Sandra Rudolph and Andreas Böker, E.ON Ruhrgas
Energy analysts are confronted with diversified tasks along the supply chain—from pricing volume and price flexibilities in gas contracts, to forecasting customer demand, to determining a portfolio optimization approach. These tasks differ in requirements concerning optimization logic, calculation time, and input and output data. This presentation illustrates how these tasks can be accomplished by using MATLAB to develop flexible modules.
Sandra Rudolph has worked for E.ON Ruhrgas as an analyst in the asset optimization department since 2008. She studied business economics, computer science, and operations research and has a her Ph.D. in operations research in the field of knowledge-based systems.
Andreas Böker has worked as an analyst in the OR department of E.ON Ruhrgas since 2008. He studied mathematics and business economics in Bonn with a focus on discrete mathematics and operational research.
Heidi Heath, Transpower
Transpower owns and operates the electricity grid in New Zealand, keeping energy flowing to 4 million residents. The New Zealand power system is long and skinny, with the major generation centres in the bottom of the South Island and the major load centre at the top of the North Island. As such, the system is very susceptible to frequency deviations and oscillations when a generator or large load unexpectedly trips off.
Transpower uses MATLAB to calculate how much spinning reserve and interruptible load must be scheduled during each half hour trading period to ensure that, in the event of a large disturbance, the system frequency doesn’t deviate to the point where it could cause cascading outages, voltage stability issues, or blackout. Detailed models of each generator and governor are built in Simulink, along with models of the HVDC link between the two islands and some basic load models. All of the detailed models are combined into a large Simulink model of the New Zealand power system. Run in conjunction with other MATLAB code files, this tool, called the Reserve Management Tool or RMT, calculates how much spinning reserve is required under a variety of conditions.
During every half hour period, RMT uses MATLAB to calculate what the largest risk to the system will be (a large generator or the HVDC link), how fast the frequency will fall if the calculated unit trips, how much load will be shed under certain contingencies due to the last-resort Automatic Under-Frequency Load Shedding scheme, what the expected governor response will be from dispatched generators, and the system inertia. It uses these parameters to calculate a final figure of how much spinning reserve and interruptible load must be scheduled for the given conditions.
Heidi Heath has been with Transpower for two years. She has a bachelor's degree in electrical engineering from Utah State University.
Edward Byrns Jr., RenRe Energy Advisors, Ltd.
In this session, Dr. Byrns focuses on his experiences with MATLAB over the past 20 years. He follows the application of the product from his days as an engineer through to RenRe’s current service-based trading platform. He touches on some of RenRe’s applications of MATLAB in various trading environments. He highlights various lessons learned in each application of those MATLAB based platforms.
Dr. Byrns has been working in commodity trading for almost 20 years. He currently is the architect for all trading systems at Renaissance Energy Advisors, Ltd. In this role, he leads a team of analysts and developers who are creating a web-based platform for energy and risk management.
Prior to his current position, he held senior quantitative management positions in the hedge fund and merchant energy sectors. He was the managing director for Global Research at Louis Dreyfus Highbridge Energy, the director of Energy Quantitative Research at Citadel Investment Group, and the director of Quantitative Analysis at Williams Energy Marketing and Trading. Before entering the trading field, Dr. Byrns worked as an analyst and consultant in Washington, D.C. While a consultant, Dr. Byrns and a colleague developed the first version of Mapping Toolbox for MATLAB.
Dr Byrns received his Ph.D. in engineering from Georgia Institute of Technology. Additionally, he received M.S. degrees in economics and in engineering, both from Georgia Tech. He received his B.S.E. from Princeton University.
Ahmad Fattahi and Michael Christopher, OSIsoft, LLC
In this presentation, we focus on showing the power of data. Using PI System as the enterprise data infrastructure, we collect samples of some physical quantities related to our office building, such as power consumption and outside temperature, on a regular basis. Over time, the data set becomes large. Leveraging the computational power of MATLAB, we integrate the two platforms, build a predictive model with MATLAB, funnel the data set, and train our model. In real time, we generate predictions of power consumption that incorporate data from a weather forecast web service. We also configure automatic notifications when power consumption deviates significantly from the "expected" level.
Ahmad Fattahi is a member of the OSIsoft Virtual Campus team. His focus is performing advanced analytics on historical data through integrating PI System with other analytical platforms such as MATLAB and R. He has done extensive research on modeling and optimizing complex systems such as communication and computer networks. He has a Ph.D. in electrical engineering from University of California, Los Angeles.
Michael Christopher is a technical support engineer at OSIsoft with a background in utilities, energy efficiency, and renewable energy. In addition to his work in support, he has worked on developing and implementing predictive algorithms using MATLAB and the PI System. Michael received his master’s in mechanical engineering from Virginia Tech.
Houda Karaki, EUtech Scientific Engineering GmbH
Using model predictive control, the intelligent optimizer reduces emissions and cuts costs all while improving combustion efficiency. This session demonstrates the end-to-end optimization solution, developed with a model-based approach and, more importantly, presents the tangible results from a large-scale power plant. The results underline the environmental and financial gains that paid off the system within a year.
The overall performance and availability of a power plant is predominantly affected by the steam-generating unit and the combustion process. Even though conventional plant control systems ensure safe and reliable operation, they do not rigorously optimize boiler operations or take care of special combustion problems. In addition, much of the information gathered by modern IT systems and advanced monitoring equipment remains untapped.
The presentation features a complete, ready-to-integrate control system for a fossil-fired power plant. EUtech applies Model-Based Design and relies on widely available tools like Simulink and Model Predictive Control Toolbox® as well as our own Thermolib, which dedicated to modeling thermodynamic systems. While it certainly is not trivial, designing and building a fully fledged intelligent controller is no longer the Herculean task it once was.
The solution devised is modular, highly customizable, and robust. Most of all, however, it requires neither major overhead on the plant operators' part nor infrastructural changes. We show the significant improvements realized in a large-scale coal-fired power plant in Germany, covering financial savings, optimized combustion, and reduced NOx and CO emissions.
Houda Karaki is a development engineer at EUtech Scientific Engineering GmbH. For the last six years, she has been working on the design, development, and integration of control systems for the energy sector. She has a B.E. in electrical engineering from The American University of Beirut and an M.Sc. from RWTH Aachen University (Germany).
Dr. Martin Wolter, 50Hertz Transmission GmbH
Especially in Germany, the fast increasing amount of energy from renewable energy sources (RESs) combined with slow grid expansions is challenging transmission system operators’ (TSOs’) ability to ensure system security. As a last resort, if all other remedial actions are not sufficient, RES infeed is curtailed. Because most renewable sources are connected to the distribution level, coordinated cooperation between TSOs and DSOs is critical. Furthermore, the TSO needs to know the regionally allocated curtailment potentials. This information is usually not available in sufficiently high resolution, however, requiring TSOs to devise suitable and efficient remedial actions without it.
This session presents the approach of 50Hertz Transmission, one of the four German TSOs. First, sensitivity indices of hand-over points to DSOs on the endangered equipment are calculated, resulting in several regions with almost equal sensitivity values. Thus, by accepting a small loss of efficiency, curtailment does not need to be defined for each busbar, but can be allocated to the specific regions. A second step aims to match the borders of these regions with DSO network groups so that curtailment potentials are easily obtainable. By curtailing only RESs with the highest impact, system security is regained using the least amount of energy and keeping the highest possible amount of RESs online.
Since 2011, Martin Wolter has been head of Interconnected Operation and System Security at 50Hertz Transmission GmbH, Berlin. From 2008 to 2011 he was chief engineer of the Institute of Electric Power Systems and vice head of the Department of Power Supply at Leibniz University Hannover. He was also teaching associate at the Institute of Technology at University Hildesheim. Martin received his diploma degree and his Ph.D. from Leibniz University Hannover, Germany. He is a member of IEEE PES, VDE ETG, and the German governmental power system advisory group Smart Grids and Smart Meters.
Tony Faris, Bonneville Power Administration
Synchronized phasors, or synchrophasors, utilize highly precise timestamping to represent power signals in terms of magnitude and phase angle components. By installing phasor measurement units (PMUs) across the power system, as well as centralized phasor data concentrators (PDCs), system operators and engineers obtain real-time visibility into the grid at unprecedented data rates. Bonneville Power Administration (BPA) has been a leader in installing PMUs and PDCs, while developing applications that harness the value of synchrophasor data. With the transition of synchrophasors from the R&D domain into an operational domain, applications have required more intensive processing and more sophisticated algorithms, while handling increasingly large data sets. Using MATLAB® developmental tools, including GUIDE and MATLAB Compiler™, BPA has created applications for synchrophasor processing, visualization, alarming, and data mining. This presentation provides an overall description of the BPA synchrophasor system and examples of some of the MATLAB based applications using synchrophasor data.
Tony Faris is an electrical engineer with the Measurement Systems group at Bonneville Power Administration in Vancouver, Washington. His roles include phasor measurement unit (PMU) testing and evaluation, contributing to the development of an operational PMU network, and developing tools and applications for processing and displaying phasor data. He has a B.S.E.E. from University of Portland and an M.S.E.E. from University of Washington, with a focus in VLSI and embedded systems.