Inexact fuzzy programming methods for energy systems planning under uncertainty

Energy has played a vital role in advancing human society and improving the world economy. Over the past three decades, excessive energy consumption has resulted in many energy-shortage crises and environmental issues, making the energy systems planning and management become an increasingly important task. To effectively support such a task, many optimization-based energy-system models were developed. However, the previous models were incapable of dealing with uncertainties associated with a variety of energy-related activities, such as energy supply/demand, pricing, and transmissions. Thus, as an extension to the previous studies, three advanced models with specific uncertainty-handling capabilities were developed in this study for supporting energy systems planning. An interval fuzzy possibilistic linear programming (IFPLP) model was developed for municipal energy system management under uncertainty. The IFPLP model effectively combined the interval programming, fuzzy possibilistic programming and mixed-integer programming approaches within a general optimization framework. The solutions of the IFPLP model could provide not only useful energy-allocation information for various energy consumers but also the associated possibilities. An inexact fuzzy-parameter quadratic programming (IFPQP) energy-system model was also developed, where the system uncertainties could be expressed as both intervals and fuzzy sets. The effects of economy-of-scale in energy systems were reflected in the quadratic objective functions. The applicability of IFPQP was well demonstrated by a hypothetical regional energy system. Finally, a hybrid fuzzy interval chance-constrained programming (FICCP) model was developed to support energy systems planning under hybrid uncertainties (expressed as stochastic, interval and fuzzy variables). The FICCP-model solutions could reflect complex tradeoffs between total cost and system reliability.