New sources of renewable energy, such as wind and solar power, and an anticipated growth in electric vehicle use are driving major change in the utility industry. At the same time, fast growing economies are experiencing an increased demand for power; putting greater pressure on the electric network. In order to forecast supplies more accurately, utility companies are turning to analytics to improve grid operations. Just as artificial intelligence (AI) and driverless cars are fuelled by data, the emerging modern electric grid is becoming increasingly data-driven, with current restrictions and limitations needing to be overcome to ensure utilities have access to the best quality data. As a result, weaknesses in data restrictions and associated limitations need to be overcome.
Constraints on the network
Many utilities use Geographic Information Systems (GIS) to model network behaviour within key operational systems, as they offer the best mechanisms to manage electrical connectivity. However, with emerging technologies on the horizon, limitations are emerging as the GIS network struggles to provide the tools required for efficient operation of the modern grid. Historically, GIS has been utilised from a design and planning (as-built) perspective rather than an operations perspective. This becomes challenging when utilities require a version of the network that reflects the existing operating state of the model. At the same time, Advanced Distribution Management Systems (ADMS) look to make operational decisions without human interaction, again requiring as-operated content. Field operators also require more timely and precise data than GIS can typically provide.
Taking GIS to the next level
Optimising GIS to support modern electric grid operations will require a number of modifications, including the following:
- improving the speed and timeliness of updates;
- more accurate reflections of the physical network;
- deployment of machine learning (ML) algorithms to harmonise phase and transformer connectivity with actual network conditions.
A form of AI, ML is an emerging technology that harmonises data to help the GIS provide the essential accuracy for operations. It enables utilities to analyse and process different types of data, ensuring that the GIS can provide the most accurate information at the right time to better inform business decisions within the ADMS platform. Field operators can analyse data during a storm, for instance, to assess where and when to distribute different sources of energy during downtime.
Utilities must be able to react quickly and effectively in complex and demanding environments. To achieve this, they must harmonise their data with actual operating conditions. Creating harmony between accurate data and as-is conditions requires an intelligent Data Management Solution (iDMS) to align process and system data. By using ML in day-to-day operations, utilities can take advantage of additional intelligence, creating a virtual circle of data quality. Many organisations understand the need to harmonise processes, systems and data in theory. However, legacy organisations and stand-alone data repositories make consolidation and aggregation difficult to achieve in practice. Finding the right model to align this data is the first step to obtaining rich data sets and enhancing modern grid operations.
Ensuring data governance success
Utilities are made up of different organisations and departments, each of which manage various processes, systems and programmes. Typically, these organisations will work separately, often duplicating data in silos rather than sharing it. However, the modern grid is causing a shift in this paradigm. With data volumes increasingly exponentially, so too are the problems associated with a lack of data governance. Utilities must therefore engage a governance model, assuring alignment between processes, systems and data to meet the demands of the modern grid.
Data governance brings together information from multiple sources, and requires blending accountability, agreed service levels and measurement. An iDMS can provide windows into service levels: for instance, by using dashboards, utilities can enforce the agreed service levels at key points within the utility and manage these restrictions. Adopting a strong governance model will therefore improve their approach to the data lifecycle.
Mastering the power of analytics
The modern grid requires an environment that thrives on high quality data. Utilities must look at their objectives of creating a safety culture to help inform the models that they need to achieve this. This includes where to start with data quality, building accountable teams, education and knowledge sharing, understanding what data quality means and assigning employee ownership.
The emergence of renewable power sources, electric cars and the need for a smarter grid are driving major disruption in the electric utility industry. To take advantage of these dynamic changes, utilities must empower operations with a level of data quality and homogeneity not typically present in the network. Key to achieving this is to optimise GIS for modern electric operations, master a data governance model and establish a data quality culture. These components will enable utilities to overcome current constraints and restrictions to master the power of analytics and enable essential operations data quality.