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keyboard_arrow_downTitleAbstractIntroductionΑ-Cut MethodSimulation ResultsConclusionReferencesAll TopicsEngineeringEnergy Engineering and Power TechnologyDownload Free PDF
Download Free PDFA possibilistic-probabilistic tool for evaluating the impact of stochastic renewable and controllable power generation on energy losses in distribution networks--A case study2011, Renewable and Sustainable Energy Reviews
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This paper proposes a hybrid possibilistic-probabilistic evaluation tool for analyzing the effect of uncertain power production of distributed generations (DG) on active losses of distribution networks. The considered DG technologies are Gas and wind turbines.
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ON THE ASSESSMENT AND MANAGEMENT OF RISK IN WIND FARM DISTRIBUTION SYSTEMSJorge MartinezAssessing and managing the risk associated with the performance of the distribution network of renewable energy projects by the exclusive means of probabilistic methods could lead to suboptimal decisions and significant risk exposure. Such probabilistic methods include, for example, those employed in the calculation of classical availability/reliability related performance indicators (such as SAIDI and SAIFI). This situation may be particularly crucial when predicting the availability of the distribution system associated with the Electrical Balance of Plant (EBoP) of wind farms. The concepts and results associated with a real-case system presented in this paper point out that risk assessments for distribution networks of renewable generation (using indicators such as Guaranteed Network Availability or calculations of Expected Generated Energy not Transferred onto the Grid, EGENT) could be underestimated when probabilistic methods that are more suitable to large-scale power system, are applied on the " small scale " distribution and grid access systems of wind farms.
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Combined Monte Carlo simulation (MCS) and market-based optimal power flow (OPF) considering different combinations of wind generation and load demand over a year are used to evaluate wind turbines (WTs) integration into distribution systems. MCS is used to model the uncertainties related to the stochastic variations of wind power generation and load demand while the social welfare is maximized by means of market-based OPF with inter-temporal constraints. The proposed probabilistic methodology allows evaluating the amount of wind power that can be injected into the grid as well as the impact of wind power penetration on the social welfare and on distribution-locational marginal prices. Market-based OPF is solved by using step-controlled primal dual interior point method considering network constraints. The effectiveness of the proposed probabilistic method in assessing the impact of wind generation penetration in terms of both technical and economic effects is demonstrated with an 84-bus 11.4-kV radial distribution system.
downloadDownload free PDFView PDFchevron_rightAnalysis of the Uncertainty Effect in Power System Losses: Uncertainties of Renewable Energy and Loadİbrahim Çağrı BARUTÇUEuropean Journal of Science and Technology, 2022
The energy loss minimization problem is increasingly gaining prominence as a result of widespread integration of renewable energy sources into the power systems. Thus, the optimal planning of power system is required to handle the technical issues due to the uncertainties in load demands and the intermittent characteristics in photovoltaic (PV) and wind turbine (WT) systems. In this paper, the impacts of various uncertainty scenarios have been considered while mitigating the total energy losses in the power network, where PV and WT systems are installed. Particle Swarm Optimization (PSO) algorithm has been implemented to determine the optimal values of control variables while taking into account the power system technical constraints. The influences of different uncertainty scenarios have been considered while alleviating total energy losses in the implementation of planning.
downloadDownload free PDFView PDFchevron_rightFuzzy based prediction of wind distributed generation impact on distribution network: Case study—Banat region, Serbiamileta zarkovicJournal of Renewable and Sustainable Energy, 2014
The paper proposes the methodology for the assessment of impact that wind distributed generation can have on distribution network. Effects of active power losses, voltage drops, and voltage total harmonic distortion are considered. The methodology uses fuzzy logic in order to address uncertainties in wind energy generation, as well as artificial neural networks for wind speed assessment. Measurement data of temperature, irradiation, and wind direction at different wind turbine hub heights are used. The predicted wind speed, active power loss variation, and elasticity of power quality constraints are presented in a form of fuzzy numbers. The proposed methodology is tested on realistic, 28-bus 35 kV voltage distribution system in the Banat region in Serbia. A steady-state voltage stability index is applied to select wind distribution generation locations. It is shown that wind distributed generation can improve voltage regulation and reduce active power losses. However, increase in vo...
downloadDownload free PDFView PDFchevron_rightA Probabilistic Model for Power Generation Adequacy EvaluationFlorin Munteanurevue.elth.pub.ro
The quality and availability required of electrical energy is directly related to the power system reliability concept. The reliability associated with a power system, in a general sense, is a measure of the overall ability of the system to generate and supply electrical energy. Due to the ...
downloadDownload free PDFView PDFchevron_rightOptimization Tools Adressing Fuzzy Uncertainty at Power FlowsEduardo Gouveia2012
Power flow studies use computational tools for the planning and operation of electrical power systems purposes. The deterministic model is the most commonly used load flow approach. In this model, the input data and the results are crisp values. Therefore, to account for uncertainties, the most common approach used is the definition of scenarios, which are characterized by crisp values. This is an impractical way to solve the problem of the uncertainty in the data. A more practical way to lead with the uncertainties is the use of probabilistic power flows. On such approach, the uncertainties are modelled through the use of probability density functions (pdf). However, that approach may be inappropriate, namely when there is no available historical data in order to construct the pdf. On such cases, the fuzzy power flows (FPF) is an interesting alternative. In this paper, a methodology named Symmetric Fuzzy Power Flow is used. That methodology uses optimization models to solve power f...
downloadDownload free PDFView PDFchevron_rightEnergy losses in a distribution line with distributed generation based on stochastic power flowAntonis MarinopoulosElectric Power Systems Research, 2011
This paper proposes a methodology for stochastic power flow in a distribution line with dispersed photovoltaic (PV) penetration. Both load and PV generation are stochastic processes. The methodology uses a probabilistic model for load demand based on measured data and an extensive stochastic modeling of PV units' power production based on historical meteorological data and commercial available PV panels. Annual power flow simulations are performed to evaluate loss reduction resulting for different penetration levels and siting strategies of PV into an urban radial distribution feeder. Finally, a cost index for the losses is defined taking into account System Marginal Price data. Results may be of interest for dimensioning, siting and cost allocation in distribution systems with dispersed generation.
downloadDownload free PDFView PDFchevron_rightReliability evaluation of distribution systems with renewable distributed generation and load variation modelsCarmen Borges2009
This paper presents a methodology for reliability evaluation of distribution systems with the presence of distributed generation (DG) based on different energy sources. The methodology considers the possibility of DG connection at the High Voltage (HV) and Medium Voltage (MV) buses of the distribution substation, as well as along the MV distribution network. The uncertainty of energy availability of time varying energy sources, such as the wind generation, and the chronological variation of the loads are included in the methodology. The network representation includes the main protection and reconfiguration devices, such as breakers, manual switches, automatic sectionalizers, reclosers and fuses. Some operative polices in relation to DG, such as islanded operation and protection schemes for network failures, as well as load shedding prioritization and load transfer between feeders, are analyzed.
downloadDownload free PDFView PDFchevron_rightA Probabilistic Framework for Active Distribution Network Optimal Energy Management Considering Correlated Uncertain VariablesJournal of Electrical and Computer Engineering Innovations2024
Background and Objectives: Distributed generations (DGs) based on renewable energy, such as PV units, are becoming more prevalent in distribution networks due to technical and environmental benefits. However, the intermittency and uncertainty of these sources lead to technical and operational challenges. Energy storage application, uncertainty analysis, and network reconfiguration are apt therapies to resist these challenges. Methods: Energy management of modern, smart, and renewable-penetrated distribution networks is tailored here considering the uncertainties correlations. Network operation costs including switching operations, the expected energy not served (EENS) index as the reliability objective, and the node voltage deviation suppression as the technical objective are mathematically modeled. Multiobjective particle swarm optimization (MOPSO) is considered as the optimization engine. Scenario generation method and Nataf transformation are used in probabilistic evaluations of the problem. Moreover, the technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) is deployed to make a final balance between different objectives to yield a unified solution. Results: To show the effectiveness of the proposed approach, the IEEE 33-node distribution network is put under extensive simulations. Different cases are simulated and interrogated to assess the performance of the proposed model. Conclusion: For different objectives dealing with different aspects of the network, remarkable achievements are attained. In brief, the final solution shows 4.50% decrease in operation cost, 13.07% improvement in reliability index, and 18.85% reduction in voltage deviation compared to the initial conditions.
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