LSTM auto‐encoder based representative scenario generation method for hybrid hydro‐PV power system-Reference-Cited by-同舟云学术

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LSTM auto‐encoder based representative scenario generation method for hybrid hydro‐PV power system

Published:2020-08-04 Issue:24 Volume:14 Page:5935-5943
ISSN:1751-8687
Container-title:IET Generation, Transmission & Distribution
language:en
Short-container-title:IET Generation, Transmission & Distribution

Author:

Yang Jingxian¹,Zhang Shuai¹,Xiang Yue¹²,Liu Jichun¹,Liu Junyong¹,Han Xiaoyan³,Teng Fei²^ORCID

Affiliation:

1. College of Electrical EngineeringSichuan UniversityChengdu610065People's Republic of China

2. Department of Electrical and Electronic EngineeringImperial College LondonLondonSW7 2AZUK

3. State Grid Sichuan Electric Power CompanyChengdu610041People's Republic of China

Funder

National Key Research and Development Program of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2020.0757

Reference49 articles.

1. Towards fully renewable energy systems: experience and trends in Denmark;Pinson P.;CSEE J. Power Energy Syst.,2017

2. Optimization of photovoltaic power system: a comparative study;Dabra V.;Prot. Control Mod. Power Syst.,2017

3. Research on joint optimal dispatching method for hybrid power system considering system security;Fu Y.;Appl. Energy,2019

4. Long‐term complementary operation of a large‐scale hydro photovoltaic hybrid power plant using explicit stochastic optimization;Li H.;Appl. Energy,2019

5. Optimal daily generation scheduling of large hydro‐ photovoltaic hybrid power plants;Ming B.;Energy Convers. Manag.,2018

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1. Stochastic distributionally robust unit commitment with deep scenario clustering;Electric Power Systems Research;2023-11

2. A spatio-temporality-enabled parallel multi-agent-based real-time dynamic dispatch for hydro-PV-PHS integrated power system;Energy;2023-09

3. Clustering and decision tree based analysis of typical operation modes of power systems;Energy Reports;2023-09

4. A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models;Energies;2023-07-25

5. Extreme scenario generation for renewable energies;IET Smart Grid;2023-07-10

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