In conjunction with the semi-annual Global Intelligent Utility Network Coalition (GIUNC) meeting, IBM announced its collaboration with CPFL Energia Holdings, the largest privately owned energy provider in Brazil, to make its energy networks more efficient and resilient. Together, the two companies will develop a smart grid strategy that drives operational efficiency and optimizes the communication network to improve customer service and workforce management.
IBM is assisting CPFL by providing consulting and assessment services to implement three of its smart grid projects: Automatic Meter Data Collection, Meter Data Management, and Optimized Communications Network. The projects are part of an investment strategy confirmed in 2009 when CPFL Energia became a member of the GIUNC, a group of energy and utility companies determined to further the adoption of smarter energy grids around the world.
"Investing in a smart grid is more than a trend; it is a market requirement, especially here in Brazil where power consumption and population are expected to increase over the coming years," said Rubens Bruncek Ferreira, Director at CPFL Energia. "Our collaboration with IBM ensures that we have the guidance, assessment tools and methodologies in place at the onset – all instrumental components to creating a roadmap that matches our current and future needs."
After CPFL joined the GIUNC, IBM teamed with the energy provider in 2010 to create a strategic plan, using the Smart Grid Maturity Model that was originally developed by the GIUNC. This model was used to outline the capabilities and technologies needed to implement an intelligent grid in Brazil. The resulting proposal served as a blueprint for the SmartGrid projects, and was instrumental in CPFL's current business restructuring.
As part of the first Automatic Meter Data Collection project, CPFL plans to install 25,000 intelligent meters by the end of 2012. Each installed meter will be connected online and will operate as a network sensor, helping the operations center to quickly identify potential faults and other events. The automated meters will also enable technicians to perform remote preventive service, reducing downtime and unnecessary field visits.
In order to support the incoming and outgoing data flow from the 25,000 intelligent meters, IBM will offer recommended technologies for the Meter Data Management project. The chosen solutions will manage both meter functionalities and data, and integrate with CPFL's business operations, as well as offer the scalability to support CPFL's planned roll-out of 6.5 million residential smart meters. The Meter Data Management project will allow CPFL to create a business model that incorporates end-to-end service management with real-time monitoring of all devices across the entire network.
Along with the Automatic Meter Data Collection phase, IBM also will evaluate the communication requirements for CPFL's planned smart grid capabilities, and develop an architecture for a new communication network. This analysis will cover core corporate communications, including voice, e-mail, and video, as well as business applications support, such as workforce communication, supervisory control and data acquisition (SCADA), and advanced meter management. The objective of this assessment is to develop a strategy and roadmap for the Optimized Communications Network project to better utilize CPFL assets and prepare for the migration from a traditional electric infrastructure to a smart grid.
As they are implemented, CPFL's projects will improve reliability of the electricity network with faster identification of energy faults, and losses; greater speed and automation in connecting and disconnecting services; and faster detection of outages. Additionally, CPFL will enhance its customer service quality by leveraging tools that identify the energy consumption and load profile of its customers – all in real time. This will enable CPFL to better inform its customers while also ensuring high dependability with improved automation, management, and control of energy flow.