Integrated Stochastic Simulation Optimization For Electric Power System Planning

Effective planning of electric power systems (EPS) plays an important role for national and/or regional sustainable development. In this study, a stochastic simulation-optimization model (SSOM) is developed for planning electric power systems (EPS) under uncertainty. SSOM integrates techniques of support-vector regression (SVR), Monte Carlo simulation, and inexact chance-constrained programming (ICP) into a general framework. SVR coupled Monte Carlo technique is used to predict the electricity consumption amount; ICP is effective for reflecting the reliability of satisfying (or risk of violating) system constraints under uncertainty. The SSOM can not only predict the electricity demand exactly, but also allows uncertainties presented as interval values and probability distributions. Different scenarios associated with SO2-emission policies are analyzed.Moreover, a robust interval-fuzzy programming (RIFP) approach is developed for planning electric power systems (EPS). RIFP can deal with multiple uncertainties expressed as fuzzy-boundary intervals and probability distributions, but also provide an effective linkage between the pre-regulated policies and the associated corrective actions against any infeasibility arising from random outcomes.Then, RIFP-based municipal-scale electric-power-systems planning (RIFP-MEP) model is formulated for the City of Shanghai, China. The results can be used to make compromises among system cost, satisfaction degree, and constraint-violation risk. The results can also address the challenges generated in the process of electric power production, this allows an increased robustness in controlling system risk in the optimization process, which permits in-depth analyses of various conditions that are associated with different robustness levels of economic penalties when the promised policy targets are violated, and thus help the decision makers to identify desired electricity-generation schemes.