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Dec. 10, 2007
Electric utility fleet optimization through process control

By John C. Kapron & Sumanth K. Makunur

A well-run business needs to know and control its process costs, optimize its performance, monitor its asset health and enhance its market value. This is a challenge for an electric utility like ours that must deal with assets and discrete technologies that span decades across multiple locations.

Detroit Edison is the largest operating subsidiary of DTE Energy. It generates and distributes electricity to 2.3 million customers in southeastern Michigan. Detroit Edison has an 11,080 megawatt system capacity and uses coal, nuclear fuel, natural gas and hydroelectric pumped storage to generate its electrical output.

In February 2004, the Fossil Generation Organization of DTE Energy’s Detroit Edison company was given the charge by senior management to be the “Best Process Control Company in the World” and bring the best value to it customers and share holders. Using existing technology assets, Fossil Generation developed a multi-layered strategy and developed a totally integrated solution across our 19-unit fossil fleet.

The Technology Framework
The framework illustrates the processing of data through technology. Development continues in each layer with horizontal and vertical integration to support ther objective of achieving performance excellence.
Our strategy displays actionable information that enables best decisions to be made with respect to operations, maintenance, budget and marketing strategies. This multi-layered approach incorporates technology solutions provided by industry leaders. Each process technology solution is totally integrated across our fleet with a focused objective. 

Process Implementation

Our technical solutions work in conjunction with our EPRI Plant Reliability Optimization business model and are coupled with our enterprise business systems (SAP/Maximo). SAP is our enterprise resource planning (ERP) system and Maximo is our work management system. All of these solutions are organized in a hierarchical system index (HSI) for reporting consistency.

All of these technologies are implemented consistently throughout our fossil fleet with the same information available to all sites, disciplines and organizations. Although there is still work to be completed, at this time each layer of the organization has at minimum a working pilot.

To optimize our fleet operations and its assets, a fleet-wide Performance Center in Ann Arbor, Mich., was put into 24x7 operation in 2005. The Performance Center analysts work closely with the power plants to help identify developing problems. The Performance Center analysts and the Merchant Operations Center analysts work side by side to make accurate capacity and cost assessment of our units when offering them to the Midwest Independent Transmission System Operator.

We started by defining a multi-layered strategy that would build on our present investments in technology and would also allow us to expand and drive performance excellence. This multi-layered integrated infrastructure would measure and optimize the performance of 19 fossil generating units across seven power plants. Our strategy requires us to use solutions from technology leaders and adherence to standards. The final goal is total integration among applications, the process and end users.

In implementing this strategy, it became evident that no single vendor was an industry technology leader across multiple layers. Our challenge was to select the best vendors, minimize the total number of vendors and install the solution fleet wide.

Historian Foundation Layer

DTE-Detroit Edison standardized on an ABB distributed control system (DCS) across our fleet, and this standardization is nearly complete. The DCS provides the source for a large set of real-time and historical process data.

OSIsoft’s PI data historians were installed at each plant to collect DCS data and also to collect data from our many non-DCS systems. The data historians are our primary data source of real-time and historical process data and provide a common user interface to access data from multiple systems.

PI is not only a valuable technology to store data, it is also an integral part of many process and business applications. PI also serves as an effective data communication conduit for many applications through its PI-to-PI interface. How does one get the most value out of 300,000 data points? By turning discrete data into actionable information. 

Engineering Applications Layer

This layer brings value to the discrete data with powerful applications.  These fleet-wide applications standardize analysis and reduce support costs. Applications include:

  • Digital Fuel Tracking Systems (DFTS) developed by Engineering Consultants Group (ECG) were installed on major units with online fuel analyzers.  The Monroe Power Plant is a 3,100MW four-unit coal fired plant with a modern coal-blending system and is an example of a plant where all coal passes through an online analyzer. The coal characteristics of each sample are tracked by the DFTS through a series of cascaded belts that feed coal into one of 28 silos supplying fuel to four boilers. The DFTS lets plant operators and combustion engineers know the characteristics of the fuel entering the boiler.  Although designed as base-load units, the generation output is normally reduced during off-peak times and fuel blends are adjusted for economics.
  • Included in the application suite developed by EGC is eNotification, which provides pager and email notification alerts for any user-selected and configured PI data points. But more than that, users can easily configure reports and trends that can then be emailed at a scheduled time or by a triggered event.
  • Matrikon’s ProcessGuard collects all plant alarms from multiple sources into a common plant database. This application includes statistical tools for alarm analysis and management. User selectable subsets of these alarms are sent to the fleet-wide Performance Center.
  • Scientech’s PMAX system prepares real-time thermal unit performance calculations  across our 19 units. Individual system and equipment performance data is continuously written to the PI historian.

Web Portal User Interface Layer

This layer, implemented across the fleet, provides a uniform method to access process data and applications. We use Matrikon’s ProcessNet because it is a robust Web Portal application that can connect to PI historians and any ODBC compliant databases. ProcessNet enables users to develop graphics and trends and share them with other users.

Combining Data
Combining Data
Combining real-time process, business and market data allows us to drive performance excellence.
The system uses our corporate single sign-on security authentication for logon and also provides transaction logs and tracks user form submittals automatically. ProcessNet eliminates the need to maintain client software and gives commonality to all applications and reports.

Months after deployment, all 6,000 DCS plant operator graphics were replicated in ProcessNet and populated by real-time PI data. These screens look and navigate just like the plant operator screens. The user also has the ability to set the clock back in history and incrementally step through an event while watching the displays. We find this to be a very powerful and useful tool.

System Dashboards Layer

To better equip our precipitator, combustion, turbine, electrical, rotating equipment, efficiency and performance experts, specific fleet-wide real-time dashboards are being developed. Not only can subject-matter experts (SME) view these displays anywhere through a VPN connection, but the displays also will be formatted for mobile devices such as  the Blackberry. SMEs will then be able to get a real-time view of the data they identify as primary indicators of asset operation through their Blackberries.

Expert Systems Layer

A fleet-wide implementation (19 units plus ten combustion turbines generators) of SmartSignal’s EPI Center uses historic data from PI to analyze and evaluate the condition of the specific asset. SmartSignal’s EPI uses similarity-based modeling built with correlated data from the actual asset.

Mobile Data
The Technology Framework provides real-time KPIs and process data to the mobile environment.
Implementation started in January 2006 and was completed in August 2006, except for the combustion turbine generators, which were completed in July 2007. Specific assets modeled include turbines, steam generators, boiler-feed pumps, air heaters, condensers, pulverizers, induced draft fans, forced draft fans, primary air fans, precipitators, feed water heaters and combustion turbine generators.

NeuCo’s CombustionOpt, a closed-loop combustion control optimization system based on the ProcessLink neural software platform, was installed on St. Clair Unit 7 and is currently in startup. Similar projects have been proposed for our Belle River and Monroe plants. Closed-loop combustion optimization implementation is going forward under the direction of one of our nationally recognized combustion experts. In conjunction with fuel analysis and other related technologies, we expect optimization to yield huge benefits. 

We are also working with NeuCo to provide a development environment for its suite of optimization solutions. The 3,200MW Monroe Power Plant uses an elaborate fuel-blending facility that continuously blends three types of coal. The plant has an online fuel analyzer and carbon monoxide grids that map the CO profile across the boiler. We hope to use this data to implement coal pile-to-stack optimization.

Business Intelligence Layer

DTE Energy offers our units to the Midwest Independent Transmission System Operator (MISO) market, which is made up of 14 utilities in the Midwest. Accurate day-ahead real-time capacities and units costs are essential for us to be competitive in this market. 

ECG has developed a robust web-based unit-capacity famework (UCF) application that manages all unit outages and unit de-rates. The UCF tracks unit costs, de-rates and fuel blends. It also determines unit capacity and is the primary source of data that feeds MISO and our Power Plant Performance Management Generation Availability Data System. 

Performance
Dashboards are an effective way for subject matter experts to evaluate equipment performance.
DTE Energy has upgraded its enterprise business systems to SAP for financials, human resources, and supply chain; and to Maximo for work management. Our hierarchical system index (HSI) data structure provides a method to roll-up costs from individual equipment, sub-systems, systems, primary systems, units, plants,and the fleet.

Enterprise business system data is correlated through a framework with the process data to enable many views of various processes. Information resides in its primary data source but the reports extract necessary information and help the organization make informed decisions. 

What is the real cost of blending a lower BTU content coal?  How does it affect boiler slagging, precipitator and mill performance? What does an automatic generation control program cost? To answer these questions one needs to know heat-rate, fuel, milling, emissions and maintenance costs.

Performance Excellence Layer

The DTE Energy Technology Framework top layer provides process costs to the sub-system level, provides asset health based on real-time process data and enables predictive analysis for our maintenance program. It also allows optimization of closed-loop control, and it accurately and competitively offers our generating units to the marketplace.

Fleet-wide implementation of business data is coupled with process data structured in a HSI. All data is organized with our EPRI Plant Reliability Optimization business model and works within corporate operating systems that support our Lean Six Sigma environment. DTE’s integrated technology, infrastructure and business processes drive performance excellence.

John C. Kapron is process technology framework development manager and Sumanth K. Makunur is lead engineer for process control at DTE Energy.

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