Planning of integrated energy-environment systems under dual interval uncertainties

Abstract

Energy-related activities are closely linked with greenhouse-gas (GHG) emissions. Such emissions should be managed through incorporating the issues of GHG mitigation within the framework of energy-environment systems planning. However, a variety of uncertain information exists in such an integrated management system, commonly expressed as intervals and dual intervals. In addition, dynamic characteristics associated with system expansions are also an important issue that needs to be addressed. Therefore, a dual-interval mixed-integer linear programming (DMLP) model is proposed and applied to the planning of integrated energy-environment systems (IEES) when GHG-emission mitigation is considered. The DMLP-IEES model integrates interval programming, dual interval programming and integer programming. The model can handle both uncertainties presented as discrete intervals, and dual uncertainties without distribution information but rough estimations of lower and upper bounds. The applicability of the developed model is demonstrated by a case study at a regional scale. The results show that the DMLP-IEES model can use the available dual uncertain information more efficiently and the solved decision variables in dual intervals have more robustness and decision flexibility than traditional methods.