Hasil untuk "Distribution or transmission of electric power"

Yao Liu, P. Ning, M. Reiter

Li

Yi Wang, D. Qiu, Fei Teng et al.

High renewable penetration has been witnessed in power systems, resulting in reduced system inertia and increasing requirements for frequency response services. Electric vehicles (EVs), owing to their vehicle-to-grid (V2G) capabilities, can provide cost-effective frequency services for transmission system operators (TSOs). However, EVs that are inherently connected to distribution networks may pose voltage security issues for distribution system operators (DSOs) when supporting TSO frequency. To coordinate both TSO frequency and DSO voltage, this paper proposes a two-stage service provision framework for multi-EVs. At stage one, EVs participate in day-ahead TSO-DSO interactions for frequency reserve schedules; at stage two, EVs make real-time dispatching behaviors in distribution networks for reserve delivery while supporting DSO voltage. Considering the potentially large EV number and environment complexity, a decentralized operation paradigm is introduced for real-time EV dispatches at stage two, while a communication-efficient reinforcement learning (RL) algorithm is proposed to reduce the communication overhead during large-scale multi-agent RL training without compromising policy performance. Case studies are carried out on a 6-bus transmission and 33-bus distribution network as well as a 69-bus distribution network to evaluate the effectiveness and scalability of the proposed method in enabling EVs for frequency service and voltage support.

Thongchart Kerdphol

To address the challenge of inertia deficiency in multi-area power systems, this paper proposes a novel approach to inertia sharing by leveraging supplementary control integrated with thyristor-controlled series capacitors (TCSC). The proposed supplementary power modulation controller (SPMC) dynamically adjusts TCSC reactance based on frequency and tie-line power deviations to facilitate coordinated inertia transfer from well-equipped areas to inertia-deficient regions. Unlike conventional strategies that rely on deploying additional energy storage systems or distributed virtual inertia units, the proposed method utilizes existing transmission infrastructure, thereby reducing implementation complexity and cost. The efficacy of the proposed control strategy is assessed using a benchmark interconnected model configured to reflect practical multi-area dynamics and intertie constraints. Simulation results confirm that the SPMC-based TCSC control improves transient frequency stability, enhances damping, and increases the efficiency of inertia sharing, even under network congestion and delay conditions. These findings highlight the potential of the proposed strategy as a scalable and practical solution for enhancing dynamic performance in renewable-rich, interconnected power grids.

Ding Lin, Jianhui Wang, Huan-Hsin Tseng et al.

Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.

Aisling Power, Cara-Lena Nies, Stefan Schulz

Controlling the crystal phase and lattice mismatch of semiconductors offers a powerful route to engineer electronic and optical properties of heterostructures. As a consequence, semiconductors in the wurtzite phase are increasingly sought after, superseding the thermodynamically favored cubic zinc blende phase. Empirical atomistic modeling, required for large scale simulations of heterostructures and their properties, relies heavily on valence force field (VFF) methods to find the equilibrium atomic positions in an alloy. For zinc blende crystals, VFF models are well-established. In the case of wurtzite, such parameters are frequently adopted without rigorous analysis, despite subtle but consequential differences from the zinc blende structure. Such an approach can compromise accuracy in describing material properties, since the structural differences between zinc blende and wurtzite directly influence electronic and optical characteristics. Based on the analytical VFF model by Tanner et al., and using structural similarities between wurtzite and [111]-oriented zinc blende, we construct a wurtzite VFF without introducing additional parameters. Our framework relies on analytic expressions and minimization routines to project zinc blende models onto wurtzite systems. Beyond elastic tensors, we train the model to reproduce bond length asymmetries and band gaps by using output of the VFF model in density functional theory calculations. Applied to wurtzite III-N compounds and BN, the model accurately reproduces targeted observables but also properties it has not been trained on, including the internal parameter u. We further validate the model on highly mismatched alloys such as (B,Ga)N and (B,In,Ga)N, exhibiting good agreement between VFF and density functional theory results when using identical supercells in these calculations.

Yiping Liu, Xiaozhe Wang, Geza Joos

The increasing penetration of electric vehicles (EVs) can provide substantial electricity to the grid, supporting the grids' stability. The state space model (SSM) has been proposed as an effective modeling method for power prediction and centralized control of aggregated EVs, offering low communication requirements and computational complexity. However, the SSM may overlook specific scenarios, leading to significant prediction and control inaccuracies. This paper proposes an extended state space model (eSSM) for aggregated EVs and develops associated control strategies. By accounting for the limited flexibility of fully charged and discharged EVs, the eSSM more accurately captures the state transition dynamics of EVs in various states of charge (SOC). Comprehensive simulations show that the eSSM will provide more accurate predictions of the flexibility and power trajectories of aggregated EVs, and more effectively tracks real-time power references compared to the conventional SSM method.

Shahabodin Afrasiabi, Sarah Allahmoradi, Mousa Afrasiabi et al.

In this paper, a robust, multi-modal deep-learning-based fault identification method is proposed for solar photovoltaic (PV) systems, capable of detecting a wide range of faults at PV arrays, inverters, sensors, and grid connections. The proposed method combines residual convolutional neural networks (CNNs) and gated recurrent units (GRUs) to effectively extract both spatial and temporal features from raw PV data. To enhance the proposed model’s robustness and accuracy, a probabilistic loss function based on the entropy theory is formulated. The proposed method is validated using both experimental data obtained from a PV emulator-based test system and simulation data, achieving over 98% accuracy in fault identification under various noise conditions. The results indicate that the proposed model outperforms conventional CNN- and MSVM-based methods, demonstrating its potential in providing precise fault diagnostics in PV systems.

Feifei Wang, Zhijun Qin

Abstract The rapid growth of offshore wind power has resulted in a mismatch between generation and demand because of its variability. To quantify the maximum wind power penetration of the transmission network, the dispatchable region is defined as the largest region in the uncertainty space. The security distance is defined as the minimum distance from the operating point to the boundaries of the dispatchable region. System operators can use security distance as a metric to assess the flexibility of the power system. This paper proposes a method to construct the dispatchable region for the AC/DC hybrid system with VSC‐HVDC by outer convex relaxation firstly. The second‐order cone relaxation is employed to reformulate non‐convex and non‐linear power flow equations. Next, a polyhedral approximation is adopted to obtain the convex hull of the dispatchable region. Subsequently, an efficient algorithm known as adaptive constraint generation (Ad‐CG) is introduced to calculate the boundaries of the dispatchable region. Furthermore, solving the Chebyshev centre problem determines the minimal security distance. The modified IEEE 5‐bus and 39‐bus system is used for validating effectiveness of the proposed method and evaluating the impact of the converter reactive power compensation capacity and generation dispatch on the dispatchable region.

Juan Avilés, Daniel Guillen, Luis Ibarra et al.

Abstract The integration of alternative energy sources, storage systems, and modern loads into the distribution grid is complicating its operation and maintenance. Variability in individual generation and consumption elements dynamically affects voltage profiles, which in turn undermines efficiency and power quality. This study proposes to address this dynamical variability using an online reconfiguration approach that involves opening and closing switches to modify the grid's topology and adjust voltage levels in response to load/generation variations. Other grid optimization techniques, based on reconfiguration, typically focus on static, fully instrumented grids with predictable parameters and homogeneous changes, aiming to minimize power losses but overlooking the dynamics of variable grid elements. This study proposes a testing approach that is dependent on the estimated transient status of the grid only using a limited number of measurement units and considering the individual‐stochastic variations of loads and generators. The proposed approach was tested on the IEEE 33‐bus test feeder with up to five varying distributed generators. The results confirm that the algorithm consistently finds a reconfiguration alternative that could enhance system efficiency and voltage profiles, even in the face of dynamic load/generator behavior, demonstrating its effectiveness and online adaptability for grid operation and management tasks.

Pikkin Lau, Lingfeng Wang, Wei Wei et al.

In this paper, a novel cyber-insurance model design is proposed based on system risk evaluation with smart technology applications. The cyber insurance policy for power systems is tailored via cyber risk modeling, reliability impact analysis, and insurance premium calculation. A stochastic Epidemic Network Model is developed to evaluate the cyber risk by propagating cyberattacks among graphical vulnerabilities. Smart technologies deployed in risk modeling include smart monitoring and job thread assignment. Smart monitoring boosts the substation availability against cyberattacks with preventive and corrective measures. The job thread assignment solution reduces the execution failures by distributing the control and monitoring tasks to multiple threads. Reliability assessment is deployed to estimate load losses convertible to monetary losses. These monetary losses would be shared through a mutual insurance plan. To ensure a fair distribution of indemnity, a new Shapley mutual insurance principle is devised. Effectiveness of the proposed Shapley mutual insurance design is validated via case studies. The Shapley premium is compared with existent premium designs. It is shown that the Shapley premium has high indemnity levels closer to those of Tail Conditional Expectation premium. Meanwhile, the Shapley premium is nearly as affordable as the coalitional premium and keeps a relatively low insolvency probability.

Nataly Bañol Arias, S. Hashemi, P. B. Andersen et al.

Trend-setting countries have promoted or even employed an increased number of electric vehicles (EVs) and other distributed energy resources (DERs) in their power systems. This development has triggered new and increasing challenges in the distribution system planning and operation, whereby the distribution systems must adapt to the increased share of DERs. However, EVs may also offer new opportunities and can be used to support the grid by providing several local and global power- and energy-based services. This paper presents a review and classification of the services potentially available from EVs for distribution systems, referred to as EV distribution system services (EV-DSS). A detailed description of recent services and approaches is given, and an assessment of the maturity of EV-DSS is provided. Moreover, challenges and prospects for future research are identified, considering key topics, such as the design of the market framework, economic assessment, battery degradation, and the impacts of the transmission system operator service provision by EVs on distribution networks. Thus, this paper offers a tool for stakeholders concerning services available from EVs and provides a broad literature framework that can be used as a base for further investigations. It is aligned with the current requirements to move toward the realistic implementations of EV-DSS.

Chizindu Stanley Esobinenwu, Oniyeburutan ET

Management of reactive power and voltage control constitute part of the major challenges in a power system. Appropriate reactive power management and control solves power quality problems, reduce losses, improve power factor, maintained a balanced voltage profile at all power transmission levels, improved system efficiency and stability. This paper examined the brief idea about the mode of operations, design characteristics of various types of reactive power compensation techniques. These techniques are used to improve the performance of AC transmission & distribution systems. They enhance the stability of the AC transmission system by increasing the active power that can be transmitted thereby enhancing the overall working of the electric power system.

Ankur Majumdar, Omid Alizadeh‐Mousavi

Abstract The intermittencies of renewable generations give rise to situations, which require both slow‐ and fast‐ramping flexibility capability from a variety of resources connected at the medium voltage (MV) andlow voltage (LV) distribution grids. Moreover, this may increase the grid reinforcement costs. To defer this reinforcement, the grid needs to be operated optimally. This paper proposes—(a) such an optimal operational methodology forthe MV and LV grids; and (b) an aggregated flexibility estimation methodology separately for fast and slow services at the primary substation (transmission interface). The methodologies based on model‐based MV grids and sensitivity coefficients‐based LV grids are suitable for LV grids where an up‐to‐date, accurate model and topology are not always available. Besides, the costs and resources associated to create full LV grid model and visibility should be prevented. The approaches, here, use the synchronised and accurate measurements from grid monitoring devices located at the LV distribution grids. They have been validated on a real MV and LV networks of a Swiss distribution grid operator equipped with such devices. The results, in terms of reducing technical losses, reducing grid violation costs,and estimating flexibility capability show the efficacy of the proposed methodologies and therefore, can be easily deployed.

Jie Shan, Bo‐Lin Xie, Yong‐Jun Zhang et al.

Abstract Salp swarm algorithm (SSA) is an excellent meta‐heuristic algorithm, which has been widely used in the engineering field. However, there is still room for improvement in terms of convergence rate and solution accuracy. Therefore, this paper proposes an enhanced parallel salp swarm algorithm based on the Taguchi method (PTSSA). The parallel trick is to split the initial population uniformly into several subgroups and then exchange information among the subgroups after a fixed number of iterations, which speeds up the convergence. Communication strategies are an important component of parallelism techniques. The Taguchi method is widely used in the industry for optimizing product and process conditions. In this paper, the Taguchi method is adopted into the parallelization technique as a novel communication strategy, which improves the robustness and accuracy of the solution. The proposed algorithm was also tested under the CEC2013 test suite. Experimental results show that PTSSA is more competitive than some common algorithms. In addition, PTSSA is applied to optimize the operation of a heatless combined cooling‐power system. Simulation results show that the optimized operation provided by PTSSA is more stable and efficient in terms of operating cost reduction.

Abolfazl Ghaffari, Alireza Askarzadeh, Roohollah Fadaeinedjad

Abstract As a well‐established renewable energy resource, wind energy is being widely used in distribution networks. However, flicker emission produced by wind turbines (WTs) is one of the main disadvantages which may cause serious problems during the system operation. Power quality‐based optimal allocation (siting and sizing) of WTs is an appropriate way to mitigate the flicker emission. Since the planning issue is a high dimensional and complex optimization problem and optimization methods suffer from premature convergence, search space reduction may be an effective approach to optimally solve the allocation problem. Here, to determine the importance of the network buses, two indices, namely, power loss index (PLI) and flicker emission index (FEI) are introduced to determine the sensitivity of the network losses and flicker emission to allocate WT on each bus. Then, based on the proposed indices, a number of important buses are identified and the allocation problem is solved with respect to these buses. Over the case study, it can be drawn that optimal allocation of WTs considerably affects the flicker emission. Moreover, search space reduction is an efficient way to optimally solve the allocation problem.

Alejandro D. Dominguez-Garcia, Madi Zholbaryssov, Temitope Amuda et al.

We address the problem of controlling the reactive power setpoints of a set of distributed energy resources (DERs) in a power distribution network so as to mitigate the impact of variability in uncontrolled power injections associated with, e.g., renewable-based generation. We formulate the control design problem as a stochastic optimization problem, which we solve online using a modified version of a projected stochastic gradient descent (PSGD) algorithm. The proposed PSGD-based algorithm utilizes sensitivities of changes in bus voltage magnitudes to changes in DER reactive power setpoints; such sensitivities are learned online via a recursive least squares estimator (rLSE). To ensure proper operation of the rLSE, the sequence of incremental changes in DER reactive power setpoints needs to be persistently exciting, which is guaranteed by a mechanism built into the controller. We analyze the stability of the closed-loop system and showcase controller performance via numerical simulations on the IEEE 123-bus distribution test feeder.

E. Gulski, R. Jongen

One of the issues faced by asset managers in the electric power industry is the condition and remaining life assessment of the high voltage (HV) assets. In contrast to other power equipment, transmission power cables are mainly subjected to corrective maintenance. To support the asset management of service aged transmission power cable circuits, this paper discusses an approach of integrated knowledge rules for condition-based maintenance based on laboratory investigations and field examples. There are very few recent publications on this subject in spite of aging power cable infrastructure; therefore, this paper aims at showing practical applications of condition-based maintenance of this important component of the transmission and distribution systems.

B. Carreras, V. Lynch, I. Dobson et al.

Guansheng Fan, Shunjiang Lin, Xiang-qian Feng et al.

With lots of distributed renewable generators and storage devices integrated into distribution networks, the coordinate economic dispatch (ED) between transmission and distribution networks is needed. A stochastic ED algorithm based on distributed approximate dynamic programming for integrated transmission and distribution networks considering the uncertain renewable generator outputs is proposed. First, the convex power flow models of transmission and distribution networks are proposed. Then, the reservoir levels of pumped-storage hydro stations and the remaining stored electric energy of battery storage stations are deemed as the storage quantities, while the renewable generator outputs are deemed as the exogenous stochastic variables to establish the stochastic storage model for the stochastic ED problem of integrated transmission and distribution networks. The approximate value functions represented by the sum of the piecewise linear functions of the storage quantities are constructed to decouple the multiperiod ED problem into a series of single-period ED problems. Finally, by using the alternating direction method of multipliers, each single-period ED problem of integrated transmission and distribution networks is solved in a distributed manner among transmission and distribution networks. Test results on the modified T6D7 system and a practical Shenzhen city network demonstrate the correctness and efficiency of the proposed algorithm.