Stable Distribution Network Research Articles

A Review of Research on The Reliability Assessment of Fresh Product Distribution Networks Considering Product Perishability Constraint

A reliable and stable distribution network is fundamental for the effective operation of fresh agricultural product logistics, and network reliability is a crucial metric for determining whether fresh agricultural product logistics can complete specified tasks promptly and smoothly. To further clarify the current research status on the reliability of fresh agricultural product distribution networks considering transportation losses, this study reviews the literature from three aspects: research on transportation losses of fresh agricultural products, reliability of stochastic distribution networks, and methods for assessing the reliability of stochastic distribution networks considering transportation losses. The studies indicate that few documents introduce both quantity deterioration and quality deterioration simultaneously into network reliability research, which tends to overlook the market demand under reduced freshness, and seldom considers the dynamic changes of deterioration factors in complex environments.

A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework

Burdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctuations when meeting the EV charging demands. Moreover, a thorough investigation is required for the coordination of the benefits to the distribution system operator (DSO), EV aggregator (EVA), and EVs. In this paper, a novel EV charging scheme considering the temporal-spatial features of EVs is proposed, achieving the coordination of DSO, EVA, and EVs in two steps. First, a mixed-integer second-order cone programming (MISOCP)-based EV charging schedule is developed for DSO. Second, the temporal-spatial features of EVs are exploited by EVA to track the charging schedule. Specifically, a dynamic charging price mechanism based on active power margin is proposed for public charging facilities and a specific load regulation is designed for private charging facilities. On this basis, the modified adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed, including adaptive flight parameter adjustment and termination mechanisms. Case studies demonstrate the proposed strategy can attenuate load variance and raise EVA revenue. Further, the impact analysis of penalty price and the price elasticity of electricity demand can provide references for stable distribution network operation, higher EVA revenue, and charging cost reduction.

Distribution network topology identification based on gradient boosting decision tree and attribute weighted naive Bayes

Topology identification is important to ensure a safe and stable distribution network operation, especially in the case of high proportion of new energy access to the distribution network. It could provide structural information for distribution network management and the foundation of distribution network system analysis. In consideration of the influence of different power flow parameters on topology identification results, this paper proposes a topology identification method based on gradient boosting decision tree (GBDT) and attribute weighted naive Bayes (AWNB). Firstly, a gradient boosting decision tree was used to calculate the importance of different power flow parameters to reduce the data dimension and computational complexity. Then each attribute is given a weight based on the result of the importance calculation. Secondly, the weighted set was used as input to train the AWNB classifier to make the model more realistic. Finally, the IEEE 30-node model was used to verify the performance of the proposed method. The results indicate that the proposed method has high accuracy and good robustness, which could maintain a high success rate of topology identification under different noise environments.

A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network

In developing distribution networks, the deployment of alternative generation sources is heavily motivated by the growing energy demand, as by environmental and political motives. Consequently, microgrids are implemented to coordinate the operation of these energy generation assets. Microgrids are systems that rely on power conversion technologies based on high-frequency switching devices to generate a stable distribution network. However, disrupting scenarios can occur in deployed systems, causing faults at the sub-component and the system level of microgrids where its identification is an economical and technological challenge. This paradigm can be addressed by having a digital twin of the low-level components to monitor and analyze their response and identify faults to take preventive or corrective actions. Nonetheless, accurate execution of digital twins of low-level components in traditional simulation systems is a difficult task to achieve due to the fast dynamics of the power converter devices, leading to inaccurate results and false identification of system faults. Therefore, this work proposes a fault identification framework for low-level components that includes the combination of Real-Time systems with the Digital Twin concept to guarantee the dynamic consistency of the low-level components. The proposed framework includes an offline trained Self Organized Map Neural Network in a hexagonal topology to identify such faults within a Real-Time system. As a case study, the proposed framework is applied to a three-phase two-level inverter connected to its digital model in a Real-Time simulator for open circuit faults identification.

Design of a DSO‐TSO balancing market coordination scheme for decentralised energy

The proliferation of distributed generation and the electrification of heat and transport pose significant challenges to distribution system operators (DSOs) and transmission system operators (TSOs). These challenges include the choice between network upgrades or operating increasingly constrained networks, with a reliance on the flexibility of distributed energy resources (DERs). This study presents a novel market-based coordination scheme, which allows both the DSO and TSO to access DER flexibility, while respecting distribution system limits. The DSO's objective in this work is to minimise the cost incurred by DSO adjustments to DERs, required to ensure stable distribution network operation. The methodology presented has the advantages of being compatible with existing TSO balancing market operation, and scalable enough to include multiple DSO markets coordinating with the TSO. The approach is demonstrated on a section of Great Britain distribution network, using high DER growth scenario data for the year 2030. The case studies demonstrate the proposed DSO market mechanism to maintain thermal and voltage limits during periods of peak demand and DER output. The DSO is given priority in using DERs to solve distribution network constraints, however, significant flexibility remains for the TSO even during periods of peak demand and maximum export.

Optimization of Distribution Network Construction Based on GIS System

With the rapid development of China’s economy and science and technology, people’s daily life and scientific research work demand more and more electricity. The traditional distribution network structure and load can hardly meet the actual demand. Therefore, it is very necessary to construct a more stable distribution network system in accordance with the actual demand for electricity. This paper mainly introduces the application of GIS system in power distribution network planning and design, and realizes the optimization and upgrading of power distribution network in coordination with the geographic information of the construction site, so as to meet the increasing demand for electricity.

Calibration of Correlation Radiometers Using Pseudo-Random Noise Signals

The calibration of correlation radiometers, and particularly aperture synthesis interferometric radiometers, is a critical issue to ensure their performance. Current calibration techniques are based on the measurement of the cross-correlation of receivers’ outputs when injecting noise from a common noise source requiring a very stable distribution network. For large interferometric radiometers this centralized noise injection approach is very complex from the point of view of mass, volume and phase/amplitude equalization. Distributed noise injection techniques have been proposed as a feasible alternative, but are unable to correct for the so-called “baseline errors” associated with the particular pair of receivers forming the baseline. In this work it is proposed the use of centralized Pseudo-Random Noise (PRN) signals to calibrate correlation radiometers. PRNs are sequences of symbols with a long repetition period that have a flat spectrum over a bandwidth which is determined by the symbol rate. Since their spectrum resembles that of thermal noise, they can be used to calibrate correlation radiometers. At the same time, since these sequences are deterministic, new calibration schemes can be envisaged, such as the correlation of each receiver’s output with a baseband local replica of the PRN sequence, as well as new distribution schemes of calibration signals. This work analyzes the general requirements and performance of using PRN sequences for the calibration of microwave correlation radiometers, and particularizes the study to a potential implementation in a large aperture synthesis radiometer using an optical distribution network.

Analysis of noise-injection networks for interferometric-radiometer calibration

The spatial resolution of current space-borne Earth observation radiometers is limited by the physical antenna aperture. This is especially critical at L-band, which exhibits high sensitivity to soil moisture and sea surface salinity. Interferometric radiometers (InR's) are currently being studied by several space agencies as a feasible alternative to overcome this problem. However, their calibration is a crucial issue since most techniques inherited from radio astronomy cannot be directly applied. Due to the large number of receiving channels, calibration techniques based on centralized noise injection from a single noise source will require a large and stable distribution network, which is technically very complex and unacceptable from the point-of-view of mass and volume. Procedures based on distributed noise injection from a set of noise sources through smaller distribution networks have been recently proposed by the authors as an alternative to alleviate these technological problems. In this paper, the analysis of these networks, the impact of the noise generated by the network losses on the calibration, and the impact of front-end reradiated noise are analyzed. Finally, the optimum circuit topologies and tolerances to which these networks have to be characterized in order to achieve the required calibration are derived. These configurations are formed by cascading basic 1:2 and 1:3 isolated power splitters. Isolators at receiver inputs have to be included in order to minimize offsets originating from the correlation of reradiation of receiver noise. It has been found that, in order to satisfy the calibration requirements of InR's, the S-parameters of the ensemble noise-injection network plus isolators have to be known to within 0.025-0.050 dB in amplitude and 0.5/spl deg/ in phase, and their physical temperature known to within 0.5/spl deg/C.