Electric Power System Planning under Uncertainty Using Inexact Inventory Nonlinear Programming Method

Abstract

ABSTRACT. In this study, an inexact inventory nonlinear programming (IINP) model is proposed for supporting electric power system planning under multiple unit prices and uncertain demands. The proposed IINP can deal with uncertainties presented as intervals and address nonlinearities in the objective function. It can also help to solve material supply problem with diverse unit prices as well as what, where, when and how much material should be purchased under uncertainty. Then, the IINP is applied to a case study of energy resources supply planning for an electric power system. Results obtained are useful for supporting (a) determination of reasonable energy resources supply scheme with global solutions, and elimination of step by step comparisons among many purchase schemes, (b) adjustment or justification purchase batches of energy resources supply and facility expansion for power-conversion technologies under different demand levels, and (c) integration of policies regarding energy resources supply, economy objective and environmental protection for in-depth analysis of tradeoff between purchase batch and unit price as well as system cost and risk.

An inexact joint-probabilistic programming method for risk assessment in water resources allocation

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“…The lower and upper bounds represents the minimum and maximum value of parameters, which can tackle uncertainties that generally cannot be quantified as either distribution functions or membership functions, since interval numbers are acceptable as its uncertain inputs (Suo et al, 2013). Consequently, to determine the hedging alternatives accounting for uncertainties expressed as discrete intervals, the concepts of IPP and MRR will be incorporated into a general framework.…”

“…Obviously, the MRR has an advantage in providing effective decision support under uncertainty; however, more uncertainties that exist in the left-hand sides and the objective function of model (2) may also need to be reflected. Intervalparameter programming (IPP) is an alternative for tackling uncertainties expressed as intervals that exist in the model's leftand/or right-hand sides as well as the objective function (Suo et al, 2013). The IPP has advantages such that (i) it allows uncertainties to be directly communicated into the optimization process, (ii) it does not lead to more complicated intermediate models, and thus has a relatively low computational requirement and (iii) it does not require distributional information for model parameters, which is particularly meaningful for practical applications because it is typically much more difficult for planners/engineers to specify distributions than to obtain intervals (Huang et al, 1995;Zhang et al, 2014).…”