Distributionally Robust Optimal DG Allocation Model Considering Flexible Adjustment of Demand Response

He, Shuaijia; Gao, Hongjun; Liu, Junyong; Liu, Jichun

doi:10.1109/pesgm40551.2019.8973569

Cited by 13 publications

(11 citation statements)

References 8 publications

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“…It is mainly divided into two methods: probability density and distance information. DRO methods based on probability density mainly include three forms: multi-discrete scene (He et al, 2019), Wasserstein distance (Zhu et al, 2019) and KL divergence (Chen et al, 2018). DRO methods based on range information mainly include two forms: deterministic range (Chen et al, 2016) and indeterminate range (Alvarado et al, 2019;Lu et al, 2018a).…”

Section: ) Distribution Robust Optimization Methodsmentioning

confidence: 99%

A review on configuration optimization of hybrid energy system based on renewable energy

Zhang

Wei

2022

Front. Energy Res.

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Hybrid energy system based on renewable energy is an important way to solve current energy and environmental problems. However, its achievable benefits need to be realized through an integrated optimization that considers optimization objectives, optimization models, and optimization algorithms. In this paper, the integrated structure and configuration optimization techniques of the hybrid energy system based on renewable energy (RE-HES) is summarized. The performance indicators, system characteristics, model construction, and solutions of RE-HES optimized configuration are comprehensively reviewed. Current review highlights the diversification of performance evaluation index, consideration of uncertainty and dynamic properties, superstructure approaches and intelligent algorithms.

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Section: ) Distribution Robust Optimization Methodsmentioning

confidence: 99%

A review on configuration optimization of hybrid energy system based on renewable energy

Zhang

Wei

2022

Front. Energy Res.

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“…Where: O p is the set that p s satisfies; N s represents the total number of scenes; A represents the configuration layer coefficient matrix; B represents the coefficient matrix of the operating layer; Ax in objective function Eq (25) represents the configuration cost "F C,1 "; By s represents the operating cost "F C,2 " in scenario s; Eq (26) represents the first stage variable related constraints, corresponding precisely to Eq (9); Eq (27) represents the second stage variable related constraints, corresponding to Eqs ( 16) to (24); Eq (28) represents the coupling constraint relationship between variables in the first and second stages, corresponding to Eqs (10) to (15); Eq (29) is the corresponding Eq (40) for the second-order cone relaxation constraint of the power flow.…”

Section: Plos Onementioning

confidence: 99%

Research on distributionally robust energy storage capacity allocation for output fluctuations in high permeability wind and solar distribution networks

Wang,

Sun,

et al. 2024

PLoS ONE

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This paper presents a novel approach to addressing the challenges associated with energy storage capacity allocation in high-permeability wind and solar distribution networks. The proposed method is a two-phase distributed robust energy storage capacity allocation method, which aims to regulate the stochasticity and volatility of net energy output. Firstly, an energy storage capacity allocation model is established, which considers energy storage’s investment and operation costs to minimize the total cost. Then, a two-stage distributed robust energy storage capacity allocation model is established with the confidence set of uncertainty probability distribution constrained by 1-norm and ∞-norm. Finally, a Column and Constraint Generation (C&CG) algorithm is used to solve the problem. The validity of the proposed energy storage capacity allocation model is confirmed by examining different wind and solar penetration levels. Furthermore, the model’s superiority is demonstrated by comparing it with deterministic and robust models.

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“…The algorithms, including analytical methods, numerical algorithms and so on, have been widely used in many real‐world problems and shown their inherent pros and cons. However, in the specific applications of DG planning, the features surely have different presentations, along with different problem‐specific techniques to enhance computation performance, which have been omitted in most existing studies; Some cutting‐edge methods have been ignored or have not been summarized in a systematic manner, for example, column and constraint generation algorithm, 43 dragonfly algorithm 44 and embedded meta evolutionary programming‐firefly algorithm‐ANN 45 …”

Section: Introductionmentioning

confidence: 99%

“…[33][34][35][36] However, they heavily rely on an accurate model developed on a set of assumptions or simplifications, making the results not suitable in practice. By contrast, numerical algorithms such as linear programming (LP), 37 mixed non-linear programming (MINLP), [38][39][40][41][42] dynamic programming (DP) 43 reduce the reliance on accurate modelling which are easy to be implemented. However, this method may obtain inaccurate solution in complex systems.…”

Section: Introductionmentioning

confidence: 99%

“…However, in the specific applications of DG planning, the features surely have different presentations, along with different problem-specific techniques to enhance computation performance, which have been omitted in most existing studies; 3. Some cutting-edge methods have been ignored or have not been summarized in a systematic manner, for example, column and constraint generation algorithm, 43 dragonfly algorithm 44 and embedded meta evolutionary programming-firefly algorithm-ANN. 45 To close the above research gaps, this paper provides a comprehensive survey of DG planning covering the most state-of-the-art techniques.…”

Section: Introductionmentioning

confidence: 99%

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Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

Yang

Chen

et al. 2020

Int J Energy Res

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Summary Distributed generation (DG) has attracted significant attention due to its great potential for enhancing economical and technical performance of power systems and reducing dependence on fossil fuels. Optimal sizing and placement are critical for stimulating such potential, about which a considerable number of models and algorithms have been proposed in past literature. This paper attempts to undertake a comprehensive review on optimal sizing and placement of DG via a systematic methodology procedure, including definition and classifications of DG, modelling and problem formulation with different technical and economic criteria, and summary of optimization algorithms. Common features and distinctive characteristics of both models and methods are identified, followed by evaluations and comparisons based on their practical performance in various test systems. Selection of DG techniques with respect to application scenarios, indispensable and optional considerations in DG planning models, and pros and cons of algorithms are listed in tables for a clearer understanding. Lastly, a total of 107 algorithms are addressed, which are classified into five categories. Particular, hybrid methods can deal with complex engineering problems with multiple objective functions and constraints most effectively and robustly. Future research trends are also highlighted with the aim of providing a comprehensive and state‐of‐the‐art survey for researchers, engineers, and other stakeholders.

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Distributionally Robust Optimal DG Allocation Model Considering Flexible Adjustment of Demand Response

Cited by 13 publications

References 8 publications

A review on configuration optimization of hybrid energy system based on renewable energy

A review on configuration optimization of hybrid energy system based on renewable energy

Research on distributionally robust energy storage capacity allocation for output fluctuations in high permeability wind and solar distribution networks

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

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