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Wind/Storage Power Scheduling Based on Time–Sequence Rolling Optimization

  • Research Article-Electrical Engineering
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Abstract

Due to that participation of energy storage in wind power dispatch can improve scheduling reliability of Grid-accessed, the effectiveness depends on energy storage capacity and feasible energy management. Daily economic dispatch model is proposed firstly under the consideration of scheduling reliability and working characteristics of energy storage. Secondly, the Time–Sequence Rolling Optimal Ultra-short-term scheduling algorithm of energy storage is developed based on dynamic deviation estimation update. To deal with the problems caused by unforeseen prediction error of wind power or energy storage SOC (state-of-charge), relaxation factor of the allowable Grid-accessed power deviation range is introduced either in the scheduling algorithm to ensure reliability of established energy storage capacity. Finally, GA (Genetic Algorithm) and EMD (Empirical Mode Decomposition) are used in reference value setting of hybrid energy storage power distribution. The feasibility of the dispatch model and energy management strategy are verified by the wind/storage simulation platform.

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Abbreviations

ARMA:

Autoregressive moving average model

ARIMA:

Autoregressive integrated moving average model

BP:

Back-propagation

BA:

Bat algorithm

PSO:

Particle swarm optimization

OPRC:

Optimization power regression curve

DWT:

Discrete wavelet transform

SVM:

Support vector machines

SBL:

Sparse Bayesian learning

EDM:

Empirical dynamic modeling

KDE:

Kernel density estimation

GP:

Gaussian Process

DYGP:

Dynamic GP

DIGP:

Direct GP

STGP:

Static GP

IDGP:

Indirect GP

G_RQ:

Gaussian process regression with the rational quadratic kernel

G_SE:

Gaussian process regression with the squared exponential kernel

G_M52:

Gaussian process regression with the matern 5/2 kernel

G_Exp:

Gaussian process regression with the exponential kernel

GMR:

Generalized mapping regressor

RF:

Random forest

O2O:

One to one

GRF:

Generalized random forest

XGB:

Extreme gradient boosting

EMD:

Empirical mode decomposition

EEMD:

Ensemble empirical mode decomposition

CEEMDAN:

Complete ensemble empirical mode decomposition with adaptive noise

ELM:

Extreme learning machine

KELM:

kernel extreme learning machine

IELM:

Improved extreme learning machine

GA:

Genetic algorithm

ADQPSO:

Adaptive disturbance quantum PSO

LLE:

Local linear embedding

PCA:

principal component analysis

LZC:

Lempel-Ziv complexity

SSA:

Salp swarm algorithms

WPD:

Wavelet packet decomposition

VMD:

Variable mode decomposition

IGWO:

Improved gray wolf optimization

OS-ELM:

Online sequential-ELM

TMD:

Two-layer mode decomposition

NN:

Neural network

ICA:

Imperialistic competitive algorithm

MWNN:

Morlet wavelet neural network

T-SFNN:

T-S fuzzy neural network

WNN:

Wavelet-based neural network

NNWT:

Neural network with wavelet transform

k-NN:

k-Nearest neighbor

SGNN:

Superposition graph neural network

ANN:

Artificial neural network

MTS:

Memory tabu search

SNN:

Spiking neural network

WNF:

Wavelet neuro fuzzy

HPA:

Hybrid PSO–ANFIS

CNN:

Convolutional neural network

GRU:

Gated recurrent unit

MIV:

Mean impact value

GRNN:

Generalized regression neural network

D.E:

Dilation and erosion

DPK:

Differential privacy protection

K-mean:

k-means clustering algorithm

FNN:

Feed-forward neural network

ANFIS:

Adaptive Neuro-Fuzzy Inference System

PER:

Persistence

DSN:

Double-Stage neural network

DSA:

Double-Stage ANFIS

DSHGN:

Double-Stage Hybrid GA-NN

DSHPN:

Double-Stage Hybrid PSO-NN

BPNN:

Back-Propagation Neural Network

AM:

Adaptive Mutation

WPD:

Wavelet packet decomposition

GMDH:

Group method data handling

RBF:

Radial basis function

ACFOA:

Adaptive chaos fruit fly optimization algorithm

T2FNN:

Type-2 Fuzzy neural network

KNEA:

kernel-based nonlinear extension Arps

LSSVM:

Least squares support vector machines

IGSA:

Improved gravitational search algorithm

BN:

Beveridge-Nelson

ALO:

Ant lion optimization

S_C:

Support Vector with the Cubic kernel

S_CG:

Support Vector with the Coarse Gaussian kernel

S_FG:

Support Vector with the Fine Gaussian kernel

S_L:

Support Vector with the Linear kernel

S_MG:

Support Vector with the Medium Gaussian kernel

S_Q:

Support Vector with the quadratic kernel

SVR:

Support vector regression

CS:

Cuckoo search

LSTM:

Long short-term memory

DLSTM:

Deep long short-term memory

EFG:

Enhanced forget-gate

MLP:

Multi-layer perceptron

CRPSO:

Cooperative random particle swarm optimization

DE:

Differential evolution

SSO:

Simplified swarm optimization

QR:

Quantile regression

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Acknowledgements

This work is a part of the National Natural Science Research Programs of China (No. 61673226, U2066203, 61973178)), considerable Natural Science Research Projects of Colleges and Universities in Jiangsu Province of China (No. BE2021063), Natural Science Foundation of Jiangsu Province (BK20200969), Nantong Science and Technology Bureau Project (JC2018116), and Jiangsu Province's fifth ‘333 high-level talent training objects’ Project. The authors would like to thank for the supports from both the Ministry of Science and Technology and National Natural Science Foundation of China.

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  1. School of Electrical Engineering, NanTong University, Nantong, 226019, China

    Jian-hong Zhu, Renji Xu, Juping Gu & Xinsong Zhang

  2. College of Energy and Electrical Engineering, HoHai University, NanJing, 210098, China

    Chenxiang Sun

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Zhu, Jh., Xu, R., Gu, J. et al. Wind/Storage Power Scheduling Based on Time–Sequence Rolling Optimization. Arab J Sci Eng 48, 6219–6236 (2023). https://doi.org/10.1007/s13369-022-07220-7

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