Optimal Stochastic Planning of a Green Hydrogen Production System via LP and Scenarios Reduction

Lucas Zenichi Terada; Juan Carlos Cortez; Luiza Higino S. Santos; Jéssica Alice A. Silva; Francisca Dulcinéia C. Gomes; Juan Camilo López; Marcos J. Rider

doi:10.1109/PESGM51994.2024.10689153

Optimal Stochastic Planning of a Green Hydrogen Production System via LP and Scenarios Reduction

Lucas Zenichi Terada^*
, Juan Carlos Cortez
, Luiza Higino S. Santos
, Jéssica Alice A. Silva
, Francisca Dulcinéia C. Gomes
, Juan Camilo López
, Marcos J. Rider

^*Corresponding author for this work

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

4 Citations (Scopus)

87 Downloads (Pure)

Abstract

An electrolyzer produces green hydrogen (H₂) using water and electricity from the main grid and local renewable energy sources (RESs). Hence, this study presents a stochastic linear programming (LP) model to efficiently sizing of distributed energy resources (DERs) for green producing and selling H₂. The proposed methodology involves a two-stage scenario reduction process, which manages computational complexities in handling numerous scenarios. The objective function aims to minimize the total average capital expenditure (CAPEX) and operating expenses (OPEX) for the project lifetime. A comprehensive test case incorporating wind, photovoltaic (PV), and battery energy storage system (BESS) is utilized to evaluate the efficacy of the model. The outcomes revealed that, for realistic investment costs and production prices, the BESS is not installed due to its high CAPEX and OPEX.

Original language	English
Title of host publication	2024 IEEE Power and Energy Society General Meeting, PESGM 2024
Publisher	IEEE
ISBN (Electronic)	9798350381832
DOIs	https://doi.org/10.1109/PESGM51994.2024.10689153
Publication status	Published - 4 Oct 2024
Event	IEEE Power and Energy Society General Meeting, PESGM 2024 - Seattle, United States Duration: 21 Jul 2024 → 25 Jul 2024

Publication series

Name	IEEE Power and Energy Society General Meeting
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	IEEE Power and Energy Society General Meeting, PESGM 2024
Abbreviated title	PESGM 2024
Country/Territory	United States
City	Seattle
Period	21/07/24 → 25/07/24

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

2025 OA procedure

Access to Document

10.1109/PESGM51994.2024.10689153

Optimal_Stochastic_Planning_of_a_Green_Hydrogen_Production_System_via_LP_and_Scenarios_ReductionFinal published version, 2.17 MBLicence: Taverne

Cite this

Terada, L. Z., Cortez, J. C., Santos, L. H. S., Silva, J. A. A., Gomes, F. D. C., López, J. C., & Rider, M. J. (2024). Optimal Stochastic Planning of a Green Hydrogen Production System via LP and Scenarios Reduction. In 2024 IEEE Power and Energy Society General Meeting, PESGM 2024 (IEEE Power and Energy Society General Meeting). IEEE. https://doi.org/10.1109/PESGM51994.2024.10689153

@inproceedings{ab607b1dbb7a4157a264e9297e98a416,

title = "Optimal Stochastic Planning of a Green Hydrogen Production System via LP and Scenarios Reduction",

abstract = "An electrolyzer produces green hydrogen (H2) using water and electricity from the main grid and local renewable energy sources (RESs). Hence, this study presents a stochastic linear programming (LP) model to efficiently sizing of distributed energy resources (DERs) for green producing and selling H2. The proposed methodology involves a two-stage scenario reduction process, which manages computational complexities in handling numerous scenarios. The objective function aims to minimize the total average capital expenditure (CAPEX) and operating expenses (OPEX) for the project lifetime. A comprehensive test case incorporating wind, photovoltaic (PV), and battery energy storage system (BESS) is utilized to evaluate the efficacy of the model. The outcomes revealed that, for realistic investment costs and production prices, the BESS is not installed due to its high CAPEX and OPEX.",

keywords = "2025 OA procedure",

author = "Terada, \{Lucas Zenichi\} and Cortez, \{Juan Carlos\} and Santos, \{Luiza Higino S.\} and Silva, \{J{\'e}ssica Alice A.\} and Gomes, \{Francisca Dulcin{\'e}ia C.\} and L{\'o}pez, \{Juan Camilo\} and Rider, \{Marcos J.\}",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; IEEE Power and Energy Society General Meeting, PESGM 2024, PESGM 2024 ; Conference date: 21-07-2024 Through 25-07-2024",

year = "2024",

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doi = "10.1109/PESGM51994.2024.10689153",

language = "English",

series = "IEEE Power and Energy Society General Meeting",

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address = "United States",

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Terada, LZ, Cortez, JC, Santos, LHS, Silva, JAA, Gomes, FDC, López, JC & Rider, MJ 2024, Optimal Stochastic Planning of a Green Hydrogen Production System via LP and Scenarios Reduction. in 2024 IEEE Power and Energy Society General Meeting, PESGM 2024. IEEE Power and Energy Society General Meeting, IEEE, IEEE Power and Energy Society General Meeting, PESGM 2024, Seattle, Washington, United States, 21/07/24. https://doi.org/10.1109/PESGM51994.2024.10689153

Optimal Stochastic Planning of a Green Hydrogen Production System via LP and Scenarios Reduction. / Terada, Lucas Zenichi; Cortez, Juan Carlos; Santos, Luiza Higino S. et al.
2024 IEEE Power and Energy Society General Meeting, PESGM 2024. IEEE, 2024. (IEEE Power and Energy Society General Meeting).

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

TY - GEN

T1 - Optimal Stochastic Planning of a Green Hydrogen Production System via LP and Scenarios Reduction

AU - Terada, Lucas Zenichi

AU - Cortez, Juan Carlos

AU - Santos, Luiza Higino S.

AU - Silva, Jéssica Alice A.

AU - Gomes, Francisca Dulcinéia C.

AU - López, Juan Camilo

AU - Rider, Marcos J.

PY - 2024/10/4

Y1 - 2024/10/4

N2 - An electrolyzer produces green hydrogen (H2) using water and electricity from the main grid and local renewable energy sources (RESs). Hence, this study presents a stochastic linear programming (LP) model to efficiently sizing of distributed energy resources (DERs) for green producing and selling H2. The proposed methodology involves a two-stage scenario reduction process, which manages computational complexities in handling numerous scenarios. The objective function aims to minimize the total average capital expenditure (CAPEX) and operating expenses (OPEX) for the project lifetime. A comprehensive test case incorporating wind, photovoltaic (PV), and battery energy storage system (BESS) is utilized to evaluate the efficacy of the model. The outcomes revealed that, for realistic investment costs and production prices, the BESS is not installed due to its high CAPEX and OPEX.

AB - An electrolyzer produces green hydrogen (H2) using water and electricity from the main grid and local renewable energy sources (RESs). Hence, this study presents a stochastic linear programming (LP) model to efficiently sizing of distributed energy resources (DERs) for green producing and selling H2. The proposed methodology involves a two-stage scenario reduction process, which manages computational complexities in handling numerous scenarios. The objective function aims to minimize the total average capital expenditure (CAPEX) and operating expenses (OPEX) for the project lifetime. A comprehensive test case incorporating wind, photovoltaic (PV), and battery energy storage system (BESS) is utilized to evaluate the efficacy of the model. The outcomes revealed that, for realistic investment costs and production prices, the BESS is not installed due to its high CAPEX and OPEX.

KW - 2025 OA procedure

UR - https://www.scopus.com/pages/publications/85207406779

U2 - 10.1109/PESGM51994.2024.10689153

DO - 10.1109/PESGM51994.2024.10689153

M3 - Conference contribution

AN - SCOPUS:85207406779

T3 - IEEE Power and Energy Society General Meeting

BT - 2024 IEEE Power and Energy Society General Meeting, PESGM 2024

PB - IEEE

T2 - IEEE Power and Energy Society General Meeting, PESGM 2024

Y2 - 21 July 2024 through 25 July 2024

ER -

Optimal Stochastic Planning of a Green Hydrogen Production System via LP and Scenarios Reduction

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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