An inexact two-stage fractional energy systems planning model

Song, Tangnyu; Huang, Guohe; Zhou, Xiong; Wang, Xiuquan

doi:10.1016/j.energy.2018.06.158

Cited by 15 publications

(3 citation statements)

References 36 publications

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“…Zhou et al (2015) proposed a chance-constrained two-stage fractional regional energy model (CTFO-REM) for British Columbia regional energy system planning under stochastic uncertainties, in order to minimize the system cost of per unit renewable energy utilization. Furthermore, an inexact two-stage fractional energy systems planning model (ITF-ESP) was devised in 2018 to address dual-objective energy system planning problems under interval featured uncertainties (Song et al, 2018). Both CTFO-REM and ITF-ESP models are practical methods to deal with conflicting issues (e.g., renewable energy utilization and system cost), however, the CTFO-REM cannot address interval uncertainties in input parameters, and the ITF-ESP was unable to analyze management schemes under different constraint-violation risk levels.…”

Section: Introductionmentioning

confidence: 99%

Development of a Chance-Constrained Dual-Objective Fractional Programming for Shandong’s Clean Power Transition

Li¹,

Huang²,

Zhang³

et al. 2022

J ENVIRON INFORM LET

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In this study, an inexact mixed-integer interval stochastic fractional model (IMSFP) is developed for Shandong’s sustainable power system management under uncertainties. Shandong has a high proportion of fossil-fuel power, which has resulted in significant greenhouse gas emissions. Future is an essential period for energy structure transition. Developed IMSFP can effectively tackle dual objective, system efficiency represented as output/input ratios, as well as uncertainties described as interval values and probability distributions in the constraints and objectives. The results indicate that the clean power transition and capacity expansion scheme are sensitive to different constraint-violation risk levels. Obtained interval solutions can provide flexible strategies for resource allocation and expansion capacities under multiple complexities. An economic single objective model (IMCLP) is also developed, which aims at minimizing the system cost. The comparative results illustrate that the IMSFP model can better characterize the real-world power system problems through optimizing a ratio between clean energy utilization and system cost. Biomass and wind power would be major developed electricity forms in the future, and solar energy has great development potential. In short, the proposed IMSFP model is advantageous in balancing conflicting dual objectives and reflecting complicated interactions among system efficiency, economic cost, system reliability, and constraint-violation scenarios.

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Section: Introductionmentioning

confidence: 99%

Development of a Chance-Constrained Dual-Objective Fractional Programming for Shandong’s Clean Power Transition

Li¹,

Huang²,

Zhang³

et al. 2022

J ENVIRON INFORM LET

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“…Abido (2006) proposed several algorithms for solving multiple objective electric dispatch problem. Linear fractional programming (LFP) have been proposed to deal with two objectives in a programming problem (Cui et al, 2015;Song et al, 2018). Based on the LFP method, various advanced technologies, and methods are integrated to address complex uncertainties in the programming model.…”

Section: Introductionmentioning

confidence: 99%

A Dual-Uncertainty Two-Stage Fractional Programming Model for Reginal Power Systems in the Province of Ontario, Canada

Huang¹,

Huang²,

Nie³

2022

J ENVIRON INFORM LET

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This study proposed a dual-uncertainty two-stage fractional power system management (DUTSF-PSM) model to deal with uncertainties and dual objectives in the power management system of Ontario. This model integrates interval linear programming (ILP), chance-constrained programming (CCP), mixed-integer linear programming (MILP), and two-stage stochastic programming (TSP) methods into the framework of a linear fractional programming (LFP) model. Two-objective issues and capacity expansion schemes under multiple uncertainties can be addressed by the DUTSF-PSM model. Economic and environmental elements are considered in the objective function of the DUTSF-PSM model at the same time in order to get maximal system benefit with minimum environmental influence. This model can tackle effectively the tradeoff between the economic and environmental objectives. Through the DUTSF-PSM model for power systems in Ontario, the maximal system efficiency based on the least environmental influence under different levels of constraint-violation probabilities can be achieved. The results indicate that both hydroelectric and wind power have development potential when the economic and environmental factors are considered in the objective function at the same time. In addition, the results of factorial analyses reflected that the effect of CO2 emission of each power generation technology on the system revenue is most significant among the chosen three factors.

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“…A multitude of studies have been conducted to help decision makers to translate the sustainable development concept into operational management plans [11][12][13][14]. Mathematical programming, as one of the most useful tools that evaluate the competition scheme between sectors such as agriculture, industry and energy for limited natural resources, receives a continued interest from water, energy and environmental practitioners [15][16][17][18][19]. Mathematical programming has its particular advantages in providing a simplified and effective way for approaching the management problem of interest and in obtaining optimal solutions that reflect the tradeoffs between conflicting interests.…”

Section: Introductionmentioning

confidence: 99%

Robust Linear Programming and Its Application to Water and Environmental Decision-Making under Uncertainty

et al. 2018

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In this study, we introduce a robust linear programming approach for water and environmental decision-making under uncertainty. This approach is of significant practical utility to decision makers for obtaining reliable and robust management decisions that are "immune" to the uncertainty attributable to data perturbations. The immunization guarantees that the chosen robust management plan will be implementable with no violation of the mandatory constraints of the problem being studied-i.e., natural resource supply constraint, environmental carrying capacity constraint, environmental pollution control constraint, etc.-and that the actual value of the objective will be no worse than the given estimation if the perturbations of data fall within the specified uncertainty set. A simplified example in regional water quality management is provided to help water and environmental practitioners to better understand how to implement robust linear programming from the perspective of application, as well as to illustrate the significance and necessity of implementing robust optimization techniques in real-world practices. Robust optimization is a growing research field that requires more interdisciplinary research efforts and engagements from water and environmental practitioners. Both may benefit from the advances of management science.

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An inexact two-stage fractional energy systems planning model

Cited by 15 publications

References 36 publications

Development of a Chance-Constrained Dual-Objective Fractional Programming for Shandong’s Clean Power Transition

Development of a Chance-Constrained Dual-Objective Fractional Programming for Shandong’s Clean Power Transition

A Dual-Uncertainty Two-Stage Fractional Programming Model for Reginal Power Systems in the Province of Ontario, Canada

Robust Linear Programming and Its Application to Water and Environmental Decision-Making under Uncertainty

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