Research On Application Of Inexact Optimization Technique In Regional Resource And Energy Allocation Systems

Natural resources, especially water resources human beings’sustainability depends on and energy resources human beings’development relies on, are of significant importance as the guarantee and foundation to the progress and prosperity of the human being society. The progress of energy and water resources utilization is closely associated and interactive with society, economy and environment systems. These close relationships lead to a variety of complexity, such as multi-period, multi-component, multi-uncertainty, interactivities and nonlinearity, exiting in energy and water resources allocation systems. However, these complexities were not appropriately handled during the previous decision-making progress, and consequently resulted in the decision dilemma for the decision makers.A sound manner for management and planning is very significant for the rational utilization of gradually-exhausting energy and water resources. However, there are obvious limitations in traditional management and planning models to deal with multiple complexities effectively. Therefore, based on system complexity identification, uncertainty parameter (or relationship) quantitative expression, critical parameter (or boundary conditions) prediction, material and energy flow analyses, water resources allocation systems and fuel allocation in the meso-and/or micro-scale multi-heat-source centralized heating system (MHSCHS) are utilized for the purpose of case study. In order to handle multiple uncertainties in different sorts of resource allocation systems, interval parameter programming (IPP), stochastic mathematical programming (SMP) and fuzzy mathematical programming (FMP) are integrated to formulate different types of optimization models. Specifically, the innovative multi-uncertain models developed in this research paper include:(1) Considering heat supply system located in one new economic zone of Liaoning Province in Northeast China during heating period as the research case, the interval single-sided fuzzy chance-constrained mixed integer programming is developed in order to deal with fuel allocation and heating capacity expansion problems under uncertainties in MHSCHS depending on the fossil fuel (gas and different coals) supply; (2) the interval two-stage double-stochastic programming method is developed on the research background of climate change and renewable (biomass) fuel supply uncertainties during heating period in Northeast China, and moreover, through coupling other inexact programming methods, different types of inexact fuel (including biomass-fuel) allocation optimization models corresponding to different MHSCHS are formulated to analyze the effects of climate change and biomass-fuel supply uncertainties on the system investigated; (3) considering a water resources allocation system containing a reservoir operation as the research case, through combing stochastic dynamic programming and fuzzy Markov Chain, which can reflect the double uncertainties embedded within inflows to the reservoir, the water resources allocation optimization model is developed under multi-uncertain environment, and meanwhile, factorial analysis technique is also coupled in the model system to analyze the effects of reliability-level variation(s) of factor(s) on the system response, and finally, the optimum reservoir operation policy, water resources allocation pattern under uncertainty and the significance of relevant factor reliability-level variation(s) to the system response are provided to the relevant decision makers.Contraposing the multiple uncertainties existing in the real heating system and water resources allocation system, several optimization models under uncertainties are developed for coping with multi-uncertainty in this paper through integrating different types of IPP, SMP and FMP, and improve upon the existing IPP, SMP and FMP models by complementing each other with their own advantages. On the basis of reflecting multiple complexities in the system, the models developed can provide the quantitive expressions of the latest policy requirements, material and energy flows in or among different subsystems and system-components, and meanwhile via reasonable solution methods, these models can not only reveal the tradeoffs among the economy, environment and reliability of the system, but also offer relative decision makers the adjustable and flexible decision intervals under different scenarios, and finally support the scientific and effective decisions for resource allocation.