Review of Uncertainty Modeling for Optimal Operation of Integrated Energy System

Fan, Hong; Wang, Cuiying; Liu, Lu; Li, Xuan

doi:10.3389/fenrg.2021.641337

Cited by 29 publications

(18 citation statements)

References 89 publications

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“…Moreover, a novel algorithm is proposed for treating ICLP problems where the analyzing process is achieved by simulating different scenarios of the uncertain environment. The scenario method is a common strategy for dealing with uncertain parameters based on scenario analysis [ 40 ]. Through better analysis than other techniques do, the analyst provides the DM with the most possible information by using the proposed terminology.…”

Section: Introductionmentioning

confidence: 99%

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Elazeem

Khalifa

Pamučar

et al. 2022

Computational Intelligence and Neuroscience

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The purpose of this study is to achieve a novel and efficient method for treating the interval coefficient linear programming (ICLP) problems. The problem is used for modeling an uncertain environment that represents most real-life problems. Moreover, the optimal solution of the model represents a decision under uncertainty that has a risk of selecting the correct optimal solution that satisfies the optimality and the feasibility conditions. Therefore, a proposed algorithm is suggested for treating the ICLP problems depending on novel measures such as the optimality ratio, feasibility ratio, and the normalized risk factor. Depending upon these measures and the concept of possible scenarios, a novel and effective analysis of the problem is done. Unlike other algorithms, the proposed algorithm involves an important role for the decision-maker (DM) in defining a satisfied optimal solution by using a utility function and other required parameters. Numerical examples are used for comparing and illustrating the robustness of the proposed algorithm. Finally, applying the algorithm to treat a Solid Waste Management Planning is introduced.

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Section: Introductionmentioning

confidence: 99%

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Elazeem

Khalifa

Pamučar

et al. 2022

Computational Intelligence and Neuroscience

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“…Amid growing concerns regarding energy supply scarcity and environmental pollution, wind and photovoltaic (PV) resources have undergone remarkable developments owing to their well-established technologies and abundant availability [1]. As a substantial energy consumer, China has witnessed rapid growth in its installed capacity for renewable energy power generation over the past decade to promote the low-carbon a significant influence on the optimal configuration of power systems [4,5]. Consequently, it is imperative to comprehensively optimize the system configuration to maintain a dynamic equilibrium between the power supply and demand within a power system characterized by high renewable energy penetration.…”

Section: Introductionmentioning

confidence: 99%

“…The objective aims to propel the transformation of the power system into a clean and low‐carbon entity, enabling future power systems to progress towards a high proportion of new energy penetration. However, the inherent stochastic and volatile nature of renewable energy output presents a formidable challenge, resulting in a lack of system flexibility and a significant influence on the optimal configuration of power systems [4, 5]. Consequently, it is imperative to comprehensively optimize the system configuration to maintain a dynamic equilibrium between the power supply and demand within a power system characterized by high renewable energy penetration.…”

Section: Introductionmentioning

confidence: 99%

A closed‐loop representative day selection framework for generation and transmission expansion planning with demand response

Liu,

Li,

Liu

et al. 2024

Energy Conversion and Econom

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In power systems with a high proportion of renewable energy resources (RES), the inherent stochasticity and volatility of RES necessitate careful consideration in power system planning. Scenario analysis is commonly employed to address the stochastic nature in power system planning. Existing studies generally adopt an open‐loop structure, where representative days are selected first and planning decisions are subsequently made. However, this method may not accurately represent the operating status of a system owing to changes in the power generation structure during the planning process. To address this limitation, this paper introduces a closed‐loop framework for representative day selection within the context of generation and transmission expansion planning (G&TEP), incorporating demand response (DR). The framework comprises three layers: representative day selection, planning decisions, and long‐term operational simulation. Initially, an approach for selecting representative days is proposed by combining the clustering and optimization‐based methods. Subsequently, a G&TEP model that incorporates DR is presented in the second layer. Lastly, the framework encompasses a three‐layer closed‐loop structure, enabling dynamic adjustments and enhancements to the representative day selection process to ensure optimality. Case studies on the reliability and operational test system of a power grid with large‐scale renewable integration (XJTU‐ROTS) demonstrate the effectiveness of our proposed framework.

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“…In this article, the two authors emphasized the control approach of the generator rotor speed while the effects of uncertainties toward the improvement of power output are lacking. The various classifications of uncertainty for optimization in the hybrid energy system are given by Fan et al (2022). In their study, they classified it into five categories.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the effect of parametric uncertainty on dynamic performances of doubly fed induction generator-based wind energy conversion system

Haile,

Tuka

2023

Wind Engineering

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The wind, stochastic in nature, is one of the fastest-growing and most promising renewable energy resources in the entire world. Thus, this paper investigates the influence of parameter uncertainties upon a dynamic performance of a grid-tied Doubly-Fed Induction Generator (DFIG)-based Wind Energy Conversion System (WECS). The main uncertain parameters found in the study are mutual and rotor winding reactances which occurred due to the variation of the angular positions of the rotor caused by varying wind speeds. The variation in the wind speed caused the generator rotor speed to deviate between 25% and 150%. Consequently, the rotor winding reactance of DFIG changes from its nominal value of 1.31 mΩ to between 0.983 and −0.655 mΩ; and the mutual reactance from its nominal value of 0.941 Ω to between 0.758 and −0.4708 Ω. As a result, the stator and rotor winding voltages and currents of the DFIG are uncertain.

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Review of Uncertainty Modeling for Optimal Operation of Integrated Energy System

Cited by 29 publications

References 89 publications

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

A closed‐loop representative day selection framework for generation and transmission expansion planning with demand response

Analysis of the effect of parametric uncertainty on dynamic performances of doubly fed induction generator-based wind energy conversion system

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