Real-time power management is a plant manager’s secret weapon

Technology that allows plant managers to have more control may improve system utilization, lower energy costs, and foster financial stability

By Shervin Shokooh, PE, and Tanuj Khandelwal, ETAP February 12, 2014

Modern power management systems require new techniques and cutting-edge technology to allow electrical power users and producers to be competitive. Technology that allows plant managers to have more control may improve system utilization, lower energy costs, and foster financial stability.

A new breed of model-based power management applications that have the capability to integrate an active blueprint of the system including system topology, engineering parameters, and other pertinent information with time-synchronized-data will help make system operation more efficient.

Advanced applications and simulation engines would also allow for improved situational awareness, a more proactive approach, and improved decision making for operators under emergency conditions. Additionally, this type of power management system could serve various levels within the enterprise such as operators, engineers, planners, and managers.

Model validation

One of the key advantages of utilizing a model-based power management system is maintaining the consistency of a network model across engineering, planning, protection, and operational departments. Traditionally, real-time systems use power system models that vary in detail and structure from the models used for offline studies. The links connecting the different models are typically not maintained, giving them incompatible data formats.

Planning and operating decisions are based on the results of power system simulations. Optimistic models can result in under-investment or unsafe operating conditions while pessimistic models can also lead to unnecessary capital investment, thereby increasing the cost of electric power. Realistic models are needed for ensuring reliable and economic power system operation.

Verifying and validating the network model with real-time and/or archived data is a crucial step. Preparing a benchmarked model will help with state estimation, monitoring, predictive simulation, forensic "root cause and effects" analysis, optimization, proactive-contingency analysis, and remedial action. Customizing a network model can be achieved through utilizing a power management system that offers traditional simulation analysis tools on the same platform as the real-time operations tools.

Intelligent monitoring

System monitoring is the base function for any power management software. In addition, seamless integration with metering devices, data acquisition, and archiving systems is essential to properly monitoring software.

All this information should be accessible to the system operator through advanced man-machine interfaces such as an interactive one-line diagram that provides a logical system-wide view. To process the telemetry data and determine the missing or faulty meter values, one should use advanced techniques such as the State and Load Estimator.

Standard power monitoring systems are inadequate because they monitor based on the "eyes" you provide in the form of digital measurement devices and can be expensive to install. An intelligent monitoring system, in contrast, should be able to compensate for the absence of physical meters through providing virtual metering devices. Virtual meters not only improve situational awareness, but also provide a means to monitor alarm equipment that may not be visible to a traditional power monitoring system.

Dashboards and thin clients

Energy dashboards summarize and record alarm conditions in case of unusual activity, providing continuous visual monitoring of user-selected parameters in any mode of operation. This provision allows for the early detection and display of problems before a critical failure occurs.

A modern power management system should not only provide monitored data via thin client, but also offer the following key advantages:

First, it uses the same electrical model as the desktop client and the offline planning model without having to recreate or maintain copies of the model. This results in significant time and cost savings when building human-machine interfaces (HMIs). Traditional power monitoring systems are inexpensive to purchase, but take up a significant amount of time, resources, and engineering cost to set up the HMIs. Extensive engineering man-hours are also spent modifying the existing HMIs, while in a model-based power management system, the offline study model can be simply transitioned and connected with real-time data.

Second, the operator is able to recall and run pre-defined scenarios to help make a simple decision. This becomes particularly important in emergency conditions, as an information overload will not only slow down every decision, it may also lead to a complete system shutdown.

Online simulation

System engineers and operators should have instant access to energy information and analysis tools to help them predict an outcome before taking actions on a power system.

To design, operate, and maintain a power system, one must first understand its behavior. The operator should have firsthand experience with the system under various operating conditions to effectively react to system changes. This will avoid an inadvertent plant outage caused by human error and equipment overload.

For industrial and generation facilities that use power system analysis applications, the ability to perform system studies and simulate hypothetical scenarios using real-time operating data is paramount. With real-time data, for example, the system operator could simulate the impact of starting a large motor without actually starting the motor.

Sequence of event playback

Recovering from a system disturbance depends on the time it takes to establish the cause of the problem and take remedial action. This requires a fast and complete review and analysis of the sequence of events prior to the disturbance.

Power management software should assist one’s operational and engineering staff to quickly identify the cause of an operating problem and determine where energy costs can be reduced. The software should also be able to reconstruct exact system conditions to check for operator actions and probe for alternative actions.

Besides reducing losses and improving data-gathering capability, an application such as this should assist in increased plant reliability and help control costs. The event playback feature is especially useful for root cause and effect investigations, improvement of system operations, exploration of alternative actions, and replay of hypothetical scenarios.

An event-playback capability can translate into savings. The savings for a typical 50 MW plant is illustrated in Figure 2. A conservative estimate of a 10% reduction in downtime for an outage that lasts an hour, for example, would yield an estimated $33,000 in savings.

Online control

An advanced power management system should provide options for full remote control to system elements such as motors, generators, breakers, load tap changers, and other protection devices.

In addition, the software should provide user-definable actions that can be added or superimposed on the existing system for automating system control. This is similar to adding PC-based processors and/or controllers or simple breaker interlocks to any part of the system.

Supervisory and advisory controls

State-of-the-art supervisory and advisory control capabilities should be used to control and optimize real-time parameters throughout the system. Through using optimization algorithms, the user could program the power management system. For energy producers in particular, this type of energy management system could minimize generation fuel costs, and optimize system operation.

Demand response or management

Another significant cost component of operations is the demand charge of an energy bill. The demand charge is 40% to 60% of the bill for sites without peak shaving generation. A single unmanaged demand charge can produce a very large hike in the power bill each month, and with "ratcheting" demand charges, the effect can linger.

An intelligent power management system could provide the current and predicted demand for each day, thus managing peak demands on a continuous basis. Loads can be shed manually or automatically, peak-shaving generators can be started, and load startup can be postponed or sequenced.

In a study performed for a large industrial facility (150MVA), advanced optimization algorithms native to the energy management system were used to reduce real and reactive power losses. Assuming a conservative power loss reduction of only 0.1% at an average electrical energy cost of $0.13/kWh, an energy management system would yield savings of more than $135,000 per year and would essentially pay for itself through the immediate realization of savings in operating and maintenance costs.

Intelligent proactive load shedding

A major disturbance in an electrical power system may result in certain areas becoming isolated and experiencing low frequency and voltage, which can result in an unstable operation. A model-based power management system can easily determine the electrical subsystems, including islanding detection. The system could then provide optimal "fast load shedding" based on the actual operating condition of the system including type and location of the disturbances.

A response time of less than 40ms can be achieved through high-speed load shedding. The longer it takes to shed a load during a disturbance, the more a load must ultimately be shed. Through adding intelligence and proactive calculations, fast response times can be achieved. Additionally, through speeding up the load shedding process, the actual amount of load that is shed will be far less than that of using the conventional methods such as frequency relay and PLC-based.

Power management systems should have the intelligence to initiate load shedding based on a user-defined Load Priority Table (LPT) and a pre-constructed Stability Knowledge Base (SKB) in response to an electrical or mechanical disturbance. Load shedding schemes through conventional frequency relays are generally a static control with fixed frequency settings. But a power management system would be able to adapt to all real-time situations and provide a true dynamic load shedding control.

The Intelligent Load Shedding built into a model-based solution also offers logic verification. Logic verification is a necessary step during a Factory Acceptance Test (FAT) as well as Site Acceptance Test (SAT). It is performed during system commissioning. With an integrated model-based solution, it is easy to capture operating data and evaluate results of Intelligent Load Shedding using dynamic stability.

An Intelligent Load Shedding component also offers an analytical and instantaneous visible confirmation that the load shedding logic will perform as expected. Every scenario can be checked by using the live system data and a transient stability program. Since the model may be expanded to accommodate additional substations or manufacturing trains, the load shedding model can be updated in the process. The entire system can be retested in a fraction of the time it would take for a conventional hardware-based system (See figure 2).

Real-time power management

Through extending the power monitoring system by equipping it with an appropriate electrical system context, simulation modules, and playback routines, the system operator and engineer will have a powerful new set of tools. These tools can help a user accurately predict the behavior of an electrical system. A standard power management system, on the other hand, evaluates collected data in a non-electrical system environment without recognizing the interdependencies of equipment.

The playback of recorded message logs into the simulator-equipped monitoring system provides the operator with an invaluable means of exploring the effects of alternative actions during historical events. Simulation techniques readily extend into power system control and can be used to perform system automation and load shedding functions.

Each of these capabilities should be included in one application. For a plant to upgrade and grow, flexibility and compatibility are essential. Real-time power management is a plant manager’s secret weapon in assuring reliable plant operations.

Shervin Shokooh is a senior principal electrical engineer and Tanuj Khandelwal is a principal electrical engineer for ETAP.