The Economic Impact and Carbon Footprint Dependence of Energy Management Strategies in Hydrogen-Based Microgrids

Rey, Jesús; Segura, Francisca; Andújar, José Manuel; Ferrario, Andrea Monforti

doi:10.3390/electronics12173703

Cited by 3 publications

(10 citation statements)

References 30 publications

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“…Additionally, each off-diagonal plot contains a scatterplot of the variable indi-cated on the left vertical axis with the variable indicated on the horizontal axis, including a pink least-squares reference line. That is, plot (1,2) shows the correlation scatterplot of variable PT112 with variable TT105. On the other hand, the number written in the left upper corner of each plot indicates the correlation coefficient.…”

Section: Results Of Feature Selection Obtained From the Novel Drl Alg...mentioning

confidence: 99%

“…But, as discussed earlier [1], to guarantee long-term cost-effectiveness and safety in electrolysers, it is necessary to define prognostics and health management (PHM) protocols that detect damages and helps to prevent catastrophic failure. With this premise, Lee et al [15] present a PHM model based on machine learning to predict the load voltage of the electrolyser for the state of health information.…”

Section: Background and Previous Workmentioning

confidence: 99%

“…The hydrogen technology deployment is heavily dependent on cost-effectiveness. Regarding hydrogen-based microgrids, their economic impact is conditioned by costs of different nature (investment costs, operation and maintenance costs, and replacement costs) [1]. Among them, operation and maintenance are the ones that are repeated along the lifespan and affect the replacement costs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Deep Reinforcement Learning (DRL) Algorithm to Apply Artificial Intelligence-Based Maintenance in Electrolysers

Abiola,

Manzano,

Andújar

2023

Algorithms

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Hydrogen provides a clean source of energy that can be produced with the aid of electrolysers. For electrolysers to operate cost-effectively and safely, it is necessary to define an appropriate maintenance strategy. Predictive maintenance is one of such strategies but often relies on data from sensors which can also become faulty, resulting in false information. Consequently, maintenance will not be performed at the right time and failure will occur. To address this problem, the artificial intelligence concept is applied to make predictions on sensor readings based on data obtained from another instrument within the process. In this study, a novel algorithm is developed using Deep Reinforcement Learning (DRL) to select the best feature(s) among measured data of the electrolyser, which can best predict the target sensor data for predictive maintenance. The features are used as input into a type of deep neural network called long short-term memory (LSTM) to make predictions. The DLR developed has been compared with those found in literatures within the scope of this study. The results have been excellent and, in fact, have produced the best scores. Specifically, its correlation coefficient with the target variable was practically total (0.99). Likewise, the root-mean-square error (RMSE) between the experimental sensor data and the predicted variable was only 0.1351.

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Section: Results Of Feature Selection Obtained From the Novel Drl Alg...mentioning

confidence: 99%

Section: Background and Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Deep Reinforcement Learning (DRL) Algorithm to Apply Artificial Intelligence-Based Maintenance in Electrolysers

Abiola,

Manzano,

Andújar

2023

Algorithms

Self Cite

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“…The cost-effectiveness analysis to be performed requires knowledge of the amount of energy supplied by the microgrid and injected into the main grid, and the amount of energy purchased from the main grid. These data have been obtained from a previous study [33] and are shown in Table 3. Taking these data into account, Equation (4) finds the savings over the lifespan of the microgrid.…”

Section: Cost-effectiveness Analysis Over Microgrid Lifespanmentioning

confidence: 99%

Profitability of Hydrogen-Based Microgrids: A Novel Economic Analysis in Terms of Electricity Price and Equipment Costs

Rey,

Segura,

Andújar

2023

Electronics

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The current need to reduce carbon emissions makes hydrogen use essential for self-consumption in microgrids. To make a profitability analysis of a microgrid, the influence of equipment costs and the electricity price must be known. This paper studies the cost-effective electricity price (EUR/kWh) for a microgrid located at ‘’La Rábida Campus’’ (University of Huelva, south of Spain), for two different energy-management systems (EMSs): hydrogen-priority strategy and battery-priority strategy. The profitability analysis is based, on one hand, on the hydrogen-systems’ cost reduction (%) and, on the other hand, considering renewable energy sources (RESs) and energy storage systems (ESSs), on cost reduction (%). Due to technological advances, microgrid-element costs are expected to decrease over time; therefore, future profitable electricity prices will be even lower. Results show a cost-effective electricity price ranging from 0.61 EUR/kWh to 0.16 EUR/kWh for hydrogen-priority EMSs and from 0.4 EUR/kWh to 0.17 EUR/kWh for battery-priority EMSs (0 and 100% hydrogen-system cost reduction, respectively). These figures still decrease sharply if RES and ESS cost reductions are considered. In the current scenario of uncertainty in electricity prices, the microgrid studied may become economically competitive in the near future.

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“…However, in this context, it is not feasible to adjust the operation of these devices to be in sync with RESs or the availability of energy storage, as indicated in references cited in [27]. Therefore, effective utilisation of RESs to reduce carbon footprints can be achieved through the implementation of local energy storage [28,29]. However, the battery capacity cannot be chosen indiscriminately as it will impact the carbon footprint of the entire investment.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Energy Consumption in Railway Signal Boxes

Kampik,

Bodzek,

Piaskowy

et al. 2023

Energies

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This study assessed hourly electricity consumption profiles in railway signal boxes located in Poland. The analyses carried out consisted of assessing the correlation among the hourly demand profile, weather indicators, and calendar indicators, e.g., temperature, cloud cover, day of the week, and month. The analysis allowed us to assess which indicator impacts the energy consumption profile and would be useful when forecasting energy demand. In total, 15 railway signal boxes were selected for analysis and grouped according to three characteristic repeatability profiles. On this basis, six of the signal boxes and one that did not fit into any of the groups were selected for further analysis. Four correlation research methods were selected for analysis: Pearson’s method, Spearman’s method, scatter plots, and distance covariance. The possibility of forecasting electricity consumption based on previously aggregated profiles and determining correlations with indicators was presented. The given indicators vary depending on the facility. Analyses showed different dependencies of the electricity demand profile. The ambient temperature and time of day have the greatest impact on the profile. Regarding the correlation with temperature, the results of the Pearson’s and Spearman’s coefficients ranged from approximately −0.4 to more than −0.8. The highest correlation coefficients were obtained when comparing the demand profile with the previous day. In this case, the Pearson’s and Spearman’s coefficients for all analysed objects range from approximately 0.7 to over 0.9.

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The Economic Impact and Carbon Footprint Dependence of Energy Management Strategies in Hydrogen-Based Microgrids

Cited by 3 publications

References 30 publications

A Novel Deep Reinforcement Learning (DRL) Algorithm to Apply Artificial Intelligence-Based Maintenance in Electrolysers

A Novel Deep Reinforcement Learning (DRL) Algorithm to Apply Artificial Intelligence-Based Maintenance in Electrolysers

Profitability of Hydrogen-Based Microgrids: A Novel Economic Analysis in Terms of Electricity Price and Equipment Costs

An Analysis of Energy Consumption in Railway Signal Boxes

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