Hasil untuk "Distribution or transmission of electric power"

V. Terzija, G. Valverde, Deyu Cai et al.

Junjie Hu, H. Morais, T. Sousa et al.

Yih-Fang Huang, S. Werner, Jing Huang et al.

Arnaud Rosseel, Bashir Bakhshideh Zad, François Vallée et al.

Tianyang Zhao, Haoyuan Yan, Xiaochuan Liu et al.

Electrification of vehicles is strengthening the interaction between power systems and transportation systems, formulating the coupled transportation power systems (CTPSs). A novel optimal traffic power flow (OTPF) problem is proposed to analyze the spatial and temporal congestion propagation on CTPSs, under congested roads, transmission lines, and charging stations. The traffic flow is depicted by the spatial and temporal distribution of electric vehicles (EVs) on roads and charging stations, connected by multilayer time-space networks (TSNs). This distribution is the solution to a mesoscale traffic assignment problem of EV fleets on TSNs, where the charging, discharging, routing, and origin–destination pairing can be optimized simultaneously. The power flow is captured by the optima of dynamic optimal power flow problems with security constraints. An extended alternating direction multiplier method algorithm with the convex-concave procedure is proposed to solve OTPFs. Results verify the effectiveness of the proposed scheme for congestion management on CTPSs.

Aamir Nawaz, Hongtao Wang, Huiting Yang et al.

Chenke He, Jizhong Zhu, Alberto Borghetti et al.

Abstract Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study.

Reza Abshirini, Mojtaba Najafi, Naghi Moaddabi Pirkolachahi

Abstract The occurrence of natural disasters has led to an alarming increase in power interruptions, with severe impacts. Compounding this problem is the uncertain nature of data, which presents significant challenges in enhancing the resiliency of power distribution systems following such events. To tackle these issues, this paper introduces a robust optimization approach for improving the resiliency of power distribution systems. The approach encompasses crew teams for switching actions as part of the restoration process, along with demand response programs and mobile generators (MGs). By simultaneously leveraging these elements and considering the uncertainty associated with electrical load and electrical price, the resiliency of the system is enhanced. The objective function is tri‐level, comprising minimum, maximum, and minimum functions. At the first level, the approach minimizes the cost of commitment of combined heat and power plants (CHPs) by taking into account the location of MGs and the reconfiguration structure in power distribution systems. The second level aims to identify the worst‐case scenario for the uncertainty variables. Finally, the third level focuses on minimizing the total operation cost under the worst‐case scenario using demand response programs. The proposed algorithm is implemented on an IEEE 33‐bus test distribution system, with four different cases investigated.

Mohammadreza Arabshahi, Younes Saeidinia, Hossein Torkaman

Abstract The high penetration of distributed generation systems poses challenges in effectively managing both DC bus voltage and power‐sharing in DC microgrids (MGs). To ensure precise voltage regulation across various distributed generation systems and maintain overall system stability, this paper studies the modelling and control of an islanded DC MG described by a set of nonlinear‐nonaffine differential equations. In the proposed DC MG, a battery storage, a photovoltaic (PV) power plant, and a back‐end power converter with DC loads have been connected to a common DC bus. First, a dynamic model of a DC MG was developed. Then, to find the maximum power point of solar PV, a straightforward method based on an artificial neural net is employed. Afterward, a nonlinear local state feedback controller through output regulation theory is applied to achieve voltage stability, overcoming the drawbacks of droop‐based techniques. Moreover, partial state feedback capability simplifies controller design. To verify the effectiveness of the proposed approach, various scenarios, including sudden load variation, illumination change, and changes in the MG configuration, have been examined. Finally, numerical studies were carried out in a simulation environment to demonstrate the superiority of the proposed nonlinear control scheme over constant feedback control.

Fangxing Li

The Editorial Board of the IEEE Open Access Journal of Power and Energy (OAJPE) would like to recognize the following best papers selected from all papers published between October 1, 2019, and September 30, 2022, in OAJPE and its preceding journal, IEEE Power and Energy Technology Systems Journal (PETS-J).

Faezeh Hosseini, Mostafa Abedi, Saba Mohammad Hosseini

Fault detection and classification of brushless DC motors (BLDCM) is considered in this paper. A novel solution is introduced to diagnose multiple electromechanical faults that includes the stator inter-turn, the rotor dynamic imbalance, the rotor static imbalance, and different combinations of them. The current signal of the BLDCM is used together with the motor torque and the motor speed to achieve the classification of a wide range of defects. The fault features of the measured signals are extracted using packet wavelet transform (PWT). These features which include the energy, in the two modes of BLDCM operation: without load and with load, are used as input data for the radial basis function (RBF) neural network. Therefore, the designed algorithm maintains its efficiency in all operating conditions of the BLDCM. Besides, by the combination of the mentioned algorithms, the relationship between the fault types and different affected parameters of the measured signals are obtained more precisely. The neural network weights are updated by the particle swarm optimization (PSO) and the genetic algorithm (GA) that improve the convergence speed and provide better flexibility for local problems. Finally, the effectiveness of the proposed methods is validated by comparing the results obtained for different combinations of the neural networks and optimization methods.

Sajjad Dadfar, Shokoofeh Balooch

Coordination between protection devices is one of the important issues in any protection scheme of an electrical power system. The tendency to increasingly use distributed generation has jeopardized the selectivity of overcurrent relays in active distribution networks. To increase the penetration level of distributed generation in these networks, an increase in the Protection Coordination Index (PCI) of the network is a must. For this purpose, in this paper, voltage-current-time characteristics and Dual Setting Directional Overcurrent Relays are used. In this case, coordination is not a trivial task and imposes further complexity. Since Dual Setting Directional Overcurrent Relays are capable to break current in both directions with separate tuning, they have the flexibility to improve the protection coordination index. Moreover, voltage-current-time characteristics employ voltage in the operation of relays. This capability can also be used in reducing the optimization of the protection scheme and provides more flexibility in coordination procedures. Afterward, the protection scheme is developed by user-adjustable voltage-current-time characteristics to improve the PCI as much as possible. The proposed scheme is modeled as a nonlinear programming approach which is solved by a Genetic Algorithm (GA).

Yongsheng Zhu, Mingming Zhang, Yan Dong et al.

Abstract In this paper, an optimal planning strategy is proposed for EV charging stations by considering travel demand based on non‐cooperative game theory. In the proposed methodology, a realistic and stochastic charging demand distribution was obtained with the travel chain of an EV and the associated probability density function. Based on the charging demand of EV owners and a multi‐operator scenario, a two‐stage effective and practical charging station planning model was established. The first stage of the planning model optimized the location of charging stations to decrease the total deadhead mileage of the EVs with the cooperation of the charging station operators. In the second stage, the operators were committed to increasing their economic benefits by developing a charging station capacity strategy in the framework of the non‐cooperative game. Furthermore, the combination of an adaptive mutation particle swarm optimization algorithm (AMPSO) and approximate Nash equilibrium had a better effect in reducing the difficulty of solving the game model and obtaining an optimal planning strategy. Finally, the results of the case study demonstrated the effectiveness of the proposed methodology for improving the charging experience of the EV owners and achieving a balance of benefits among multiple charging station operators.

Kishan Prudhvi Guddanti, Yang Weng, Antoine Marot et al.

High penetration of distributed energy resources (DERs) changes the flows in power grids causing thermal congestions which are managed by real-time corrective topology switching. It is crucial to consider voltage stability margin (VSM) as a constraint when modifying grid topology. However, it is nontrivial to exhaustively search using AC power flow (ACPF) for all control actions with desired VSM. Sensitivity methods are used to solve this issue of “power flow-free VSM estimation” to screen candidate control actions. However, due to the volatile nature of DERs, sensitivity methods do not perform well near nonlinear operating regions which is overcome by solving ACPF. Here, we propose a new VSM estimation method that performs well at nonlinear operating regions without solving ACPF. We achieve this by formulating the learning of graph neural networks like the matrix-free power flow algorithms. We empirically demonstrate how this similarity bypasses the inaccuracy issues and performs well on unseen operating conditions and topologies without further re-training. The effectiveness is demonstrated on a power network with realistic load and generation profiles, various generation mix, and large control actions. The benefits are showcased in terms of speed, reliability to identify insecure controls, and adaptability to unseen scenarios and grid topologies.

Neda Azizi, Hassan Moradi CheshmehBeigi, Kumars Rouzbehi

Abstract High‐voltage direct current breaker is among the essential components of high‐voltage direct current grids. Such a breaker generally needs a direct current reactor to reduce the fault currents rate. However, direct current reactors have destructive effects on the multi‐terminal high‐voltage direct current grid dynamic stability, and in such a system, despite the variety of controllers, the system dynamics are highly sensitive to the operating point. Therefore, additional damping control will be needed. This paper proposes a modification to be applied to the traditional droop controller of high‐voltage direct current grids to cope with the influence of these large reactors, improving the direct voltage stability and decreasing power variations in the transient events by introducing a direct current power system stabilizer. The proposed method for direct voltage control has been investigated through the analytical model of the system. Stability improvement has been studied following the application of the proposed method by investigating zeros, poles, and frequency response analysis. Moreover, a method is proposed for optimal design and optimal placement of direct current power system stabilizer. The system analysis and time‐domain simulations demonstrate a decent damping improvement attained by the proposed method. All simulations and analytical studies are conducted on Cigré DCS3 test high‐voltage direct current grid in MATLAB/Simulink.

Sijia Geng, Ian A. Hiskens

The paper describes an inverter control scheme which incorporates both a phase-locked loop (PLL) for voltage synchronization and power-frequency droop for load sharing. As such, it is a hybrid grid-forming/following controller and offers beneficial characteristics of both. The model describing the dynamic behaviour of the inverter control scheme is presented and connections with grid-forming and grid-following control strategies are considered. A process for black-starting the PLL-based inverter control scheme is described. Case studies explore controller small- and large-disturbance performance under a variety of system conditions.

Dan Xu, Fang Liang, Qian Cheng et al.

Abstract The reserve sharing amongst the interconnected power systems will facilitate the improvements on their anti‐disturbance capability and operating economy. Based on the design of reserve sharing mechanism across different areas, a multi‐area generation‐reserve sharing model is constructed. The stochastic fluctuation of load and wind power will cause the power grid to be in an unpredictable random state when the reserve is needed, and the transmission capacity constraint of the power grid may restrict the effective deployment of the reserve. To address these issues, a max‐min bi‐level optimization model is proposed to check the reserve availability. The joint generation and reserve optimization sub‐model, together with the reserve check sub‐model, constitutes the two‐stage robust optimization model in this paper. The column‐and‐constraint generation (C&CG), algorithm is adopted to solve the proposed model. The modified IEEE 118‐node test case is used to verify the proposed model and algorithm. The test results indicate that the proposed method can realize the multi‐area generation‐reserve joint optimization under the condition of guaranteeing the reserve availability, and it possesses the advantage of high efficiency in dealing with stochastic scenarios.

Sadegh Ghaedi, Saeed Abazari, Gholamreza Arab Markadeh

Abstract This paper offers novel non‐linear control of a doubly fed induction generator (DFIG) and unified power flow controller (UPFC) for transient stability increment with transient energy function (TEF) and sliding mode observer in a power system. First, the TEF technique is considered for the damping control in a power system with a synchronous generator (SG), DFIG, and UPFC, and then the controller time‐derivative signals are estimated by a second‐order sliding mode observer. The importance of the issue is in the employ of a complete UPFC model. Also, a one‐axis model is used for DFIG that is similar to the SG model. Another characteristic of the novel non‐linear technique is robust versus system topology changes and variable time delay of the control signals. To evaluate the performance of the novel non‐linear control method for increment transient stability, simulation studies are performed on a two‐machine connected to an infinite bus (TMIB), the IEEE 9‐bus, and the New England Standard 39‐bus power system. The results determine that the novel non‐linear control method decreases the first swing of fluctuations admissibly and enhancement stability margins considerably.

Sajad Samadinasab, Alireza Jalilian

Abstract The growing use of non‐linear loads is one of the main reasons for harmonic distortions in distribution systems. Power quality disturbances have caused improper performance of electrical equipment and have created many technical and economic problems for industries and customers. One approach to overcome these problems and also the correct detection of PQ disturbances is power quality monitoring (PQM). Measurement and estimation are two essential tools to understand and investigate PQ problems in distribution networks. Here, a new estimation model is used to simultaneously extract the fundamental and harmonic phasors information of the system. In other words, the proposed estimation model has been used to access system‐level data. Also, the recursive variational mode extraction (RVME) approach efficiency has been investigated for detecting power quality disturbances (PQDs). Finally, to validate the effectiveness of the proposed PQDs detection method, six different scenarios of disturbances are investigated. The IEEE 13‐bus and IEEE 34‐bus standard distribution test networks are used to evaluate the efficiency of the proposed method. Also, the real‐time PQ disturbances are used for validating the proposed PQDs detection method. Results obtained demonstrate the efficiency and high accuracy of the proposed approach in correctly diagnosing real‐time PQ disturbances.

Mrutyunjaya Mangaraj, Anup Kumar Panda

Abstract Sparse least mean square algorithm is proposed for the DSTATCOM as an optimal current harmonic extractor to cope with the intermittent nature of loadings. Sparse least mean square is the improved version of adaptive least mean square learning mechanism with regards to sparsity. This innovative approach is utilized for better parameter estimation due to its algorithmic simplicity and parallel computing nature. Hence, sparse least mean square is expected to reduce the computation and storage requirements significantly. This suggested controller consists of six subnets. Three subnets for active and another three for reactive weight component are used to extract the fundamental component of the load current. Several factors like previous weight, normalizing weight and learning rate are involved in the sparse least mean square based weight‐updating rule to have better dynamic performance, reduced computational burden and better estimation speed etc. The detailed control algorithm is formulated using MATLAB/Simulink, and validated using experimental analysis. Among these two algorithms, the sparse least mean square offers better voltage regulation, voltage balancing, source current harmonic reduction and power factor correction under various loading scenarios.