Hasil untuk "Distribution or transmission of electric power"

Ugur S. Selamogullari, Ibrahim Evkay

Savvas Panagi, Chrysovalantis Spanias, Petros Aristidou

The growing electrification of transportation and heating through Electric Vehicles (EVs) and Heat Pumps (HPs) introduces both flexibility and complexity to Active Distribution Networks (ADNs). These resources provide substantial operational flexibility but also create tightly coupled thermal-electrical dynamics that challenge conventional network management. This paper proposes a unified co-optimization framework that integrates a calibrated 3R2C grey-box building thermal model into a network-constrained Optimal Power Flow (OPF). The framework jointly optimizes EVs, HPs, and photovoltaic systems while explicitly enforcing thermal comfort, Distributed Energy Resource (DER) limits, and full power flow physics. To maintain computational tractability, Second-Order Cone Programming (SOCP) relaxations are evaluated on a realistic low-voltage feeder. The analysis shows that, despite network heterogeneity violating some theoretical exactness conditions, the relaxation remains exact in practice. Comparative assessments of convex DistFlow, bus injection, and branch flow formulations reveal that convex DistFlow achieves sub-second runtimes and near-optimal performance even at high DER penetration levels. Simulations confirm the effectiveness of coordinated scheduling, yielding reductions of 41% in transformer aging, 54% in losses, and complete elimination of voltage violations, demonstrating the value of integrated thermal-electrical coordination in future smart grids.

Chenghao Huang, Jiarong Fan, Weiqing Wang et al.

As power systems advance toward net-zero targets, behind-the-meter renewables are driving rapid growth in distributed energy resources (DERs). Virtual power plants (VPPs) increasingly coordinate these resources to support power distribution network (PDN) operation, with EV charging stations (EVCSs) emerging as a key asset due to their strong impact on local voltages. However, in practice, VPPs must make operational decisions with only partial visibility of PDN states, relying on limited, aggregated information shared by the distribution system operator. This work proposes a safety-enhanced VPP framework for coordinating multiple EVCSs under such realistic information constraints to ensure voltage security while maintaining economic operation. We develop Transformer-assisted Lagrangian Multi-Agent Proximal Policy Optimization (TL-MAPPO), in which EVCS agents learn decentralized charging policies via centralized training with Lagrangian regularization to enforce voltage and demand-satisfaction constraints. A transformer-based embedding layer deployed on each EVCS agent captures temporal correlations among prices, loads, and charging demand to improve decision quality. Experiments on a realistic 33-bus PDN show that the proposed framework reduces voltage violations by approximately 45% and operational costs by approximately 10% compared to representative multi-agent DRL baselines, highlighting its potential for practical VPP deployment.

Yi Ju, Lunlong Li, Jingchun Wang et al.

Rapid growth in electric-vehicle (EV) charging demand is placing increasing stress on distribution power networks (DPNs), whose hosting capacity is often limited and spatially uneven. Beyond demonstrating that coordination can help, this paper answers an open question that is central for planners: what is the maximal achievable benefit of EV demand flexibility in reducing overload-driven distribution upgrades at a regional scale? Establishing such an upper bound is computationally challenging, as it entails solving and certifying near-optimal solutions to population-scale optimization problems with millions of variables and both spatial and temporal coupling. We introduce MAC (Mobility-Aware Coordinated EV charging), a framework that quantifies the maximum potential of leveraging EV demand flexibility to mitigate DPN overloading risk without interrupting drivers' travel needs. (i) MAC expands feasible scheduling by coupling charging decisions over a full mobility horizon: instead of enforcing per-session energy recovery, it only requires the EV state-of-charge (SOC) to remain sufficient for upcoming trips. (ii) MAC is computationally scalable via an ADMM-based decomposition with custom subproblem solvers, and admits a decentralized interpretation in which dual variables act as locational-temporal price signals that implement the social optimum as a competitive equilibrium. Using high-resolution mobility trajectories and feeder hosting-capacity data in a future-oriented 30% EV adoption scenario for the San Francisco Bay Area, we show that MAC can dramatically reduce overload-driven upgrade requirements relative to unmanaged charging. This paper illustrates how trajectory-coupled flexibility and scalable, certifiable optimization can provide actionable best-case benchmarks for DPN planning and operations.

Saad Ullah Khan, Muhammad Sajid Khan, Hamza Farooq

Changgang Wang, Wei Liu, Yu Cao et al.

In the context of the rising share of new energy generation, accurately generating new energy output scenarios is crucial for day-ahead power system scheduling. Deep learning-based scenario generation methods can address this need, but their black-box nature raises concerns about interpretability. To tackle this issue, this paper introduces a method for day-ahead new energy scenario generation based on an improved conditional generative diffusion model. This method is built on the theoretical framework of Markov chains and variational inference. It first transforms historical data into pure noise through a diffusion process, then uses conditional information to guide the denoising process, ultimately generating scenarios that satisfy the conditional distribution. Additionally, the noise table is improved to a cosine form, enhancing the quality of the generated scenarios. When applied to actual wind and solar output data, the results demonstrate that this method effectively generates new energy output scenarios with good adaptability.

Wang Zhaoqing, Chang Yanzhao, Chen Jianlei et al.

Abstract The complexity of power quality (PQ) concerns is intensifying in tandem with the proliferation of inverter‐based renewable energy systems. The integration of power electronic devices within the distribution network exacerbates the complexity and introduces greater temporal variability to signal components. This paper introduces an advanced, online optimization technique for the decomposition and identification of PQ disturbances (PQDs). Initially, an improved variational mode decomposition (IVMD) method is presented, leveraging an energy ratio criterion for precise decomposition of concurrent PQDs. Subsequently, utilizing the characteristic attributes derived from IVMD, an optimized support vector machine (OSVM) algorithm is developed through the synthesis of diverse kernel functions. The OSVM strategically employs distinct kernel functions to augment the discriminability of the feature set. The synergy of IVMD and OSVM enables the detection of multiple PQDs, remarkably even with a minimal amount of training data. A series of experiments have been conducted to validate the effectiveness of the proposed methodology. The results corroborate that the formulated framework exhibits robust learning capabilities and a high degree of resistance to noise interference. Moreover, the hardware platform experiments prove that the proposed method has a satisfactory real‐time performance for its practicability.

Huibin Jia, Weiran Hou, Tao Zheng et al.

Abstract Reliability is a fundamental requirement of wide‐area backup protection (WABP) systems, and it is one of the most crucial performance indicators for such a WABP system. Two definitions are introduced: the reliability of WABP systems and the undetectable probability of a power line. They are determined by the specific placement of communication links (CLs) and phasor measurement units (PMUs). A simultaneous optimization model was developed to minimize the construction cost of WABP systems by optimizing the partitioning and placement of PMUs, protection centers, and CLs. The model considers several constraints, including latency, regional balance, and system reliability. To reduce the computational complexity, a cluster‐based genetic algorithm was developed to determine the optimal solution. Finally, numerical simulations were conducted using IEEE test cases. The results demonstrate that the proposed method can minimize the construction costs of WABP systems while increasing their reliability.

Xiaojie Fan, Yongning Chi, Zhibing Wang

Abstract The grid‐following control‐based hybrid cascaded high‐voltage direct current (HC‐HVDC) links may contribute to the worsening of inertia decline and instability issues, especially in situations characterized by a weak grid. To address the challenge, this paper proposes a grid‐forming (GFM) based optimal frequency support scheme (OFSS) for HC‐HVDC integrated wind farms. Firstly, a GFM control for hybrid converter is proposed and its stability under weak grid conditions is evaluated through a small‐signal model. Then, DC current–voltage droop control is introduced to the line commutated converter (LCC). This control approach ensures the consistency of reactive power in the LCC by regulating the DC voltage and current in the same direction, thereby mitigating AC voltage fluctuations. It also facilitates active power control in the LCC in response to grid frequency variations. Furthermore, the OFSS can convey the frequency of receiving‐end grid to the rectifier and wind farms without communication for precise power control. Finally, to validate the effectiveness of OFSS, a point‐to‐point HC‐HVDC and a 4‐machine test model are constructed on PSCAD/EMTDC. The OFSS significantly enhances the frequency support capability of HC‐HVDC links and improves the robustness in weak grid.

Hamid Helmi, Taher Abedinzadeh, Jamal Beiza et al.

Abstract This study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting.

Fezan Rafique, Ling Fu, Ruikun Mai

Hossein Jalali Farahani, Mahdi Rezvanyvardom, Amin Mirzaei

Abstract A high step‐up non‐isolated DC–DC converter based on a switched‐inductor/switched‐capacitor network is proposed in this paper. The proposed converter takes advantage of both active and passive switched inductor cells. Inductors L1 and L2 can be connected in series to increase the voltage gain. Furthermore, the conduction losses of the proposed converter are low which leads to an increase in its efficiency. The proposed converter takes advantage of the voltage multiplier at the output. As a result, the voltage across all output capacitors is half the output voltage. Moreover, the switched capacitor network in the proposed converter can be extended to achieve higher voltage gain. The comparison is done with other similar topologies in detail to prove the effectiveness of the proposed converter. Operation modes, design procedures, and the control scheme are analyzed. A 200‐W DC–DC converter is implemented and tested. The experimental results confirm the theoretical analysis and its successful operation. The maximum efficiency is above 94%.

Abdelhamid Kherbachi, Aissa Chouder, Ahmed Bendib et al.

Abstract Computation of active and reactive powers is a crucial step in droop‐controlled single‐phase voltage source inverters (VSIs) in standalone microgrid since the performance and stability of the power‐sharing strategy are strongly influenced by its speed and accuracy, especially in the case of non‐linear loads. Here, an improved performance of power‐sharing among single‐phase droop‐controlled VSIs in an islanded microgrid, considering DC component and nonlinear loads is presented. To achieve this goal, an enhanced power‐sharing control scheme including a Multiple Enhanced Second‐Order Generalized Integrator Frequency‐Locked Loop (MESOGI‐FLL) for power calculation is proposed. As a result, the proposed power computation technique provides high rejection capability of DC component and current harmonics, hence, perfect estimation of the fundamental component of the inverter output current and its 90◦ phase‐shifted component. This strategy makes the power calculation method‐based control scheme immune to disturbance effects of the DC component and the high current harmonics. Detailed analysis, mathematical modelling of MESOGI, as well as a comparison with recent methods, are also provided. Simulation and experimental tests were carried out and the obtained results have shown the effectiveness and robustness of the proposed power‐sharing controller even under nonlinear load operating conditions.

Soheil Ranjbar

Abstract This paper presents an online protective scheme of estimating critical inter‐area angle for controlling the inter‐area oscillations based on evaluating the rate of changes of rotor angle oscillations as an adaptive tripping index (ATI)‐based protective zone. For this purpose, by using wide area measuring system technology, oscillatory signals Δδ and Δω are measured which based on correlation coefficients criteria, coherent groups are identified which based on evaluating oscillating areas, inter‐area signals ΔδCOI‐AB and ΔωCOI‐AB are provided. In the case of evaluating an inter‐area oscillation, the proposed ATI scheme is activated which the first zero crossing point (ZCP0) of δCOI‐AB is determined as the critical criteria for estimating unstable situation. The proposed ATI approach consists of two adaptive protective zones ATI1 and ATI2 to estimate inter‐area oscillatory and inter‐area transient instabilities, respectively. The proposed ATI is an online and non‐model‐based scheme which evaluates the system dynamic stability through two straight and sinusoidal‐based adaptive zones. The effectiveness of the proposed ATI approach is evaluated on three different test systems IEEE 39‐bus, modified IEEE 39‐bus with converter‐based low inertia sources and a practical Iran nation power grid with presenting positive effects of damping inter‐area oscillations through different operational and topological conditions.

Ahmadreza Alavi‐Koosha, Turaj Amraee, Salar Saberi Oskouee

Abstract This paper presents a comprehensive under frequency load shedding (UFLS) model that can be implemented in a multi‐area power system with real network characteristics. Conventional single‐area UFLS models operate on the basis of an equivalent center‐of‐inertia (COI) model, which ignores the local dynamics of system frequency response (SFR) and the impacts of load shedding location. Unlike the single‐SFR model, that is commonly utilized in previous works, the suggested multi‐area or multi‐SFR UFLS plan of this research has the distinct benefit of taking into account the dynamics of power transfer across different electric areas. The proposed multi‐area UFLS design incorporates a flywheel energy storage system (FESS) to support the inertial system frequency response and alleviate more than 30% load shedding while improving the frequency nadir by 25%. In order to investigate the performance of the proposed method under high penetrations of inertia‐free renewables, the inertia of the power network is reduced by around 30%; therefore the proposed UFLS scheme is assessed under a low inertia scenario. The proposed multi‐stage UFLS scheme is formulated as a mixed‐integer linear programming problem, and the optimal settings, including frequency set‐points and load shedding, are then optimized. The efficiency of the proposed model is verified using the IEEE‐118 Bus dynamic test system.

Qingyu Su, Cong Chen, Jian Li

Abstract To ensure the stable operation of power systems, critical nodes need to be identified for key protection. The LeaderRank algorithm is a fast and accurate algorithm for identifying key nodes, but it has obvious inappropriateness when targeting the power system. For this reason, this paper considers the scheduling function of the information network and the power flow betweenness of the power grid. A TrendRank algorithm is proposed to identify key nodes in complex power grids. TrendRank values can be computed iteratively by a weighted distribution strategy of internally linked nodes and then ranked. The performance of the TrendRank method has been fully tested and benchmarked on IEEE39 and IEEE118. The comparison of four performance metrics fully validates the effectiveness and superiority of the method. The TrendRank algorithm provides an idea in protecting the power system, which makes the economic cost of protecting the power system lower.

Yemao Zhang, Jilai Xu, Ni Li et al.

Abstract In order to build UHV AC transmission lines at high altitudes, it is necessary to analyze the audible noise (AN) characteristics of the existing EHV transmission lines at high‐altitude areas to guide the design. The typical test data of AN for 750‐kV single‐circuit transmission lines measured at the Guanting long‐term observation station, located at an altitude of 1854 m, were collected. Two methods of sorting the test data of AN and extracting valid data were given. Then the levels of AN under different weather conditions were analyzed, which showed that AN was raised with the increase of instantaneous rainfall and snowfall. Typical frequency spectrums of AN under different weather were also obtained, the pure tone at 100 Hz was more prominent under rainy and snowy weather, and the pure‐tone oscillation attenuated at different positions. The statistical results and the calculated difference of AN over 5.5 h with continuous rainfall were obtained. For the 750‐kV transmission line in the quiet plateau areas, the calculated difference between AN during rainy days and fair weather can be taken as 25 dB, and the calculated value of the altitude correction coefficient of 2.85 dB/1000 m is better consistent with the measured value.

Mohammad Vahabi Khah, Rahim Zahedi, Reza Eskandarpanah et al.

The use of renewable energy is necessary to achieve the goals of sustainable development, and sooner or later all countries are forced to plan and make policies for the use of this equipment. Considering the growing trend of smart systems and the ability of these systems to control and use renewable resources, it is necessary to investigate how to control and optimally use these resources in smart systems. Considering the geographical conditions and significant solar energy radiation in Iran, the most suitable option for using renewable energy in residential buildings is solar energy. Among the types of solar energy used around the world, photovoltaic panels are used more due to their wide range, being cheaper than other sources of electric power from solar energy and more durable than other sources. In order to reduce widespread losses and reduce the cost of transmission and distribution, increase efficiency, the possibility of the presence of private sector investors and increase the security and stability of the power grid, distributed production of electrical energy at consumption locations using small-scale units is the most cost-effective way to use home solar panels. Also, the production of energy from wind turbines can be done in the areas where anemometer data determine it to be suitable. The combination of solar energy and wind energy can effectively reduce the need for batteries, but studies show that this combination is only economically viable when it is used on a large scale and with high powers, which requires a lot of investment. Large initial capital is one of the biggest problems of distributed production systems, so the use of artificial intelligence methods for accurate capacity determination of renewable energy production systems becomes doubly important. The economic results show that the least cost of electricity and net price cost are 0.44 $ per kWh and 15.0 million $ respectively, when the converter size was gradually changed, with a renewable fraction of 46.7%.

Arash Rohani, Ali Nahavandi, Mahyar Abasi et al.

Abstract This paper presents the optimal scheduling of active distribution network (ADN) with linear and non‐linear loads in the presence of an integrated unit of electric spring and electric vehicle parking lot (IUEE). The proposed scheme is formulated in the form of three‐objective optimization, which minimizes the expected operating cost of the ADN, voltage deviation function (VDF), and the total harmonic distortion (THD) of the voltage in separate objective functions. Constraints include harmonic power flow (HPA) equations, operation and power quality limits, and operation model of the IUEE. This scheme has uncertainties of load, energy price, and energy demand of EVs, which are modelled here using stochastic programming. Next, the sum of the weighted functions method as a subset of the Pareto optimization technique is adopted to extract the integrated single‐objective problem. Then, Sine‐Cosine Combined Algorithm (SCA) and Gray Wolf Optimization (GWO) are used to achieve an optimal solution. In the end, by examining the numerical results obtained from the implementation of the proposed scheme on a 69‐bus ADN, the capability of the design in enhancing the economic situation, operation, and power quality of the ADN compared to network power flow studies obtained by optimal scheduling of the IUEE is confirmed.

Dabo Zhang, Zhuwei Chu, Qianjin Gui et al.

Abstract Equipment maintenance decision is a very critical technology in power grid asset management. The traditional maintenance decision focuses on the analysis of the operation performance of the equipment itself, but lacks the analysis and description of the fuzziness of power equipment outage parameters. Therefore, this paper establishes the reliability assessment model based on equipment health condition monitoring, which makes the maintenance decision transition from equipment level to system level, and the fuzzy mathematics theory is introduced to establish a model describing the fuzziness of equipment failure rate parameter to make system reliability assessment and equipment maintenance decision scheme. Firstly, this paper proposes a fuzzy failure rate model of power transformer based on condition monitoring. Then the fuzzy parameters are combined with the conventional probabilistic reliability assessment method to establish the fuzzy probability hybrid reliability assessment model of the transmission system, and the fuzzy maintenance reliability benefit index is defined and deduced. Finally, a maintenance strategy of transformer based on fuzzy probability hybrid reliability assessment and fuzzy set theory is proposed, and the case study is carried out on a regional power grid in East China. The results show that the proposed model provides an improved maintenance strategy of power equipment.