Hybrid modeling of energy storage system and electrical loads in a pilot-microgrid

Silva, Danilo P. e; Queiroz, Macello D.; Fardin, Jussara Farias; Sales, Jose Leandro F.; Orlando, M. T. D.

doi:10.1109/induscon.2018.8627180

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…A large number of studies have been carried out in the literature. Indeed, Izquierdo-Monge O and al, propose in their paper, a methodology for optimizing electrical energy consumption in a distribution network by equipping the network with intelligence (Izquierdo-Monge et al 2021); Mohandes B. and al, optimize demand management by sizing the network powered by renewable energy sources, solar with a storage system; in (E Silva et al 2019), the authors propose a microgrid optimization based on a hybridization system of renewable energy resources; Mah AXY and al. present an optimization for the design and operation of an autonomous microgrid with electric and Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

Section: Context and Issuesmentioning

confidence: 99%

Comparative Study of Electrical Loads Forecasting Based on Hybrid Approaches: Discrete Wavelet Transforms with Adaboost and LSTM

Moyème,

Yao,

Sedzro

et al. 2024

Preprint

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The dynamic evolution and variation of electrical loads is now a priority for optimum management and, above all, forecasting. Indeed, these dynamic load variations require computer tools able to implement optimal load forecasting models. Scientific research into automated models for forecasting electrical loads therefore represents a challenge for scientific researchers. Several research studies have therefore been carried out. These include machine learning approaches such as: LSTM (Long Short-Term Memory), Support Vector Machine (SVM) and others. This article proposes a comparison study between a hybrid model based on the wavelet transform coupled with the adaboost method, and LSTM, for forecasting electricity consumption loads. Python 3.10 was used for the studies. The first results obtained consisted in the study of electricity load forecasts over four (04) data sets of the year (January, April, August and December). These results are very satisfactory, showing a good correlation between the real results of the electrical load consumption data, and the results of the implemented model. The electrical load consumption data are collected from a renewable energy production source: photovoltaic solar energy. The model test results (R2) obtained range from 0.919 to 0.958. These results are representative of real data and reflect the model's performance. The second set of results concerns a comparative study between the methods used by Y. Xie et al (CNN, MLP, LSTM-AM and LSTM-AM-MLP), and our approach. The prediction results obtained between the approaches show a prediction gap ranging from 0.15 to 0.78 for RMSE and 0.83 for MAPE. These results show significant differences between the methods. The minimization of errors by our model, therefore, reflects the model's performance in terms of accuracy, which is necessary for the optimal management of consumer electricity load forecasts in order to ensure balance between supply and demand.

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Section: Context and Issuesmentioning

confidence: 99%

Comparative Study of Electrical Loads Forecasting Based on Hybrid Approaches: Discrete Wavelet Transforms with Adaboost and LSTM

Moyème,

Yao,

Sedzro

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…[33] present a study on the application of a model predictive control approach to the problem of efficiently optimizing microgrid operations while satisfying time-varying demand and operating constraints; the overall problem is formulated using integer linear programming with Matlab as the solution tool; Li Guo and al. [34] present a two-stage optimal planning and design method for a combined cooling, heat and power microgrid system to simultaneously minimize total net present cost and carbon dioxide emission; in [35], the authors propose a microgrid optimization based on a hybridization system of renewable energy resources; Mah AXY et al present an optimization for the design and operation of an autonomous microgrid with electric and hydrogen loads showing a significant reduction in load costs [36] ; moreover, a strategy for controlling and managing the energy supply of a microgrid in order to achieve higher efficiency, reliability and economy, are proposed in [37,38]. Aiswariya L. and al.…”

Section: Bibliographical Reviewsmentioning

confidence: 99%

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming and Onsset Method

Moyème,

Yao,

Sedzro

et al. 2024

Preprint

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Optimal planning and design of microgrids is a priority for electrification of off-grid areas. Indeed, according to one of the Sustainable Development Goals (SDG) 7, the UN recommends universal access to electricity for all but, at the lowest cost. To achieve this goal, several optimization methods with different strategies have been proposed in the literature. This paper proposes a microgrid installation and planning model based on a combination of several techniques. Python 3.10, the programming language, has been used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. The results obtained enabled us to simulate the model obtained, with a cluster considered as a case study, based on the elbow and k-means clustering method; then, in a second phase, to size the microgrid with a capacity of 40 kW, by optimizing all the resources available on the site: the example of the various resources in the case of Togo was considered (solar, wind, hydropower). This study therefore highlighted the optimal resources obtained by integrating battery systems by the optimization model formulated on the basis of the various technology costs, such as investment, maintenance and operating costs, based on the technical limits of the various technologies. For the optimum results obtained, solar systems account for 80% of the maximum load considered, compared with 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed, based on the constraints of a voltage stability limit estimated at 10% of the maximum voltage drop. The substation capacity limit was also taken into account. The results obtained from the case studied enabled us again, to present selective results for load nodes in relation to the substation node. And finally, the spatial technology planning tool made it possible to obtain results on the different strategies for the various technologies required for the electrification planning study of the Togo case. The various results obtained from the different techniques provide the leads needed for a feasibility study for optimal electrification of off-grid areas, using microgrid systems.

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“…Li Guo et al [34] presented a two-stage optimal planning and design method for a combined cooling, heat, and power microgrid system to simultaneously minimize total net present cost and carbon dioxide emission. In [35], the authors proposed microgrid optimization based on a hybridization system of renewable energy resources. Mah AXY et al presented the optimized design and operation of an autonomous microgrid with electric and hydrogen loads, showing a significant reduction in load costs [36].…”

Section: Bibliographical Reviewsmentioning

confidence: 99%

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Kabe,

Bokovi,

Sedzro

et al. 2024

Energies

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Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

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Hybrid modeling of energy storage system and electrical loads in a pilot-microgrid

Cited by 5 publications

References 12 publications

Comparative Study of Electrical Loads Forecasting Based on Hybrid Approaches: Discrete Wavelet Transforms with Adaboost and LSTM

Comparative Study of Electrical Loads Forecasting Based on Hybrid Approaches: Discrete Wavelet Transforms with Adaboost and LSTM

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming and Onsset Method

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

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