Hasil untuk "Distribution or transmission of electric power"

Chengming Song, Ruirong Yang, Zhendong Cui

Qirui Zheng, Dan Wu, Franz-Erich Wolter et al.

The widespread adoption of renewable energy poses a challenge in maintaining a feasible operating point in highly variable scenarios. This paper demonstrates that, within a feasible region of a power system that meets practical stability requirements, the power flow equations define a smooth bijection between nodal voltage phasors (angle and magnitude) and nodal active/reactive power injections. Based on this theoretical foundation, this paper proposes a data-based power flow evaluation method that can imply the associated power flow manifold from a limited number of data points around a single operating point. Using techniques from differential geometry and analytic functions, we represent geodesic curves in the associated power flow manifold as analytic functions at the initial point. Then, a special algebraic structure of the power flow problem is revealed and applied to reduce the computation of all higher-order partial derivatives to that of the first-order ones. Integrating these techniques yields the proposed data-based evaluation method, suggesting that a small number of local measurements around a single operating point is sufficient to imply the entire associated power flow manifold. Numerical cases with arbitrary directional variations are tested, certifying the efficacy of the proposed method.

Tossaporn Surinkaew, Watcharakorn Pinthurat, Boonruang Marungsri et al.

Reconfiguration in low-inertia microgrids (MGs) can often result in a critical small-signal stability margin. In this condition, the ability of inverter-based resources (IBRs) to provide voltage and frequency support may be insufficient. To maintain stable operation without interruptions, this paper presents a control strategy that first evaluates the effect of MG reconfiguration on system stability and then employs controllable loads as an enhancement mechanism to improve small-signal stability in scenarios involving reconfigurable MGs, particularly during islanded operation or high-demand situations such as sudden load changes or fault recovery. Mathematical models of system reconfiguration are presented. Then, we demonstrate how reconfiguration in MGs can result in marginal small-signal stability. The proposed framework operates in two stages: (i) assessing optimal breaker/switch configurations to ensure a baseline stability margin, and (ii) using controllable loads to fine-tune and improve damping performance. It is shown that the proposed framework can shift stability from critical or unstable levels to an acceptable range, making the initial conditions of reconfigured MGs feasible. Simulation results in a reconfigurable MG with different portions of IBRs and controllable loads demonstrate the effectiveness of the proposed framework in using controllable loads to successfully enhance small-signal stability. The proposed strategy ensures that the reconfigured MGs remain stable after reconfigurations.

A. Jodaei, Z. Moravej, M. Pazoki

Dingrui Li, Yiwei Ma, Yu Su et al.

A microgrid (MG) may lose its grounding provided by the main distribution grid in islanded mode, which could cause equipment insulation damage, hazards to personnel, and protection malfunction. Existing MG grounding schemes include the grounding transformer-based scheme and distributed energy resource (DER) transformer-based scheme. However, the grounding transformer-based scheme will increase MG’s cost, and the DER transformer approach will affect the main grid in the grid-connected mode. Moreover, future MGs may have multiple source locations. In each source location, the source and critical load can potentially operate as a sub-MG, requiring a grounding when it stands alone. In this scenario, the drawbacks of existing grounding schemes will be further magnified. In this paper, a novel controllable DER transformer-based grounding scheme is proposed, where a controllable switch is added to the neutral wire of the transformer. The proposed scheme can disable grounding capability in the grid-connected mode and enable it in the islanded mode by changing the transformer connection. The proposed approach can avoid impacts on the main distribution grid and eliminate the need for additional transformers. The design methodology of the proposed grounding scheme is provided. Simulation verification is conducted on a realistic MG model and experimental verification is conducted.

Arun Suresh, Sumit Paudyal, Sukumar Kamalasadan

Mohamed Ibrahim Abdelaziz Shekeew, Bala Venkatesh

The security-constrained unit commitment (SCUC) challenge is solved repeatedly several times every day, for operations in a limited time. Typical mixed-integer linear programming (MILP) formulations are intertemporal in nature and have complex and discrete solution spaces that exponentially increase with system size. Improvements in the SCUC formulation and/or solution method that yield a faster solution hold immense economic value, as less time can be spent finding the best-known solution. Most machine learning (ML) methods in the literature either provide a warm start or convert the MILP-SCUC formulation to a continuous formulation, possibly leading to sub-optimality and/or infeasibility. In this paper, we propose a novel ML-based variables reduction method that accurately determines the optimal schedule for a subset of trusted generators, shrinking the MILP-SCUC formulation and dramatically reducing the search space. ML indicators sets are created to shrink the MILP-SCUC model, leading to improvement in the solution quality. Test results on IEEE systems with 14, 118, and 300 busses, the Ontario system, and Polish systems with 2383 and 3012 busses report significant reductions in solution times in the range of 48% to 98%. This is a promising tool for system operators to solve the MILP-SCUC with a lower optimality gap in a limited-time operation, leading to economic benefits.

Yuhan Du, Javad Mohammadi

The growing integration of distributed energy resources drives the centralized power system towards a decentralized multi-agent network. Operating multi-agent networks significantly relies on inter-agent communications. Computation granularity in this context refers to the number of nodes overseen by an agent. The impact of granularity on equitable power coordination, particularly among marginalized customers with limited communication bandwidth (e.g., intermittent internet connectivity) is not well studied. This work explores different levels of computation granularity for agent-based energy dispatch and studies their impact on equitable coordination. We will leverage and utilize the Consensus + Innovations approach to model the equitable coordination of a multi-agent power system.

Umar Jamil

Power losses in electrical power systems especially, distribution systems, occur due to several environmental and technical factors. Transmission & Distribution line losses are normally 17% and 50% respectively. These losses are due to the inappropriate size of the conductor, long distribution lines, low power factor, overloading of lines etc. The power losses cause economic loss and reduce the system's reliability. The reliability of electrical power systems can be improved by decreasing network power loss and by improving the voltage profile. In radial distribution systems, power loss can also be minimized through Distributed Generation (DG) system placement. In this thesis, three different grid strategies including real power sharing, reactive power injection and transformer tap changing are discussed and used to minimize line losses. These three proposed grid strategies have been implemented using a power flow study based on Newton-Raphson (NR) and Genetic Algorithm (GA). To minimize line losses, both methods have been used for each grid strategy. The used test system in this research work is the IEEE-30 bus radial distribution system. Results obtained after simulation of each grid strategy using NR and GA shows that real load sharing is reliable with respect to minimization of line loss as compared to reactive power injection and transformer tap changing grid strategy. Comparative analysis has been performed between GA and NR for each grid strategy, results show that Genetic Algorithm is more reliable and efficient for loss minimization as compared to Newton-Raphson. In the base case for optimum power flow solution using genetic algorithm and Newton-Raphson, real line losses are 9.481475MW and 17.557MW respectively. So, GA is preferable for each proposed grid strategy to minimize line losses than NR.

Stefan Geng, Thomas Schulte, Jürgen Maas

Plug-in hybrid electric vehicles (PHEVs) are developed to reduce fuel consumption and the emission of carbon dioxide. Common powertrain configurations of PHEVs (i.e., the configuration of the combustion engine, electric motor, and transmission) can be operated either in series, parallel, or power split hybrid mode, whereas powertrain configurations with multimode transmissions enable switching between those modes during vehicle operation. Hence, depending on the current operation state of the vehicle, the most appropriate mode in terms efficiency can be selected. This, however, requires an operating strategy, which controls the mode selection as well as the torque distribution between the combustion engine and electric motor with the aim of optimal battery depletion and minimal fuel consumption. A well-known approach is the equivalent consumption minimization strategy (ECMS). It can be applied by using optimizations based on a prediction of the future driving behavior. Since the outcome of the ECMS depends on the quality of this prediction, it is crucial to know how accurate the predictions must be in order to obtain acceptable results. In this contribution, various prediction methods and real-time capable ECMS implementations are analyzed and compared in terms of the achievable fuel economy. The basis for the analysis is a holistic model of a state-of-the-art PHEV powertrain configuration, comprising the multimode transmission, corresponding powertrain components, and representative real-world driving data.

Matthew Deakin, Thomas Morstyn, Dimitra Apostolopoulou et al.

Abstract Distributed Generators that use reactive power for voltage control in distribution networks reduce renewable curtailment but can significantly increase network losses, undermining the effectiveness of this control. This paper proposes Voltage Control Loss Factors (VCLFs) as a means of understanding the interactions between reactive power flows, losses and curtailment, focusing on commercial‐scale generators in radial systems. The metric uses a substitution‐based method, whereby a system with voltage control is compared against a counterfactual with no such control. The proposed method studies this metric by coupling numerically precise black‐box simulations with analytic results from a Two‐Bus network representation. The latter provides a physical explanation for the numerical simulation results in terms of power, voltage and impedance parameters, providing clear explainability which is absent in traditional approaches for determining distribution loss factors. The whole solution space of the Two‐Bus system is explored, and VCLFs are calculated for six cases on three unbalanced test networks to illustrate the approach. Relative losses as high as 30% are found in a system with high branch resistance‐reactance ratio and large voltage rise. The results have implications for the design of loss allocation algorithms in distribution networks, and the optimal sizing of power‐electronic interfaced Distributed Generators.

Salman Sanaei, Mahmud‐Reza Haghifam, Amir Safdarian

Abstract The distribution system's economic operation is significantly impacted by the management of distributed generation (DG) resources, energy storage (ES), and controllable loads. The paper employs a smart distribution system that incorporates dispatchable and non‐dispatchable DG resources, as well as battery storage, in addition to the demand response (DR) scheme. New modelling was performed in hourly steps to achieve the optimal unit commitment. In smart homes, appliances are prioritized and classified into four types: adjustable, interruptible, shiftable, and uncontrollable loads. Load reduction in smart homes is also considered based on load prioritization and customer participation in the DR scheme to achieve the proposed scheme's objectives. The proposed method considers costs associated with microturbines (MTs), including manufacturing, start‐up, shutdown, and pollution. Additionally, planning is conducted to purchase or sell electricity to the upstream network. The simulation is run on an IEEE 33‐bus system to demonstrate the proposed method's effectiveness. The system is assumed to be capable of operation in both island and grid‐connected modes. The results demonstrate the proposed approach's efficacy in load reduction, operation cost, and execution time.

Amir Hossein Ataee‐Kachoee, Hamed Hashemi‐Dezaki, Abbas Ketabi

Abstract Much attention has been paid to optimizing smart grids (SGs) and microgrids (MGs) protection schemes. The SGs’ operation in different operating modes (especially grid‐connected and islanded conditions) and various system configurations (such as the outage of each of the distribution generations) adversely influence the protection system. The adaptive protection schemes using different setting groups are suitable and reliable solutions to achieve a fast protective system. However, the literature shows a research gap in developing optimized adaptive protection schemes, focusing on constraint reduction, besides the optimal selection of time‐current characteristics for direction overcurrent relays (DOCRs) and high‐set relays (HSRs). This research aims to fill such a research gap. The power system analyses, such as power flow and short circuit studies, are done in DIgSILENT, and the genetic algorithm (GA) is used to find the optimum solutions. Test results of the IEEE 38‐bus distribution system illustrate the advantages of this study compared to existing ones. The comparative test results emphasize that 31.78% and 21.62% decrement in time of the protective scheme in different topologies for the distribution networks of the IEEE 38‐bus and IEEE 14‐bus test systems could be achievable by simultaneously optimizing relay characteristics and HSRs compared to existing approaches.

Hesam Mazaheri, Moein Moeini‐Aghtaie, Mahmud Fotuhi‐Firuzabad et al.

Abstract The concept of flexibility is defined as the power systems’ ability to effectively respond to changes in power generation and demand profiles to maintain the supply–demand balance. However, the inherent flexibility margins required for successful operation have been recently challenged by the unprecedented arrival of uncertainties, driven by constantly changing demand, failure of conventional units, and the intermittent outputs of renewable energy sources (RES). Tackling these challenges, energy storage systems (ESS) as one important player of the new power grids can enhance the system flexibility. It, therefore, calls for an efficient planning procedure to ensure flexibility margins by considering ESS's role in modern power systems. This paper proposes a novel mixed integer linear programming (MILP) model for transmission expansion planning (TEP) framework taking into account the role of compressed air energy storage (CAES) integration on improvements in system flexibility. The proposed framework is housed with a quantitative metric of grid‐scale system flexibility, while a new offline repetitive mechanism is suggested to account for the N − 1 reliability criterion. The model is applied to different test systems, where the numerical results demonstrate the impacts of CAES units on system flexibility, investment plans, and the total costs.

Peng Xu, Beibei Wang, Yue Zhang et al.

Abstract The increasing use of distributed generation (DG) in power systems can result in frequent online voltage problems. In scenarios in which substantial DG prediction errors occur because of high DG accommodation levels, traditional technical solutions cannot meet the online voltage regulation requirements. Hence, new resources for online voltage regulation are needed. Here, flexible network reconfiguration is proposed to coordinate with the existing resources for severe online voltage deviations. For online topology‐based voltage regulation (OTVR), the authors develop a deep reinforcement learning (DRL) algorithm based on the following specially designed modelling to enhance the computational performance. The mechanism of action incorporates the concepts of local research, branch exchange, and action separation, and it effectively simplifies the action dimension and action space. In addition, for the graph data in OTVR, a graph convolution network (GCN) is applied to obtain better feature extraction. Case studies performed on IEEE 14‐bus, 33‐bus, 141‐bus systems and a practical system verify that our proposed algorithm can obtain close to optimal solutions in 2 s which can meet the needs of online voltage regulation. Moreover, we verify that the developed OTVR effectively increases DG penetration and decreases the need for investment in additional regulating devices.

Ryan T. Goolsby

Yufan Zhang, Qian Ai, Zhaoyu Li

Abstract Under the digitalization trend in the energy sector, utilities are devoted to providing better service to their customers by mining knowledge in fine‐grained electricity consumption data. Understanding the group behaviour of customers by clustering method is essential to achieving this end. Different from shape‐based clustering methods, an f‐divergence based hierarchical clustering model is proposed to group customers by their dynamic electricity consumption patterns. Modelling the electricity consumption by Markov chains, the customers’ consumption patterns are first summarized into transition probability matrixes. Then, dissimilarity measures based on f‐divergence are calculated. Specifically, due to their superiority, squared Hellinger distance and total variation distance are used. The hierarchical clustering is then conducted based on the obtained distance matrixes. Using real‐world smart meter dataset, the proposed method is compared with other dynamic clustering candidates by using the revised silhouette score. And consumers’ dynamic consumption patterns are not only analysed from the global to local levels, but also the relationship between clustering results and external factors are delved into. The results show that the proposed method can produce highly representative clusters, and is able to provide insights on the implementation of the demand‐side management program.

Maulik Raichura, Nilesh Chothani, Dharmesh Patel

Abstract To increase the classification accuracy of a protection scheme for power transformer, an effective convolution neural network (CNN) extreme gradient boosting (XGBoost) combination is proposed in this work. Data generated from various test cases are fed to one‐dimensional CNN for high‐level feature extraction. After that, an efficient classifier tool XGBoost is used to properly discriminate different transformer internal faults against outside abnormalities. A portion of an Indian power system is considered and simulated in PSCAD software using the multi‐run feature to collect a large number of data for various fault/abnormal situations. The generated data are used in MATLAB software where the proposed algorithm is programmed. A high‐performance CPU is used for training and testing purpose of the projected artificial intelligent technique. The obtained results for classification accuracy as well as discrimination time shows that the proposed scheme is competent enough to properly discriminate transformer operational conditions. Further, the combined CNN‐XGBoost technique is compared with existing relevance vector machine and hierarchical ensemble of extreme learning machine classifier techniques. Moreover, a hardware experiment is performed in a laboratory prototype of 50 kVA, 440/220 V transformer to verify the authenticity of the developed protective scheme. After analyzing a variety of experiments, the authors note that the presented method provides promising classification accuracy within a short time period.

Wangling He, Baoquan Wan, Yunpeng Liu et al.

Abstract At high altitudes, the corona discharge around a conductor surface is severe, and the induced audible noise (AN) is irritating; this is due to the low air density at high altitudes. Therefore, AN has become a crucial limiting factor in the design of ac power lines of 500 kV or higher at high altitudes. An investigation of the spectral characteristics of AN should help provide a greater understanding of corona noise; however, only a few studies have investigated the spectral characteristics of AN in practical bundled conductors at high altitude. Therefore, it is difficult for power utility companies to select suitable conductors. In this study, the AN spectral characteristics of 6 × LGJ400, 6 × LGJ720, and 8 × LGJ500 bundled conductors were investigated using an ultra‐high‐voltage corona cage (8 × 8 × 35 m) in Xining, Qinghai Province (altitude: 2261 m). The AN equivalent A‐weighted level and the 1/3‐octave frequency characteristics of these three conductors were obtained, and the influence of the electric field (E‐field) on these characteristics was analysed. Subsequently, the relationship between the AN A‐weighted level and the 8‐kHz level was examined. We found that, with the increase of the E‐field, the low‐frequency components of AN level did not exhibit an obvious trend, but in the high‐frequency band (1.6–20 kHz), a clear positive correlation was observed between the spectrum level and E‐field strength. Among these three conductors, the 8 × LGJ500 conductor was the optimal conductor for reducing the AN levels at high altitude. The results obtained in this study can provide a data reference for the construction of high‐altitude ac power lines.

Mohamed E. Ibrahim, Samy M. El‐Behiry, A. A. Hussien et al.

Abstract Effect of adding nanofillers on transformer oil breakdown voltage and its thermal performance finds a great interest from researchers and scientists. The impact of oil circulation considering oil forced cooling method on nanoparticles stability in transformer oil is investigated. The stability is considered from breakdown voltage and thermal performance points of view. Titanium dioxide (TiO2) nanoparticles are chosen due to the safety of TiO2 nanomaterial as well as its good performance. First, the optimal concentration of nanosized TiO2 at which maximum breakdown voltage of nanofilled transformer oil is determined and chosen to study the effect of oil circulation on breakdown voltage as well as thermal performance of nanofilled oil. Two reduced experimental models are designed. The first model consists of a galvanized steel tank containing TiO2‐nanofilled transformer oil, at the pre‐determined optimal concentration. This tank is designed to simulate the natural oil cooled transformer. However, the second model that uses a similar tank is designed to simulate a forced cooled transformer. This model consists of a tank containing TiO2‐nanofilled transformer oil, at the pre‐determined optimal concentration, and an oil pump. Breakdown voltage and thermal performance considering the two adapted models are measured in a time period of 60 days.