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99332

A Hybrid Dynamic Programming and Neural Network Approach to Unit Commitment with High Renewable Penetration.

Article

Last updated: 04 Jan 2025

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Subjects

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Tags

Electrical Engineering

Abstract

This paper presents a solution of the unit commitment (UC) problem for an electrical grid, which contains conventional sources and renewable energy sources as well as storage units. To ensure economical with the stochastic nature of renewable sources, it is essential to develop an efficient forecasting model for renewable power generation. Forecasting model was built by using a hybrid Markov to forecast solar radiation, while, autoregressive integrated moving average model is used to predict wind speed. UC problem incorporates with forecasting, the proposed formulation aims to minimize total production cost. The total production cost includes the fuel costs, environmental cost, operation and maintenance cost (O&M cost), start-up cost, and shutdown cost. UC formulation is subject to multi-constraints. These constraints are system constraint, thermal unit constraints, renewable sources constraints and storage unit's constraint. Also, reserve coefficient is modified to overcome the variation and error of renewable source forecasting by developing two new reserves; up reserve and down spinning reserve. The unit commitment algorithm is solved by simple, fast, and accurate optimization technique. So, hybrid optimization technique used to solve UC is dynamic programming based on neural network. The proposed hybrid techniquemakes the solution faster and more accurate compared with the other techniques. The system under study in this paper is the standard IEEE 30 bus system, with wind speed and solar radiation data of the city of Florida, USA

DOI

10.21608/bfemu.2020.99332

Keywords

Unit Commitment, Renewable Forecasting, storage system, Dynamic Program, Neural network

Authors

First Name

Sahar

Last Name

Kaddah

MiddleName

Sedky

Affiliation

Prof, Electrical Engineering Department, Faculty of Engineering Mansoura University, Egypt, She is the head of Electrical Engineering Department

Email

skaddah@mans.edu.eg

City

-

Orcid

-

First Name

K.

Last Name

Abo-Al-Ez

MiddleName

M.

Affiliation

-

Email

-

City

-

Orcid

-

First Name

Tamer

Last Name

Megahed

MiddleName

F.

Affiliation

Electrical Engineering Department Faculty of Engineering Mansoura University

Email

-

City

-

Orcid

-

First Name

M.

Last Name

Osman

MiddleName

G.

Affiliation

Electrical Engineering Department Faculty of Engineering Mansoura University

Email

-

City

-

Orcid

-

Volume

41

Article Issue

1

Related Issue

14715

Issue Date

2016-03-01

Receive Date

2015-10-02

Publish Date

2020-06-30

Page Start

7

Page End

17

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 41_Issue 1_Pages 7-17.pdf

PDF . 1.2MB

Link

https://bfemu.journals.ekb.eg/article_99332.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=99332

Order

2

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

A Hybrid Dynamic Programming and Neural Network Approach to Unit Commitment with High Renewable Penetration.

Details

Type

Article

Created At

22 Jan 2023