Recent studies have discovered that North America is facing a pressing situation with slow load growth, soaring energy prices, rising inflation, and grid reliability and resiliency becoming an ever-present concern. The immensely fragmented utilities sector is making the grid more complex to operate, as distributed energy resources introduce an influx of new information and variables into the system. In the era of digital transformation, the utilities sector is embracing Advanced Distribution Management Systems (ADMS) and Digital Twins as game-changing technologies. These innovative solutions offer utilities unprecedented visibility, control, and optimization capabilities.
In smart grids, Big Data analytics and domain aware Artificial Intelligence enable dynamic energy management. Smart grids allow for the exchange of data and power between customers and network operators. This aids in power optimization in terms of dependability, energy efficiency, and power sustainability. It also allows energy users and producers to play a more active part in the electrical market and, as a result, in the management of dynamic energy. Effective dynamic power management is heavily reliant on load forecasts and renewable energy output.
In this article, we explore how ADMS and Digital Twins can leverage enormous amounts of data generated in the utilities sector to unlock a new era of operational efficiency, grid resilience, and customer satisfaction.
Challenges with Data Integration and Interoperability
- Data formats and standards: Operation Technology (OT) systems such as SCADA and AMI may generate data in different formats and structures, making it difficult to seamlessly integrate and exchange information. Inconsistent data formats and lack of standardized protocols hinder interoperability efforts. Utilities need to establish common data standards and promote industry-wide adoption to ensure compatibility and smooth data integration.
- Data silos and legacy systems: Utilities often have multiple systems and legacy infrastructure that operate in isolation, resulting in data silos. Data integration becomes challenging when information is scattered across disparate systems and not readily accessible. Migrating data from legacy systems to modern platforms can be complex and time-consuming, requiring careful planning and system compatibility considerations.
- Scalability and data volume: SCADA and AMI systems generate massive amounts of data in real-time. Managing and processing this vast volume of data can strain existing IT infrastructure and networks. Ensuring scalability to accommodate increasing data volumes is essential to avoid bottlenecks and enable efficient integration and analysis.
- Data quality and consistency: Data quality is crucial for meaningful insights and accurate predictions. Incomplete, inaccurate, or inconsistent data can significantly impact the performance and reliability of predictive models. Data cleansing and validation processes need to be implemented to ensure data integrity, consistency, and reliability throughout the integration process.
- Data security and privacy: Integrating data from multiple systems involves sharing sensitive information across different platforms and systems. Ensuring data security and privacy becomes a critical concern. Utilities must implement robust security measures, such as encryption, access controls, and data anonymization techniques, to safeguard data and comply with privacy regulations.
- System interoperability: ADMS and Digital Twins need to interact seamlessly with other utility systems, GIS, SCADA, AMI, customer information systems, and billing systems. Achieving interoperability between these diverse systems requires well-defined interfaces such as CIM (Common Information Model), protocols such as ICCP (Inter-Control Center Communications Protocol), and integration frameworks. Collaboration between vendors and industry stakeholders is necessary to establish common standards and facilitate system interoperability.
Addressing these challenges requires a strategic and systematic approach, involving collaboration among utilities, technology providers, and industry standards organizations. Establishing common data standards, investing in scalable infrastructure, implementing data governance practices, and promoting interoperability frameworks are crucial steps toward effective data integration and interoperability for ADMS and Digital Twins in the utilities sector.
How ADMS and Digital Twins Can Make Utilities Data More Constructive
With grid modernization becoming more imperative to manage the complex power distribution and network challenges, increasing adoption of digital technologies are driving a dramatic shift toward ADMS and Digital Twins. Several utilities are embracing the concept of "Digital Twins" for their distribution networks to enhance operational efficiency, improve decision-making, and adapt to the changing dynamics of the energy industry. By harnessing and combining operational and grid management systems data with Geographic Information Systems (GIS) and Enterprise Resource Planning (ERP) systems, utilities can create comprehensive Digital Twins of their distribution networks.
- Data collection and integration: ADMS and Digital Twins gather data from various sources, such as GIS, SCADA, AMI, weather forecasts, and historical records. This diverse data is collected, integrated, and standardized to ensure consistency and accuracy. The data may include information on asset performance, operational parameters, weather conditions, customer demand, and grid events.
- Data preparation and cleansing: Once the data is collected, it undergoes preprocessing and cleansing. This step involves removing duplicates, handling missing values, and addressing data inconsistencies. Data normalization and transformation techniques may be applied to ensure compatibility and enable accurate analysis.
- Enhanced grid management: ADMS enables utilities to monitor and manage the grid more effectively. Operators can visualize the grid in real-time, identify areas of congestion or overloading, and make informed decisions to optimize power distribution and ensure grid stability.
- Better asset management: Digital Twins provide a virtual representation of physical assets, allowing utilities to monitor and analyze asset health and performance. This information facilitates condition-based maintenance, extending the life of assets and optimizing maintenance schedules.
- Model development: Using the prepared data and engineered features, predictive analytics models are developed. Various algorithms, such as regression, decision trees, random forests, or neural networks, can be employed depending on the specific use case and the nature of the data. The models are trained on historical data, learning patterns and relationships between input variables and the desired output.
- Model validation and evaluation: To ensure the accuracy and reliability of the predictive models, they are validated and evaluated using separate datasets. The models are tested against unseen data to assess their performance and predictive capabilities. Evaluation metrics, such as accuracy, precision, recall, or mean squared error, are calculated to measure the model's effectiveness.
- Predictive insights and decision-making: Once the predictive models are validated, they can be deployed to generate actionable insights. These insights can assist utilities in making informed decisions related to asset maintenance, load forecasting, outage management, demand response, or grid optimization. For example, predictive analytics can help utilities anticipate equipment failures and schedule maintenance proactively, avoiding costly unplanned downtime.
- Continuous model refinement: Predictive analytics is an iterative process. As new data becomes available and models are applied in real-world scenarios, feedback is collected to refine and improve the predictive models. This continuous learning and refinement process ensures that the models stay up-to-date and adapt to changing conditions and evolving patterns.
By utilizing data from ADMS and Digital Twins, utilities can unlock the power of predictive analytics to optimize operations, improve asset performance, and enhance decision-making. These insights enable proactive planning, resource optimization, and cost reduction, contributing to overall operational efficiency and improved grid reliability.
How Cyient Aids in Making Utilities Tech-Ready
Cyient understands networks, systems, operations, and digital technology. From addressing supply and distribution issues to compliance challenges, we help utilities meet data and quality requirements that drive advanced distribution management systems.
We help utilities strengthen grid control and monitoring capabilities, enhance the safety and resiliency of their networks, minimize service disruption in the face of extreme natural events, and manage their data assets. With our core strength being GIS, we help with GIS integrations with ADMS, to increase operational efficiency and reduce outage time. Some of the key areas where we help our customers make data more quantifiable are:
Smart Grid solution: Our comprehensive portfolio consists of industry-specific services, including consulting, solution engineering, and implementation of Supervisory Control & Data Acquisition (SCADA), Distributed Management System (DMS), Advanced Metering Infrastructure (AMI) and Outage Management System (OMS).
Minimize energy supply disruption with DOME: Our Disaster Operations Management (DOME) solution helps utilities better manage disaster-related risks. We leverage our strong engineering and industry domain expertise to provide both geospatial and location-based services as part of this technology-agnostic solution. DOME enables early risk assessment and impact visualization to help utilities develop an effective disaster response plan.
Intelligent Data Management Solution (iDMS): Cyient’s intelligent Data Management Solution (iDMS) enables utilities to assess and validate GIS data against other relevant systems and govern data quality for Advanced Data Management System (ADMS) readiness. It helps increase data accuracy to better predict outages and improve fault identification. By leveraging this solution, utilities can work in tandem with GIS, asset management, customer, meter, and other data-intensive systems to ensure reliable reporting. More importantly, iDMS turns underutilized capital investments into productive resources by leveraging smart meter data to automate network data validation. Our solution is technology-agnostic, supporting all commercial off-the-shelf products and platforms through published APIs.
About the Author
Ilango Ganesan leads Technology solutions for Electric, Gas and Water Utility industry clients at Cyient. He holds Masters in Software Systems from BITS. He has vast experience in IT / OT integration, Network analytics and Data governance framework in Utility segment. He has more than 30 years’ experience in end-to-end development of intelligent products, complex systems and enterprise wide solutions for Utility, Energy & Automotive specific client needs.
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