A Chance-Constrained Interval-Inexact Energy Systems Planning Model (CCIESM) for City B Based on Power Demand Probabilistic Forecasting

Energy management systems (EMS) are fraught with uncertainties, while current EMS models always deal with deterministic factors. The uncertainties in EMS could be expressed as interval values and probabilistic distributions. To tackle these uncertainties within EMS, a chance-constrained interval-inexact energy system planning model (CCIESM) was developed in this study, and the probability distribution of power demand was addressed with CCP, and interval values in the left and right hand was addressed with ILP. This probabilistic distribution was calculated through three models including relative electricity model, middle/long-term power demand prediction model and Shapiro-Wilk statistical model. The results of case study in city B indicated that CCIESM would have advantages of addressing interval-value and probabilistic distribution in EMS.

A Chance-Constrained Interval-Inexact Energy Systems Planning Model (CCIESM) for City B Based on Power Demand Probabilistic Forecasting Conferences