Albert Einstein said, “A man should look for what is, and not for what he thinks should be.” While the quote referenced the disposal of prejudices in the context of the scientific process, the same philosophy applies to today’s power system. Restated, “The grid should be operated to what is, and not to what the assumptions say should be.”
Oncor’s recent Smart Grid Demonstration Project (SGDP) — cofunded by the U.S. Department of Energy (DOE) — employs Einstein’s counsel by jettisoning assumptions for real-time information in a key area: thermal ratings for overhead transmission lines. Through the SGDP, Oncor quantified the economic costs of thermally constrained lines and compared traditional line rating methodology with real-time monitoring methods. There were several operations breakthroughs of the SGDP, and the results are already being extended to new areas.
Oncor is a U.S.-regulated electric transmission and distribution service provider that serves 10 million customers across the state of Texas. The Oncor transmission system consists of approximately 1,500 circuits (Figure 1). The needs of the grid have tremendous variability across time. Texas has seen rapid load growth in pockets as a result of oil and gas developments, while wind and coal create more changes on the generation side. On top of these larger trends, pricing varies day to day, which increases the unpredictability of the load-carrying requirements on any particular line.
Figure 2 plots congestion over a three-year period. When congestion is displayed by date and line, the volatility of congestion in a market-driven environment is apparent. The plot shows a few lines have chronic congested behavior and the financial impacts vary from a few dollars to millions of dollars over a day’s operating period. Each bar represents a day’s financial impact for a given transmission line. Over the three-year period, approximately 200 lines would experience sporadic to chronic congestion under N-1, N-1-1 or N-2 conditions.
Since the Electric Reliability Council of Texas (ERCOT) dispatches the system to avoid contingency overloads, these constraints account for approximately US$172 million in annual congestion costs, even though the lines are seldom loaded to their limits in actual operation. If the utility could use more thermal capacity from existing lines, customer costs could be reduced not only through access to lower-cost generation but also through reduced transmission cost-recovery charges.
The data in Figure 2 points to the need for a flexible solution to meet the uncertainty of transmission grid needs. As grid topology changes — due to changes in load growth, generation (construction, mothballing, market pricing and distributed resources) and transmission grid enhancements of upgrades, rebuilds and new construction — the demand on individual transmission lines can be difficult to anticipate. Loads can appear and disappear within the planning and construction duration of a traditional upgrade or construction of a new line.
Precisely and quickly meeting the line-capacity requirements of today’s grids requires smart grid technologies. In many cases, the latent capacity within the existing transmission assets can bridge this dynamic environment.