A dynamic optimization approach for nonrenewable energy resources management under uncertainty

Abstract

This paper introduces an integrated dynamic optimization approach for nonrenewable energy (NRE) resources management under uncertainty. A hybrid inexact chance-constrained mixed-integer linear programming (ICCMILP) method is proposed, with an objective of maximizing economic return under constraints of resources availability and environmental regulations. In its solution process, the ICCMILP is transformed into two deterministic submodels, which correspond to the upper and lower bounds for the desired objective function value. Interval solutions, which are feasible and stable in the given decision space, can then be obtained by solving the two submodels sequentially. Thus, decision alternatives can be generated by adjusting decision variable values within their solution intervals. The obtained solutions are useful for decision makers to optimally allocate limited NRE resources over time for acquiring maximized benefit. Meanwhile, regional air quality could be maintained to keep the communities from health damage. Results of a hypothetical case study indicate that reasonable solutions for dynamic planning of NRE resources allocation in a regional system have been obtained. A number of decision alternatives were generated based on the ICCMILP solutions as well as the projected applicable conditions.