PyLESA: a Python modelling tool for planning-level local, integrated, and smart energy systems analysis

Andrew Lyden; Graeme Flett; Paul G. Tuohy

doi:10.1016/j.softx.2021.100699

PyLESA: a Python modelling tool for planning-level local, integrated, and smart energy systems analysis

Andrew Lyden, Graeme Flett, Paul G. Tuohy

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Abstract

PyLESA is a modelling tool for the planning-level design of local, integrated and smart energy systems. It was developed to tackle gaps in existing planning-level tools: (i) adaptable and transparent source code; (ii) temperature dependence for heat pump models; (iii) stratification model for thermal storage models; (iv) modelling of evolving electricity markets; and (v) model predictive control. PyLESA uses a flexible object-orientated approach to model thermal and electrical supply, demand, and storage technologies following fixed order and model predictive control strategies. Functionality is illustrated to size heat pumps and hot water tanks for a wind power integrated district heating system.

Original language	English
Article number	100699
Number of pages	5
Journal	SoftwareX
Volume	14
Early online date	24 Apr 2021
DOIs	https://doi.org/10.1016/j.softx.2021.100699
Publication status	Published - 30 Jun 2021

Keywords

energy system modelling
smart energy systems
energy storage
model predictive control

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10.1016/j.softx.2021.100699Licence: CC BY 4.0

Lyden-etal-SoftwareX-2021-A-Python-modelling-tool-for-planning-level-local-integrated-and-smart-Energy-Systems-analysisFinal published version, 958 KBLicence: CC BY 4.0

Cite this

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title = "PyLESA: a Python modelling tool for planning-level local, integrated, and smart energy systems analysis",

abstract = "PyLESA is a modelling tool for the planning-level design of local, integrated and smart energy systems. It was developed to tackle gaps in existing planning-level tools: (i) adaptable and transparent source code; (ii) temperature dependence for heat pump models; (iii) stratification model for thermal storage models; (iv) modelling of evolving electricity markets; and (v) model predictive control. PyLESA uses a flexible object-orientated approach to model thermal and electrical supply, demand, and storage technologies following fixed order and model predictive control strategies. Functionality is illustrated to size heat pumps and hot water tanks for a wind power integrated district heating system.",

keywords = "energy system modelling, smart energy systems, energy storage, model predictive control",

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doi = "10.1016/j.softx.2021.100699",

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T2 - a Python modelling tool for planning-level local, integrated, and smart energy systems analysis

AU - Lyden, Andrew

AU - Flett, Graeme

AU - Tuohy, Paul G.

PY - 2021/6/30

Y1 - 2021/6/30

N2 - PyLESA is a modelling tool for the planning-level design of local, integrated and smart energy systems. It was developed to tackle gaps in existing planning-level tools: (i) adaptable and transparent source code; (ii) temperature dependence for heat pump models; (iii) stratification model for thermal storage models; (iv) modelling of evolving electricity markets; and (v) model predictive control. PyLESA uses a flexible object-orientated approach to model thermal and electrical supply, demand, and storage technologies following fixed order and model predictive control strategies. Functionality is illustrated to size heat pumps and hot water tanks for a wind power integrated district heating system.

AB - PyLESA is a modelling tool for the planning-level design of local, integrated and smart energy systems. It was developed to tackle gaps in existing planning-level tools: (i) adaptable and transparent source code; (ii) temperature dependence for heat pump models; (iii) stratification model for thermal storage models; (iv) modelling of evolving electricity markets; and (v) model predictive control. PyLESA uses a flexible object-orientated approach to model thermal and electrical supply, demand, and storage technologies following fixed order and model predictive control strategies. Functionality is illustrated to size heat pumps and hot water tanks for a wind power integrated district heating system.

KW - energy system modelling

KW - smart energy systems

KW - energy storage

KW - model predictive control

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VL - 14

JO - SoftwareX

JF - SoftwareX

M1 - 100699

ER -

PyLESA: a Python modelling tool for planning-level local, integrated, and smart energy systems analysis

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Keywords

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Doctoral Training Partnership (DTP - University of Strathclyde) | Lyden, Andrew

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PyLESA: a Python modelling tool for planning-level local, integrated, and smart energy systems analysis

Abstract

Keywords

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Doctoral Training Partnership (DTP - University of Strathclyde) | Lyden, Andrew

Cite this