A Dynamic Analysis of Energy Storage With Renewable and Diesel Generation Using Volterra Equations

Sidorov, Denis; Muftahov, Ildar; Tomin, Nikita; Karamov, Dmitriy; Panasetsky, Daniil; Dreglea, Aliona; Liu, Fang; Foley, Aoife

doi:10.1109/tii.2019.2932453

Cited by 67 publications

(61 citation statements)

References 38 publications

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“…In recent years many numerical or semi-analytical methods have been presented to solve the linear and non-linear first kind IEs [7; 8; 18; 26; 27; 30]. In this paper, we develop further the numerical methods for solution of such integral equations and continue our previous work [22] where we demonstrated how these novel models can be applied for storage modeling. In this work, the Taylor-collocation method [4; 6; 11; 20] is considered to solve the mentioned problem based on the stochastic arithmetic and the approximate results are validated by using the CESTAC method.…”

Section: Introductionmentioning

confidence: 86%

Control of Accuracy on Taylor-Collocation Method for Load Leveling Problem

Noeiaghdam¹,

Sidorov²,

Muftahov³

et al. 2019

Bull.ISU.Ser.Math.

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High penetration of renewable energy sources coupled with decentralization of transport and heating loads in future power systems will result even more complex unit commitment problem solution using energy storage system scheduling for efficient load leveling. This paper employees an adaptive approach to load leveling problem using the Volterra integral dynamical models. The problem is formulated as solution of the Volterra integral equation of the first kind which is attacked using Taylor-collocation numerical method which has the second-order accuracy and enjoys self-regularization properties, which is associated with confidence levels of system demand. Also the CESTAC method is applied to find the optimal approximation, optimal error and optimal step of collocation method. This adaptive approach is suitable for energy storage optimization in real time. The efficiency of the proposed methodology is demonstrated on the Single Electricity Market of the Island of Ireland.

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Section: Introductionmentioning

confidence: 86%

Control of Accuracy on Taylor-Collocation Method for Load Leveling Problem

Noeiaghdam¹,

Sidorov²,

Muftahov³

et al. 2019

Bull.ISU.Ser.Math.

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“…The cost k endured per kWh not supplied within the MG is set to 2 euro/kWh. The MG parameters are taken from (Sidorov et al, 2019). The typical behaviour of the policy is illustrated in Figure 15 (test data).…”

Section: Batterymentioning

confidence: 99%

Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources

2019

Self Cite

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The problem of optimally activating the flexible energy sources (short-and long-term storage capacities) of electricity microgrid is formulated as a sequential decision making problem under uncertainty where, at every time-step, the uncertainty comes from the lack of knowledge about future electricity consumption and weather dependent PV production. This paper proposes to address this problem using deep reinforcement learning. To this purpose, a specific deep learning architecture has been used in order to extract knowledge from past consumption and production time series as well as any available forecasts. The approach is empirically illustrated in the case of off-grid microgrids located in Belgium and Russia.

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“…L. Jiang et al [10] employed the Taguchi method to search an optimal charging pattern for 5-stage constant-current charging strategy and improved the lithium-ion battery charging efficiency by 0.6-0.9%. D. Sidorov et al [11] presented a review of battery energy storage and an example of battery modeling for renewable energy applications and demonstrated an adaptive approach to solve the load leveling problem with storage. Hu et al [12] employed advanced sparse Bayesian predictive modeling (SBPM) methodology to capture the underlying correspondence between capacity loss and sample entropy.…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Prediction for Batteries in Data Centers

Haider

Zhao

2020

Energies

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Prediction of a battery's health in data centers plays a significant role in Battery Management Systems (BMS). Data centers use thousands of batteries, and their lifespan ultimately decreases over time. Predicting battery's degradation status is very critical, even before the first failure is encountered during its discharge cycle, which also turns out to be a very difficult task in real life. Therefore, a framework to improve Auto-Regressive Integrated Moving Average (ARIMA) accuracy for forecasting battery's health with clustered predictors is proposed. Clustering approaches, such as Dynamic Time Warping (DTW) or k-shape-based, are beneficial to find patterns in data sets with multiple time series. The aspect of large number of batteries in a data center is used to cluster the voltage patterns, which are further utilized to improve the accuracy of the ARIMA model. Our proposed work shows that the forecasting accuracy of the ARIMA model is significantly improved by applying the results of the clustered predictor for batteries in a real data center. This paper presents the actual historical data of 40 batteries of the large-scale data center for one whole year to validate the effectiveness of the proposed methodology.

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A Dynamic Analysis of Energy Storage With Renewable and Diesel Generation Using Volterra Equations

Cited by 67 publications

References 38 publications

Control of Accuracy on Taylor-Collocation Method for Load Leveling Problem

Control of Accuracy on Taylor-Collocation Method for Load Leveling Problem

Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources

Cluster-Based Prediction for Batteries in Data Centers

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