Electric-power systems planning and greenhouse-gas emission management under uncertainty

In this study, a multistage interval-stochastic integer programming model is formulated for managing greenhouse gas (GHG) emissions and planning electric-power systems under uncertainty. The developed model can reflect dynamic, interactive, and uncertain characteristics of energy systems. Besides, the model can be used for answering questions related to types, times, demands and mitigations of energy systems planning practices, with the objective of minimizing system cost over a long-time planning horizon. The solutions can help generate electricity-generation schemes and capacity-expansion plans under different GHG-mitigation options and electricity-demand levels. Tradeoffs among system cost, energy security, and emission management can also be tackled. A high system cost will increase renewable energy supply and reduce GHG emission, while a desire for a low cost will run into risks of a high energy deficiency and a high GHG emission.

Highlight
► A multistage stochastic integer programming model is developed for planning electric-power systems.
► Uncertain and dynamic information can be incorporated within a multilayer scenario tree.
► This can help minimize system cost under random energy demand and greenhouse gas (GHG) abatement goal.
► Results can support decisions of facility expansion, electricity supply and GHG mitigation.