Hasil untuk "Distribution or transmission of electric power"

Shiva Poudel, Poorva Sharma, Abhineet Parchure et al.

The uncertainty in distribution grid planning is driven by the unpredictable spatial and temporal patterns in adopting electric vehicles (EVs) and solar photovoltaic (PV) systems. This complexity, stemming from interactions among EVs, PV systems, customer behavior, and weather conditions, calls for a scalable framework to capture a full range of possible scenarios and analyze grid responses to factor in compound uncertainty. Although this process is challenging for many utilities today, the need to model numerous grid parameters as random variables and evaluate the impact on the system from many different perspectives will become increasingly essential to facilitate more strategic and well-informed planning investments. We present a scalable, stochastic-aware distribution system planning application that addresses these uncertainties by capturing spatial and temporal variability through a Markov model and conducting Monte Carlo simulations leveraging modular cloud-based architecture. The results demonstrate that 15,000 power flow scenarios generated from the Markov model are completed on the modified IEEE 123-bus test feeder, with each simulation representing an 8,760-hour time series run, all in under an hour. The grid impact extracted from this huge volume of simulated data provides insights into the spatial and temporal effects of adopted technology, highlighting that planning solely for average conditions is inadequate, while worst-case scenario planning may lead to prohibitive expenses.

Sander Lid Skogen, Jose Luis Rueda Torres

As the integration of renewable energy accelerates, ensuring power system stability becomes increasingly critical. This research utilized a Root Mean Square (RMS) synthetic model of the future 380 kV Dutch power system towards 2050 to analyze its oscillatory stability under high renewable penetration and the impact of grid-forming converters under various parametrizations. The presented case study shows that grid-forming (GFM) converters significantly improve frequency stability and damping performance across different perturbations, particularly at higher GFM penetration levels, improving frequency and damping parameters. However, various oscillatory modes present potential stability risks at high penetration levels. The case study also shows minimal differences in controller selection in large-scale models, except under certain conditions. Additionally, the analysis of controller parameters highlighted the critical importance of tuning active power parameters to ensure system stability. The investigation provides essential insights for future power systems, where large-scale integration of renewable energy will necessitate the implementation of converters able to provide ancillary services. The findings emphasize the importance of optimizing GFM converter settings and penetration levels to maintain system resilience, offering valuable guidance for future system planning and regulatory frameworks.

Amritanshu Pandey, Shimiao Li, Larry Pileggi

Miguel A. Diaz-Ibarra, Daniel U. Campos-Delgado, Carlos A. Gutierrez et al.

In this paper, we study the problem of utility maximization in the uplink of vehicle-to-infrastructure communication systems. The studied scenarios consider four practical aspects of mobile radio communication links: i) Interference between adjacent channels, ii) interference between roadside units along the way, iii) fast and slow channel fadings, and iv) Doppler shift effects. We present first the system model for the IEEE 802.11p standard, which considers a communication network between vehicles and roadside infrastructure. Next, we formulate the problem of utility maximization in the network, and propose a distributed optimization scheme. This distributed scheme is based on a two-loop feedback configuration, where an outer-loop establishes the optimal signal to interference-noise ratio (SINR) that maximizes the utility function per vehicle and defines a quality-of-service objective. Meanwhile, inner-control loops adjust the transmission power to achieve this optimal SINR reference in each vehicle node regardless of interference, time-varying channel profiles and network latency. The computation complexity of the distributed utility maximization scheme is analyzed for each feedback loop. Simulation results indicate that the proposed scheme reaches the objective SINRs that maximize utility and improve energy efficiency in the network with a low time cost. The results also show that the maximum utility is consistently achieved for different propagation scenarios inside the vehicular communication network.

Santosh Sharma

The power distribution system is evolving in the form of an intelligent grid. The proliferation of distributed energy resources (DERs) makes the previously passive system active and more complicated. With the adoption of de-carbonization principles, large-scale coal and nuclear power plants are being gradually replaced by renewables and carbon-free DERs. With this rapid transformation, the power system operates with less inertia and minimal margins. In recent years, power systems have been facing apocalyptic weather events more frequently, and large-scale blackouts have become regular. After the complete or partial blackouts, the power system goes through different stages before it reaches the normal operating condition. The load restoration is the stage where the power system is fully established after the blackouts; however, due to the limiting ramping rates of centralized generation, the energization of large amounts of loads is delayed by some time. To mitigate the negative impact of ramping rates of centralized generation, DERs in distribution systems are proposed to serve the loads in both transmission and distribution systems in coordination with limited centralized generation in the transmission system. The problem is formulated as a centralized or integrated transmission and distribution (T$\&$D) coordination model. The modified IEEE 14 bus test case and IEEE 13 node test feeders are used to validate the proposed strategy; the results indicate the validity of the proposed model.

Yizhao Wang, Chuan Feng, Jian Liu et al.

Abstract Currently, theoretical analysis of single‐phase grounding (SPG) faults in low‐current grounding systems has reached a high level of refinement, and the corresponding detection methods have matured. However, there is a significant lack of research on successive grounding (SG) faults, particularly in the area of detecting cross‐line same‐phase successive grounding (CSSG) faults in arc suppression coil grounding systems. To address these issues, a novel faulty line detection method based on transient characteristics of CSSG is proposed. Firstly, a transient equivalent circuit is established, and the characteristics of transient zero‐sequence current (ZSC) are theoretically analysed. Secondly, variational mode decomposition (VMD) is employed to extract low and high‐frequency information from intrinsic mode functions (IMFs). Subsequently, a fault initiation criterion based on kurtosis theory and the Teager‐Kaiser energy operator (TKEO) is proposed, which can accurately detect the occurrence time of secondary faults. Finally, a method for faulty line detection based on cross‐correlation distance (CCD) and transient energy (TE) is proposed. The results of PSCAD simulation under different conditions and field test affirm the accuracy of the analysis and the effectiveness of the proposed method.

Nattavit Piamvilai, Somporn Sirisumrannukul

Abstract This research proposes a new comprehensive methodology aimed at optimally managing electric vehicle (EV) charging in an unbalanced distribution system with consideration of grid capacity and voltage quality constraints as well as the stochastic driving and charging behaviour of EV users. A data mining algorithm is designed to generate suitable spatial and temporal EV and charger input data for EV scheduling that is decomposed into two subproblems. The lower‐level subproblem identifies the maximum load at each node and time and the upper‐level subproblem allocates charging slots for each EV being charged. Both subproblems are solved by developed particle swarm optimisation (PSO) algorithms. The effectiveness and robustness of the algorithms have been thoroughly validated by conducting rigorous tests on a modified IEEE 37‐bus distribution system with different EV penetration scenarios. The test results have confirmed the algorithms' performance in accommodating the increasing load associated with EV charging and successfully improving the system performance by maximising the system load factor, minimising load unbalance, and reducing the system power loss. All operational constraints on node voltages, and distribution transformer and feeder capacities of the existing power distribution infrastructure are fully respected while maintaining high user satisfaction represented by the average state of charge (SoC) of all EVs.

Pooja Algikar, Yijun Xu, Somayeh Yarahmadi et al.

In this paper, we propose a robust data-driven process model whose hyperparameters are robustly estimated using the Schweppe-type generalized maximum likelihood estimator. The proposed model is trained on recorded time-series data of voltage phasors and power injections to perform a time-series stochastic power flow calculation. Power system data are often corrupted with outliers caused by large errors, fault conditions, power outages, and extreme weather, to name a few. The proposed model downweights vertical outliers and bad leverage points in the measurements of the training dataset. The weights used to bound the influence of the outliers are calculated using projection statistics, which are a robust version of Mahalanobis distances of the time series data points. The proposed method is demonstrated on the IEEE 33-Bus power distribution system and a real-world unbalanced 240-bus power distribution system heavily integrated with renewable energy sources. Our simulation results show that the proposed robust model can handle up to 25% of outliers in the training data set.

Hossein Nourianfar, Hamdi Abdi

Global Warming and progression of modern power networks have profoundly changed traditional power grids in terms of fossil fuel consumption and emission of toxic gases. Therefore, auxiliary power plants and ancillary services have been introduced as an effective alternative, to overcome these new challenges in power systems. In this work, the dynamic environmental economic dispatch (DEED) problem, is investigated by considering the plug-in electric vehicles (PEVs), minimizing the fuel cost and greenhouse gas emissions from fossil fuel units. In the mentioned problem, to make it more practical, various operational constraints, including valve-point loading effect (VPLE), ramp rate limits (RRLs) and generation capacity limits are considered. This paper proposes a new multi-objective exchange market algorithm (EMA) based on the non-dominated sorting theory to find the Pareto front. In addition, the impacts of PEVs, as an uncertainty source, on the mentioned problem are analysed in four different charging scenarios. The efficiency of the proposed method has been detailed on three experimental systems and the obtained results are compared with other algorithms in this field. The results show that the maximum percentage reduction in costs for test cases 1 to 3, are about 2.13, 2.69, and 39.48, respectively, and bout 45.96, 48.20 and 44.07, for emission, respectively. The comparative analysis verify the proposed method efficiency, and accuracy in solving the suggested problem.

Yongjun Zhang, Jingxu Yang, Qinhao Li et al.

Abstract In view of the limited capacity for public transformers to accept electric vehicles (EVs) and the large cost of putting in new transformers to supply power in the parking lot of urban buildings, a special transformer sharing mode (STSM) is proposed, where charging station operators utilize the special transformers of buildings to supply power for charging stations. To enhance the applicability of STSM, single regulation and joint regulation methods for the charging price strategy of charging stations are put up, and the mathematical model of joint regulation is focused on. First of all, a joint regulation mechanism is established, in which EVs are guided to charge off‐peak and off‐station through the joint regulation. Secondly, integrating charging fees, queuing costs, and transfer costs, the equivalent charging prices for EV users are put forward. Based on the difference of the equivalent charging prices, transfer probability model for EVs’ charging periods and charging stations is proposed. Furthermore, the overall objective of joint regulation is built based on the net income of all parties. And an optimization method for the joint regulation of charging prices is proposed. Then, regulation lifting ratio is put forward to reflect the superiority of joint regulation over single regulation. Based on this, an evaluation model for joint station diameter threshold is established. The practical result proves that through single regulation and joint regulation, approximately 200–500 additional EVs can be powered, which will result in significant savings in infrastructure construction and investment costs.

Mehdi Davoudi, Mohammad Hossein Barmayoon, Moein Moeini‐Aghtaie

Abstract With the increasing rate of population in big cities around the world, the tendency to build new buildings in the suburb of main cities or to build large apartments in the main cities has been highlighted. In this regard, building residential complexes has seen a dramatic increase in these areas as it makes it possible to build a large number of residential units within a reasonable space. Although these complexes have brought numerous benefits, they are some challenges regarding their construction processes. One main concern associated with these complexes is how to optimally install energy components such as transformers, combined heat and power (CHP) units, boilers etc., in the shared area of apartments in the residential complex. To address this issue, this paper models the energy system of a residential complex as an energy hub and proposes a novel framework to obtain the optimal planning of such an energy hub. In order to address the conflicting desires of the residential complex's builders and the future residents of the residential units, a multi‐objective (MO) optimization problem has been considered in the proposed method that simultaneously optimizes the investment costs, operation costs, and the reliability of energy supply. In this regard, a Multi‐objective Particle Swarm Optimization (MOPSO) algorithm combined with classical linear programming (LP) optimization method has been proposed to solve the MO optimization problem. In order to demonstrate the effectiveness of the proposed method, a case study including a residential complex with 300 residential units is considered, and the proposed method is implemented in this case study. The numerical results show that the proposed framework can appropriately optimize investment costs, operation costs, and the reliability index simultaneously, and the obtained Pareto frontier gives the investors the freedom to opt for any point from this surface.

Zitong Huang, Yonghai Xu, Lin Chen et al.

Abstract Faced with the uncertainty of wind and photovoltaic power output and load fluctuation caused by the increase of new energy penetration in active distribution network, the demand for operational flexibility and the construction demand for flexible resources of distribution network are gradually increasing. The flexible operation of active distribution network can be realized by coordinated planning of the soft open point integrated with energy storage system (ESOP) and flexible resources. Firstly, the flexibility resource adjustability evaluation and margin indicators are proposed for the response model of typical flexibility resources. Secondly, a two‐stage distributionally robust coordinated planning model considering the coordination planning scheme of distributed generation, flexibility resource, and ESOP as well as the comprehensive norm uncertainty of wind power and photovoltaic outputs multi‐operation scenarios is established with the distribution network construction cost, annual operation cost, and annual power sales revenue as the objective functions meanwhile the investment and flexibility resource operation as constraints. Finally, the column constraint generation algorithm is used to solve the problem, and the effectiveness of the proposed model is verified by the modified IEEE33 node system.

M. I. Saleem, S. Saha, L. Ang et al.

Abstract With the emergence of renewable energy sources (RESs), the power grid all over the world is going through a paradigm shift. The inverter based RESs are replacing the conventional rotating synchronous generators and this trend is expected to continue in coming years. Consequently, the grid strength is decreasing, which can pose significant challenges on the grid stability, especially during integration of IRESs. This paper presents a thorough analysis on the integration challenges of IRESs in weak grids. The paper also presents a comprehensive investigation on the impacts of factors, such as available fault level at the point of interconnection (POI), feeder length and grid nature on the POI voltage, and maximum allowable power injection by IRES. This is followed by maximum allowable IRES power injection sensitivity analysis to quantify the impacts of the aforementioned factors. The paper concludes with a thorough case study on the IEEE 39‐bus test system to numerically show the impact of diminishing grid strength on the integration of IRESs.

Natanael Vieyra, Cesar Angeles‐Camacho, Paul Maya‐Ortiz

Abstract Controlling and operating modern distribution networks are challenging tasks. The implementation of monitoring systems and the development of tools are helping to address this challenge. State estimators are one of the most promising tools for this purpose. These are used to reconstruct the current state of a system based on measurement outputs, which can then be used to design controllers that can adjust the inputs to achieve the desired output. This article deals with the problem of estimating the state of a real‐world distribution network. A combination of conventional supervisory control and data acquisition is used to estimate the state, together with a synchrophasor monitoring system. The proposed method uses the Fortescue transformation to separate the three‐phase coupled equations into three independent modal measurement equations. Single‐phase estimators based on the extended Kalman filter are used to solve three sub‐problems associated with the sequence reference frame, which correctly capture the transient and steady‐state behaviour of the distribution network. The proposed estimation method is easy to implement and robust to measure model noise and uncertainties. Furthermore, an already‐established distribution network tests the estimation scheme under various conditions.

Farhad Ilahi Bakhsh, Sanjeevikumar Padmanaban, Khadim Moin Siddiqui et al.

Binrui Cao, Xiong Wu, Bingwen Liu et al.

Abstract Hydrogen‐based energy systems (HESs) have shown great potential to promote the process of decarbonization. Conventional studies mainly focus on the sizing and operation of HESs in a determined static situation, and the dynamic planning model of HESs considering large‐scale uncertain scenarios of future developments should be considered. This paper proposes a multi‐stage stochastic programming (MSP) long‐term planning model to find the optimal sequential planning results of the grid‐connected HES. The planning model considers the long‐term uncertainties of the investment cost decrease and the load increase. Additionally, the short‐term uncertainties of renewable energies are also considered to obtain robust results in each stage. The improved stochastic dual dynamic integer programming (SDDIP) is then employed to solve the MSP long‐term planning model with consideration of the realized uncertainties. Specifically, the sequential planning order is developed to improve the efficiency of the SDDIP. Numerical case studies are constructed to show the convergence process of the improved SDDIP and the planning results of the HES. Moreover, the improved SDDIP shows greater efficiency compared with the traditional SDDIP and the method which solves the model directly.

Kang Ma, Huaqiu Liu, Qiang Wang et al.

Gas-insulated transmission lines (GILs) have a wide application prospect in special occasions such as cross-terrain power transmission. Metal particle contamination is an important reason for low insulation performance of GIL. To better suppress metal particle contamination, it is necessary to master the motion characteristics of metal particles. Based on that, this article took the metal particle dust (MPD), which is common in engineering, as the research object. The boundary element method (BEM) was introduced, and the boundary integral equations (BIEs) of multimedia domain Poisson equation in 3-D were given. The electric field distribution considering charged particles was obtained. Then the motion model of MPD was established. Combined with experiments, the movement and distribution characteristics of MPD in direct-current (dc) GIL were studied. The results show that the randomness of MPD movement in dc GIL is strong, and near the ground, MPD presents a “sandstorm” movement. Second, due to the influence of charged metal particles on electric field distribution, the collision between metal particles, and the randomness of the collision between metal particles and wall, the range of MPD’s motion in the axial direction of GIL chamber is wide, which increases the probability of MPD adhering to the insulator. The adhesion of MPD is more obvious on the convex surface of the insulator near the high-voltage electrode. The above research results can be used to guide the suppression of metal particle contamination in dc GIL.

Fellow S.K. Mazumder

I. B. Sperstad, G. Kjølle, O. Gjerde

Abstract Electric power systems are critical infrastructures subject to the possibility of extraordinary events with high societal consequences. Although such possibilities are often associated with very low probabilities of being realized, it is nevertheless crucial to be able to identify and understand the vulnerabilities of power systems related to extraordinary events. The objective of the work presented in this article is to establish a methodological basis for vulnerability analysis that is complementary to conventional risk and reliability analysis of power systems. It presents a comprehensive framework of definitions, indicators and methods that can be used to classify, analyse and monitor vulnerabilities in power transmission and distribution systems. Its main components include (1) a conceptual framework of definitions that forms the basis for understanding and classifying vulnerabilities, (2) an assessment methodology for identifying vulnerabilities related to extraordinary events and barriers to mitigate them, and (3) vulnerability indicators for quantifying and monitoring power system vulnerabilities. The applicability of the vulnerability analysis framework is demonstrated through several studies of real power systems. Moreover, the concept of power system vulnerability elaborated in this article is also related to the concept of power system resilience.

Damian Owerko, Fernando Gama, Alejandro Ribeiro

Optimal power flow (OPF) is a critical optimization problem that allocates power to the generators in order to satisfy the demand at a minimum cost. Solving this problem exactly is computationally infeasible in the general case. In this work, we propose to leverage graph signal processing and machine learning. More specifically, we use a graph neural network to learn a nonlinear parametrization between the power demanded and the corresponding allocation. We learn the solution in an unsupervised manner, minimizing the cost directly. In order to take into account the electrical constraints of the grid, we propose a novel barrier method that is differentiable and works on initially infeasible points. We show through simulations that the use of GNNs in this unsupervised learning context leads to solutions comparable to standard solvers while being computationally efficient and avoiding constraint violations most of the time.