Search Results (249)

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

Virtual power plants (VPPs) coordinate distributed energy resources (DERs) to collectively meet grid dispatch instructions. When a dispatch command is issued to a VPP, it must be disaggregated optimally among the individual DERs to minimize overall operational costs. However, existing methods for VPP dispatch instruction disaggregation often require solving complex optimization problems for each instruction, posing challenges for real-time applications. To address this issue, we propose a multi-parametric programming-based method that yields an explicit mapping from any given dispatch instruction to an optimal DER-level deployment strategy. In our approach, a parametric optimization model is formulated to minimize the dispatch cost subject to DER operational constraints. By applying Karush–Kuhn–Tucker (KKT) conditions and recursively partitioning the DERs’ adjustable capacity space into critical regions, we derive analytical expressions that directly map dispatch instructions to their corresponding resource allocation strategies and optimal scheduling costs. This explicit solution eliminates the need to repeatedly solve the optimization problem for each new instruction, enabling fast real-time dispatch decisions. Case study results verify that the proposed method effectively achieves the cost-efficient and computationally efficient disaggregation of dispatch signals in a VPP, thereby improving its operational performance. Full article

Advanced Non-Intrusive Load Monitoring (NILM) research is important to help reduce energy consumption. Very-low-frequency approaches have traditionally faced challenges in separating appliance uses due to low discriminative information. The richer signatures available in high-frequency electrical data include many harmonic orders that have the potential to advance disaggregation. This has been explored to some extent, but not comprehensively due to a lack of an appropriate public dataset. This paper presents the development of a cost-effective energy monitoring system scalable for multiple entries while producing detailed measurements. We will detail our approach to creating a NILM dataset comprising both aggregate loads and individual appliance measurements, all while ensuring that the dataset is reproducible and accessible. Ultimately, the dataset can be used to validate NILM, and we show through the use of machine learning techniques that high-frequency features improve disaggregation accuracy when compared with traditional methods. This work addresses a critical gap in NILM research by detailing the design and implementation of a data acquisition system capable of generating rich and structured datasets that support precise energy consumption analysis and prepare the essential materials for advanced, real-time energy disaggregation and smart energy management applications. Full article

Load disaggregation has emerged as an effective tool for enabling smarter energy management in residential and commercial buildings. By providing appliance-level energy consumption estimation from aggregate data, it supports energy efficiency initiatives, demand-side management, and user awareness. However, several challenges remain in improving the accuracy of energy disaggregation methods. For instance, the amount of noise in energy consumption datasets can heavily impact the accuracy of disaggregation algorithms, especially for low-power consumption appliances. While disaggregation performance depends on hyperparameter tuning, the influence of data characteristics, such as noise, on hyperparameter selection remains underexplored. This work investigates the hypothesis that appliance-specific noise information can guide the selection of algorithm hyperparameters, like the input sequence length, to maximize disaggregation accuracy. The appliance-to-noise ratio metric is used to quantify the noise level relative to each appliance’s energy consumption. Then, the selection of the input sequence length hyperparameter is investigated for each case by inspecting disaggregation performance. The results indicate that the noise metric provides valuable guidance for selecting the input sequence length, particularly for user-dependent appliances with more unpredictable usage patterns, such as washing machines and electric kettles. Full article

Climate change and global warming are causing growing environmental concerns, prompting many countries to increase investments in renewable energies. The high growth rate of renewables in the energy systems brings significant intermittency challenges. Demand-side flexibility is presented as a viable solution to address this phenomenon. In this framework, this research study proposes a novel methodology to evaluate the flexibility potential that residential consumers can offer to the Distribution System Operator (DSO). Moreover, it pretends to provide guidelines and design of standardized parameters to disaggregate the aggregated energy consumption data of end-users. This step is essential to identify and characterize the primary energy consumption processes in the residential sector, laying the groundwork for future flexibility evaluation. Furthermore, the modeling of the energy consumption curves will enhance residential sector demand-side flexibility enabling end-users to modify their usual consumption patterns. The implemented methodology has been applied to real consumer data provided by the DSO of a Spanish municipality of about 29,000 habitants in the Alicante Province (Spain). Results achieved allowed the validation of the proposed methodology enabling the disaggregation of residential energy profiles and facilitating the subsequent dynamic assessment of residential end-user’s demand flexibility. Moreover, this work will provide valuable guidelines to carry out short-term energy resource planning and solve operational problems of the energy systems. Full article

If new technologies can increase production efficiency and reduce the consumption of natural resources, they can also bring new environmental risks. This dynamic is particularly relevant for the automotive industry, since it is one of the sectors that invests most in R&D, but at the same time also contributes a significant portion of greenhouse gas emissions and consumes a large amount of energy. This article aims to analyze the feasibility of meeting the environmental targets in place within 32 European countries in light of the recent technological trajectory of the automotive industry, namely with regard to the adoption of the propulsion model’s alternative to oil and diesel. Using data disaggregated by countries from 2000 up until 2020, in this paper, the estimated regressions aimed to not only verify whether electrical vehicles had a positive impact on CO₂ emissions found in the European market, but to also assess whether they will meet the target set for the next 30 years, with attention to the economy recovery after 2025 and a more robust EV market penetration in replacement of traditional fossil fuels cars. Full article

The increasing need for efficient energy consumption monitoring, driven by economic and environmental concerns, has made Non-Intrusive Load Monitoring (NILM) a cost-effective alternative to traditional measurement methods. Despite its progress since the 1980s, NILM still lacks standardized benchmarks, limiting objective performance comparisons. This study introduces several key contributions: (1) the development of five new converters with 13-digit timestamp support and harmonic inclusion, improving the data collection accuracy by up to 25%; (2) the implementation of an advanced disaggregation software, achieving a 10–15% increase in the F1-score for certain appliances; (3) a detailed analysis of harmonics’ impact on NILM, reducing the Mean Normalized Error in Assigned Power by up to 40%; and (4) the design of open-source measurement hardware to enhance reproducibility. This study also evaluates open hardware platforms and compares five common household appliances using NILM Toolkit metrics. Results demonstrate that open hardware and software foster reproducibility and accelerate innovation in NILM. The proposed approach contributes to a standardized and scalable NILM framework, facilitating real-world applications in energy management and smart grid optimization. Full article

This study investigates how various renewable energy technologies influence national carbon intensity (CO₂ emissions per unit of GDP) across 184 countries over the period 2000–2020. In the context of Sustainable Development Goals (SDG 7 and SDG 13) and the post-Paris-Agreement policy landscape, it addresses the gap in understanding technology-specific decarbonization effects and the role of governance. A dynamic panel framework employing the Dynamic Common Correlated Effects (DCCE) estimator accounts for cross-sectional dependence and temporal persistence, while disaggregating total renewables into hydropower, wind, solar, and geothermal generation. Environmental regulation is incorporated as a moderating variable using the World Bank’s Regulatory Quality index. Empirical results demonstrate that higher renewable generation is associated with statistically significant reductions in carbon intensity, with hydropower showing the most consistent negative effect across all income groups. Solar and geothermal technologies yield substantial carbon-reducing impacts in lower-middle-income settings once supportive policies are in place. Wind exhibits heterogeneous outcomes: positive or insignificant effects in some high- and upper-middle-income panels prior to 2015, shifting toward neutral or negative after more stringent regulation. Interaction terms reveal that stronger regulatory environments amplify renewable-driven decarbonization, particularly for intermittent sources such as wind and solar. Key contributions include (1) a comprehensive global assessment of four disaggregated renewable technologies; (2) integration of regulatory quality into decarbonization pathways, illustrating post-2015 policy moderations; and (3) methodological advancement through a large-sample DCCE approach that captures unobserved common shocks and heterogeneous country dynamics. These findings inform targeted policy measures—such as prioritizing hydropower where feasible, strengthening regulatory frameworks, and tailoring technology strategies—to accelerate low-carbon energy transitions worldwide. Full article

Non-intrusive load monitoring (NILM) enables the disaggregation of appliance-level energy consumption from aggregate electrical signals, offering a scalable solution for improving efficiency. This study compared the performance of traditional NILM algorithms (Mean, CO, Hart85, FHMM) and deep neural network-based approaches (DAE, RNN, Seq2Point, Seq2Seq, WindowGRU) under various experimental conditions. Factors such as sampling rate, harmonic content, and the application of power filters were analyzed. A key aspect of the evaluation was the difference in testing conditions: while traditional algorithms were evaluated under multiple experimental configurations, deep learning models, due to their extremely high computational cost, were analyzed exclusively under a specific configuration consisting of a 1-s sampling rate, with harmonic content present and without applying power filters. The results confirm that no universally superior algorithm exists, and performance varies depending on the type of appliance and signal conditions. Traditional algorithms are faster and more computationally efficient, making them more suitable for scenarios with limited resources or rapid response requirements. However, significantly more computationally expensive deep learning models showed higher average accuracy (MAE, RMSE, NDE) and event detection capability (F1-SCORE) in the specific configuration in which they were evaluated. These models excel in detailed signal reconstruction and handling harmonics without requiring filtering in this configuration. The selection of the optimal NILM algorithm for real-world applications must consider a balance between desired accuracy, load types, electrical signal characteristics, and crucially, the limitations of available computational resources. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

The Open Radio Access Network (O-RAN) paradigm promises unprecedented flexibility and cost efficiency for 6G networks but introduces critical security risks due to its disaggregated, AI-driven architecture. This paper proposes a secure optimization framework integrating zero-trust principles and privacy-preserving Federated Learning (FL) to address vulnerabilities in O-RAN’s RAN Intelligent Controllers (RICs) and xApps/rApps. We first establish a novel threat model targeting O-RAN’s optimization processes, highlighting risks such as adversarial Machine Learning (ML) attacks on resource allocation models and compromised third-party applications. To mitigate these, we design a Zero-Trust Architecture (ZTA) enforcing continuous authentication and micro-segmentation for RIC components, coupled with an FL framework that enables collaborative ML training across operators without exposing raw network data. A differential privacy mechanism is applied to global model updates to prevent inference attacks. We validate our framework using the DAWN Dataset (5G/6G traffic traces with slicing configurations) and the OpenRAN Gym Dataset (O-RAN-compliant resource utilization metrics) to simulate energy efficiency optimization under adversarial conditions. A dynamic DU sleep scheduling case study demonstrates 32% energy savings with <5% latency degradation, even when data poisoning attacks compromise 15% of the FL participants. Comparative analysis shows that our ZTA reduces unauthorized RIC access attempts by 89% compared to conventional O-RAN security baselines. This work bridges the gap between performance optimization and trustworthiness in next-generation O-RAN, offering actionable insights for 6G standardization. Full article

With global efforts intensifying towards achieving carbon neutrality, accurately monitoring and managing energy consumption in industrial sectors has become critical. Non-Intrusive Load Monitoring (NILM) technology presents a cost-effective solution for industrial energy management by decomposing aggregate power data into individual device-level information without extensive hardware requirements. However, existing NILM methods primarily tailored for residential applications struggle to capture complex inter-device correlations and production-dependent load dynamics prevalent in industrial environments, such as cement plants. This paper proposes a novel sequence-to-sequence-based non-intrusive load disaggregation method that integrates Convolutional Neural Networks (CNN) and Transformer architectures, specifically addressing the challenges of multi-device load disaggregation in industrial settings. An innovative time–application attention mechanism was integrated to effectively model long-term temporal dependencies and the collaborative operational relationships between industrial devices. Additionally, global constraints—including consistency, smoothness, and sparsity—were introduced into the loss function to ensure power conservation, reduce noise, and achieve precise zero-power predictions for inactive equipment. The proposed method was validated on real-world power consumption data collected from a cement production facility. Experimental results indicate that the proposed method significantly outperforms traditional NILM approaches with average improvements of 4.98%, 3.70%, and 4.38% in terms of accuracy, recall, and F1-score, respectively. These findings underscore its superior robustness in noisy conditions and under device fault conditions, further affirming its applicability and potential for deployment in industrial settings. Full article

The use of load-monitoring systems in residential homes is fundamental in the context of smart homes and smart grids. Specifically, these systems will allow, for example, the provision of efficient energy management and/or load forecasting for residential homes. To achieve this goal, these systems can be based on the concept of a smart meter. However, a smart meter provides aggregate power consumption, which makes it extremely complex to identify individual home appliances, even using advanced algorithms. In line with this, this paper proposes to analyze the impact of power definitions on the disaggregation of home appliance loads. Moreover, it will also consider the distortion of the voltage grid, which is usually not addressed in the resolution of this problem. This effect will be verified through an approach that is based on a genetic algorithm. The approach will be tested through the use of several scenarios, in which an aggregation of home appliances is used. Full article

This research seeks to elucidate the relationship between economic activities, energy consumption, and CO₂ emissions, thereby contributing to a deeper understanding of the economic dimensions of climate change mitigation efforts within the European context, which may be useful for developing policies to mitigate CO₂ emissions and promote sustainable development. This study investigates world CO₂ emissions and their relation to population growth and finds a strong positive relation based on data from 1969 to 2023. The World Kaya Identity has been applied to understand how changes in the involved factors affect CO₂ emissions over time. When studying the more complex relation between the variables by controlling for energy use, GDP, and carbon intensity based on the Kaya Identity, the authors identified an overall long-term coupling of all factors. Considering short-term variations, population growth appears to have an insignificant effect, and carbon intensity appears most influential on CO₂ emissions. As a next step, we take a disaggregated view on different country settings, economic sectors, and energy sources to further analyze the role carbon intensity plays for increased CO₂ emissions. Here, we lay a special focus on the European perspective. This descriptive analysis lets us draw some general conclusions regarding strategies for reducing the negative impact of CO₂ emissions and political efforts for sustainability transformations. This study is important for the current state of science, since a clear economic assessment of the negative effects of carbon dioxide is necessary for planning measures and costs in the ecological sphere, the correct assessment of the impact on the health of the population, the prospective implementation of preventive measures at all levels, and financing measures to reduce the negative effects of carbon dioxide. The authors found a significant positive effect of GDPpc, energy intensity, and carbon intensity on impact and an insignificant effect on the population. Thus, an unexpected increase in the population likely does not have short-term effects on CO₂ emissions, and the responses to GDPpc and energy intensity both decrease after some periods, while the shock in carbon intensity shows a significant effect even after 10 years. This is reasonable in the sense that both increases in GDP and energy intensity might be alleviated by technological progress and, thus, only show a short-term positive effect on CO₂ emissions. The carbon intensity of energy consumption is more crucial for the long-term change of CO₂ emissions. For this reason, we study the decomposition of energy use in more detail by considering descriptive statistics over time and over different sectors and countries. Full article

Disaggregated data on electricity generation from bioenergy are very helpful for investigating the economic and technical effects of this form of renewable energy on the German power sector with a high temporal and spatial resolution. But the lack of high-resolution feed-in data for Germany makes it necessary to apply numerical simulations to determine the electricity generation from biomass power plants for a time period and geographic region of interest. This article presents how such a simulation model can be developed using public power plant data as well as open information from German TSOs as input data. The physical model is applied to an ensemble of 20,863 biomass power plants, most of which are in continuous operation, to simulate their electricity generation in Germany for the year 2020. For this period, the spatially aggregated simulation results correlate well with the official electricity feed-in from bioenergy. The disaggregated time series can be used to analyze the electricity generation at any spatial scale, as each power plant is simulated with its technical parameters and geographical location. Furthermore, this article introduces the electricity generation landscape of bioenergy as a high-resolution map and at the federal state level with meaningful energy figures, enabling comprehensive assessments of this form of renewable energy for different regions of Germany. Full article