Search Results (558)

This study utilises the Open Source Energy Modelling System (OSeMOSYS) in conjunction with the Climate, Land, Energy, and Water systems (CLEWs) framework to investigate Thailand’s energy transition, which is designed to achieve carbon neutrality by 2050. Two scenarios have been devised to evaluate the long-term trade-offs among energy, water, and land systems. Data were sourced from esteemed international organisations (e.g., the IEA, FAO, and OECD) and national agencies and organised into a tailored OSeMOSYS Starter Data Kit for Thailand, comprising a baseline and a carbon neutral trajectory. The baseline scenario, primarily reliant on fossil fuels, is projected to generate annual CO₂ emissions exceeding 400 million tons and water consumption surpassing 85 billion cubic meters by 2025. By the mid-century, the carbon neutral scenario will have approximately 40% lower water use and a 90% reduction in power sector emissions. Under the carbon neutral path, renewable energy takes the front stage; the share of renewable electricity goes from under 20% in the baseline scenario to almost 80% by 2050. This transition and large reforestation initiatives call for consistent investment in solar energy (solar energy expenditures exceeding 20 billion USD annually by 2025). Still, it provides notable co-benefits, including greater resource sustainability and better alignment with international climate targets. The results provide strategic insights aligned with Thailand’s National Energy Plan (NEP) and offer modelling evidence toward achieving international climate goals under COP29. Full article

Intensive energy consumption in the building sector remains one of the primary contributors to climate change and global warming. Within Renewable Energy Communities (RECs), improving energy management is essential for promoting sustainability and reducing environmental impact. Accurate forecasting of energy consumption at the community level is a key tool in this effort. Traditionally, engineering-based methods grounded in thermodynamic principles have been employed, offering high accuracy under controlled conditions. However, their reliance on exhaustive building-level data and high computational costs limits their scalability in dynamic REC settings. In contrast, Artificial Intelligence (AI)-driven methods provide flexible and scalable alternatives by learning patterns from historical consumption and environmental data. This study investigates three Machine Learning (ML) models, Decision Tree (DT), Random Forest (RF), and CatBoost, and one Deep Learning (DL) model, Convolutional Neural Network (CNN), to forecast community electricity consumption using real smart meter data and local meteorological variables. The study focuses on a REC in Loureiro, Portugal, consisting of 172 residential users from whom 16 months of 15 min interval electricity consumption data were collected. Temporal features (hour of the day, day of the week, month) were combined with lag-based usage patterns, including features representing energy consumption at the corresponding time in the previous hour and on the previous day, to enhance model accuracy by leveraging short-term dependencies and daily repetition in usage behavior. Models were evaluated using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination $R^2$. Among all models, CatBoost achieved the best performance, with an MSE of 0.1262, MAPE of 4.77%, and an $R^2$ of 0.9018. These results highlight the potential of ensemble learning approaches for improving energy demand forecasting in RECs, supporting smarter energy management and contributing to energy and environmental performance. Full article

Modern utilities face unprecedented pressures as trends in digital transformation and democratized energy choice empower consumers to engage in peak shaving, flexible load management, and adopt grid automation and intelligence solutions. A powerful confluence of architectural, technological, and socio-economic forces is transforming the U.S. electricity market, triggering significant changes in electricity production, transmission, and consumption. Utilities are embracing digital twins and repurposed Utility 2.0 concepts—distributed energy resources, microgrids, innovative electricity market designs, real-time automated monitoring, smart meters, machine learning, artificial intelligence, and advanced data and predictive analytics—to foster operational flexibility and market efficiency. This analysis qualitatively evaluates how digitalization, Battery Energy Storage Systems (BESSs), and adaptive strategies to mitigate rebound effects collectively advance smart duck curve management. By leveraging digital platforms for real-time monitoring and predictive analytics, utilities can optimize energy flows and make data-driven decisions. BESS technologies capture surplus renewable energy during off-peak periods and discharge it when demand spikes, thereby smoothing grid fluctuations. This review explores the benefits of targeted digital transformation, BESSs, and managed rebound effects in mitigating the duck curve problem, ensuring that energy efficiency gains translate into actual savings. Furthermore, this integrated approach not only reduces energy wastage and lowers operational costs but also enhances grid resilience, establishing a robust framework for sustainable energy management in an evolving market landscape. Full article

Cross-border energy trading activity via interconnection has received much attention in Southern Asia to help the South Asian Association for Regional Cooperation (SAARC) region’s energy deficit states. This research article proposed a smart metering system to reduce energy losses and increase distribution sector efficiency. The implementation of smart metering systems in utility management plays a pivotal role in advancing several Sustainable Development Goals (SDGs), i.e.; SDG (Affordable and Clean Energy), and SDG Climate Action. By enabling real-time monitoring, accurate measurement, and data-driven management of energy resources, smart meters promote efficient consumption, reduce losses, and encourage sustainable behaviors among consumers. The adoption of a smart metering system along with Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis, socio-economic analysis, current challenges, and future prospects was also investigated. Besides the economics of the electrical distribution system, one feeder with non-technical losses of about 16% was selected, and the cost–benefit analysis and cost–benefit ratio was estimated for the SAARC region. The import/export ratio is disturbing in various SAARC grids, and a solution in terms of community microgrids is presented from Pakistan’s perspective as a case study. The proposed work gives a guidelines for SAARC countries to reduce their losses and improve their system functionality. It gives a composite solution across multi-faceted evaluation for the betterment of a large region. Full article

Aiming at the economic problems of industrial users with wind power, photovoltaic, and small hydropower resources in clean energy consumption and trading with superior power grids, this paper proposes a distributed pumped storage capacity optimization configuration method considering clean energy systems. First, considering the maximization of the investment benefit of distributed pumped storage as the upper goal, a configuration scheme of the installed capacity is formulated. Second, under the two-part electricity price mechanism, combined with the basin hydraulic coupling relationship model, the operation strategy optimization of distributed pumped storage power stations and small hydropower stations is carried out with the minimum operation cost of the clean energy system as the lower optimization objective. Finally, the bi-level optimization model is solved by combining the alternating direction multiplier method and CPLEX solver. This study demonstrates that distributed pumped storage implementation enhances seasonal operational performance, improving clean energy utilization while reducing industrial electricity costs. A post-implementation analysis revealed monthly operating cost reductions of 2.36, 1.72, and 2.13 million RMB for wet, dry, and normal periods, respectively. Coordinated dispatch strategies significantly decreased hydropower station water wastage by 82,000, 28,000, and 52,000 cubic meters during corresponding periods, confirming simultaneous economic and resource efficiency improvements. Full article

This study presents an innovative methodology to optimize the operation of distribution transformers through the estimation of hourly load curves, aimed at minimizing technical losses due to oversizing, particularly in systems lacking advanced metering infrastructure. The proposed approach combines clustering techniques, K-Means with DTW, to identify representative daily consumption patterns and a supervised model based on LightGBM to estimate hourly load curves for unmetered transformers, using customer characteristics as input. These estimated curves are integrated into a process that calculates technical losses, both no-load and load losses, for different transformer sizes, selecting the optimal rating that minimizes losses without compromising demand. Empirical validation showed accuracy levels of 95.6%, 95.29%, and 98.14% at an individual transformer, feeder, and a complete electrical system with 16,864 transformers, respectively. The application of the methodology to a real distribution system revealed a potential annual energy savings of 3004 MWh, equivalent to an estimated economic reduction of 150,238 USD. Full article

Microbial fuel cells (MFCs) are bio-electrochemical systems that harness microorganisms to convert organic pollutants in wastewater directly into electricity, offering a dual solution for sustainable wastewater treatment and renewable energy generation. This paper presents a holistic techno-economic and environmental feasibility assessment of large-scale MFC deployment in Dhaka’s industrial zone, Bangladesh, as a relevant case study. Here, treating 100,000 cubic meters of wastewater daily would require a capital investment of approximately USD 500 million, with a total project cost ranging between USD 307.38 million and 1.711 billion, depending on system configurations. This setup has an estimated theoretical energy recovery of 478.4 MWh/day and a realistic output of 382 MWh/day, translating to a per-unit energy cost of USD 0.2–1/kWh. MFCs show great potential for treating wastewater and addressing energy challenges. However, this paper explores remaining challenges, including high capital costs, electrode and membrane inefficiencies, and scalability issues. Full article

To alleviate the impact of economic and environmental detriments caused by the increased demands of electric vehicle battery production and disposal, the use of spent batteries in second-life stationary applications such as energy storage for renewable sources or backup power systems, offers many benefits. This paper focuses on reducing the energy consumption cost and greenhouse gas emissions of Internet-of-Things-enabled campus microgrids by installing solar photovoltaic panels on rooftops alongside energy storage systems that leverage second-life batteries, a gas-fired campus power plant, and a wind turbine while considering the potential loads of a prosumer microgrid. A linear optimization problem is derived from the system by scheduling energy exchanges with the Ontario grid through net metering and solved by using Python 3.11. The aim of this work is to support Sustainable Development Goals, namely 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action). A comparison between a base case scenario and the results achieved with the proposed scenarios shows a significant reduction in electricity cost and greenhouse gas emissions and an increase in self-consumption rate and renewable fraction. This research work provides valuable insights and guidelines to policymakers. Full article

The energy system of Ukraine needs to be decentralized, which aligns entirely with its intention to join the EU. The study focuses on regional peculiarities in establishing solar energy cooperatives and provides practical guidance on developing an energy cooperative in Ukraine. The article studies the different elements of electricity tariff composition for households, compares the existing support schemes (feed-in tariff and net metering), and defines which regions are the most suitable for establishing energy cooperatives (using solar installation). The primary methods employed are descriptive analysis, net present value analysis, and the integral assessment method, which collectively provide a comprehensive framework for evaluating both the economic viability and regional suitability of solar energy cooperatives. The findings indicate that the most suitable regions for solar energy cooperatives in Ukraine are located in the northeast and southwest of the country. The study highlights the importance of tailoring regional programs for energy cooperatives to enhance energy security and support the country’s low-carbon energy transition. The findings may be of interest and applicable in Ukraine and beyond. Full article

Electricity theft remains a pressing challenge in modern smart grid systems, leading to significant economic losses and compromised grid stability. This paper presents a sensor-driven framework for electricity theft detection that leverages data collected from smart meter sensors, key components in smart grid monitoring infrastructure. The proposed approach combines prototype learning and meta-level ensemble learning to develop a scalable and accurate detection model, capable of identifying zero-day attacks that are not present in the training data. Smart meter data is compressed using Principal Component Analysis (PCA) and K-means clustering to extract representative consumption patterns, i.e., prototypes, achieving a 92% reduction in dataset size while preserving critical anomaly-relevant features. These prototypes are then used to train base-level one-class classifiers, specifically the One-Class Support Vector Machine (OCSVM) and the Gaussian Mixture Model (GMM). The outputs of these classifiers are normalized and fused in a meta-OCSVM layer, which learns decision boundaries in the transformed score space. Experimental results using the Irish CER Smart Metering Project (SMP) dataset show that the proposed sensor-based detection framework achieves superior performance, with an accuracy of 88.45% and a false alarm rate of just 13.85%, while reducing training time by over 75%. By efficiently processing high-frequency smart meter sensor data, this model contributes to developing real-time and energy-efficient anomaly detection systems in smart grid environments. Full article

The water–energy nexus (WEN) is of great significance due to the strong interdependence between the energy and water sectors. Nevertheless, water leakage in water distribution networks (WDNs), which is often ignored in existing WEN operation models, causes notable water and energy losses. In this research, a cooperative operation model for WEN considering WDN water leakage is put forward. A hybrid stochastic–information gap decision theory (IGDT) method was tailored in this study to properly manage the probabilistic uncertainties associated with renewable generation, electrical and water demand in the WEN, and water leakage with limited information to enhance the robustness of the operation strategies of the WEN under complex operational conditions. The proposed model and method were validated on a modified IEEE 33-bus system integrated with a 15-node commercial WDN. The co-optimization model reduced the operational cost by 23.01% compared to the independent operation model. When considering water leakage, the joint optimization resolved the water supply shortage issue caused by ignoring leakage and reduced the water purchase volume by 94.44 cubic meters through coordinated optimization. These quantitative results strongly demonstrate the effectiveness of the proposed framework. Full article

Electricity consumption is increasing as a result of increasing people’s needs, such as lighting, heating, and comfort. Different needs come into play day by day in the houses where people live and in places used as common areas, and this increases the need for electricity. Studies have observed that almost half of the world’s electricity consumption is made by buildings. Public buildings, shopping malls, hospitals, and hotels are typical examples of such structures. However, hospitals have an important place among all building types as they contain a wide range of devices and are of critical importance to many systems. Consumption in hospitals is a necessity rather than a desire for comfort in places such as hotels and shopping malls. Therefore, analysis of the energy consumed by hospitals is one of the important things to perform to reduce the damage caused by electricity consumption to the environment. In this study, the energy analysis of a green hospital with an installed area of 55,000 square meters in Istanbul was conducted, and machine learning techniques can be used in the analysis. Among many methods used for building energy analysis, long short-term memory (LSTM) has been chosen. The available data set was analyzed with the various LSTM methods and classification and prediction operations were carried out. Error rates for each method were compared. With the results obtained, it has been observed that the vanilla LSTM method provides acceptable results in building energy analysis. Full article

Non-Intrusive Load Monitoring (NILM), a technique that extracts appliance-level energy consumption information through analysis of aggregated electrical measurements, has become essential for smart grids and energy management applications. Given the increasing diversification of electrical appliances, real-time NILM systems require continuous integration of knowledge from new client-side appliance data to maintain monitoring effectiveness. However, current methods face challenges with inter-client knowledge conflicts and catastrophic forgetting in distributed multi-client continual learning scenarios. This study addresses these challenges by proposing a confidence-based collaborative distributed continual learning framework for NILM. A lightweight layer-wise dual-supervised autoencoder (LWDSAE) model is initially designed for smart meter deployment, supporting both load identification and confidence-based collaboration tasks. Clients with learning capabilities generate new models through one-time fine-tuning, facilitating collaboration among client models and enhancing individual client load identification performance via a confidence judgment method based on signal reconstruction deviations. Furthermore, an anomaly sample detection-driven model portfolios update method is developed to assist each client in maintaining optimal local performance under model quantity constraints. Comprehensive evaluations on two public datasets and real-world applications demonstrate that the framework achieves sustained performance improvements in distributed continual learning scenarios, consistently outperforming state-of-the-art methods. Full article

The management of energy demand in systems lacking smart metering presents a significant challenge for electric distributors, primarily due to the absence of real-time data. This research assesses the efficacy of the K-Means algorithm when applied to the monthly billing records of 221,401 residential customers from Empresa Eléctrica Ambato Regional Centro Norte S.A. (EEASA) (Ecuador) over the period 2023–2024. The methodology encompassed data cleaning, Z-score normalization, and validation employing the Silhouette (0.55) and Davies–Bouldin (0.51) indices. Additionally, linear regression (LR) and Random Forest (RF) models were utilized to forecast demand, with the latter yielding an R² of 0.67. The findings delineated eight distinct clusters, facilitating the formulation of more representative rates, the identification of outliers through the interquartile range (IQR) method, and the enhancement of consumption estimation. It is concluded that this unsupervised segmentation approach constitutes a robust and cost-effective tool for energy planning in network environments devoid of smart infrastructure. Full article

This study describes the step-by-step development of a simplified system which can be implemented in industrial facilities with the help of their own surplus of plastic waste, mainly packaging waste, to reach a level of energy autonomy or semi-autonomy. This waste is converted to about 57,500 L of synthetic pyrolysis oil, which can then be used to power industries, being fed into a Combined Heat and Power system. To achieve this goal, the design has hydrocarbon stability at elevated temperature and restricted oxygen exposure, so that they can be converted to new products. Pyrolysis is a key process which causes thermo-chemical changes—ignition and vaporization. The research outlines the complete process of creating a basic small-scale pyrolysis system which industrial facilities can use to generate energy from their plastic waste. The proposed unit processes 360 tons of plastic waste yearly to produce 160 tons of synthetic pyrolysis oil which enables the generation of 500 MWh of electricity and 60 MWh of heat. The total investment cost is estimated at EUR 41,000, with potential annual revenue of up to EUR 45,000 via net metering. The conceptual design proves both environmental and economic viability by providing a workable method for decentralized waste-to-energy solutions for Small and Medium-sized Enterprises. Full article