Renewable On-Site Power Generation for Manufacturing Companies—Technologies, Modeling, and Dimensioning

Schulz, Julia; Leinmüller, Daniel; Misik, Adam; Zaeh, Michael F.

doi:10.3390/su13073898

Cited by 12 publications

(4 citation statements)

References 71 publications

(72 reference statements)

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“…The purpose of the study is to systematically enhance EDM procedures by optimising machining settings using a combination of mathematical programming and machine learning. The second uses machine learning to build prediction models for in-the-moment judgment while the first theme utilises mathematical programming to systematically probe the parameter space [26]. The study focuses on how the improved EDM methods might contribute to a more sustainable world.…”

Section: E Discussionmentioning

confidence: 99%

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Ritu Gupta

2023

IJRITCC

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This study explores the combination of machine guidance and several developing approaches to enhance both precision and effectiveness during Electricity-discharged Machining (EDM) business operations. The studies on quality control, energy efficiency, sustainable development, mathematical modelling within EDM optimization, and machine learning applications in EDM optimisation are all examined in this study. It highlights significant gaps in scientific knowledge, providing a pathway for the development of state-of-the-art EDM methods. The outcomes show that material decrease, energy efficiency, along EDM technique optimisation can all be enhanced. This study offers valuable information for future research within the field and contributes to the ongoing conversation about advanced manufacturing techniques. This project intends to revolutionise EDM by merging mathematical programming and machine learning. Three primary topics are investigated machining parameter optimisation, efficiency improvement using machine learning and environmental effect assessment. The goals of the study are met by using the deductive method, which gives a formal setting in which to examine hypotheses. Descriptive research designs allow for in-depth analyses of previously published works, mathematical models and automated learning programs. Finding commonalities and trends in qualitative data is the goal of the thematic data analysis technique. The results of this study provide useful resources, standards and sustainable perspectives for enhancing EDM procedures in manufacturing settings.

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Section: E Discussionmentioning

confidence: 99%

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Ritu Gupta

2023

IJRITCC

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“…Middelhauve et al [2] further discuss the optimization of communitylevel renewable energy hubs, identifying districts as critical entities in local energy systems. Schulz et al [17] and Bashir et al [18] demonstrate the potential for substantial energy cost savings and the maximization of self-consumption through optimized photovoltaic systems and the strategic use of storage and demand response solutions. Araujo et al [19] investigate the integration of decentralized solar PV systems in urban settings, focusing on the challenges of energy surplus management within regulated markets.…”

Section: State Of the Artmentioning

confidence: 99%

Power to the People: On the Role of Districts in Decentralized Energy Systems

Schnidrig,

Chuat,

Terrier

et al. 2024

Energies

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The transition towards renewable and decentralized energy systems is propelled by the urgent need to address climate concerns and advance sustainable development globally. This transformation requires innovative methods to integrate stochastic renewable sources such as solar and wind power and challenging traditional energy paradigms rooted in centralized and continuous energy production. The present study focuses on the Swiss energy system to explore the optimization of energy planning strategies that incorporate decentralized energy production within a centralized framework. Here, we show that a strategic approach to decentralization can significantly reduce annual system costs by 10% to CHF 1230 per capita and increase self-consumption to 68% of the decentralized PV production, emphasizing the need for a hybrid energy-planning model that balances centralized and decentralized models for enhanced system resilience, efficiency, and cost-effectiveness. This research underscores the strategic importance of diversifying energy sources, enhancing energy storage, improving grid flexibility, and laying a foundational framework for policy making and strategic planning. It encourages further investigation into climate impacts, technology synergy, and the integration of district heating, aiming to establish a resilient, sustainable, and autonomous energy future.

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“…Schulz et al [20] present key technologies for on-site power generation at manufacturing companies. They also describe an approach for modeling and dimensioning the technologies.…”

Section: Designing Industrial Energy Supply Systemsmentioning

confidence: 99%

Decentral Production of Green Hydrogen for Energy Systems: An Economically and Environmentally Viable Solution for Surplus Self-Generated Energy in Manufacturing Companies?

et al. 2023

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Power-to-X processes where renewable energy is converted into storable liquids or gases are considered to be one of the key approaches for decarbonizing energy systems and compensating for the volatility involved in generating electricity from renewable sources. In this context, the production of “green” hydrogen and hydrogen-based derivatives is being discussed and tested as a possible solution for the energy-intensive industry sector in particular. Given the sharp, ongoing increases in electricity and gas prices and the need for sustainable energy supplies in production systems, non-energy-intensive companies should also be taken into account when considering possible utilization paths for hydrogen. This work focuses on the following three utilization paths: “hydrogen as an energy storage system that can be reconverted into electricity”, “hydrogen mobility” for company vehicles and “direct hydrogen use”. These three paths are developed, modeled, simulated, and subsequently evaluated in terms of economic and environmental viability. Different photovoltaic system configurations are set up for the tests with nominal power ratings ranging from 300 kWp to 1000 kWp. Each system is assigned an electrolyzer with a power output ranging between 200 kW and 700 kW and a fuel cell with a power output ranging between 5 kW and 75 kW. There are also additional variations in relation to the battery storage systems within these basic configurations. Furthermore, a reference variant without battery storage and hydrogen technologies is simulated for each photovoltaic system size. This means that there are ultimately 16 variants to be simulated for each utilization path. The results show that these utilization paths already constitute a reasonable alternative to fossil fuels in terms of costs in variants with a suitable energy system design. For the “hydrogen as an energy storage system” path, electricity production costs of between 43 and 79 ct/kWh can be achieved with the 750 kWp photovoltaic system. The “hydrogen mobility” is associated with costs of 12 to 15 ct/km, while the “direct hydrogen use” path resulted in costs of 8.2 €/kg. Environmental benefits are achieved in all three paths by replacing the German electricity mix with renewable energy sources produced on site or by substituting hydrogen for fossil fuels. The results confirm that using hydrogen as a storage medium in manufacturing companies could be economically and environmentally viable. These results also form the basis for further studies, e.g., on detailed operating strategies for hydrogen technologies in scenarios involving a combination of multiple utilization paths. The work also presents the simulation-based method developed in this project, which can be transferred to comparable applications in further studies.

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Renewable On-Site Power Generation for Manufacturing Companies—Technologies, Modeling, and Dimensioning

Cited by 12 publications

References 71 publications

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Power to the People: On the Role of Districts in Decentralized Energy Systems

Decentral Production of Green Hydrogen for Energy Systems: An Economically and Environmentally Viable Solution for Surplus Self-Generated Energy in Manufacturing Companies?

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