Risk-Oriented Renewable Energy Scenario Clustering for Power System Reliability Assessment and Tracing

Yang, Wenhua; Cao, Maosen; Ge, Pengjiang; Hu, Bo; Qu, Gaoqiang; Xie, Kaigui; Cheng, Xin; Peng, Lvbing; Yan, Jun; Li, Yufei

doi:10.1109/access.2020.3027435

Cited by 14 publications

(5 citation statements)

References 29 publications

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“…(1, ) m  is a user-defined weight index reflecting the amount of fuzziness [32], [33], and ε denotes the convergency tolerance. m, ε take the value of 2 and 10 -6 in this paper.…”

Section: Scenario Generating Methodologymentioning

confidence: 99%

Complementary operation of multi-renewable energy system with pumped storage

2021

CSEE JPES

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Complementarity is an important attribute between different renewable energy sources (RESs). Multi-renewable energy system (MRES) offers an alternative to aggregate diverse RESs and make the best of their mutual characteristics. However, few papers conduct convincing research into the complementary operation of MRES. This paper investigates the complementarities in MRES that contains wind, photovoltaic, run -off hydro, pumped storage power plants and incorporates complementarity into optimal operation of MRES. The complementarity is theoretically summarized and firstly classified into natural and synthetical one. To effectively capture the uncertainty and complementarity of RESs, the time-dependent clustering simulation technique is employed to generate the joint-scenarios. An equivalent run-off hydropower plant index is proposed, based on which a novel complementary operation model that considers volatility and self-sufficient adequacy is established. Results are presented and compared in the case study, which demonstrate the validity and scalability of the proposed methodology, reveal the complementarities under multiple time-scales, and illustrate the significance of complementary operation.

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“…(1, ) m  is a user-defined weight index reflecting the amount of fuzziness [32], [33], and ε denotes the convergency tolerance. m, ε take the value of 2 and 10 -6 in this paper.…”

Section: Scenario Generating Methodologymentioning

confidence: 99%

Complementary operation of multi-renewable energy system with pumped storage

2021

CSEE JPES

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“…This paper discusses the equivalence of distributed generations (DGs) in distribution networks to two types of models [31]. The first model regards DG as a generator set, while the second model views DG as a stochastic power supplier.…”

Section: Distributed Generation Modeling In Distribution Networkmentioning

confidence: 99%

“…The constraints (4), ( 9), ( 10), ( 11), ( 12), ( 20), ( 21), ( 24), ( 25), (30), (31), (32), (33), (41), (42), (45), ( 46) and (48) of the text are linearized using the Big-M method of 4.3, The results after linearization are as follows: ) , ∀i j ∈ Υ.…”

Section: Appendix Amentioning

confidence: 99%

“…In this paper, the scenario‐based method [31] is used to model PV equivalent to stochastic power supply.…”

Section: Basis Of Reliability Assessment For Distribution Networkmentioning

confidence: 99%

“…In this paper, DG in distribution networks is equivalent to two types of models [31]. The first model is equivalent to DG as generator set, and the second model is equivalent to DG as stochastic power supply.…”

Section: Proposed Model Based On Linear Programming Considering Da An...mentioning

confidence: 99%

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A novel reliability assessment method for distribution networks based on linear programming considering distribution automation and distributed generation

Li,

Kong,

Wang

et al. 2023

IET Renewable Power Gen

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The primary methods of assessing the reliability of distribution networks comprise analytic and simulation methods. However, both approaches require the identification and computation of network topology, which precludes their expression in explicit, continuous functions, consequently impeding the incorporation of reliability constraints into planning and operational optimization models. To tackle this restriction, the present work puts forth a novel linear‐programming‐based reliability assessment method that is mathematically formulated, considering distribution automation (DA) and distributed generations (DGs), consisting of both conventional and renewable energy sources. In this paper, the clustering method and the scenario‐based method are used to model DGs. Next, a mixed integer linear programming (MILP) model, considering the DA and DGs with the System Average Interruption Duration Index (SAIDI) as the optimization objective, is proposed. Finally, the feasibility and effectiveness of the proposed method are verified in a 37‐node distribution network system.

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