Planning of Electric Power Generation Systems under Multiple Uncertainties and Constraint-Violation Levels

Abstract

Regional electric power generation systems (REPGS) planning involves multiple sectors, multiple facilities, and multiple uncertainties, leading a variety of complexities. In this study, lower-side attainment degrees based inexact fuzzy chance-constraint programming (LA-IFCCP) was proposed to support the planning of REPGS under such a complex situation. LA-IFCCP was developed by integrating lower-side attainment degrees based fuzzy programming (LA-FLP) into an interval chance-constraint programming (ICCP) framework. It was able to tackle uncertainties expressed as intervals, fuzzy sets, probabilistic distributions as well as their combinations. At the same time, fuzzy relationships between conversion efficiencies of technologies and availabilities of energy resources could be transformed into corresponding deterministic ones via the lower-side attainment degree index without introducing any additional constraints, and thus guaranteed enhanced computation efficiency. Moreover, constraint-violation levels about renewable energy resource availabilities could be quantified through the adoption of various pi levels, which could represent the reliability of the system. The relationships between systems costs and reliability could be reflected via analyzing the solutions under different pi levels, which was very important for the management of power generation. A hypothetical but representative regional electric power generation system was adopted for demonstrating its applicability. Reasonable solutions were generated. They provided desired plans regarding energy supply, electricity generation, capacity expansion and emission mitigation to achieve a minimized system cost.