Energy Resources Research Articles

National-level resilience: Innovative framework for Energy-Water-Food nexus evaluation

The escalating demand for energy, water, and food (EWF) resources, alongside external stressors such as climate change, is placing significant strain on national systems. Therefore, this paper introduces a novel framework for assessing the resilience of interconnected EWF systems at a national level. The framework utilizes a hybrid approach of network systems modeling and optimization models, integrating sectoral and dependency impacts to offer a comprehensive perspective on the interconnected resilience of EWF components. Applied to Qatar's EWF nexus, the framework revealed significant interdependencies, particularly between water and energy components, that led to cascading failures and challenged nexus resilience. Under single failure scenarios, unmet demands for energy and water ranged from 9 to 59 GWh and 97,000 to 359,000 m3, respectively, with resilience indices from 0.86 to 0.99. In double failure scenarios, significantly higher unmet demands caused the resilience index to drop to 0.43. Due to limited freshwater availability, the food sector is highly vulnerable to groundwater supply disruptions, resulting in an 85.6 % reduction in total crop production under extreme scenarios. Furthermore, the study highlighted resilience-enhancement strategies, such as utilizing treated wastewater for agricultural, industrial, and commercial activities and diversifying energy sources to reduce the risk of cascading failures, which increased the nexus's resilience by 17 %. The proposed framework serves as an essential tool for identifying critical components within the EWF nexus and planning strategies to enhance both operational and structural resilience.

A new method to extract coal-covered area in open-pit mine based on remote sensing

ABSTRACT Coal is a prominent energy resource in China. Therefore, it is crucial to accurately extract the coal-covered areas. Taking Xinjiang’s Zhundong mining region as the study area, Landsat 8 OLI data were employed to assess the spectral curves of four typical ground objects, including vegetation, bare land, water body and bare coal. The Normalized Difference Bare Coal Index (NDBCI) and the Normalized Difference Coal Index (NDCI) were developed to extract coal-covered area. Higher-resolution Sentinel-2B data for the same period were used for verification, with extraction accuracy evaluated by five metrics including Kappa coefficient, Overall accuracy, Checking accuracy, Checking completeness and F1-score. The results of extracting coal-covered areas showed that (1) the NDBCI showed ‘internal fragmentation’ and the NDCI demonstrated ‘pixel overflow’ during the extraction process. Therefore, we determined the optimal thresholds −0.03 for NDBCI and 0.04 for NDCI. (2) NDBCI distinguished pixels with lower grey-scale values, such as water body, road and gangue. However, some dump zones and shed patches were misclassified. (3) NDCI generated clear boundaries and more complete interiors, and the dump zone and shed could be distinguished. However, some water body parts were misclassified as coal-covered areas. (4) Combined application of NDBCI and NDCI generated a ‘complementary’ effect better than both individual modes. Kappa coefficient, Overall accuracy and F1-score reached 0.95, 98.76% and 0.75, respectively. This study successfully extracted coal-covered areas by developing a remote sensing index based on spectral traits and a priori knowledge of the study area. The proposed combined extraction mode achieved high accuracy for rapid and reliable identification of coal-covered areas.

Coordination in islanded microgrids: Integration of distributed generation, energy storage system, and load shedding using a new decentralized control architecture

For an islanded microgrid (MG) to work reliably, it is essential to manage the control of distributed energy resources, including generation and storage units, as well as loads, in a coordinated manner. In islanded microgrids, the safe energy storage limits must be accounted for coordination to avoid rapid damage or degradation to the storage units. In this paper, a novel control method is introduced to coordinate distributed generation (DG) and energy storage systems (ESS) in an islanded MG to enhance penetration and complete exploitation of DGs and ESSs and maintain balanced state of charge (SoC). A decentralized modified droop function (MDF) is suggested to share power and to control voltage and frequency without communication link. This controller uses virtual impedance to separate active and reactive power in the primary level control loop. Moreover, based on the ESS's charge state and its control as a frequency control support system, the proposed controller enables DGs to operate in both voltage and frequency control mode (VFCM) and active power control mode (APCM) to avoid overcharging. Likewise, to prevent from over discharging and maintain the frequency within permissible range, a new load shedding strategy is implemented to minimize the cycles of load connection and disconnection. The validation and effectiveness of the proposed control system are demonstrated by simulation results.

Graphene-Infused Sustainable Rubber-Based Triboelectric Nanogenerator For Real-Time Human Motion Monitoring.

Triboelectric nanogenerators (TENG) are promising alternatives for clean energy harvesting. However, the material utilization in the development of TENG relies majorly on polymers derived from non-renewable resources. Therefore, minimizing the carbon footprint associated with such TENG development demands a shift toward usage of sustainable materials. This study pioneers using natural rubber (NR) as a sustainable alternative in TENG development. Infusing graphene in NR, its dielectric constant and tribonegativity are optimized, yielding a remarkable enhancement. The optimized sample exhibits a dielectric constant of 411 (at 103Hz) and a contact potential difference (CPD) value of 1.85V. In contrast, the pristine NR sample showed values of 6 and 3.06V for the dielectric constant and CPD. Simulation and experimental studies fine-tune the TENG's performance, demonstrating excellent agreement between theoretical predictions and practical studies. Sensors developed via stencil printing technique possess a remarkably low layer thickness of 270µm, and boast a power density of 420mWm-2, a staggering 250% increase over conventional NR. Moreover, the material is pressure sensitive, enabling precise real-time human motion detection, including finger contact, finger bending, neck bending, and arm bending. This versatile sensor offers wireless monitoring, empowering healthcare monitoring based on the Internet of Things.

A novel environmentally-friendly filler of the asphalt mastic: Organic modified waste shells

The shell farming industry generates significant waste shells annually, often improperly disposed of, causing environmental pollution. Concurrently, pavement engineering faces a shortage of limestone resources. Utilizing waste shells as a substitute for limestone fillers could simultaneously address both environmental and resource challenges. To analyze the feasibility of this strategy, this work collected the mussel, pectinid, oncomelania and oyster as the raw materials, and the method of organic modification was adopted on these shells to enhance the compatibility between the filler and asphalt. Asphalt. The physical and rheological properties of the prepared asphalt mastics were evaluated, and the modification effect was determined by the contact angle test and the surface energy parameters. Results indicated that the asphalt mastics with modified shells had higher penetration and ductility while lower softening point compared to the mastics with unmodified shells. Moreover, the modification with aluminate coupling agent had more significant impact on the physical properties of the asphalt mastics than that using amino silicone oil. For the rheological properties, shell asphalt mastics exhibited an extremely complex rheological behavior due to the various polarity and the organic content of the shell fillers. Besides, the contact angle of the shells increased after the modification, implying a better compatibility with asphalt. Moreover, the results of the surface energy parameters indicated that the non-polar component of the shells increased obvious after modification. In comparison with the conventional limestone, it can be found that the modified shells enhanced the adhesion between the aggregates and the asphalt mastics, which can improve the resistance of asphalt mixture to water damage. This work verifies the feasibility to replace the conventional mineral filler with modified shell filler, which was beneficial to recycling the waste shell and reducing the consumption of non-renewable mineral resources.

Flexible supply-demand side management towards a sustainable decentralized distribution network: A net-negative Water-energy-emissions Nexus assessment

Severe water and energy shortfalls hinder widespread access and challenge sustainable resource management in various supply systems. This study proposed a new decentralized distribution network with net-negative emissions concept and flexible strategies to ensure efficient water and mix-energy with low-carbon emissions The proposed net-negative water-energy emission decentralized distribution network (N-NWEE-D2N) was formulated by assessing spatio-temporal interactions at local-regional scale of multi-sector water and energy demand and supply using flexible nexus regulation strategies. The feasibility of self-production and CO2-based regeneration from biogas recovery in activated sludge process and non-renewable energy generation, respectively, was investigated. The N-NWEE-D2N-based water-energy distribution showed an effective balance between supply and demand, with low deficits, efficient surplus utilization, and reduced CO2 emissions. The water and energy management efficiencies based on N-NWEE-D2N improved by 67.196% and 78.11%, respectively, compared to scenarios without such considerations, due to the effective decentralized distribution of water and energy among the regions. Thus, proposed N-NWEE-D2N can contribute to the sustainable operation of WWTPs and promoting long-term sustainability in water and energy distribution transmission.

Stochastic Techno-Economic Optimization of Hybrid Energy System with Photovoltaic, Wind, and Hydrokinetic Resources Integrated with Electric and Thermal Storage Using Improved Fire Hawk Optimization

In this paper, a stochastic techno-economic optimization framework is proposed for three different hybrid energy systems that encompass photovoltaic (PV), wind turbine (WT), and hydrokinetic (HKT) energy sources, battery storage, combined heat and power generation, and thermal energy storage (Case I: PV–BA–CHP–TES, Case II: WT–BA–CHP–TES, and Case III: HKT–BA–CHP–TES), with the inclusion of electric and thermal storage using the 2m + 1 point estimate method (2m + 1 PEM) utilizing real data obtained from the city of Espoo, Finland. The objective function is defined as planning cost minimization. A new meta-heuristic optimization algorithm named improved fire hawk optimization (IFHO) based on the golden sine strategy is applied to find the optimal decision variables. The framework aims to determine the best configuration of the hybrid system, focusing on achieving the optimal size for resources and storage units to ensure efficient electricity and heat supply simultaneously with the lowest planning cost in different cases. Also, the impacts of the stochastic model incorporating the generation and load uncertainties using the 2m + 1 PEM are evaluated for different case results compared with the deterministic model without uncertainty. The results demonstrated that Case III obtained the best system configuration with the lowest planning cost in deterministic and stochastic models and. This case is capable of simply meeting the electrical and thermal load with the contribution of the energy resources, as well as the CHP and TESs. Also, the IFHO superiority is proved compared with the conventional FHO, and particle swarm optimization (PSO) achieves the lowest planning cost in all cases. Moreover, incorporating the stochastic optimization model, the planning costs of cases I–III are increased by 4.28%, 3.75%, and 3.57%, respectively, compared with the deterministic model. Therefore, the stochastic model is a reliable model due to its incorporating the existence of uncertainties in comparison with the deterministic model, which is based on uncertain data.

Real time implementation of scaled droop control in hybrid microgrid with hydrogen storage for regulation of voltage and frequency.

The incorporation of renewable energy resources (RERs) into smart city through hybrid microgrid (HMG) offers a sustainable solution for clean energy. The HMG architecture also involves linking the AC-microgrid and DC-microgrid through bidirectional interconnection converters (ICC). This HMG combines AC sources like wind-DFIG with DC sources such as solar PV and solid oxide fuel cell (SOFC), supported by battery energy storage systems (BESS) and hydrogen storage units (HSU). The HSU can generate and store hydrogen during RER surplus. This stored hydrogen can be further employed for production of electrical power along with numerous other applications. The HSU is emerged as a competent tool which can be utilised alone/in combination with BESS to enhance the system reliability. Harvesting power from clean and green sources requires its optimal operation and control while feeding to the existing grid. The existing strategies of controlling ICC are complex and not efficient; hence, a novel intelligent scaled droop control structure (SDCS) is proposed, utilizing frequency, DC voltage, and active power. The SDCS regulate voltage and frequency in both islanded mode (IM) and grid connected mode (GCM) of HMG. Experimental validation demonstrates its simplicity and effectiveness, making it suitable for smart city environments, ensuring uninterrupted power for critical loads with improved air quality.

Support for Advanced Nuclear Power Growing in the United States

Global clean energy and decarbonization goals are forcing governments around the world to take a hard look at their energy resource portfolios and future plans for meeting increasing electricity demand. While the last several years have seen a significant uptick in the deployment of solar, both terrestrial and offshore wind, and battery storage here in the United States, nuclear energy is once again being considered a critical energy generation source. Nuclear power, and in particular small modular nuclear reactors, are being considered as carbon‐free power generation sources to strategically meet new loads.

Thermoelectric supercapacitors: materials, challenges and future outlook

Abstract Self-powered devices are the most interesting research subject in recent time, focusing on the advancement of the flexible and wearable electronics market. A variety of self-powered systems have been designed using different energy harvesting (solar cells, mechanical as well as thermal energy harvester) and storage devices such as supercapacitors. Environmental degradation, the inadequacy in the supply of existing fossil fuel, as well as fast-rising energy demand have all raised the alarm bells for our planet's long-term viability. To address the aforementioned challenges, researchers must pursue steadfast studies on an urgent needs by using "green" energies such as wind, solar, tidal, mechanical, as well as geothermal sources. Because these green energy resources are intermittent, new energy harvesting as well as storage devices must be designed to keep and distribute the captured energy gradually, efficiently and meticulously. The low-grade thermal energy generally squandered without usage can be particularly beneficial for consistently powering electronic equipment, including sensors and wearable electronics. This review deals with a detailed discussion on the mechanism and fabrication of thermoelectric supercapacitors. The challenges, possible solutions, and the prospects of thermoelectric supercapacitors have also been highlighted.

SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems

SecuriDN v. 0.1 is a tool for the representation of the assets composing the IT and the OT subsystems of Distributed Energy Resources (DERs) control networks and the possible cyberattacks that can threaten them. It is part of a platform that allows the evaluation of the security risks of DER control systems. SecuriDN is a multi-formalism tool, meaning that it manages several types of models: architecture graph, attack graphs and Dynamic Bayesian Networks (DBNs). In particular, each asset in the architecture is characterized by an attack graph showing the combinations of attack techniques that may affect the asset. By merging the attack graphs according to the asset associations in the architecture, a DBN is generated. Then, the evidence-based and time-driven probabilistic analysis of the DBN permits the quantification of the system security level. Indeed, the DBN probabilistic graphical model can be analyzed through inference algorithms, suitable for forward and backward assessment of the system’s belief state. In this paper, the features and the main goals of SecuriDN are described and illustrated through a simplified but realistic case study.

Modeling the ecological footprint and assessing its influential factors: A systematic review.

Various factors have been found responsible for the increment in ecological footprint resulting difficulties in maintaining environmental sustainability. This has been noticed through a modeling perspective. Identifying the factors affecting Ecological Footprint helps policymakers to formulate policies regarding sustainability. However, studies conducted based upon systematic reviews on Ecological Footprint through modeling are still limited. This study intends to identify influential factors associated with ecological footprint through a systematic review. ProQuest, Science Direct, Scopus, and Web of Science databases were used to search literature systematically. Particular keywords and Boolean operators were applied to dig out relevant studies for the review. Peer-reviewed research articles published in the English language till September 13, 2023, were incorporated for the analysis. Following the guidelines of Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA), 1011 articles were identified from four different databases and only 37 research papers were eligible for this study. These articles were assessed and relevant information was extracted and then amalgamated into the systematic review. Gross domestic product, urbanization, energy consumption, renewable energy, non-renewable energy, natural resources, bio-capacity, human capital, foreign direct investment, trade openness, and financial development were observed as key factors of the ecological footprint. Factors known to influence ecological footprint need to be addressed properly for environmental sustainability including widespread use of renewable energy.

Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm

Evaluating, mapping, and monitoring high-quality Direct Normal Irradiance (DNI) is crucial for the design, financing, and operation of solar power plants, especially those utilizing concentrating technologies. In China, limited number of first-class meteorological stations provide DNI observations. To bridge this gap, this study has reconstructed a comprehensive 41-year (1982–2022) daily DNI dataset for China (CHDNI). This dataset integrated CMA ground-based observations with ERA5 and MERRA-2 reanalysis data, employing a spatial resolution of 10 km. The data was processed using the REST2 radiation transfer model combined with Stacking ensemble machine learning. Validation with ground-based measurements indicated that the Stacking model exhibited high and consistent performance. This was evidenced by its sample-based cross-validation results, showing a correlation coefficient (R) of 0.92, a root-mean-square error (RMSE) of 32.04 W/m2, a mean absolute error (MAE) of 23.68 W/m2, and a global performance indicator of 3.86. Among the four analyzed DNI products (CERES, ERA5, SWGDN, and CHDNI), CHDNI demonstrated the closet alignment with ground observations, evidenced by the least deviation (MAE = 21.67 W/m2 and RMSE = 31.00 W/m2) and the highest R value (0.92). Aerosol optical depth and cloud cover emerged as the primary factors influencing the quality and accuracy of DNI products under clear-sky and total-sky conditions. The spatial distribution of DNI across China is complex, showcasing DNI values ranging from 25.82 W/m2 to 194.22 W/m2, with an average value of 98.08 W/m2 over 41-year. This dataset is a valuable resource for analyzing regional climate change, photovoltaic applications, and solar energy resources assessment.

Revisiting the cost analysis of importing liquefied green hydrogen

Hydrogen emerges as a promising energy carrier in achieving carbon neutrality, especially in countries such as South Korea and Japan, where renewable energy resources are scarce. Consequently, economically importing hydrogen from overseas is paramount to a stable supply. However, previous studies have often oversimplified the estimation of hydrogen import costs with optimistic assumptions, raising doubts about their reliability. This study comprehensively assesses costs incurred from green hydrogen production to liquefaction and transportation. The cost analysis includes detailed design considerations for hydrogen liquefaction and re-conditioning plants, alongside thermodynamic simulations to estimate the boil-off gas rate of liquid hydrogen during transport. Focusing on a case study of importing liquefied green hydrogen from Australia to South Korea, our findings reveal that the levelized cost of hydrogen is approximately 30.2 USD/kgH2 as of 2023. Projections suggest a decrease to around 18.3 USD/kgH2 by 2050, assuming technological advancements, significantly exceeding values reported in the existing literature. Sensitivity analysis highlights the renewable electricity price in Australia and the boil-off rate during shipping as key factors that significantly influence the levelized cost of hydrogen.

Robust-mv-M-LSTM-CI: Robust Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic

The digitalization of the global landscape of electricity consumption, combined with the impact of the pandemic and the implementation of lockdown measures, has required the development of a precise forecast of energy consumption to optimize the management of energy resources, particularly in pandemic contexts. To address this, this research introduces a novel forecasting model, the robust multivariate multilayered long- and short-term memory model with knowledge injection (Robust-mv-M-LSTM-CI), to improve the accuracy of forecasting models under uncertain conditions. This innovative model extends the capabilities of mv-M-LSTM-CI by incorporating an additional branch to extract energy consumption from adversarial noise. The experiment results show that Robust-mv-M-LSTM-CI demonstrates substantial improvements over mv-M-LSTM-CI and other models with adversarial training: multivariate multilayered long short-term memory (adv-M-LSTM), long short-term memory (adv-LSTM), bidirectional long short-term memory (adv-Bi-LSTM), and linear regression (adv-LR). The maximum noise level from the adversarial examples is 0.005. On average, across three datasets, the proposed model improves about 24.01% in mean percentage absolute error (MPAE), 18.43% in normalized root mean square error (NRMSE), and 8.53% in R2 over mv-M-LSTM-CI. In addition, the proposed model outperforms “adv-” models with MPAE improvements ranging from 35.74% to 89.80% across the datasets. In terms of NRMSE, improvements range from 36.76% to 80.00%. Furthermore, Robust-mv-M-LSTM-CI achieves remarkable improvements in the R2 score, ranging from 17.35% to 119.63%. The results indicate that the proposed model enhances overall accuracy while effectively mitigating the potential reduction in accuracy often associated with adversarial training models. By incorporating adversarial noise and COVID-19 case data, the proposed model demonstrates improved accuracy and robustness in forecasting energy consumption under uncertain conditions. This enhanced predictive capability will enable energy managers and policymakers to better anticipate and respond to fluctuations in energy demand during pandemics, ensuring more resilient and efficient energy systems.

Study of a Novel Updraft Tower Power Plant Combined with Wind and Solar Energy

This study presents a novel solar updraft tower power plant (SUTPP) system, which has been designed to achieve the simultaneous utilization of solar and wind energy resources in desert regions, in response to the pressing demand for sustainable and efficient renewable energy solutions. The aim of this research was to develop an integrated system that is capable of harnessing and converting these abundant energy sources into electrical power, thereby enhancing the renewable energy portfolio in arid environments. The methodology of this study involved the design and construction of a prototype SUTPP, comprising a 53 m high tower, a 6170 m2 collector, five horizontal-axis wind turbines, and a thermal energy storage layer made up of pebbles and sand. The experimental setup was meticulously detailed, and experiments were conducted to collect data on the system’s performance under various environmental conditions. Subsequently, three-dimensional numerical simulations were performed to explore the effects of ambient wind speed and solar radiation on the output power of the SUTPP. The results indicate that the output power of the system increases with the increase in ambient wind speed and solar radiation. The impact of solar irradiation on output power was observed to diminish as ambient wind speeds increased. Notably, as the inlet wind speed rose from 4 m/s to 12 m/s, the output power showed a substantial increase of 727%. The numerical simulations revealed that ambient wind speed has a more pronounced effect on power output compared to solar radiation. Furthermore, it was found that the influence of solar radiation is significant at low wind speeds, with its impact decreasing as wind speed increases. This research provides essential guidance for the design and engineering of highly efficient solar thermal energy utilization projects, representing a significant advancement in the field of renewable energy technology deployment in desert environments.

Model predictive control of 3L‐NPC inverter to enhance fault ride through capability under unbalanced grid conditions

AbstractUnbalanced voltages are becoming increasingly common in distribution networks due to the growing integrations of distributed renewable energy resources as well as large loads such as electric vehicle chargers. Such unbalanced voltages introduce significant control challenges as they produce ripples in the controlling signals, impacting the operation of inverters during the unbalanced conditions. Therefore, this paper proposes a controlling algorithm for three‐level inverters aimed at suppressing these ripples on controlling signals; improving the overall performance of inverters, and consequently enhancing the fault ride‐through capability under unbalanced conditions and faults. The proposed controlling algorithm enables inverters to operate in either a balanced current output mode or a suppressed‐ripple active power output mode, depending on the specific event scenario and demands from the grid operators. Current harmonics are suppressed even under severe faults. For improving the voltage balance on the DC capacitors in three‐level inverters, an enhanced method of neutral point current prediction is presented. The proposed method ensures higher balancing accuracy through the use of weighted switching costs, avoiding the requirement of a balancing controller or current extrapolation. Simulation and experimental results demonstrate the effectiveness of the proposed controller for reliable grid operation.

Resilient and Reversible Phosphotungstic Acid Passivated Zinc Anode.

Aqueous zinc ion batteries (ZIBs) present a compelling solution for grid-scale energy storage, which is crucial for integrating renewable energy resources into the electric infrastructure. The cycling stability of ZIBs hinges on the electrochemical reversibility of the zinc anode, which is often compromised by corrosion and dendritic zinc deposition. Here, we present a straightforward surface passivation strategy that significantly enhances the cycling stability of zinc anodes. By immersing zinc in a solution of phosphotungstic acid, we promoted the dominance of the 002 plane of zinc's hexagonal structure. This process facilitates the creation of a uniform nucleation and protective layer on the native zinc surface, resulting in a more uniform plating-stripping process and increased corrosion resistance. In symmetric cells, the passivated zinc exhibits a capacity retention of 68.7% after 1000 cycles at a current density of 1.0 Ag1-, whereas untreated zinc anodes retain only 7.4% of capacity under identical conditions. In full cell zinc iodine batteries employing the passivated zinc anode, over 1000 stable charge-discharge cycles were achieved at a current density of 20 mA cm-2, with approximately 96% Coulombic efficiency (CE), 86% voltage efficiency (VE), and 82% energy efficiency (EE). This study demonstrates a promising pathway for the construction and upscaling of flow batteries with high capacity and low cost.

Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability

Smart grid is a transformative advancement that modernized the traditional power system for effective electricity management, and involves optimized energy distribution by load forecasting. Precise load forecasting provides the best utilization of energy resources and increases sustainability. Dynamic changes of several connected factors, such as temporal and geographical variability, pose challenges to accurate load prediction. Integrating Artificial Intelligence (AI) in the smart grid can enhance the performance of the forecasting process by capturing these changes. This study investigated load forecasting tasks on four different datasets. Several preprocessing and augmentation techniques are applied to increase the data quality. An attention-based 1D-CNN-GRU model is proposed to capture the temporal patterns from the time-series data, and the hyperparameters of the model are optimized using a particle swarm optimization (PSO) algorithm that also accelerates the convergence and results in an efficient training session. Empirical evaluations highlight that the proposed model substantially minimizes the loss, reflecting the ability to make accurate predictions. It obtains MAE values of 0.12, 0.8, 16.48, and 82.64 for the four datasets. Moreover, the explainable AI (XAI) technique is applied using Shapley Additive explanations (SHAP) to interpret the model prediction, providing the feature ranking based on their prediction score. Moreover, this study utilizes federated learning, enables collaborative training, maintains the privacy of the grid data, and secures the process comprehensively. The aggregation mechanism in federated learning is modified using pruning-based methods that reduce the parameters and computational cost, resulting in a more efficient framework. Integrating all these approaches provides valuable insights for developing a load forecasting model and outlines potential contributions in the smart grid domain.

High-dimensional private and social optimal policy valuation model for non-renewable natural resource extraction projects for multivariate public policy decisions

We present a model to find the value and sensitivity function associated with the exploitation of a non-renewable natural resource both for the mining company (private optimum) and for the government (social optimum). Improvements over previous studies are achieved by achieving a multivariate model of optimal policy valuation under a Markov decision process with stopping times based on multiple real options, proposing an iterative algorithm of stochastic approximation with supervised dynamic programming decision under Monte Carlo scenarios until obtaining convergence. Applied to a mining project (Tía María) and extended to the total reserves of a country (Peru), it provides additional decision criteria for fiscal public policy, negotiations, concessions, tax levels, community reparations, as well as benefit estimation, discounted from the potential environmental cost in countries abundant in natural resources that wish to achieve sustainability.