A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties

Zhang, Xiaoyue; Huang, Guohe; Zhu, Hua; Li, Y. P.

doi:10.1016/j.energy.2017.01.072

Cited by 20 publications

(8 citation statements)

References 31 publications

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“…(1) Monte Carlo simulation method was used in [2] to construct a decision-making model for fuzzy stochastic decision-making problems in groundwater-agricultural systems. In [3], a fuzzy stochastic dynamic fractional programming was proposed to deal with stochastic fuzzy dual uncertainty. In [1], a multiple objective programming was discussed for interactive fuzzy stochastic programming issues, and some interactive algorithms were proposed to seek a satisfying solution of DMs.…”

Section: Advantages Of the Proposed Methodmentioning

confidence: 99%

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An Intuitionistic Fuzzy Stochastic Decision‐Making Method Based on Case‐Based Reasoning and Prospect Theory

Yang

Wei

2017

Mathematical Problems in Engineering

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According to the case-based reasoning method and prospect theory, this paper mainly focuses on finding a way to obtain decision-makers’ preferences and the criterion weights for stochastic multicriteria decision-making problems and classify alternatives. Firstly, we construct a new score function for an intuitionistic fuzzy number (IFN) considering the decision-making environment. Then, we aggregate the decision-making information in different natural states according to the prospect theory and test decision-making matrices. A mathematical programming model based on a case-based reasoning method is presented to obtain the criterion weights. Moreover, in the original decision-making problem, we integrate all the intuitionistic fuzzy decision-making matrices into an expectation matrix using the expected utility theory and classify or rank the alternatives by the case-based reasoning method. Finally, two illustrative examples are provided to illustrate the implementation process and applicability of the developed method.

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Section: Advantages Of the Proposed Methodmentioning

confidence: 99%

“…With the increasing complexity of socioeconomic environments, it is very hard for decision-makers (DMs) to express their preferences with crisp values. Fuzzy set (FS) theory was proposed and has been deeply studied from different disciplines [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Intuitionistic Fuzzy Stochastic Decision‐Making Method Based on Case‐Based Reasoning and Prospect Theory

Yang

Wei

2017

Mathematical Problems in Engineering

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“…and if (0.75,1] Fractional programming (FP) is an effective tool for tackling dual-objective optimization problems. Integrating type-2 fuzzy variables into the fractional programming framework will enhance the relevant decision robustness [1,34]. A general linear fractional programming (LFP) problem can be denoted as follows [27,28]:…”

Section: Type-2 Fuzzy Mathematical Programmingmentioning

confidence: 99%

“…However, the sustainable management of EPS faces many challenges. For example, decision-makers should consider the tradeoff between rising electric demand and growing environmental/health concerns, the reflection of dynamic characteristics of the installed capacity of power generation facilities issues, as well as uncertainties of input information, such as the forecast values of power demands and renewable resource availabilities [1][2][3][4]. Therefore, efficient systems analysis methods for planning of EPS under these complexities and uncertainties are desired.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Zhu et al presented a chance-constrained stochastic FP approach for ratio optimization problems involving facilities expansion and stochastic information in electric power systems [27]. Zhang et al advanced a dynamic fuzzy stochastic FP method for supporting sustainable management of EPS and balancing conflicting objectives under dual uncertainties [1].…”

Section: Introductionmentioning

confidence: 99%

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A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks

2019

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In this study, a type-2 fuzzy chance-constrained fractional integrated programming (T2FCFP) approach is developed for the planning of sustainable management in an electric power system (EPS) under complex uncertainties. Through simultaneously coupling mixed-integer linear programming (MILP), chance-constrained stochastic programming (CCSP), and type-2 fuzzy mathematical programming (T2FMP) techniques into a fractional programming (FP) framework, T2FCFP can tackle dual objective problems of uncertain parameters with both type-2 fuzzy characteristics and stochastic effectively and enhance the robustness of the obtained decisions. T2FCFP has been applied to a case study of a typical electric power system planning to demonstrate these advantages, where issues of clean energy utilization, air-pollutant emissions mitigation, mix ratio of renewable energy power generation in the entire energy supply, and the displacement efficiency of electricity generation technologies by renewable energy are incorporated within the modeling formulation. The suggested optimal alternative that can produce the desirable sustainable schemes with a maximized share of clean energy power generation has been generated. The results obtained can be used to conduct desired energy/electricity allocation and help decision-makers make suitable decisions under different input scenarios.

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Generation capacity expansion economic-environmental planning under uncertainty of demand and supply

et al. 2020

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A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties

Cited by 20 publications

References 31 publications

An Intuitionistic Fuzzy Stochastic Decision‐Making Method Based on Case‐Based Reasoning and Prospect Theory

An Intuitionistic Fuzzy Stochastic Decision‐Making Method Based on Case‐Based Reasoning and Prospect Theory

A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks

Generation capacity expansion economic-environmental planning under uncertainty of demand and supply

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