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

• Published in: Journal of environmental informatics Vol. 23; no. 1; pp. 55 - 64
• Main Authors: HU, Q, HUANG, G. H, CAI, Y. P, SUN, W
• Format: Journal Article
• Language: English
• Published: Regina ISEIS 01-03-2014; International Society for Environmental Information Sciences
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Computer science; control theory; systems; Electric power; Electrical engineering. Electrical power engineering; Electrical power engineering; Energy; Energy resources; Exact sciences and technology; Natural energy; Operation. Load control. Reliability; Planning; Power networks and lines; Studies; Theoretical computing; Costs; Quantization; Constraint satisfaction; Set computation; Pollutant emission; Modeling; Energy level; Interval arithmetic; Renewable resources; constraint-violation; emission mitigation; Energy supply; Uncertain system; Efficiency; Deterministic approach; Availability; Electric generator; Probabilistic approach; electric power generation system; Power system planning; System reliability; lower-side attainment degree; multiple system uncertainties; Confidence interval; Constrained optimization; Fuzzy logic; Renewable energy; Energy management
• ISSN: 1726-2135; 1684-8799
• DOI: 10.3808/jei.201400257

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, constraintviolation 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. [PUBLICATION ABSTRACT]