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9:47 pm
August 19, 2013
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Lessons From The Road: Closing In On A Smarter Grid

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The journey has been a long, sometimes bumpy one from both supply- and demand-side perspectives. But today, with a range of advanced technologies and methodologies on board, the destination is almost in sight.

By Tanuj Khandelwal, ETAP

Utility stakeholders, owners, operators, managers and other personnel have set out on an important journey to transform an obsolete electrical grid into an infrastructure that is smart, self-healing, predictable and efficient. The lessons learned along the way aren’t just about identifying weakness in the system: They also involve exploring what can be done with current and future technology. This article presents some snapshots of where we’ve been and where we’re going.

One of the first priorities for utilities is to assure a constant supply of power to its customers. Prompt troubleshooting and power restoration after outages is the performance benchmark of a utility. With the increased implementation of smart-grid technologies and the quest to add new renewable power sources to it, there is growing pressure on utility owners, designers and operators to implement power-management solutions that put them in control of planning, operation and maintenance of their systems.

Thanks to real-time predictive software tools, utility managers can now have a clearer picture of grid performance—and thus preempt problems before they cause service interruptions. Utilizing a life-cycle-management approach will result in optimum system utilization, lower costs and maintained financial stability.

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Fig. 1. Power-system monitoring and simulation (PSMS) determines the appropriate system response to a variety of changes and disturbances by using electrical and physical parameters, loading and generation levels, network topology and control logics. PSMS can also determine the source of potential. 

Power-system monitoring and simulation
As shown in Fig. 1, power-system monitoring and simulation (PSMS) can proactively determine the appropriate system response to a variety of grid changes and disturbances by using electrical and physical parameters, loading and generation levels, network topology and control logics. But that’s not all PSMS can do: It also can determine the source of potential problems and advise on corrective actions to avoid interruptions.

Intelligent monitoring
System monitoring is the base function for any power-management software. Seamless integration with metering devices, data acquisition and archiving systems are essential to monitoring software. Real-time or snapshot data are linked to an online model of the system for proper presentation of actual operating status.

All this information should be accessible to the system operator through advanced man-machine interfaces (MMIs), such as an interactive one-line diagram that provides logical system-wide views.

The next step is to process the telemetry data and determine the missing or faulty meter values using advance techniques like a state and load estimator (SLE).

The system should also be able to compensate for the absence of physical meters by providing virtual metering of devices. Graphic watch windows summarize and record alarm conditions in case of unusual activity and provide continuous visual monitoring of user-selected parameters in any mode of operation. This provision would allow early detection and display of problems before a critical failure takes place. Periodic validation of the measuring devices is critical to any power-management solution. Online real-time validation of these devices with deviation alarming is part of the technology that differentiates the next-generation power-management solutions available today.

Online predictive simulation
Intelligent monitoring can be taken a step further with the ability to analyze the acquired data. System engineers and operators must have instant access to energy information and analysis tools that allow them to predict an outcome before actions are taken on the system.

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

For industrial and generation facilities that utilize power-system analysis applications, the ability to perform system studies and simulate “what if” scenarios using real-time operating data on demand is of the essence. Consider this example: Using real-time data, a system operator can iteratively simulate the impact of starting a large motor without actually starting the motor.

Sequence of events playback
The ability to recover 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 operation and engineering staff to quickly identify the cause of operating problems 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 ones—after the fact. This important tool serves as an ongoing learning process for the operator.

Besides reducing losses and improving data-gathering capability, such an application should assist in increasing plant reliability and controlling 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  “what if” scenarios. Event playback capability translates into savings. For example, a conservative estimate of 10% reduction in downtime for an outage that lasts an hour yields about $33,000 in savings.

Online control
An advanced power-management system should provide the options for full remote control to the system elements such as motors, generators, breakers, load tap changers and other protection devices directly or through existing Supervisory Control and Data Acquisition (SCADA) systems.

Moreover, 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/controllers (kV, kW, kvar, PF, etc.) or simple breaker interlocks to any part of the system by means of the software.

Supervisory and advisory controls
State-of-the-art supervisory and advisory capabilities should be used to control and optimize in real-time various parameters throughout the system. Using optimization algorithms, users can program the power-management system (i.e., assist energy consumers by automatically operating their systems to minimize system losses, reduce peak load consumption or minimize control adjustment). For energy producers, this energy-management system can be set up to minimize generation fuel costs and optimize system operation.

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

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Fig. 2. By extending traditional data-acquisition systems with an intelligent power-management solution, operators, dispatchers, engineers and other decision-makers have control of operations, maintenance and planning their electrical systems.

 

Real-time intelligent energy management
An intelligent energy-management software control system (Fig. 2) is designed to reduce energy consumption, improve utilization of the system, increase reliability and predict electrical-system performance—as well as optimize energy usage to reduce cost. The next-generation smart-grid energy-management applications will use real-time data such as frequency, actual generation, tie-line load flows and plant-unit controller status to effect system changes.

There are many objectives of energy-management software, including an application to maintain the frequency of a power-distribution system and to keep tie-line power close to scheduled values. In intelligent energy-management software, scheduled values will be maintained by adjusting the MW outputs of the automatic generation control (AGC) generators so as to accommodate fluctuating load demands. The energy-management software application will also calculate the required parameters to optimize the operation of the generation units under energy-management action. By providing a user interface that allows for interchange scheduling, the operator has the capability to schedule energy transfer from one control area to another while considering wheeling, scheduling ancillary services and financial tracking of energy transactions.

Dedicated for electricity power exchange and scheduling, interchange scheduling incorporates energy scheduling, transaction management and energy cost analysis and report creation of energy transactions for each location. This interface allows the user to specify separate contracts for each location and assign multiple non-overlapping schedules to each location.

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Fig. 3. Intelligent load shedding offers fast load shedding that can dynamically manage the stability of your system by responding faster to disturbances.

Intelligent 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. The power-management system should have the intelligence to initiate load shedding (Fig. 3) based on a user-defined Load Priority Table (LPT) and a pre-constructed Stability Knowledge Base (SKB) in response to electrical or mechanical disturbances in the system. Load-shedding schemes by conventional frequency relays are generally a static control with fixed frequency settings. Based on Neural Networks, a power-management system would be able to adapt to all real-time situations and provide a true dynamic load-shedding control. This would allow the operator to optimize load preservation, reduce downtime for critical loads and simulate/test the load-shedding recommendations.

Another significant cost component of operations is demand charge of the 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 a facility’s power bill each month, and with “ratcheting” demand charges, the local power utility Network Access Charge (NAC) applies to the highest recorded demand above the notified demand for 12 consecutive months. That means the penalty is in effect for an entire year. An intelligent combination of smart applications can provide the current and predicted demand for each day, thus managing peak demands on a continuous basis. Loads can be shed intelligently and automatically, peak-shaving generators can be started, load startup can be postponed or sequenced or a penalty can be paid if certain processes are vital.

Conclusion
A typical power-management system evaluates collected data in a non-electrical-system environment without recognizing the interdependencies of equipment. Extending the power-monitoring system by equipping it with an appropriate electrical-system context, simulation modules and playback routines will provide the system operator and engineer with a powerful new set of tools. Using these tools, users can accurately predict the behavior of their electrical systems in response to a variety of changes. 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.

These simulation techniques will provide a revolutionary training tool to effectively prepare smart-grid operators of the future. Never before has the industry been able to take an electrical-system model from the design environment and readily extend it as an operator-training asset.

Finally, it’s important to note that all of these capabilities should be included in one application with the flexibility and compatibility that allows for expansion and upgrade of the power-management system as needs grow. MT

Tanuj Khandelwal is Vice President, Product and Industry Strategy for ETAP®. Founded in 1986 and based in Irvine, CA, ETAP is a provider of software solutions for the design, optimization and on-line operation of mission-critical electrical power infrastructure. It recently announced the opening of a new regional office in Houston, TX, that it says will serve as the Energy Sector’s Center of Excellence for the Gulf Coast Region.

 

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