Development of an Energy Management System for a Microgrid Using Neural Networks. Case Study: San Cristobal Island, Galapagos Archipelago

G. Ampuño-Avilés; R. López-Marcillo; D. Andrade-Núñez

doi:10.1007/978-981-19-6347-6_5

Development of an Energy Management System for a Microgrid Using Neural Networks. Case Study: San Cristobal Island, Galapagos Archipelago

G. Ampuño-Avilés
, R. López-Marcillo
, D. Andrade-Núñez

Sustainability, Management and Regulation of Telecommunications and Energy (SMART-TECH)

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

Abstract

In this paper, an energy management system EMS hourly energy management system for a renewable energy system (HRES) is presented. The proposed HRES is composed of hybrid wind turbine (WT), solar photovoltaic (PV) panels, a diesel generator (DG) and a Distributed Collector System (DCS), as primary energy sources. In turn, an energy storage system (ESS), which is a battery sub-system. The wind turbine, PV panels and DCS system are made to work at peak power, while the battery acts as storage. The EMS uses intelligent rule-based controllers and optimizers to meet the energy demanded by the load and maintain the state of charge (SOC) of the battery between certain target margins, while trying to optimize the utilization cost and lifetime of the BESS. Simulation results show that optimization-based control meets the objectives set for the HRES EMS and achieves a total cost savings of 23.5% over other simpler control state-based EMSs.

Original language	English
Title of host publication	Communication and Applied Technologies - Proceedings of ICOMTA 2022
Editors	Paulo Carlos López-López, Ángel Torres-Toukoumidis, Andrea De-Santis, Óscar Avilés, Daniel Barredo
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	47-57
Number of pages	11
ISBN (Print)	9789811963469
DOIs	https://doi.org/10.1007/978-981-19-6347-6_5
State	Published - 2023
Event	International Conference on Communication and Applied Technologies (ICOMTA 2022) - EC, Cuenca, Ecuador Duration: 7 Sep 2022 → 9 Sep 2022 https://icomta.net/

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	318
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	International Conference on Communication and Applied Technologies (ICOMTA 2022)
Country/Territory	Ecuador
City	Cuenca
Period	7/09/22 → 9/09/22
Internet address	https://icomta.net/

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Artificial neural networks
EMS
Fuzzy logic control
Renewable energy

CACES Knowledge Areas

727A Industrial and process design

Access to Document

10.1007/978-981-19-6347-6_5

Cite this

Ampuño-Avilés, G., López-Marcillo, R., & Andrade-Núñez, D. (2023). Development of an Energy Management System for a Microgrid Using Neural Networks. Case Study: San Cristobal Island, Galapagos Archipelago. In P. C. López-López, Á. Torres-Toukoumidis, A. De-Santis, Ó. Avilés, & D. Barredo (Eds.), Communication and Applied Technologies - Proceedings of ICOMTA 2022 (pp. 47-57). (Smart Innovation, Systems and Technologies; Vol. 318). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-6347-6_5

Ampuño-Avilés, G. ; López-Marcillo, R. ; Andrade-Núñez, D. / Development of an Energy Management System for a Microgrid Using Neural Networks. Case Study : San Cristobal Island, Galapagos Archipelago. Communication and Applied Technologies - Proceedings of ICOMTA 2022. editor / Paulo Carlos López-López ; Ángel Torres-Toukoumidis ; Andrea De-Santis ; Óscar Avilés ; Daniel Barredo. Springer Science and Business Media Deutschland GmbH, 2023. pp. 47-57 (Smart Innovation, Systems and Technologies).

@inproceedings{2d7acbe561eb43398d6a9a57629c2f0b,

title = "Development of an Energy Management System for a Microgrid Using Neural Networks. Case Study: San Cristobal Island, Galapagos Archipelago",

abstract = "In this paper, an energy management system EMS hourly energy management system for a renewable energy system (HRES) is presented. The proposed HRES is composed of hybrid wind turbine (WT), solar photovoltaic (PV) panels, a diesel generator (DG) and a Distributed Collector System (DCS), as primary energy sources. In turn, an energy storage system (ESS), which is a battery sub-system. The wind turbine, PV panels and DCS system are made to work at peak power, while the battery acts as storage. The EMS uses intelligent rule-based controllers and optimizers to meet the energy demanded by the load and maintain the state of charge (SOC) of the battery between certain target margins, while trying to optimize the utilization cost and lifetime of the BESS. Simulation results show that optimization-based control meets the objectives set for the HRES EMS and achieves a total cost savings of 23.5\% over other simpler control state-based EMSs.",

keywords = "Artificial neural networks, EMS, Fuzzy logic control, Renewable energy",

author = "G. Ampu{\~n}o-Avil{\'e}s and R. L{\'o}pez-Marcillo and D. Andrade-N{\'u}{\~n}ez",

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Ampuño-Avilés, G, López-Marcillo, R & Andrade-Núñez, D 2023, Development of an Energy Management System for a Microgrid Using Neural Networks. Case Study: San Cristobal Island, Galapagos Archipelago. in PC López-López, Á Torres-Toukoumidis, A De-Santis, Ó Avilés & D Barredo (eds), Communication and Applied Technologies - Proceedings of ICOMTA 2022. Smart Innovation, Systems and Technologies, vol. 318, Springer Science and Business Media Deutschland GmbH, pp. 47-57, International Conference on Communication and Applied Technologies (ICOMTA 2022), Cuenca, Ecuador, 7/09/22. https://doi.org/10.1007/978-981-19-6347-6_5

Development of an Energy Management System for a Microgrid Using Neural Networks. Case Study: San Cristobal Island, Galapagos Archipelago. / Ampuño-Avilés, G.; López-Marcillo, R.; Andrade-Núñez, D.
Communication and Applied Technologies - Proceedings of ICOMTA 2022. ed. / Paulo Carlos López-López; Ángel Torres-Toukoumidis; Andrea De-Santis; Óscar Avilés; Daniel Barredo. Springer Science and Business Media Deutschland GmbH, 2023. p. 47-57 (Smart Innovation, Systems and Technologies; Vol. 318).

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

TY - GEN

T1 - Development of an Energy Management System for a Microgrid Using Neural Networks. Case Study

T2 - 2022 International Conference on Communication and Applied Technologies ICOMTA 2022

AU - Ampuño-Avilés, G.

AU - López-Marcillo, R.

AU - Andrade-Núñez, D.

PY - 2023

Y1 - 2023

N2 - In this paper, an energy management system EMS hourly energy management system for a renewable energy system (HRES) is presented. The proposed HRES is composed of hybrid wind turbine (WT), solar photovoltaic (PV) panels, a diesel generator (DG) and a Distributed Collector System (DCS), as primary energy sources. In turn, an energy storage system (ESS), which is a battery sub-system. The wind turbine, PV panels and DCS system are made to work at peak power, while the battery acts as storage. The EMS uses intelligent rule-based controllers and optimizers to meet the energy demanded by the load and maintain the state of charge (SOC) of the battery between certain target margins, while trying to optimize the utilization cost and lifetime of the BESS. Simulation results show that optimization-based control meets the objectives set for the HRES EMS and achieves a total cost savings of 23.5% over other simpler control state-based EMSs.

AB - In this paper, an energy management system EMS hourly energy management system for a renewable energy system (HRES) is presented. The proposed HRES is composed of hybrid wind turbine (WT), solar photovoltaic (PV) panels, a diesel generator (DG) and a Distributed Collector System (DCS), as primary energy sources. In turn, an energy storage system (ESS), which is a battery sub-system. The wind turbine, PV panels and DCS system are made to work at peak power, while the battery acts as storage. The EMS uses intelligent rule-based controllers and optimizers to meet the energy demanded by the load and maintain the state of charge (SOC) of the battery between certain target margins, while trying to optimize the utilization cost and lifetime of the BESS. Simulation results show that optimization-based control meets the objectives set for the HRES EMS and achieves a total cost savings of 23.5% over other simpler control state-based EMSs.

KW - Artificial neural networks

KW - EMS

KW - Fuzzy logic control

KW - Renewable energy

UR - https://www.scopus.com/pages/publications/85144187143

U2 - 10.1007/978-981-19-6347-6_5

DO - 10.1007/978-981-19-6347-6_5

M3 - Conference contribution

AN - SCOPUS:85144187143

SN - 9789811963469

T3 - Smart Innovation, Systems and Technologies

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BT - Communication and Applied Technologies - Proceedings of ICOMTA 2022

A2 - López-López, Paulo Carlos

A2 - Torres-Toukoumidis, Ángel

A2 - De-Santis, Andrea

A2 - Avilés, Óscar

A2 - Barredo, Daniel

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 7 September 2022 through 9 September 2022

ER -

Ampuño-Avilés G, López-Marcillo R, Andrade-Núñez D. Development of an Energy Management System for a Microgrid Using Neural Networks. Case Study: San Cristobal Island, Galapagos Archipelago. In López-López PC, Torres-Toukoumidis Á, De-Santis A, Avilés Ó, Barredo D, editors, Communication and Applied Technologies - Proceedings of ICOMTA 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 47-57. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-19-6347-6_5

Development of an Energy Management System for a Microgrid Using Neural Networks. Case Study: San Cristobal Island, Galapagos Archipelago

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

CACES Knowledge Areas

Access to Document

Other files and links

Cite this