Planning and Scheduling for Industrial Demand-Side Management: State of the Art, Opportunities and Challenges under Integration of Energy Internet and Industrial Internet

Chen, Songsong; Gong, Feixiang; Ming-qiang, Zhang; Yuan, Jianguang; Liao, Siyang; Chen, Hongyin; Li, Dezhi; Tian, Shiming; Hu, Xiaoming

doi:10.3390/su13147753

Cited by 15 publications

(7 citation statements)

References 56 publications

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“…Other contributions such as [7] focus on the information technology side of the implementation of Industrial Demand-Side Integration, which offers more flexibility but is also far more complex to apply. Chen et al focus more on the data-driven planning and scheduling as well as market mechanisms [8].…”

Section: Discussionmentioning

confidence: 99%

“…However, it is missing the important aspect of monetisation options. Other contributions propose solutions for the implementation of IDSI through information technology platforms [7] or by detailing options for data-driven planning and scheduling [8] but they do not provide insights about the IDSI measures themselves. In the literature there is also research about the behaviour of energy markets and price development such as [9].…”

Section: Introductionmentioning

confidence: 99%

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A Methodology for the Classification and Characterisation of Industrial Demand-Side Integration Measures

et al. 2022

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In the context of the ongoing climate change and increasingly strict climate goals of the European Green Deal, industry faces a growing challenge to decrease its high demand for electrical energy and its greenhouse gas emissions. Demand-Side Integration measures have a great potential to reduce the greenhouse gas emissions of the industrial sector. However, there is still no definition and consistent characterising terms for Industrial Demand-Side Integration. The lack of clarity in concepts and definitions may impose hurdles in the transfer of results and methodologies from research activities and thus, in the implementation of measures in the industry. Furthermore, the economic value of implementing these measures is often unclear but of high relevance to industrial consumers. This paper proposes a comprehensive Industrial Demand-Side Integration definition and a methodology to classify and characterise its measures. The methodology is aimed at helping industrial consumers decide which measures can be implemented in their specific setting and how these measures can be monetised. The methodology is validated by applying it to eight relevant use cases in the ETA Research Factory.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Methodology for the Classification and Characterisation of Industrial Demand-Side Integration Measures

et al. 2022

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“…Studies have progressed to analyzing methods of self‐scheduling and closed‐loop decision‐making processes to automate industrial demand response scheduling 117,118 . The increasing availability of process data and metrics opens doors to improved modeling of scheduling and better integration with control methodologies 119–121 . Though different than direct process control during production, process scheduling can be just as important for industrial facilities in responding to demand signals while still achieving production requirements.…”

Section: Five Improvement Areasmentioning

confidence: 99%

Grid‐responsive smart manufacturing: A perspective for an interconnected energy future in the industrial sector

Billings

Powell

2022

AIChE Journal

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With the growing amount of renewable energy sources, the grid has become responsible for accounting for intermittency and the flexibility needed to utilize dynamic sources. Expensive peaking plants and energy storage systems have been proposed as ways to mitigate those problems. There is a large group of energy consumers that can respond to grid conditions. Historically, these consumers have been residential and commercial users, but with modern innovations and practices, industrial consumers have the potential to become a major player in this space. Grid‐responsive smart manufacturing can be used to utilize modern tools in manufacturing innovation as enablers for grid response. These modern tools already exist but are not widely used for industrial grid‐side energy management. This article defines grid‐responsive smart manufacturing, identifies five major barriers to its widespread implementation, and portrays the path to getting industrial users to be key players in grid stability and flexibility.

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“…However, applying the conventional demand response models directly to power systems with integrated IDCs leads to challenges [15]. These challenges arise due to the interests of cloud service providers (CSP) [16], the time-varying nature of computing workloads affecting DR availability [17], and the uncertainties caused by predicting interactive task flows and local generator outputs [18].…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy Resource Exploitation through Co-Optimization of Power System and Data Centers with Uncertainties during Demand Response

et al. 2023

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This paper presents a robust bi-level co-optimization model that promotes the active participation of Internet Data Centers (IDCs) in demand response (DR) programs, thereby enhancing the flexibility of power systems. Our approach involves leveraging virtual power lines to migrate workloads among IDCs, optimizing resource allocations, and benefiting both domains. The model incorporates a Gaussian Process Regression (GPR)-constructed DR price–amount curve, which largely contributes to the simplification of the optimization problem with high accuracy and computational efficiency. It also respects the information barriers between the two domains of power systems and IDCs, and thus safeguards the privacy and flexibility of IDCs. The uncertainty in IDC operations is considered by incorporating the variance in GPR into the demand response curve. By integrating IDCs as DR resources, the framework of this research enhances the flexibility of power systems and the efficiency of cross-domain co-optimization. The model and algorithm are validated using modified IEEE test systems.

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Planning and Scheduling for Industrial Demand-Side Management: State of the Art, Opportunities and Challenges under Integration of Energy Internet and Industrial Internet

Cited by 15 publications

References 56 publications

A Methodology for the Classification and Characterisation of Industrial Demand-Side Integration Measures

A Methodology for the Classification and Characterisation of Industrial Demand-Side Integration Measures

Grid‐responsive smart manufacturing: A perspective for an interconnected energy future in the industrial sector

Distributed Energy Resource Exploitation through Co-Optimization of Power System and Data Centers with Uncertainties during Demand Response

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