An optimization model for regional micro-grid system management based on hybrid inexact stochastic-fuzzy chance-constrained programming

Micro-grid system management considering air pollutant control and carbon dioxide (CO2) mitigation is a challenging task, since many system parameters such as electric demand, resource availability, system cost as well as their interrelationships may appear uncertain. To reflect these uncertainties, effective inexact system-analysis methods are desired. In this study, a hybrid inexact stochastic-fuzzy chance-constrained programming (ITSFCCP) was developed for micro-grid system planning, and interval-parameter programming (IPP), two-stage stochastic programming (TSP) and fuzzy credibility constrained programming (FCCP) methods were integrated into a general framework to manage pollutants and CO2 emissions under uncertainties presented as interval values, fuzzy possibilistic and stochastic probabilities. Moreover, FCCP allowed satisfaction of system constraints at specified confidence level, leading to model solutions with the lowest system cost under acceptable risk magnitudes. The developed model was applied to a case of micro-grid system over a 24-h optimization horizon with a real time and dynamic air pollutant control, and total amount control for CO2 emission. Optimal generation dispatch strategies were derived under different assumptions for risk preferences and emission reduction goals. The obtained results indicated that stable intervals for the objective function and decision variables could be generated, which were useful for helping decision makers identify the desired electric power generation patterns, and CO2 emission reduction under complex uncertainties, and gain in-depth insights into the trade-offs between system economy and reliability.