Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Journal Article · Sat Jul 01 00:00:00 EDT 2017 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/TPWRS.2016.2626958· OSTI ID:1427532

Zhan, Yiduo; Zheng, Qipeng P.; Wang, Jianhui; Pinson, Pierre

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets' planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE Office of Electricity Delivery and Energy Reliability
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1427532
• Journal Information: IEEE Transactions on Power Systems, Vol. 32, Issue 4; ISSN 0885-8950
• Publisher: IEEE
• Country of Publication: United States
• Language: English