A Multistage Stochastic Transmission Expansion Algorithm for Wide-Area Planning under Uncertainty

Technical Report · Fri May 13 04:00:00 EDT 2022

DOI:https://doi.org/10.2172/1737833· OSTI ID:1737833

Webster, Mort D. ^[1]

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1. Pennsylvania State Univ., University Park, PA (United States); Pennsylvania State University

The overall objective for this project was to develop and demonstrate a set of methods for solving the transmission investment problem for a large network considering many possible scenarios of future conditions and multiple decision points when investments can be made. Project sub-objectives achieved this goal through a succession of extending the methods to apply to problems with increasing complexity or additional features, including the number of decision points, whether generation and transmission are co-optimized, and whether AC or DC power flow is used. A transmission model was developed for the Western Electric Coordinating Council (WECC) region, the high-voltage transmission system that serves the western third of the continental U.S. Using a dataset provided by WECC and by researchers from John Hopkins University, we have validated and demonstrated the model and used it to compare the new method for solving multi-stage stochastic transmission planning to several state-of-the-art techniques. The project has resulted in several key outcomes and achievements: The covariance-based method for choosing a small set of hours to represent short-term variability has superior performance in terms of accuracy to existing methods, including K-means clustering and Importance Sampling; The combined partitioning method for long-term uncertainty with the nested clustering approach for choosing representative hours for each long-term group has superior accuracy for equivalent computational effort compared with existing methods; Using the partitioning/clustering method combined with Sample Average Approximation provides both statistical bounds on the quality of the solution and at the same time, a complete investment plan for all contingencies in the full uncertainty set; no existing methods can provide both at the same time; The method is demonstrated to work well for choosing both transmission and generation investments; A variant on the method allows for both scenario selection and simultaneous correction for the error from the DC power flow approximation to provide a tractable method for AC power flow-based transmission planning under uncertainty; The method applied to the WECC case study demonstrates the additional value to the system operator and the consumer of identifying flexible investment options in the near-term decisions. In particular, the case study exhibits significant option value in postponing some transmission additions that appear useful but in some long-term system states create new congestion problems.

• Research Organization: Pennsylvania State Univ., University Park, PA (United States)
• Sponsoring Organization: USDOE Office of Electricity (OE)
• DOE Contract Number: OE0000881
• OSTI ID: 1737833
• Report Number(s): DOE-PSU-0000881
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
29 ENERGY PLANNING, POLICY, AND ECONOMY
97 MATHEMATICS AND COMPUTING
electric power transmission planning
scenario reduction
stochastic programming
uncertainty