| Over the past decades,the world has undergone rapid economic development and social revolution corresponding to the increasing energy demand.Meanwhile,the infrastructural investments and pollutant emissions associated with power industry have adverse impacts on environment.Therefore,how to effectively balance the contradiction between energy demand-supply reliability and air quality improvement continues to be great challenges faced by decision makers.A municipal-scale energy system(MES)is complicated with uncertainties related to various economic and technical parameters,such as energy demand/supply,electricity production,transmission,consumption,facility-capacity expansion plans,as well as air-pollutant emissions.These uncertainties could not only affect the related optimization processes,but also affect the generated MES decision schemes.In such case,how to identify these uncertainties and how to reveal their interactions to the modeling outputs,as well as how to explore desired optimization approaches still restrict effectively planning MES management problems.Therefore,this paper aims to develop series of inexact optimization methods to deal with such problems under the background of multi-objective,multi-period,multi-scenario and multi-element.The detailed optimization methods include:(a)a fuzzy-stochastic simulation-optimization model(FSSOM).FSSOM integrates techniques of SVM(support vector machine),Monte Carlo simulation,and FICMP(fractile interval chanceconstrained mixed-integer programming).In FSSOM,uncertainties expressed as fuzzy boundary intervals and random variables can be effectively tackled.In addition,SVM coupled Monte Carlo technique is used for predicting the peak-electricity demand.The FSSOM is applied to planning EPS for the City of Qingdao,China.Solutions of electricity generation pattern to satisfy the city's peak demand underdifferent probability levels and p-necessity levels have been generated,(b)a robust flexible probabilistic programming(RFPP)method is developed for planning municipal energy system(MES)with considering peak electricity prices(PEPs)and electric vehicles(EVs),where multiple uncertainties regarded as intervals,probability distributions and flexibilities as well as their combinations can be effectively reflected.The RFPP-MES model is then applied to planning Qingdao's MES,where electrical load of 24-h time is simulated based on Monte Carlo.Results can help decision makers improve energy supply patterns,reduce energy system costs and abate pollutant emissions as well as adjust end-users' consumptions.(c)an interval-possibilistic basic-flexible programming(IPBFP)method is proposed for planning MES of Qingdao,where uncertainties expressed as interval-flexible variables and interval-possibilistic parameters can be effectively reflected.Support vector machine(SVM)is used for predicting electricity demand of the city under various scenarios.Solutions of EVs stimulation levels and satisfaction levels in associationwith flexible constraints and predetermined necessity degrees are analyzed,which can help identify the optimized energy-supply patterns that could plunk for improvement of air quality and hedge against violation of soft constraints.(d)a flexible-possibilistic stochastic programming(FPSP)method is developed for dealing with multiple uncertainties expressed as soft constraints and flexibilities,fuzzy possibility distributions and probability distributions as well as their combinations(i.e.flexible-possibilistic-stochastic).FPSP has advantages of tackling uncertain constraints with high variability through integration of fuzzy possibility distributions and probability distributions.It can also reveal the individual and interactive effects of uncertain parameters on system cost through introducing sensitive analysis,avoiding the loss of uncertain information and enhancing the robustness of solutions.The FPSP method is then applied to planning municipal-scale energy system(MES)of Beijing under considering the impacts of renewable energies and EVs.Solutions in association with different constraint-violation levels,satisfaction degrees and confidence levels have been obtained,(e)a copula-based flexible-stochastic programming(CFSP)method has been proposed for planning the energy system within a regional scale under multi-uncertainty.CFSP simultaneously reflects interactive features of random variables and deals with uncertain parameters in target value of goals and soft constraints.Its applicability has been verified for planning RES of the urban agglomeration of Beijing and Tianjin.Based on the proposed CFSP method,a CFSP-RES model has been formulated.Issues of energy demand-supply security,minimum system cost and environment mitigation,as well as multiple uncertainties are reflected in the CFSP-RES.(f)a copula-based stochastic fuzzy-credibility programming(CSFP)method is developed for planning regional-scale electric power systems(REPS).CSFP cannot only deal with multiple uncertainties presented as random variables,fuzzy sets,interval values as well as their combinations,but also reflect uncertain interactions among multiple random variables owning different probability distributions and having previously unknown correlations.Then,a CSFP-REPS model is formulated for planning the electric power systems(EPS)of the Jing-Jin-Ji region,where multiple scenarios with different joint and individual probabilities as well as different credibility levels are examined.Based on the uncertainty analysis,multiple planning strategies for the MES or EPS could be obtained in a cost-and environment-effective way.Results cover all aspects of energy systems,includine energy supply and demand,electricity production and supply.pollutant emission control,as well as minimize of system cost.These solutions can support the adjustment of the existing plans and policies,and facilitation of dynamic analysis for decisions of capacity expansion and development plans.Findings will help decision makers to realize the unification of economic gains,social improvements and environmental benefits. |
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