Distributed generators
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The rising energy demand, substantial transmission and distribution losses, and inconsistent power quality in remote regions highlight the urgent need for innovative solutions to ensure a stable electricity supply. Microgrids (MGs), integrated with distributed generation (DG), offer a promising approach to address these challenges by enabling localized power generation, improved grid flexibility, and enhanced reliability. This paper introduces the Improved Lyrebird Optimization Algorithm (ILOA) for optimal sectionalizing and scheduling of multi-microgrid systems, aiming to minimize generation costs and active power losses while ensuring system reliability. To enhance search efficiency, ILOA incorporates the Levy Flight technique for local search, which introduces adaptive step sizes with long-distance jumps, improving the exploration-exploitation balance. Unlike conventional local search strategies that rely on fixed step sizes, Levy Flight prevents premature convergence by allowing the algorithm to escape local optima and explore the solution space more effectively. Additionally, a chaotic sine map is integrated to enhance global search capability, ensuring better diversity and superior optimization performance compared to traditional algorithms. Simulation studies are conducted on a modified 33-bus distribution system segmented into three independent microgrids. The algorithm is evaluated under single-objective scenarios (cost and loss minimization) and a multi-objective optimization framework combining both objectives. In single-objective optimization, ILOA achieves a generation cost of $19,254.64/hr with 0.7118 kW of power loss, demonstrating marginal improvements over the standard Lyrebird Optimization Algorithm and significant gains over Genetic Algorithm (GA) and Jaya Algorithm (JAYA). In multi-objective optimization, ILOA surpasses competing methods by achieving a generation cost of $89,792.18/hr and 10.26 kW of power loss. The optimization results indicate that, for the IEEE-33 bus system without considering EIR, the proposed ILOA algorithm achieves savings of approximately 0.0014%, 0.0041%, and 0.657% in operation costs compared to LOA, JAYA, and GA, respectively, when MG-1, MG-2, and MG-3 are operational. The analysis of real power loss reduction demonstrates that, in the IEEE-33 bus system without considering EIR, the proposed ILOA algorithm effectively minimizes power loss by approximately 0.692%, 1.696%, and 1.962% in comparison to LOA, JAYA, and GA, respectively, under the operational conditions of MG-1, MG-2, and MG-3. Additionally, reliability constraints based on the Energy Index of Reliability (EIR) are effectively incorporated, further validating the robustness of the proposed approach. Considering EIR, the real power loss analysis for the IEEE-33 bus system highlights that the proposed ILOA algorithm achieves a reduction of approximately 1.319%, 2.069%, and 2.134% in comparison to LOA, JAYA, and GA, respectively, under the operational scenario where MG-1, MG-2, and MG-3 are active. The results confirm that ILOA is a highly efficient and reliable solution for distributed generation scheduling and multi-microgrid sectionalizing, showcasing its potential for real-world applications such as dynamic economic dispatch and demand response integration in smart grid systems.
Cassava is a staple food in many countries, and this food source differs from other crops in that its processing generates a highly polluting and toxic residue (manipueira) that requires further treatment. The present study analyzed the economic feasibility of anaerobic digestion of manipueira for producing clean electricity through distributed generation (DG) while simultaneously eliminating toxic compounds. This eliminates the toxic residues. For this, an approach for the sizing of DG plants from manipueira biogas was presented, a non-trivial task which is not widespread in the literature. For two plants with different capacities, a deterministic economic analysis was carried out based on the criteria of Net Present Value, Internal Rate of Return, and Discounted Payback. Finally, the project risk was assessed through a sensitivity and stochastic analysis using Monte Carlo Simulation. The empirical verification was done on Brazilian data. When considering the NPV criterion, the results indicate a feasibility probability of 9.25% and 81.21% for scenarios 01 and 02, respectively. The results show that scale gains were important in reducing the impact of the investment and, at the same time, the larger scale of the project makes the cost of capital more relevant to the result. These findings show the need for subsidies for the investment, in addition to the promotion of specific credit lines that enable small-scale generation, or that can improve results in greater capacity.
- Klíčová slova
- Manipueira, discounted cashflow, distributed generation, financial viability, investment,
- MeSH
- biopaliva * MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Brazílie MeSH
- Názvy látek
- biopaliva * MeSH
The distributed long-range sensing system, using the standard telecommunication single-mode optical fiber for the distributed sensing of mechanical vibrations, is described. Various events generating vibrations, such as a walking or running person, moving car, train, and many other vibration sources, can be detected, localized, and classified. The sensor is based on phase-sensitive optical time-domain reflectometry (ϕ-OTDR). Related sensing system components were designed and constructed, and the system was tested both in the laboratory and in the real deployment, with an 88 km telecom optical link, and the results are presented in this paper. A two-fiber sensor unit, with a double-sensing range was also designed, and its scheme is described. The unit was constructed and the initial measurement results are presented.
- Klíčová slova
- distributed fiber optic sensor, mechanical vibrations, vibration sensor, ϕ-OTDR,
- MeSH
- design vybavení MeSH
- lidé MeSH
- optická vlákna MeSH
- technologie optických vláken * MeSH
- vibrace * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.
- Klíčová slova
- Epilepsy, deep brain stimulation, distributed computing, implantable devices, seizure detection, seizure prediction,
- Publikační typ
- časopisecké články MeSH
In recent years, advancements in air quality monitoring have been driven by the development of various sensor technologies, each with distinct advantages and limitations. Among these, polymer-based Distributed Bragg Reflectors (DBRs) have garnered significant interest for use in cost-effective, portable colorimetric sensors for detecting volatile organic compounds (VOCs). However, a key challenge in the fabrication of polymer-based DBRs lies in achieving an adequate refractive index contrast between the individual polymer layers. In this work, we fabricate plasmonic DBR sensors by a combination of low-temperature plasma-based techniques with reduced environmental footprint, investigate their potential as VOC sensors, and propose an optical model that links the sensors' optical properties and microstructure. Plasmonic nanoparticles of silver (Ag) are synthesized by gas aggregation and embedded into thermally evaporated poly(lactic acid) (PLA) layers to create nanocomposites with an enhanced refractive index (∼2.0). A 6-bilayer plasmonic DBR sensor is then produced by alternating depositions of plain PLA and nanocomposite layers as low and high refractive index materials, respectively. The resulting DBR achieves a 77% reflectance at 570 nm. The potential use-case of such a DBR as a VOC sensor is highlighted by its optical response upon exposure to ethanol (a model VOC) vapors as well as other VOCs (water, propanol, acetone, hexane). In an ethanol atmosphere, swelling of the polymer layers occurs, resulting in a red-shift of the reflection peak to 640 nm and a change in the DBR color. We take advantage of a generalized Maxwell-Garnett approach to create an advanced model that accurately reproduces the DBR spectra and captures swelling and degradation by accounting for structural changes and the behavior of isolated and coalesced Ag NPs within individual layers. Despite a decrease in the sensing performance with the number of swelling cycles, these plasmonic DBRs offer a promising solution for low-cost real-time VOC sensing.
- Klíčová slova
- VOC sensing, distributed Bragg reflector, gas aggregation source, nanocomposites, plasmonic nanoparticles,
- Publikační typ
- časopisecké články MeSH
The distributed nature of modern research emphasizes the importance of collecting and sharing the history of digital and physical material, to improve the reproducibility of experiments and the quality and reusability of results. Yet, the application of the current methodologies to record provenance information is largely scattered, leading to silos of provenance information at different granularities. To tackle this fragmentation, we developed the Common Provenance Model, a set of guidelines for the generation of interoperable provenance information, and to allow the reconstruction and the navigation of a continuous provenance chain. This work presents the first version of the model, available online, based on the W3C PROV Data Model and the Provenance Composition pattern.
- Klíčová slova
- Common Provenance Model, Provenance Composition, Provenance information, W3C PROV, distributed processes,
- MeSH
- biologické vědy * MeSH
- reprodukovatelnost výsledků MeSH
- Publikační typ
- časopisecké články MeSH
The optimal siting and sizing of DGs are vital for the efficient operation of both radial and microgrid distribution systems. From an operational perspective, minimizing real power loss, reducing voltage deviation, and improving voltage stability index are the three primary objectives considered in this study. This manuscript addresses these issues by proposing a novel quasi-oppositional forensic-based investigation (QOFBI) algorithm, an evolutionary meta-optimization technique designed to optimize the location and sizing of DGs under various operating conditions, while adhering to system constraints. The approach introduces a weighting factor-based multiobjective formulation, where optimal weighting factors are computed dynamically. This ensures a balanced approach to minimizing power loss, voltage deviation, and enhancing voltage stability. Extensive simulations were conducted on the IEEE 33-bus and IEEE 69-bus standard distribution systems, evaluating the impact of DG placement with varying power factors under operational constraints. The results demonstrate the superiority of the proposed approach in terms of faster convergence, reduced complexity, and improved performance compared to existing optimization methods. The QOFBI algorithm achieves a 94.44% reduction in active power loss, highlighting its robust performance across different operational scenarios. These findings underscore the potential of QOFBI as a highly effective tool for DG optimization in modern distribution systems, offering both operational efficiency and system reliability.
The article presents a synthesis method to design electrical circuit elements with fractional-order impedance, referred to as a Fractional-Order Element (FOE) or Fractor, that can be implemented by Metal-Oxide-Semiconductor (MOS) transistors. This provides an approach to realize this class of device using current integrated circuit manufacturing technologies. For this synthesis MOS transistors are treated as uniform distributed resistive-capacitive layer structures. The synthesis approach adopts a genetic algorithm to generate the MOS structures interconnections and dimensions to realize an FOE with user-defined constant input admittance phase, allowed ripple deviations, and target frequency range. A graphical user interface for the synthesis process is presented to support its wider adoption. We synthetized and present FOEs with admittance phase from 5 to 85 degrees. The design approach is validated using Cadence post-layout simulations of an FOE design with admittance phase of 74 ± 1 degrees realized using native n-channel MOS devices in TSMC 65 nm technology. Overall, the post-layout simulations demonstrate magnitude and phase errors less than 0.5% and 0.1 degrees, respectively, compared to the synthesis expected values in the frequency band from 1 kHz to 10 MHz. This supports that the design approach is appropriate for the future fabrication and validation of FOEs using this process technology.
- Klíčová slova
- Distributed element, Fractional-order element, Fractor, Genetic algorithm, MOS transistor,
- Publikační typ
- časopisecké články MeSH
Reduction of fossil fuel usage, clean energy supply, and dependability are all major benefits of integrating distributed energy resources (DER) with electrical utility grid (UG). Nevertheless, there are difficulties with this integration, most notably accidental islanding that puts worker and equipment safety at risk. Islanding detection methods (IDMs) play a critical role in resolving this problem. All IDMs are thoroughly evaluated in this work, which divides them into two categories: local approaches that rely on distributed generation (DG) side monitoring and remote approaches that make use of communication infrastructure. The study offers a comparative evaluation to help choose the most efficient and applicable IDM, supporting well-informed decision-making for the safe and dependable operation of distributed energy systems within electrical distribution networks. IDMs are evaluated based on NDZ outcomes, detection duration, power quality impact, multi-DG operation, suitability, X/R ratio reliance, and efficient functioning.
- Klíčová slova
- Artificial neural network, Distributed generation, Islanding detection, Microgrid, Non-detection zone, Renewable energy, Signal processing,
- Publikační typ
- časopisecké články MeSH
The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.
- Klíčová slova
- Artificial intelligence, Cognitive science, Distributed generation, Energy management, Microgrid, Optimization, Predictive modeling, Renewable energy, Support vector regression,
- Publikační typ
- časopisecké články MeSH
