A non-uniform grid approach for scheduling considering electricity load tracking and future load prediction

Ave, Giancarlo Dalle; Harjunkoski, Iiro; Engell, Sebastian

doi:10.1016/j.compchemeng.2019.06.031

Cited by 12 publications

(7 citation statements)

References 25 publications

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“…4). More information about the non-uniform grid approach applied to an example case study about demand side management of an steel plant can be found in (Dalle Ave et al 2019). Even though the time scales in this work are much longer than in the aforementioned paper, the time-grid ratio between the near and the far future shows similarly elongated time intervals.…”

Section: Time Gridsmentioning

confidence: 78%

Integration of maintenance scheduling and planning for large-scale asset fleets

et al. 2021

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Large fleets of engineering assets that are subject to ongoing degradation are posing the challenge of how and when to perform maintenance. For a given case study, this paper proposes a formulation for combined scheduling and planning of maintenance actions. A hierarchical approach and a two-stage approach (with either uniform or non-uniform time grid) are considered and compared to each other. The resulting discrete-time linear programming model follows the Resource Task Network framework. Asset deterioration is considered linearly and tackled with an enumerator-based formulation. Advantages of the model are its computational efficiency, scalability, extendability and adaptability. The results indicate that combined maintenance planning and scheduling can be solved in appropriate time and with appropriate accuracy. The decision-support that is delivered helps the choice of the specific maintenance action to perform and proposes when to conduct it. The paper makes a case for the benefits of optimally combining long-term planning and short-term scheduling in industrial-sized problems into one system.

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Section: Time Gridsmentioning

confidence: 78%

Integration of maintenance scheduling and planning for large-scale asset fleets

et al. 2021

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Section: Electricity Market Modelmentioning

confidence: 99%

“…On both markets, participants have to submit their bids and thus commit to the purchase before the final electricity prices are fixed. For an extended description of a detailed market model, we refer to Dalle Ave et al 41 Figure 1 provides an overview of the market model which we use in our SP-based scheduling. Considering short-term trading for the next day, we make use of the following assumptions on market participation and market prices similar to existing work.…”

Section: Electricity Market Modelmentioning

confidence: 99%

Demand response potential of industrial processes considering uncertain short‐term electricity prices

2022

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Time-varying electricity prices on the day-ahead and intraday market incentivize demand response of industrial processes. In prior work (Schäfer et al. AIChE J. 2020;66:1-14), we studied the demand response potential with a generalized process model, but neglected the intraday market. Extending our prior investigation, we account for uncertain intraday prices in a mixed-integer linear stochastic programming-based scheduling, that is, we minimize expected cost and conditional value-atrisk in a bi-objective optimization. We find that for very broad variations of the generalized process parameters, the conditional value-at-risk can be reduced significantly without drastically increasing the expected cost. Furthermore, simultaneously improving multiple process parameter leads to synergetic benefits. Moreover, the savings of three electrolysis processes can be more than doubled by marketing flexibility on the intraday market in addition to the day-ahead market. Overall, our model allows for a rapid early assessment of the demand response potential considering the two markets.

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“…The method does not utilize actual future plan of dairy farmers related to infrastructural utensils for achieving European Union (EU) environmental targets. Ave et al 29 presented demand side management (DSM) scheduling formulation based on discrete‐time RTN model for solving the market constraints. However, the method allowed more realistic DSM model by considering day‐ahead, and intraday markets in same formulation.…”

Section: Introductionmentioning

confidence: 99%

Taylor ant lion optimization‐based generative adversarial networks for forecasting electricity consumption

Poongavanam

Kasinathan²,

Kanagasabai

2023

Concurrency and Computation

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Summary This article proposes a Taylor ant lion optimization‐based generative adversarial method (TaylorALO‐based GAN) for predicting renewable energy. The proposed renewable energy prediction mechanism involves four different modules, namely, data transformation, extraction of technical indicator, feature selection, and the prediction. At first, the time‐series data is presented to data transformation module where the process is performed using Yeo–Johnson transformation. The transformed data is subjected to the technical indicator extraction module, where the technical indicators are efficiently extracted for further processing. After that, features are extracted based on wrapper selection model. Finally, renewable energy prediction is completed using GAN, which is trained using developed TaylorALO, which is the amalgamation of Taylor series and ant lion optimization algorithm (ALOA). The proposed technique achieved the better performance with the minimal mean square error (MSE) of 8.536 and minimal root mean square error of 2.921 based on per capita consumption.

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A non-uniform grid approach for scheduling considering electricity load tracking and future load prediction

Cited by 12 publications

References 25 publications

Integration of maintenance scheduling and planning for large-scale asset fleets

Integration of maintenance scheduling and planning for large-scale asset fleets

Demand response potential of industrial processes considering uncertain short‐term electricity prices

Taylor ant lion optimization‐based generative adversarial networks for forecasting electricity consumption

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