Iet Generation Transmission & Distribution最新文献

For cooperation among distributed generations in a DC microgrid (MG), distributed control is widely applied. However, the delay in distributed communication will result in steady-state bias and the risk of instability. This paper proposes a novel distributed control for time-delayed DC MGs to achieve accurate current proportional sharing and weighted average voltage regulation. Firstly, by utilizing an advanced observer based on the PI consensus algorithm, the steady-state bias problem is addressed. Then, using the passivity theory, stability analysis is conducted to reveal the principle of system instability caused by communication delay. On this basis, to offset the adverse effects of communication delay on the system stability, scattering transformation is introduced in the observer-based distributed control. Moreover, considering the potential delay from the measurement stage in real-life scenarios, the sufficient condition of the system stability is concluded by constructing the Lyapunov–Krasovskii functional. Finally, the performance of the proposed control and conclusions of stability analysis are verified by hardware-in-loop tests.

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.

As an emerging multi-energy consumption subject, data centres (DCs) are bound to become crucial energy users for integrated energy systems (IES). Therefore, how to fully tap the potential of the collaborative operation between DCs and IES to improve total energy efficiency and economic performance is becoming a pressing need. In this article, the authors research an optimization coordinated by the energy scheduling and information service provision within the scenario of an integrated energy system with a data centre (IES-DC). The mathematical model of IES-DC is first established to reveal the energy conversion process of the electricity-heat-gas IES and the DC's energy consumption affected by the scale of active IT equipment. For dynamical providing multi-energy and computing service by coordinating scheduling energy and information equipment, the formulations of IES-DC scheduling, which is described as a Markov decision process (MDP), are presented, and it is solved by introducing the twin-delayed deep deterministic policy gradient (TD3), which is a model-free deep reinforcement learning (DRL) algorithm. Finally, the numerical studies show that compared with benchmarks, the proposed method based on the TD3 algorithm can effectively control the operation of energy conversion equipment and the number of active servers in IES-DC.

It is difficult to identify an arc fault accurately when the loads on the user side are more complicated, which hinders the development of low-voltage monitoring and pre-warning inspection. This study acquired a series of arc-fault signals according to IEC 62606. The main time-frequency features were strengthened with high efficiency by applying the generalized S-transform to them with a bi-Gaussian window. Further, the power spectrum density determination allowed for the detection of imperceptible high-frequency harmonic energy reflections, thus increasing the rate of arc-fault diagnosis and making it suitable for arc-fault monitoring of non-linear loads. The final samples were trained and classified using a 2D convolutional neural network and the overall accuracy of identification was observed to be 98.13%, which involved various domestic loads, thus providing a reference for follow-up arc-fault monitoring and inspection research.

This study thoroughly investigates the NoisyNet Deep Deterministic Policy Gradient (DDPG) for frequency regulation. Compared with the conventional DDPG method, the suggested method can provide several benefits. First, the parameter noise will explore different strategies more thoroughly and can potentially discover better policies that it might miss if only action noise were used, which helps the actor achieve an optimal control strategy, resulting in enhanced dynamic response. Second, by employing the delayed policy update policy work with the proposed framework, the training process exhibits faster convergence, enabling rapid adaptation to changing disturbances. To substantiate its efficacy, the scheme is subjected to simulation tests on both an IEEE three-area power system, an IEEE 39 bus power system, and an IEEE 68 bus system. A comprehensive performance comparison was performed against other DDPG-based methods to validate and evaluate the performance of the proposed LFC scheme.

Bird-related outages greatly threaten the safety of overhead transmission and distribution lines, while electrocution and collisions of birds with power lines, especially endangered species, are significant environmental concerns. Automatic bird recognition can be helpful to mitigate this contradiction. This paper proposes a method for automatic classification of bird species related to power line faults combining deep convolution features with error-correcting output codes support vector machine (ECOC-SVM). An image dataset of about 20 high-risk and 20 low-risk bird species was constructed, and the feed-forward denoising convolutional neural network was used for image preprocessing. The deep convolution features of bird images were extracted by DarkNet-53, and taken as inputs of the ECOC-SVM for model training and bird species classification. The gradient-weighted class activation mapping was used for visual explanations of the model decision region. The experimental results indicate that the average accuracy of the proposed method can reach 94.39%, and its performance was better than other models using different feature extraction networks and classification algorithms.

The increasing penetration of the distributed energy resources (DER) in the power grid, which, while having significant advantages, also pose significant challenges. The behaviors of DERs differ from those of synchronous generators, particularly in abnormal conditions. For this reason, the power grid enforces grid codes to ensure that DERs perform properly in different conditions. For instance, short circuit faults and unbalanced grid voltage are severe transient events that inverters need to be able to pass through without disconnecting from the grid. Furthermore, the inverters are required to support the grid voltage by regulating the active and reactive power injections. This article proposes a voltage support control scheme to support grid voltage during asymmetrical voltage drop by utilizing an optimization problem. In this optimization problem, the active and reactive powers injected into the grid will be obtained optimally by considering constraints such as instantaneous active and reactive power oscillation magnitudes and peak current limitation. To aid in this purpose, the corresponding mathematical formulations such as instantaneous active and reactive power oscillation magnitudes will be obtained by using the currents and voltages in stationary reference frame. The proposed scheme will be verified by simulating it in MATLAB/Simulink under three different scenarios and tested on a real-time experimental Opal-RT platform.

This article proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short-term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this article, the model training is formulated as a bi-level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower-level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full connected Neural Network (FNN) to generate interval boundary. In upper-level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper-parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state-of-the-art algorithms, achieving a 15% reduction in prediction error and a 20% decrease in computational time.

With the increasing deployment of renewable energy resources, the scale of DC networks and renewable capacity continues to grow. System security is challenged by the decrease in inertia from traditional synchronous generators. In order to accommodate high-penetration renewable energy, voltage stability should be considered in the renewable energy integration planning. Herein, first, a voltage stability-constrained minimum startup index and algorithm for conventional thermal power plants are proposed. Then, based on time series production simulation, a renewable energy integration capacity analysis algorithm is designed considering voltage stability and peak shaving constraints. Finally, based on the boundary conditions of the “Fifteen-Five Plan”, the renewable energy capacity considering voltage stability and peak shaving constraints for Northwest and East China power grids are analysed to verify the effectiveness and engineering practicality of the proposed methodology. The results demonstrate that the proposed method can maintain the renewable energy integration goal outlined in the “Fifteen-Five Plan” while maintaining the voltage stability in these regions.

Electric power systems are currently undergoing a transformation towards a decentralized paradigm by actively involving prosumers, through the utilization of distributed multi-energy sources. This research introduces a fully decentralized multi-community peer-to-peer electricity trading mechanism, which integrates iterative auction and pricing methods within local electricity markets. The mechanism classifies peers in all communities on an hourly basis depending on their electricity surplus or deficit, facilitating electricity exchange between sellers and buyers. Moreover, communities engage in energy exchange not only within and between themselves but also with the grid. The proposed mechanism adopts a fully decentralized approach known as the alternating direction method of multipliers. The key advantage of this approach is that it eliminates the need for a supervisory node or the disclosure of private information of the involved parties. Furthermore, this study incorporates the flexibility provided by residential heating systems and energy storage systems into the energy scheduling of some prosumers. Case studies illustrate that the proposed multi-community peer-to-peer electricity trading mechanism effectively enhances local energy balance. Specifically, the proposed mechanism reduces average daily electricity costs for individual prosumers by 63% compared to scenarios where peer-to-peer electricity trading is not employed.