A general MILP based optimization framework to design Energy Hubs

Gotze, Jens; Dancker, Jonte; Wolter, Martin

doi:10.1515/auto-2019-0059

Cited by 10 publications

(1 citation statement)

References 33 publications

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“…In recent literature, numerous optimization models and frameworks have been presented [2,10], for example the oemof framework [12], Ehub Modeling Tool by EMPA (Switzerland) [4], or the DER‐CAM model by Lawrence Berkeley National Laboratory [21]. In addition, user‐friendly applications for designing energy systems have been developed: The National Renewable Energy Laboratory, developed the free and open‐source webtool REopt for designing electrical systems (microgrids with photovoltaics [PV], wind turbines, and batteries), which uses an MILP in the calculation [17].…”

Section: Introductionmentioning

confidence: 99%

EHDO: A free and open‐source webtool for designing and optimizing multi‐energy systems based on MILP

Wirtz

Remmen

Müller

2020

Comp Applic In Engineering

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This paper presents a novel webtool, called Energy Hub Design Optimization tool, for designing and optimizing complex multi‐energy systems. The tool determines the optimal technology selection and sizing of all energy conversion units of a supply system while satisfying heat, cold, electricity, and hydrogen demands. A large variety of different technologies including renewable energies (wind power, photovoltaic, solar thermal, and hydroelectricity) and energy carriers like natural gas, hydrogen, biomass, and waste can be considered. Energy demands are provided by the user with an hourly resolution and all technical and economic model parameters can be tailored to specific use cases. The objective functions of the design optimization are total annualized costs, CO2 emissions, or trade‐offs between both objectives (multi‐objective optimization). The calculation is based on mathematical optimization and uses a mixed‐integer linear program. The webtool is accessible online for free and the optimization model is open‐source. The webtool has been developed to support academic teaching in energy engineering courses. With the tool, students understand how energy supply systems with intermittent renewable energies and cross‐sectoral conversion and storage technologies can be designed with innovative planning approaches.

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Section: Introductionmentioning

confidence: 99%

EHDO: A free and open‐source webtool for designing and optimizing multi‐energy systems based on MILP

Wirtz

Remmen

Müller

2020

Comp Applic In Engineering

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Operational planning of distributed energy generation for a semiconductor fabrication plant

Poemsl

Siddiqui

2022

Elektrotech. Inftech.

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The semiconductor industry is well known for its high resource consumption due to its clean room usage and power-intensive manufacturing technology. This is a significant financial burden for semiconductor fabrication plant owners who wish to minimize the cost needed to cover the electricity and gas loads for their daily operations. From a historical perspective, those electricity and gas cost reductions have been enabled by more energy-efficient manufacturing technologies. Due to the complexity of factors influencing the energy system of a semiconductor plant, other aspects have often been neglected and also not been studied intensively in the academic literature so far. One possibility for reducing energy costs is the integration of a microgrid as on-site distributed energy resources (DER) may offer a less cost-intensive way to supply the energy needed. Therefore, this paper seeks to answer the following research question: How much expected cost can be saved and risk mitigated at a semiconductor fabrication plant via DER? More precisely, in this paper, a microgrid at a semiconductor fabrication plant that has installed DER with combined heat and power (CHP) applications and demand response (DR) is used to simulate the expected annual energy costs under given uncertain electricity and gas prices at different installed capacity levels. A simulation-based approach is used to evaluate several DER capacity sizes. The results of the simulation show that installations of DER at a semiconductor microgrid can dominate the base case of doing nothing, considering the expected minimized cost, the expected CO2 emissions, and conditional value-at-risk (CVaR). In fact, DER can reduce the expected annual energy bill by up to 6%, whereas annual expected CO2 emissions can even be reduced by up to 22%. Moreover, with DER, a microgrid’s risk is reduced as it can react to market conditions and local demand. Additionally, the true potential of microgrid cost savings can only be enabled when DR is allowed and proves particularly effective when energy prices are more volatile.

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Nonlinear operational optimization of an industrial power-to-heat system with a high temperature heat pump, a thermal energy storage and wind energy

Walden,

Bähr,

Glade

et al. 2023

Applied Energy

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A general MILP based optimization framework to design Energy Hubs

Cited by 10 publications

References 33 publications

EHDO: A free and open‐source webtool for designing and optimizing multi‐energy systems based on MILP

EHDO: A free and open‐source webtool for designing and optimizing multi‐energy systems based on MILP

Operational planning of distributed energy generation for a semiconductor fabrication plant

Nonlinear operational optimization of an industrial power-to-heat system with a high temperature heat pump, a thermal energy storage and wind energy

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