The importance of probabilistic assessment in distribution systems is very high due to the increasing penetration of renewable energy sources (RESs) with their fluctuating behavior. Also, there are some other uncertai...
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The importance of probabilistic assessment in distribution systems is very high due to the increasing penetration of renewable energy sources (RESs) with their fluctuating behavior. Also, there are some other uncertain variables in distribution systems such as loads fluctuations. More awareness of the system state by considering many uncertainties provides more certainty in decision making and causes better risk management. Moreover, the distribution static compensator (DSTATCOM) has been recently implemented due to its efficient abilities in the distribution systems operation especially when RESs are integrated into them. This article investigates the probabilistic assessment of DSTATCOM operation in a distribution system using the k-means-based data clustering method (DCM). The uncertainty of load demands, wind speed, and solar radiation are considered in this study. The performance of DCM is compared to the Monte Carlo simulation (MCS) and Latin Hypercube sampling (LHS) methods in terms of accuracy and computational burden. The efficiency of k-means-based DCM is investigated in the IEEE 69-node test system.
Renewable energies have a significant portion in supplying energy demands in modern distribution networks. Due to the wide use of power electronic devices, these networks may have power quality problems. The unpredict...
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Renewable energies have a significant portion in supplying energy demands in modern distribution networks. Due to the wide use of power electronic devices, these networks may have power quality problems. The unpredictable nature of renewable energies, besides the effect of non-linear loads brings out serious planning and operating challenges for distribution systems. Basicly, harmonic distortion is a severe problem for both electric efficiency and power energy customers. This study proposes an optimal scheduling strategy for wind turbine's integrated distribution networks with non-linear loads using a multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm and the best solution is selected via the TOPSIS technique. In the proposed strategy, energy storage systems are optimally scheduled besides wind turbines, and reactive power compensators. Also, to use the distribution network more efficiently, an optimal network reconfiguration is applied. The wind turbine's output and load demands have probabilistic nature. The proposed scheme reduces the total harmonic distortion as well as total costs. The efficacy of the proposed management scheme is investigated using the IEEE standard 33 bus distribution network. Also, the performance of the multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm is compared with the multi-objective particle swarm optimization algorithm.
In a smart grid framework, relations between the system operator (SO) and terminal consumers will become interactive and demand-side response capacities can be integrated as dispatch-able resources. This paper propose...
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In a smart grid framework, relations between the system operator (SO) and terminal consumers will become interactive and demand-side response capacities can be integrated as dispatch-able resources. This paper proposes a systematic analysis on demand-side response mechanism in smart grid. A multi-agent (MA) system is established to describe interactive relations between the SO and different kinds of consumers. On this basis, a novel mechanism is proposed to reflect the process of interactive response, which consists of three schemes: dataclustering and release scheme, demand-side interactive response capability (DIRC) submission scheme, and submission correction scheme. Then, a standard data format is defined to formulate the submission of DIRC from basic consumers and a fuzzy-C-mean clusteringmethod is implemented to generate and release typical interactive response modes (IRM) for different kinds of consumers. Moreover, a correction method based on similarity identification is developed to modify submission of DIRC by taking into account deviations between historical submissions and real performances. Finally, a simulation case verifies the effectiveness and rationality of the proposed mechanism, models and methods. (C) 2012 Elsevier B.V. All rights reserved.
Renewable energy sources (RESs) such as wind turbines (WTs) and photovoltaic (PV) cells have become more widespread in recent years due to their superior technical, economical, and environmental benefits. These types ...
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Renewable energy sources (RESs) such as wind turbines (WTs) and photovoltaic (PV) cells have become more widespread in recent years due to their superior technical, economical, and environmental benefits. These types of energy sources are innately unpredictable, owing to the uncertain nature of their primary resources and bring about more challenges in the distribution networks. Moreover, there may be a high correlations between uncertain input variables which intensify the mentioned challenges. On the other hand, power electronic-based devices have had an effective role in improving the performance of distribution networks. Distribution network flexible AC transmission system (D-FACTS) devices such as distribution static compensator (D-STATCOM) can be efficiently used for making the modern distribution networks with high penetration of RESs more flexible. In this article, probabilistic assessment of the D-STATCOM operation is considered in the distribution networks, including RESs such as WTs and PV cells. For this purpose, the probabilistic extended forward-backward load flow with D-STATCOM modeling has been introduced. The uncertainties are considered in power demands, wind speed, and solar radiation. Also, the correlations between uncertain variables are taken into account. Probabilistic assessment for this problem is conducted by k-means based data clustering method for the first time. In addition to the comparison of the results with the Latin hypercube sampling, the results are validated with the Monte Carlo simulation method as a reference technique. The IEEE 33-node and 69-node test networks are used as case studies and show the effectiveness of the proposed method.
Flexible alternating-current transmission system (FACTS) devices as power electronic-based technologies have been developed to improve the performance of power systems. This paper proposes a probabilistic framework fo...
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Flexible alternating-current transmission system (FACTS) devices as power electronic-based technologies have been developed to improve the performance of power systems. This paper proposes a probabilistic framework for optimal allocation of unified power flow controller (UPFC) as one of the most beneficial FACTS devices to increase the reliability of the power system. For this purpose, the expected power not served (EPNS) is considered as a reliability index that is obtained based on the summation of required load shedding in all buses during single contingencies, including transmission lines and generators outage. Power demands and wind speed are considered as uncertain input variables. In addition, the correlations among these variables are included in the proposed study method. The well-known particle swarm optimization (PSO) algorithm is used to optimize the proposed objective function. Also, the firefly algorithm (FA) is implemented to determine the total amount of load shedding for the calculation of EPNS, considering each possible generated solution by the PSO. The k-means based data clustering method (DCM) is used for probabilistic evaluation of the problem, for the first time. Also, the correlations among uncertain input variables are modeled with the Cholesky decomposition method. Extraction of statistical information of UPFC parameters for improving power system reliability in a probabilistic framework is one of the most important achievements of the proposed method. This information is very important in deciding on the UPFC sizing. In order to evaluate the effectiveness of the proposed method, the IEEE 14-bus and 30-bus test systems have been used.
Gradual depletion of fossil fuel resources, poor energy efficiency, and environmental pollution problems have led to the use of renewable energy sources (RESs) such as wind turbines (WTs) and solar photovoltaic (PV) c...
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Gradual depletion of fossil fuel resources, poor energy efficiency, and environmental pollution problems have led to the use of renewable energy sources (RESs) such as wind turbines (WTs) and solar photovoltaic (PV) cells in distribution networks all around the world. The uncertain nature of these sources, along with network power demands, necessitates probabilistic evaluation to extract results with high applicability and efficiency. Distribution network flexible AC transmission system (D-FACTS) devices such as distribution static compensator (D-STATCOM) can be efficiently used for making the modern distribution networks with high penetration of RESs more flexible. This paper presents a probabilistic technique for optimal allocation of the D-STATCOM, considering the correlation between uncertain variables. The proposed solution method helps to mitigate expected active power losses, improve expected voltage deviation index (VDI), and decrease D-STATCOM expected installation cost for radial/mesh distribution networks. The k-means based data clustering method (DCM) and Latin hypercube sampling (LHS) method are used for probabilistic evaluation of distribution networks. In addition, the particle swarm optimization (PSO) algorithm is employed as the optimization tool. The proposed algorithm is applied to the IEEE 69 node test network, and the results are compared with the Monte Carlo simulation (MCS) method. Also, the efficacy of the proposed study method has been investigated for a real meshed distribution network.
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