Digital Assistants – Networks
Optimizing Energy and Water Supply Networks
Reliable, environmentally-friendly power supply networks rely on computers and high-performance information technology and control systems—as do water networks in major cities. An optimal solution for these applications is provided by Siemens’ Spectrum Power CC network monitoring and control system.
The Spectrum Power CC control system from Siemens can monitor and regulate energy supply grids—such as São Paulo’s complete water network (below)
Weather services have issued a winter storm warning for gale-force winds, rapidly falling temperatures, and icy roads. As a precautionary measure, executives at a power supply company quickly put together a crisis-management team that immediately goes to work rolling out giant maps of the power grid in an attempt to identify those sections of the network most likely to fail in the storm. The technicians work with pencils and erasers because the weather information they have available to them can change at any moment. To ensure that no major blackout occurs, it’s especially important to have accurate information as to which distribution stations might experience a sharp drop in voltage. Fortunately, all overhead lines remain intact, and the storm passes without a power interruption.
Power supply companies need to plan for emergencies of this sort all the time. Unlike 20 years ago, however, no power company today would go into action with just pencils and paper. Instead, highly automated facilities like the control center at Evonik New Energies GmbH in Saarbrücken, Germany, are equipped with computers that evaluate several thousand bits of data from some 15,000 measurement points every minute. These measurement points provide information on the current operating conditions of the power grid, and the data is translated by software modules into a depiction of the network on monitors, complete with all sub and switching stations. The modules also provide information on what’s happening at each measuring point at any given moment.
SCADA (Supervisory Control and Data Acquisition) is one of the key applications of this system platform that collects and processes all information from the power grid. Screens in the control center show colored process images and graphs of the current level of supply. These are based on program modules that know what the acceptable limits are, and can therefore calculate within seconds where and when they have been exceeded. "A grid control center can optimally monitor and regulate its entire supply network," says Thomas Vogl, product manager for Power Grid Systems at Siemens Power Distribution in Nuremberg.
Linking Power Plants. Evonik New Energies GmbH is now using intelligent monitoring and analysis instruments to coordinate three large hard-coal power plants in and around Saarbrücken with a total output of some 2,000 MW. The same systems also check on additional outputs in excess of 100 MW at 21 smaller facilities across Germany, including biomass and wind power plants, and a geothermal unit in Bavaria.
"We’ve linked the distributed facilities across all control areas, which means our security of supply is now very high," says Franz-Josef Blug, who manages Evonik New Energies’ control center. At the heart of the facility is the Spectrum Power CC network control system from Siemens, which collects all process data from linked power plants and distributed facilities, and then evaluates it on the basis of a virtual power plant sample.
Spectrum Power CC not only brings together everything needed for optimal control; it also integrates planning for power plant use and contact information regarding external energy dealers. Monitors display up-to-the-minute calculations of grid capacity utilization, as well as simulations that provide helpful information on the most efficient and least expensive ways of using available power generation units. Also precisely depicted is the current level of energy exchange with partners. Special forecast features enable the software to look into the future—for example, by analyzing daily energy consumption figures—to identify patterns displayed by regions or customer groups; they then use this data to draw conclusions about future consumption levels. Mathematical techniques can then be applied to generate load forecasts that very accurately predict the weekly electricity requirement of a region or major customer. These calculations form the basis on which additional power supply deals are made. As Blug points out, "We don’t just generate power—we’re also a service provider and contractor for industrial customers who outsource their complete energy needs to us."
Such comprehensive energy management requires real-time data processing and detailed graphic depictions, both of which are also crucial for systems that address disturbances and power outages. Gone are the days when it took hours to determine the cause of a voltage drop. Today’s technicians receive information at the speed of light, thanks to fiber optic cables that connect a grid’s generation and distribution stations with its control center. Spectrum Power CC automatically analyzes incoming data to identify the location of any errors that may have occurred and determine what parts of the grid they may be affecting. It then sends the results of this analysis to plant personnel. If necessary, a so-called switching-sequence management program can run a simulation to identify the most favorable sequence for shutting down parts of the network. This shutdown plan can also be used to draw up maintenance assignments.
Digital Water Management. Intelligent software tools from Siemens also play an important role in modern water networks, especially in terms of resource conservation and pinpointing leaks. Sabesp of São Paulo, Brazil, is a semi state-owned firm and the fourth largest water-services company in the world. Sabesp uses the Siemens Power CC monitoring and control system to more efficiently regulate the water supply in the huge Brazilian metropolis.
The company was helped by Chemtech, a Siemens subsidiary in Brazil, which in less than a year equipped São Paulo with South America’s most modern water distribution system. Rather than pumping water into reservoirs every time water levels decline, the Siemens software system adopts a revolutionary approach. Specifically, it utilizes typical consumption profiles to regulate water levels in individual reservoir basins. "A comparison of reservoir levels with actual requirements lowers the cost of making water available," says Ingo Goldak, a Siemens sales representative in Nuremberg. That’s because targeted refilling reduces pump operation times.
A major challenge here involves the comparative measurements of water pressure in the network’s pipes. Measurements are taken by the monitoring system at various times of the day and night. In a process similar to that used in electricity and gas grids, Power CC has been analyzing data from São Paulo water pumping stations, reservoir basins, and extraction points since the fall of 2006.
The system also evaluates weather data such as the external temperature and precipitation. The system then uses this information to generate consumption forecasts that technicians incorporate into their pumping system planning processes.
That’s not all, however. If consumption in a specific residential area rises above a statistical average value at a certain time, the control system sounds an alarm, causing a red warning message to appear on Sabesp’s monitors. The control center team can then determine the exact location where an unusual drop in pressure has been recorded in the extensive network. "This technology enables us to monitor practically any disturbance in the system and correct it much more rapidly than was previously the case," says Hélio Luiz Castro, Sabesp’s chief engineer for Water Distribution.
New Horizons. Communication between control centers, specialized departments, and external business partners has created new challenges for power and water networks. "Open electricity markets require new business-focused applications," Vogl explains.
Initial approaches being used here include Internet portals where sellers of electrical energy or available transmission capacity can publicly offer their services. Such contracting requires very precise knowledge of a company’s own resources, the stability of the entire network, and contractual safeguards regarding a reliable supply.
From a technical point of view, such transactions can be carried out only via a homogenous information technology landscape with standardized communication links and data formats. "A viable network control system therefore requires international standards and compatible data models," says Vogl.
Andreas Beuthner
In these days of electricity market liberalization, accurate market forecasts have become more and more important when purchasing electric power. It’s crucial to know, for example, whether prices are likely to rise or fall in the near future, and thus be able to determine the best possible time to buy large amounts of power. Such decisions aren’t easy for Norbert Fuchs from Siemens Corporate Supply Chain and Procurement, or Fritz Bullrich-Mörlbach from Siemens Real Estate District Munich. Their job is to purchase the Group’s annual electrical power requirement (approximately 2.3 TWh at 580 locations) on the "best" days possible. "We have to very closely monitor the market in order to identify savings potential and exploit fluctuations," says Fuchs. Decision-making assistance is provided here by Dr. Hans Georg Zimmermann, Principal Research Scientist in the Learning Systems department at Siemens Corporate Technology. "We use neural networks to predict price developments in the electricity market," Zimmermann explains. Experts refer to such movements as "price dynamics that need to be identified." Here, the short-term price fluctuates by up to 1 €/MWh as supply and demand change. Use of a patented mathematical model based on the "Software Development Environment for Neural Networks" (German acronym: SENN) enables Zimmermann and his team to monitor the electricity market and calculate price movements for the coming 12 months. Their success here has been impressive. "We’ve achieved accuracy rates of up to 80 % for short-term monthly forecasts," says Dr. Ralph Grothmann, a member of Zimmermann’s team. The biggest challenge with annual forecasts is risk assessment. "We use the results of the calculation as a basis, but of course there’s always an increasing risk of deviation from the forecast price," Fuchs explains. Nevertheless, the electricity procurement specialists always turn to researchers at Siemens Corporate Technology for advice, as they no longer feel comfortable about simply following hunches.