Contingency Constrained Optimal Power Flow For Deregulated Electricity Markets

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ECS-9810288 Clements Deregulation of the electric power industry will require dynamic optimal power flow (OPF) algorithms, new tools that can efficiently schedule power system operation over a time horizon while including transmission and distribution costs, transmission line limits, generation ramp rate limits, energy constraints, price-responsive loads, and a long list of operational contingencies. In the deregulated environment, transmission and distribution costs and line limits will be significant because of wheeling through third-party transmission systems operating at or near capacity. Ramp limits and energy constraints will control load following and peak support during periods of large changes in load. The active electricity spot market will cause load fluctuations as large industrial customers, for example, shift productions schedules to capture lower energy prices. All must be accommodated within a secure operating environment that can efficiently reschedule to correct for a range of contingencies. The proposed research will extend the classic static OPF tool to incorporate all of these issues. Drawing on prior experience with unit commitment, economic dispatch, and a variety of nonlinear optimization techniques, the proposers will develop an interior-point-based, nonlinear programming approach to dynamic OPF that incorporates rescheduling and price-responsive loads. Key ideas in the development of these tools are constraint relaxation and problem reduction to control problem size, the use of Lagrangian relaxation to decouple ramp rate constraints (vis-a-vis direct solution of the full time-coupled problem), and techniques such as artificial variables to accommodate unfeasibility.