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A Day in the Life of Transmission Congestion
A forecast for California on Aug. 16, 2006
an accompanying detailed transmission network model. The OPF simulation uses the initial Market Analytics zonal solution and cost-and-performance characteristics of generators, combined with a detailed electrical model of the entire transmission network-including important constraints associated with the electrical network-to minimize power costs subject to generator bids or costs.
The OPF simulation tests for congestion and, if present, optimally redispatches generator units to relieve this congestion. The OPF model will calculate and report bus level prices and other operational data. The Market Analytics LMP platform provides a complete solution to calculating nodal prices and allows an assessment of the impact of nodal prices to each market participant's asset portfolio.
The need for location-specific price information is driven by regional transmission organizations that have adopted LMP market structures, as well as for market participants in regions that have yet to adopt nodal pricing but need to assess locational impacts. Moreover, the use of LMPs as part of a congestion management system is favorably viewed by FERC for its ability to convey appropriate price signals to market participants. It creates the need for market participants to forecast nodal prices to assess generation or transmission system improvements, and to assess the value of financial transmission rights (FTR) in mitigating congestion exposure.
LMPs capture the cost of supplying the next megawatt of load at a specific location, and are calculated using a security-constrained unit commitment dispatch model that goes beyond security constraints typically included in zonal models—such as operating reserves, unplanned outages at generating facilities, and transportation-like representation of key regional transmission paths—to introduce additional constraints tied to a detailed description of the transmission network. These include transmission links and interface limits, and complex operating schedules tied to multiple interfaces.
To illustrate the breadth of information associated with nodal analysis, this analysis considers one week from a simulation of the California markets. An aggregated view of the California market is shown in Figure 1. Since California is the net importer of power in general, and in this week in particular, hourly generation within California is less than hourly load requirements. The balance of energy is scheduled over California's extensive interstate transmission system to deliver power from out of state generators. Figure 1 also shows an aggregated view of prices based on the underlying bus price at each node in the transmission network.
Figure 2 shows the distribution in nodal prices for the peak hour, underlying the nodal prices shown in Figure 1. The figure also shows that wide distribution in bus prices develop due to locational differences inside California. These locational differences are driven by congestion. Figure 2 reveals prices much lower and much higher than the statewide average price. Indeed, it is the locational price signal that is intended to provide market participants better information to use in managing operational and investment-related decisions affecting their asset portfolio.
It is clear from this figure that there are winners and losers, depending on location and whether one is injecting power into the grid ( i.e., a generator) or removing power from it ( i.e., a load-serving