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Managing the smart grid will require real-time, sub-second monitoring of a complex network composed of millions of devices, compounded by the unpredictable ramifications of renewable energy generators and demand response programmes. Traditional energy management systems (EMS) were not designed for such operating scenarios, but Alstom’s EMS enhanced with synchrophasor solutions provides tools to do this more effectively.
Business needs are driving change, too. As Jay Giri, Director of Power Systems Technology and Strategic Initiatives with Alstom Grid, points out, “Spurred by deregulation and electricity markets, operators want to maximise available transmission capacity even more, and this can result in pushing the grid closer to dynamic stability limits. So those limits need to be updated in real time, and to do this you need computationally intensive applications that simulate grid dynamic behaviour in the face of disturbances.”
Wide-area management systems (WAMS) that use synchronised measurement technologies generate very high volumes of measurement data. This needs to be converted into useful, operator-actionable information. Today, analytics using synchronized measurement data extract such information independently without a system model and without full observability. This approach is particularly valuable for large interconnections in which individual operators do not have full observability or models of the entire system.
Potential benefits coming from the availability of PMU data range from improving routine tasks to blackout prevention. For example, operational limits for transmission corridors factor in a safety margin to protect the grid against disturbances; the operator enters these limits into the EMS manually, and they are not changed very often. This means that the margins are necessarily pessimistically large and the network is operating well below its potential; traditionally this would mean that additional capacity would be needed, so extra power lines would have to be built. Apart from the intrinsic expense of adding lines, this is also very time-consuming because of obstacles to obtaining rights of way, building permits and so on. As Giri goes on to say, “having time-stamped, synchrophasor data will allow us to operate the system closer to its actual limit by intelligently updating this limit in real time, while maintaining system integrity. This results in cost savings to the utility and in environmental benefits, since you don’t need to invest in new lines and transmission pylons.”
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