A Very Short-term Forecasting Approach for Virtual Power Plant Using a Self-adaptive Hybrid Algorithm

Yu Zhu; Qiuyu Lu; Yinguo Yang; Bo Li; Yingming Lin; Yan Tan; Zhongkai Yi; Kang Wang; Yinliang Xu

doi:10.1109/ISGT-Asia.2019.8881352

A Very Short-term Forecasting Approach for Virtual Power Plant Using a Self-adaptive Hybrid Algorithm

Yu Zhu
, Qiuyu Lu
, Yinguo Yang
, Bo Li
, Yingming Lin
, Yan Tan
, Zhongkai Yi^*
, Kang Wang
, Yinliang Xu

^*Corresponding author for this work

China Southern Power Grid
Tsinghua University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Virtual power plant (VPP) emerges as a new concept to promote the efficient utilization of renewable energy resources (RES) and flexible loads. RES forecasting approach is an important item to ensure the stability and economy in VPP dispatching and bidding. In this paper, considering multienvironmental factors, a very short-term photovoltaic (PV) forecasting approach based on a self-adaptive simulated annealing hybrid genetic algorithm (SA-GA) and backpropagation neural network (BP) algorithm is proposed. Numerical studies illustrate that the proposed approach achieves a satisfactory forecasting accuracy and offers a high computation efficiency, which indicates its promising application value in RES forecasting for VPP.

Original language	English
Title of host publication	2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3485-3489
Number of pages	5
ISBN (Electronic)	9781728135205
DOIs	https://doi.org/10.1109/ISGT-Asia.2019.8881352
State	Published - May 2019
Externally published	Yes
Event	2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019 - Chengdu, China Duration: 21 May 2019 → 24 May 2019

Publication series

Name	2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

Conference

Conference	2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019
Country/Territory	China
City	Chengdu
Period	21/05/19 → 24/05/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

SA-GA-BP algorithm
Virtual power plant
multi-environmental factors
very short-term PV forecasting

Access to Document

10.1109/ISGT-Asia.2019.8881352

Cite this

Zhu, Y., Lu, Q., Yang, Y., Li, B., Lin, Y., Tan, Y., Yi, Z., Wang, K., & Xu, Y. (2019). A Very Short-term Forecasting Approach for Virtual Power Plant Using a Self-adaptive Hybrid Algorithm. In 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019 (pp. 3485-3489). Article 8881352 (2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISGT-Asia.2019.8881352

@inproceedings{a2d51e16339d49b5a1d2af50bc2dfa47,

title = "A Very Short-term Forecasting Approach for Virtual Power Plant Using a Self-adaptive Hybrid Algorithm",

abstract = "Virtual power plant (VPP) emerges as a new concept to promote the efficient utilization of renewable energy resources (RES) and flexible loads. RES forecasting approach is an important item to ensure the stability and economy in VPP dispatching and bidding. In this paper, considering multienvironmental factors, a very short-term photovoltaic (PV) forecasting approach based on a self-adaptive simulated annealing hybrid genetic algorithm (SA-GA) and backpropagation neural network (BP) algorithm is proposed. Numerical studies illustrate that the proposed approach achieves a satisfactory forecasting accuracy and offers a high computation efficiency, which indicates its promising application value in RES forecasting for VPP.",

keywords = "SA-GA-BP algorithm, Virtual power plant, multi-environmental factors, very short-term PV forecasting",

author = "Yu Zhu and Qiuyu Lu and Yinguo Yang and Bo Li and Yingming Lin and Yan Tan and Zhongkai Yi and Kang Wang and Yinliang Xu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019 ; Conference date: 21-05-2019 Through 24-05-2019",

year = "2019",

month = may,

doi = "10.1109/ISGT-Asia.2019.8881352",

language = "英语",

series = "2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3485--3489",

booktitle = "2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019",

address = "美国",

}

Zhu, Y, Lu, Q, Yang, Y, Li, B, Lin, Y, Tan, Y, Yi, Z, Wang, K & Xu, Y 2019, A Very Short-term Forecasting Approach for Virtual Power Plant Using a Self-adaptive Hybrid Algorithm. in 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019., 8881352, 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019, Institute of Electrical and Electronics Engineers Inc., pp. 3485-3489, 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019, Chengdu, China, 21/05/19. https://doi.org/10.1109/ISGT-Asia.2019.8881352

A Very Short-term Forecasting Approach for Virtual Power Plant Using a Self-adaptive Hybrid Algorithm. / Zhu, Yu; Lu, Qiuyu; Yang, Yinguo et al.
2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3485-3489 8881352 (2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Zhu, Yu

AU - Lu, Qiuyu

AU - Yang, Yinguo

AU - Li, Bo

AU - Lin, Yingming

AU - Tan, Yan

AU - Yi, Zhongkai

AU - Wang, Kang

AU - Xu, Yinliang

PY - 2019/5

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AB - Virtual power plant (VPP) emerges as a new concept to promote the efficient utilization of renewable energy resources (RES) and flexible loads. RES forecasting approach is an important item to ensure the stability and economy in VPP dispatching and bidding. In this paper, considering multienvironmental factors, a very short-term photovoltaic (PV) forecasting approach based on a self-adaptive simulated annealing hybrid genetic algorithm (SA-GA) and backpropagation neural network (BP) algorithm is proposed. Numerical studies illustrate that the proposed approach achieves a satisfactory forecasting accuracy and offers a high computation efficiency, which indicates its promising application value in RES forecasting for VPP.

KW - SA-GA-BP algorithm

KW - Virtual power plant

KW - multi-environmental factors

KW - very short-term PV forecasting

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ER -

Zhu Y, Lu Q, Yang Y, Li B, Lin Y, Tan Y et al. A Very Short-term Forecasting Approach for Virtual Power Plant Using a Self-adaptive Hybrid Algorithm. In 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3485-3489. 8881352. (2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019). doi: 10.1109/ISGT-Asia.2019.8881352

A Very Short-term Forecasting Approach for Virtual Power Plant Using a Self-adaptive Hybrid Algorithm

Abstract

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UN SDGs

Keywords

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