wind power), consumer demand for faster and more affordable services, cyber security and big data. Gathering data to harvest insights and forecast more accurately has huge potential to optimise the way that utilities operate. In the same manner that driverless cars require accurate data and roadway mapping to operate effectively, the emerging modern grid demands accurate data and electric network information. Given these business imperatives, utilities must overcome current constraints and limitations to enable essential operations data quality.
The importance of high quality data
Utilities need accurate data to become a top operator within the industry and to achieve maximum quartiles in SAIDI and cost of operations. Good quality data enables the utility to understand network and asset behavior, operating conditions, and their impact on customer service. Electric networks change routinely, and operations reflect a dynamic condition. Therefore, quality data must be timely given the context of use. The utility network must enable accurate measurements of network behaviour to assure that accurate observations and the ability to optimise measures in response to current, accurate data. The ability to assure correct inputs from system and operations data enables the utility to substantially improve the quality and cost efficiency of its operations.
At the same time, regulators are applying pressure to utilities to take advantage of rich data sets to improve grid operation and asset management and provide a better customer experience. For example, New York launched Reforming the Energy Vision (REV) in 2015, which aimed to improve customer choice and affordability of services through the overhaul of regulatory structures relating to renewables adoption, energy use and distributed energy resources.
What data limitations exist in the Modern Grid?
The GIS network has long been the system of record that the utility uses to model the behaviour of the network within key operations systems. The GIS platform offers the best tools and mechanisms to manage the topology of the connections that represent the electric network. However, while the platform is able to model the network behavior to meet advancing market conditions, a number of constraints limit GIS from meeting the needs of today’s modern grid requirements. A summary of these constraints is as follows:
In summary, the GIS network is the most appropriate source of network information, yet substantial limitations exist which limit its ability to drive the modern grid.
The GIS network provides the essential technical capabilities that grid management activities require. However, a number of limitations prevent the GIS from effectively serving the modern grid. GIS typically reflects the initial network construction but rarely incorporate essential operating characteristics. The work of constructing the network generates a great deal of data and information. In addition to the GIS network, the utility typically stores this data in an asset management system. Electric utilities typically operate in a regulated environment and therefore, generate a great deal of inspection data requiring organisation, storage and access. Finally, there is customer specific data includes location, service experience, amount and quality of electric data. Interestingly, the new imperatives of the modern grid require harmonising data sources into a cohesive story that can help make immediate decisions based on customer demand.
Utilities generate substantial volumes of data and while the Internet of Things (IoT) proliferates across networks thanks to smart devices, it creates multiple new data points that can put pressure on infrastructure. BI Intelligence estimates that the global installed base of smart meters will increase from 450 million in 2015 to 930 million in 2020. On top of this, distributed energy resources (DER) and legacy IT systems bring fresh challenges to utilities having to manage and interpret greater volumes of information. For example, thousands of mini generation plants can sit all over the network, bringing in new data points every minute. A system is therefore required to gather and maintain multiple sources of data.
Putting the data to work in the modern grid
Consolidating and aligning the many platforms that the data resides on is essential. ADMS, which lies at the heart of the Modern Grid, requires utilities to combine data from multiple utility business functions. Utility control centre personnel can then use this insight to manage all aspects of the distribution system. A strong ADMS model requires high quality data to support the mathematical analysis of the distribution system.
By adopting this tool, utilities can improve their resilience and ability to withstand or recover from a natural disaster quickly as well as accommodate larger quantities of DER, which enables them to implement more renewables on offer. ADMS enables reliability, efficiency, and survival in a Distributed Energy Resource world, thus enabling utilities to remain compliant with new regulations. Maintenance of data quality and continued data model update is a key consideration for ongoing support of ADMS.
Several keys exist to enable the Utility to make better use of its data and to achieve the mandates of the Modern Grid. Three keys:
Extend GIS into electric operations
GIS is the optimal tool for network management and utilities need to make it suitable to support electric operations. This requires key modifications to do the following:
Machine learning (ML) is an emerging data science that harmonises vast amounts of data to help the GIS provide the essential accuracy for operations. ML can leverage many different types of data available. One of the most useful is voltage data, which is available in modern metering systems. ML helps the GIS provide the most accurate information at the right time to contribute good decision within the ADMS platform. Current and accurate data trusted by field operations personnel is especially useful during times of crisis. For example, when a natural disaster occurs, utilities must act on where to distribute the different sources of energy to reduce downtime.
The modern grid enables the utility to react quickly and effectively in a complex and demanding environment. To enable this intelligence, they must harmonise data with actual operating conditions. Creating this harmony between data and as-is or as-switched conditions requires an Intelligent Data Management Solution (IDMS) to align utility process and system data. By harnessing ML, asset behaviour can add additional intelligence into the network creating 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. Finding the right model and system to align this data is the first step to obtaining high quality, actionable data and improving modern grid services quality.
Mastering a data governance model
Electric utilities are large organisations with many discrete organisations, each of which manage various programs, processes and systems. Typically, these organisations work separately, often duplicating, not sharing data. As a result, harmonising data systems and processes is a radically new concept. Simply put, data harmony had not been essential to a top utility operator. 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 a need 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. For example, dashboards that can help management enforce the agreed service levels at key points within the utility and manage these constraints. Therefore, adopting a strong governance model will improve their approach to the data lifecycle.
Establishing a data quality culture
Ultimately, the modern grid requires a culture that thrives on the generation and assimilation of high quality data. Achieving this standard begins to align toward a quality culture. We can look to our objectives of creating a safety culture to help inform the models that we need to achieve this. These ideas include: where data quality begins, building accountable teams, education and knowledge management, understanding what data quality means and ownership at the employee level.
The emergence of renewable power, electric vehicles and the need for a smarter grid is disrupting the electric utility industry. One of the key initiatives in harnessing these dynamic changes is to empower operations with a level of data quality and homogeneity not typically present in the utility. Keys to this modern state of data is to (a) optimise GIS for modern electric operations, (b) master a data governance model and (c) establish a data quality culture. These components will enable the utility to overcome current constraints and limitations to enable essential operations data quality.