Search Results (265)

Electricity distribution systems face increasing challenges due to demand growth, regulatory requirements, and the integration of distributed generation. In this context, distribution companies must make strategic and well-supported investment decisions, particularly in asset reinforcement actions such as reconductoring. This paper presents a multi-stage methodology to optimize reconductoring investments under load forecasting uncertainties. The approach combines a decomposition strategy with Monte Carlo simulation to capture demand variability. By discretizing a lognormal probability density function and selecting the largest loads in the network, the methodology balances computational feasibility with modeling accuracy. The optimization model employs exhaustive search techniques independently for each network branch, ensuring precise and consistent investment decisions. Tests conducted on the IEEE 123-bus feeder consider both operational and regulatory constraints from the Brazilian context. Results show that uncertainty-aware planning leads to a narrow investment range—between USD 55,108 and USD 66,504—highlighting the necessity of reconductoring regardless of demand scenarios. A comparative analysis of representative cases reveals consistent interventions, changes in conductor selection, and schedule adjustments based on load conditions. The proposed methodology enables flexible, cost-effective, and regulation-compliant investment planning, offering valuable insights for utilities seeking to enhance network reliability and performance while managing demand uncertainties. Full article

The reduction of environmental pollutant emissions—including greenhouse gases, particulate matter, and other harmful substances—represents one of the foremost challenges in climate policy, economics, and industrial management today. Excessive emissions of CO₂, NO_X, and suspended particulates exert significant impacts on climate change as well as human health and welfare. Consequently, numerous studies and regulatory and technological initiatives are underway to mitigate these emissions. One critical area is intra-plant transport within manufacturing facilities, which, despite its localized scope, can substantially contribute to a company’s total emissions. This paper aims to assess the potential of computer simulation using FlexSim software as a decision-support tool for planning inter-operational transport, with a particular focus on environmental aspects. The study analyzes real operational data from a selected production plant (case study), concentrating on the optimization of the number of transport units, their routing, and the layout of workstations. It is hypothesized that reducing the number of trips, shortening transport routes, and efficiently utilizing transport resources can lead to lower emissions of carbon dioxide (CO₂) and nitrogen oxides (NO_X). The findings provide a basis for a broader adoption of digital tools in sustainable production planning, emphasizing the integration of environmental criteria into decision-making processes. Furthermore, the results offer a foundation for future analyses that consider the development of green transport technologies—such as electric and hydrogen-powered vehicles—in the context of their implementation in the internal logistics of manufacturing enterprises. Full article

In the field of marine science, the measurement of ocean currents is essential for the conduction of marine surveys, the understanding of ocean dynamics, and also the interpretation of oceanic climate change. The expendable current profiler (XCP) is an equipment employed in oceanographic research, capable of providing detailed profiles of oceanic flow by measuring the velocity and direction of currents at various depths when it falls from surface to bottom. The performance of the XCP largely relies upon the precision and stability of its electrodes. Silver/silver chloride (Ag-AgCl) electrodes, renowned for their superior electrochemical stability and low-noise characteristics, are frequently selected as the electrode material for XCP. This paper focuses on four pairs of Ag-AgCl electrodes, designated as Electrodes I, II, III, and IV, where Electrodes I and II are custom-made from a company, Electrode III is a self-developed electrode, and Electrode IV is an improved self-developed electrode. A detailed description of the fabrication process of Electrode III is provided in this study. Multiple experiments were conducted on these four pairs of electrodes to investigate their self-noise, power spectral density, and frequency response under various experimental conditions. The experimental results indicate that, in the absence of an external electric field, the power spectral density at 1 Hz for Electrodes I, II, and III is in the tens of nanovolts per square root hertz (nV/√Hz) of magnitude. The performance of Electrode IV is superior, with a power spectral density of only a few nV/√Hz at 1 Hz when without an external electric field, and its frequency response within the 13–18 Hz range, which is of utmost concern to XCP, is also fundamentally stable, meeting the requirements for sea trial utilization of XCP. Full article

Palm oil production generates substantial underutilized biomass wastes, including empty fruit bunches, fiber, palm kernel shells, and palm oil mill effluent (POME). Waste-to-energy systems offer a viable pathway to convert these residues into electricity and fertilizer, supporting circular economy goals and sustainability targets. This study takes an example of palm oil waste from the Indragiri Hulu region in Riau Province in Indonesia. It develops a multi-objective optimization framework to evaluate palm oil mill WtE systems from economic, environmental, and energy output. Three scenarios are analyzed: maximal profit (MP), maximal profit with carbon tax (MPCT), and all waste processing (AWP). The MP scenario favors high-return technologies such as gasification and incineration, leading to significant greenhouse gas emissions. The MPCT scenario favors lower-emission technologies like composting and excludes high-emission, low-profit options such as POME digestion. In contrast, the AWP scenario mandates the processing of all wastes, leading to the lowest profits and the highest emissions among all scenarios. The sensitivity analysis reveals that POME processing is not feasible when electricity prices are below the government-set rate, but becomes viable once prices exceed this threshold. These findings offer valuable insights for companies and policymakers seeking to develop and implement effective strategies for optimal waste utilization. Full article

This paper discusses the development of an Integrated Building Energy Model (IBEM) in MATLAB (R2024b) for a university campus building. In the general context of the development of integrated energy district models to guide the evolution and planning of smart energy grids for increased efficiency, resilience, and sustainability, this work describes in detail the development and use of an IBEM for a university campus building featuring a heat pump-based heating/cooling system and PV generation. The IBEM seamlessly integrates thermal and electrical aspects into a complete physical description of the energy performance of a smart building, thus distinguishing itself from co-simulation approaches in which different specialized tools are applied to the two aspects and connected at the level of data exchange. Also, the model, thanks to its physical, white-box nature, can be instanced repeatedly within the comprehensive electrical micro-grid model in which it belongs, with a straightforward change of case-specific parameter settings. The model incorporates a heat pump-based heating/cooling system and photovoltaic generation. The model’s components, including load modeling, heating/cooling system simulation, and heat pump implementation are described in detail. Simulation results illustrate the building’s detailed power consumption and thermal behavior throughout a sample year. Since the building model (along with the whole campus micro-grid model) is implemented in the MATLAB Simulink environment, it is fully portable and exploitable within a large, world-wide user community, including researchers, utility companies, and educational institutions. This aspect is particularly relevant considering that most studies in the literature employ co-simulation environments involving multiple simulation software, which increases the framework’s complexity and presents challenges in models’ synchronization and validation. Full article

In this work, we present a novel approach for predicting short-term electrical energy consumption. Most energy consumption methods work well for their case study datasets. The proposed method utilizes a cloud computing platform that allows for integrating information from different sources, such as weather data and historical energy consumption, while employing machine learning techniques to achieve higher accuracy in forecasting. We collected detailed weather data from the “Weather Underground Company” website, known for its accurate records. Then, we studied past energy consumption data provided by PJM (focusing on DEO&K, which serves Cincinnati and northern Kentucky) and identified features that significantly impact energy consumption. We also introduced a processing step to ensure accurate predictions for holidays. Our goal is to predict the next 24 h of load consumption. We developed a hybrid, generalizable forecasting methodology with deviation correction. The methodology is characterized by fault tolerance due to distributed cloud deployment and an introduced voting mechanism. The proposed approach improved the accuracy of LSTM, SARIMAX, and SARIMAX + SVM, with MAPE values of 5.17%, 4.21%, and 2.21% reduced to 1.65%, 1.00%, and 0.88%, respectively, using our CM-LSTM-DC, CM-SARIMAX-DC, and CM-SARIMAX + SVM-DC models. Full article

This study investigates the impact of human resource management (HRM) practices—specifically planning, recruitment, training, and motivation—on dimensions of career quality (job security, promotion equity, and participatory decision-making) among employees of the Jordan Electricity Distribution Company (JEDCO). Utilizing a quantitative cross-sectional survey design, data were collected from 173 employees, allowing for an in-depth exploration of their perceptions and experiences regarding HRM practices. The findings reveal that both training and motivation significantly enhance career quality, with employees who receive advanced training reporting a stronger sense of job security and an increased likelihood to participate in decision-making processes. In contrast, the effects of recruitment and planning practices were found to be marginal due to perceived biases and strategies that fail to adequately address the long-term needs of the workforce. Despite moderate overall career quality scores, key areas for improvement were identified, particularly in job security and employee involvement. This study offers actionable recommendations for JEDCO, such as implementing AI-driven recruitment tools to mitigate nepotism and developing gamified training modules to enhance skill development. Furthermore, it underscores the importance of integrating HRM reforms into Jordan’s National Energy Strategy, thereby supporting Sustainable Development Goal 8. This research represents the first empirical examination linking HRM practices to career quality in Jordan’s energy sector, offering a framework applicable to public utilities in emerging economies (e.g., Lebanon’s EDL). This research extends Social Exchange Theory into non-Western hierarchical contexts, demonstrating how bureaucratic inertia and tribal affiliations weaken reciprocity dynamics—a novel boundary condition contrasting Western-centric SET models. Full article

Maintaining a balance between electricity generation and consumption is vital for ensuring grid stability and preventing disruptions. Spatial load forecasting (SLF) predicts geographical electricity demand, thereby aiding in power system planning. However, conventional methods like multiple linear regression and autoregressive integrated moving average struggle to capture the complex spatiotemporal patterns in historical data. Advanced methods like spatiotemporal graph transformers, graph convolutional networks, and improved scale-limited dynamic time warping better capture these dependencies, thereby enhancing prediction accuracy. Despite the advancements, challenges persist, particularly in developing economies with limited reliable data. This paper presents a novel SLF approach that divides the grid into predefined clusters based on regional characteristics and economic activity. For each cluster, a customized long short-term memory (LSTM) model captures unique spatiotemporal dependencies for more accurate predictions. The proposed method was tested across five load clusters using real-world data from Cameroon’s National Electricity Transmission Company and Energy Utilities. The results, compared against a linear regression model, demonstrated the superior performance of the LSTM approach across metrics like the mean absolute percentage error, root-mean-square error, mean absolute error, and R² score. This approach highlights the potential for enhanced, localized load forecasting in regions with data constraints. Full article

The paper presents the design and development of a universal simulation model named SustainSIM, intended for optimizing the carbon footprint in mechanical engineering production. The objective of this model is to enable enterprises to accurately quantify, monitor, and simulate CO₂ emissions generated during various manufacturing processes, thereby identifying and evaluating effective reduction strategies. The paper thoroughly examines methodologies for data collection and processing, determination of emission factors, and categorization of emissions (Scope 1 and Scope 2), utilizing standards such as the GHG Protocol and associated databases. Through a digital simulation environment created in Unity Engine, the model interactively visualizes the impacts of implementing green technologies—such as solar panels, electric vehicles, and heat pumps—on reducing the overall carbon footprint. The practical applicability of the model was validated using a mechanical engineering company as a case study, where simulations confirmed the model’s potential in supporting sustainable decision-making and production process optimization. The findings suggest that the implementation of such a tool can significantly contribute to environmentally responsible management and the reduction of industrial emissions. In comparison to existing methods such as SimaPro/OpenLCA (detailed LCA) and the Corporate Calculator (GHG Protocol), SustainSIM achieves the same accuracy in calculating Scopes 1/2, while reducing the analysis time to less than 15% and decreasing the requirements for expertise. Unlike simulation packages like Energy Plus, users can modify parameters without scripting, and they can see the immediate impact in CO₂e. Full article

The rapid increase in agricultural waste in recent years has led to significant losses and challenges for agro-industrial companies. At the same time, the growing demand for energy to support daily human activities has prompted these companies to seek new and sustainable methods for generating electric energy, which is crucial. Sucrose extracted from fruit waste can act as a carbon source for microbial fuel cells (MFCs), as bacteria metabolize sucrose to generate electrons, producing electric current. This research aims to evaluate the potential of sucrose as an additive to enhance the use of asparagus waste as fuel in single-chamber MFCs. The samples were obtained from CUC SAC in Trujillo, Peru. This study utilized MFCs with varying sucrose concentrations: 0% (Target), 5%, 10%, and 15%. It was observed that the MFCs with 15% sucrose and 0% sucrose (Target) produced the highest electric current (5.532 mA and 3.525 mA, respectively) and voltage (1.729 V and 1.034 V) on the eighth day of operation, both operating at slightly acidic pH levels. The MFC with 15% sucrose exhibited an oxidation-reduction potential of 3.525 mA, an electrical conductivity of 294.027 mS/cm, and a reduced chemical oxygen demand of 83.14%. Additionally, the MFC-15% demonstrated the lowest internal resistance (128.749 ± 12.541 Ω) with a power density of 20.196 mW/cm² and a current density of 5.574 A/cm². Moreover, the microbial fuel cells with different sucrose concentrations were connected in series, achieving a combined voltage of 4.56 V, showcasing their capacity to generate bioelectricity. This process effectively converts plant waste into electrical energy, reducing reliance on fossil fuels, and mitigating methane emissions from the traditional anaerobic decomposition of such waste. Full article

Detecting fraudulent behaviors in electricity consumption is a significant challenge for electric utility companies due to the lack of information and the complexity of both constructing patterns and distinguishing between regular and fraudulent consumers. This study proposes a methodology based on data analytics that, through the processing of information, generates lists of suspicious metering systems for fraud. The database provided by the electrical distribution company contains 266,298 records, of which 15,013 have observations for possible frauds. One of the challenges lies in managing the different variables in the training data and choosing appropriate evaluation metrics. To address this, a balanced database of 27,374 records was used, with an equitable division between fraud and non-fraud cases. The features used in the identification and construction of patterns for non-technical losses were crucial, although additional techniques could be applied to determine the most relevant variables. Following the process, several popular classification models were trained. Hyperparameter optimization was performed by using grid search, and the models were validated by using cross-validation techniques, finding that the ensemble methods Categorical Boosting (CGB), Light Gradient Boosting Machine (LGB) and Extreme Gradient Boosting (EGB) are the most suitable for identifying losses, achieving high performance and reasonable computational cost. The best performance was compared by measuring accuracy (Acc) and F1 score, which allows for the evaluation of various techniques and is a combination of two metrics: detection rate and precision. Although CGB achieved the best performance in terms of accuracy (Acc = 0.897) and F1 (0.894), it was slower than LGB, so it is considered the ideal classifier for the data provided by the electrical distribution company. This research study highlights the importance of the techniques used for fraud detection in electricity metering systems, although the results may vary depending on the characteristics of the training, the number of variables, and the available hardware resources. Full article

With the establishment of a competitive electricity retail market, how to optimize the retail electricity price mechanism has become the core of all kinds of retail companies to explore. Aiming at the pricing problem of time-of-use electricity price, this paper proposes a pricing strategy based on the master–slave game model. Firstly, considering the user’s electricity utility and satisfaction factors, the comprehensive benefit function of the electricity selling company with electricity price as the decision variable and the user’s comprehensive benefit function with electricity consumption as the decision variable are established, respectively. Then, a master–slave game model is established with the electricity selling company as the leader and the user as the follower, and the reverse induction method is used to solve the model. Finally, considering the influencing factors of user response ability, different electricity price types and user types are set up for simulation. The results show that the revenue of electricity retailers can be increased by up to 170,000 yuan, and the average electricity price of users can be reduced by up to 8 yuan. It is verified that the model can effectively achieve a win-win situation for both sides and promote peak shaving and valley filling. At the same time, it is proved that the role of the model is positively related to electricity price flexibility and user response capability. Full article

Electric vehicles are regarded as key players in reducing CO₂ emissions. However, managing the end-of-life (EoL) of lithium-ion batteries (LIBs) poses significant environmental and technical challenges. This presents a daunting task for governments, companies, and academics when discussing and developing initiatives for the EoL of LIBs. As more LIBs reach the end of their vehicular use, it becomes essential to identify key challenges. This research aims to analyze possible pathways, identify LIBs’ challenges in reaching the appropriate destinations, and propose actions to overcome these obstacles. Additionally, this study addresses those responsible for each challenge. A narrative review was employed as a methodological approach to achieve the proposed objectives, utilizing available literature on EoL LIB management. The research findings highlight various challenges, including safety, commercialization, and disassembly. To address these issues, this work recommends strategies such as extended producer responsibility, automation, and regulation. The study also emphasizes the necessity for a collaborative effort, particularly highlighting the key roles of government and industry in developing regulations, implementing effective waste management strategies, and driving market expansion, while academia contributes through research and technological advancements. The research contributes to a better understanding of sustainable LIB management, advocating for responsible disposal and reducing environmental and economic impacts. Full article

Based on the renewable energy assessment in 2023, it was found that only 21% of total electricity is generated using renewable sources. As the global demand for electricity rises in the AI world, the need for electricity management will increase and must be optimized. Based on research, many companies are working on green AI electricity management, but few companies are working on predicting shortages. To identify the rising electricity demand, predict the shortage, and to bring attention to consumption, this study focuses on the optimization of solar electricity generation, tracking its consumption, and forecasting the electricity shortages well in advance. This system demonstrates a novel approach using advanced machine learning, deep learning, and reinforcement learning to maximize solar energy utilization. This paper proposes and develops a community-based model that manages and analyzes multiple buildings’ energy usage, allowing the model to perform both distributed and aggregated decision-making, achieving an accuracy of 98.2% using stacking results of models with reinforcement learning. Concerning the real-world problem, this paper provides a sustainable solution by combining data-driven models with reinforcement learning, contributing to the current market need. Full article

The dedieselization program is one of the PLN’s (Indonesia’s state-owned utility company) programs to reduce the greenhouse gas effect. The program manifestation is the integration of photovoltaic (PV) systems into isolated island networks by substituting diesel generators. This condition introduces challenges such as diminished system strength, specifically, decreased frequency and voltage stability. This study focuses on Panjang Island, one of the target locations for the PLN’s dedieselization program, which currently relies entirely on diesel generators for electricity. As part of the transition to a PV-based power supply, retired diesel generators are proposed for conversion into synchronous condensers (SCs) to enhance system stability by providing inertia and reactive power support. By employing system modeling, steady-state analysis, and dynamic simulations, this study evaluates the effects of SC penetration on Panjang Island. The findings demonstrate that SCs improve grid stability by offering voltage support, increasing short-circuit capacity, and contributing to system inertia. Furthermore, a system assessment flowchart is also proposed to guide SC deployment based on network characteristics. Short-circuit ratios (SCRs) and voltage drops are evaluated as key parameters to determine the feasibility of SC penetration in a system. Converting retired diesel generators into SCs provides a resilient, stable grid as renewable energy penetration increases, optimizing system performance and reducing network losses. Full article