Figure : Averaged Size of the Blackout as a Function of the Number of Outages in the Initiating Event for Four Different Network Models Figure : The Correlation of the Size of the Blackout With the Number of Overloads in the Initiating Event Shows a Clear Precursor When the System Noise Is Low Having found a number of system parameters that strongly correlate with blackout probability, and even more importantly with extreme event size, it is possible to consider monitoring these quantities in the real system. The goal there would be to see (1) whether they show variations that are meaningful and the same correlations exist, and (2) if so, whether the noise level is low enough to make any of them useful as a precursor measure—the ultimate objective of the work in this section. 5.4.4 Impact of Distributed Generation With the increased utilization of often renewable, power sources coupled with a drive for decentralization, the fraction of electric power generation that is is growing and “distributed” set to grow even faster. It is often held that moving toward more distributed generation would have a generally positive impact on the robustness of the transmission grid. This intuited improvement comes simply from the realization that less power would need to be moved long distances, and the local mismatch between power supply and demand would be reduced. The project approached the issues of system dynamics and robustness with this intuitive understanding in mind and with the underlying question to be answered, there an optimal “is balance of distributed versus central generation for network robustness?” In the interest of understanding the effects of different factors, the investigation was initiated by intentionally ignoring the differences in the economics of centralized vs. distributed generation and trying to approach the question in a hierarchical manner, starting from the simplest model of distributed generation and then adding more complexity. Since the network robustness is being explored as characterized by the risk of large failures and temporal dynamics, the OPA simulation model was used. To understand the impact of distributed and renewable generation, and thereby improve the realism of the model, a new class of generation was added to OPA. This distributed generation class allows the project to vary: (1) the fraction of power from distributed generation, (2) the fraction of nodes with distributed generation, (3) the variability of the distributed generation, (4)
Figure : Averaged Size of the Blackout as a Function of the Number of Outages in the Initiating Event for Four Different Network Models Figure : The Correlation of the Size of the Blackout With the Number of Overloads in the Initiating Event Shows a Clear Precursor When the System Noise Is Low Having found a number of system parameters that strongly correlate with blackout probability, and even more importantly with extreme event size, it is possible to consider monitoring these quantities in the real system. The goal there would be to see (1) whether they show variations that are meaningful and the same correlations exist, and (2) if so, whether the noise level is low enough to make any of them useful as a precursor measure—the ultimate objective of the work in this section. 5.4.4 Impact of Distributed Generation With the increased utilization of often renewable, power sources coupled with a drive for decentralization, the fraction of electric power generation that is “distributed” is growing and set to grow even faster. It is often held that moving toward more distributed generation would have a generally positive impact on the robustness of the transmission grid. This intuited improvement comes simply from the realization that less power would need to be moved long distances, and the local mismatch between power supply and demand would be reduced. The project approached the issues of system dynamics and robustness with this intuitive understanding in mind and with the underlying question to be answered, “is there an optimal balance of distributed versus central generation for network robustness?” In the interest of understanding the effects of different factors, the investigation was initiated by intentionally ignoring the differences in the economics of centralized vs. distributed generation and trying to approach the question in a hierarchical manner, starting from the simplest model of distributed generation and then adding more complexity. Since the network robustness is being explored as characterized by the risk of large failures and temporal dynamics, the OPA simulation model was used. To understand the impact of distributed and renewable generation, and thereby improve the realism of the model, a new class of generation was added to OPA. This distributed generation class allows the project to vary: (1) the fraction of power from distributed generation, (2) the fraction of nodes with distributed generation, (3) the variability of the distributed generation, (4)
CEC-500-2013-031.docx