Optimal configuration of battery energy storage systems using for rooftop residential photovoltaic to improve voltage profile of distributed network

Due to the power mismatches between residential load and rooftop residential photovoltaic (PV) generation, voltage profile of low-voltage distribution system (LVDN) may exceed the allowable limit during the daytime and is well below the lower limit at night. To solve this problem, an optimal configuration of battery energy storage (BES) systems is used for rooftop residential PV to improve the voltage profile of LVDN. Firstly, typical curves of residential electric load and PV generation are analysed to demonstrate the principle of integrated voltage regulation method. Secondly, two-stage voltage regulation strategy is proposed to minimise the installed capacity of BES. More specifically, to limit the voltage with reasonable level, the reactive power capability of PV inverter can be utilised in inner stage, and BES is used in outer stage in term of storage charging at off-peak time and discharging at the peak time. Finally, optimal configuration model is achieved by combining the two-stage voltage control method and the constraint condition of total reduced PV generation. Simulation results indicate that the voltage level in LVDN could be controlled in a proper range by the proposed optimal configuration scheme and the total installed capacity of BES is the minimum

Control capacity and bimodality in target control

Controlling large networks is a fundamental problem and a great challenge in network science. Typically, full control is not necessary and infeasible. In many cases, only a preselected subset of nodes is required to be controlled, which is the target control problem. Each node does not participate in controlling the target set with equal probability, prompting us to quantify their contributions for target control. Here we develop a random sampling method to estimate the likelihood of each node participating as a driver node in target control configurations and demonstrate the unbiasedness of sampling. Each node is assigned with a role of critical, intermittent or redundant as it appears in all, some and none of the minimum driver node sets accordingly. We apply the method to Erdős-Rényi (ER) and scale-free (SF) networks and find that the fractions of critical and intermittent nodes increase as the scale of the target set increases. Furthermore, when the size of target node is fixed in SF networks, the fraction of redundant nodes may show a bimodal behavior as the networks become denser, leading to two control modes: centralized control and distributed control. The findings help understand the dynamics of control and offer tools for target control in complex systems

Wind-Photovoltaic-Energy Storage System Collaborative Planning Strategy Considering the Morphological Evolution of the Transmission and Distribution Network

The collaborative planning of a wind-photovoltaic (PV)-energy storage system (ESS) is an effective means to reduce the carbon emission of system operation and improve the efficiency of resource collaborative utilization. In this paper, a wind-PV-ESS collaborative planning strategy considering the morphological evolution of the transmission and distribution network is proposed. Firstly, aiming at the optimal economy of transmission and distribution network and considering the constraints of safe and stable operation of the system, the planning model of the transmission network based on DC power flow and the planning model of the distribution network based on AC power flow are constructed. Further, considering the coupling interaction between the transmission and distribution networks, a collaborative planning model of transmission and distribution networks based on second-order cone relaxation (SOCR) is constructed. Secondly, in order to reduce the computational complexity of the model and ensure the global optimality of the model solution, a fast model solution method based on heterogeneous decomposition architecture is proposed. Thirdly, the multiple driving factors of the morphological evolution of transmission and distribution network are analyzed, the morphological evolution path and typical characteristics of transmission and distribution network are determined, and a wind-PV-ESS collaborative planning strategy considering the morphological evolution of a transmission and distribution network is proposed. Finally, the results show that, compared with the sprouting period, the overall economy of the development period and maturity period is improved by 3342 k$and 5751 k$ respectively, and the effectiveness and necessity of the collaborative planning strategy proposed in this paper is verified

Recombination and selection in the major histocompatibility complex of the endangered forest musk deer (Moschus berezovskii)

The forest musk deer (Moschus berezovskii) is a high elevation species distributed across western China and northern Vietnam. Once abundant, habitat loss and poaching has led to a dramatic decrease in population numbers prompting the IUCN to list the species as endangered. Here, we characterized the genetic diversity of a Major Histocompatibility Complex (MHC) locus and teased apart driving factors shaping its variation. Seven DRB exon 2 alleles were identified among a group of randomly sampled forest musk deer from a captive population in the Sichuan province of China. Compared to other endangered or captive ungulates, forest musk deer have relatively low levels of MHC genetic diversity. Non-synonymous substitutions primarily occurred in the putative peptide-binding region (PBR), with analyses suggesting that recombination and selection has shaped the genetic diversity across the locus. Specifically, inter-allelic recombination generated novel allelic combinations, with evidence for both positive selection acting on the PBR and negative selection on the non-PBR. An improved understanding of functional genetic variability of the MHC will facilitate better design and management of captive breeding programs for this endangered species

A Novel Receiving End Grid Planning Method with Mutually Exclusive Constraints in Alternating Current/Direct Current Lines

The large-scale application of high-voltage direct current (HVDC) transmission technology introduces mutually exclusive constraints (MEC) into the power grid planning, which deepens the complexity of power grid planning. The MECs decrease the planning efficiency and effectiveness of the conventional method. This paper proposes a novel hybrid alternating current (AC)/direct current (DC) receiving end grid planning method with MECs in AC/DC lines. The constraint satisfaction problem (CSP) is utilized to model the MECs in candidate lines and then the detailed planning model, in which mutually exclusive candidate lines are described by mutually exclusive variable and constraint sets. Additionally, the proposed planning model takes the hybrid AC/DC power system stability into consideration by introducing the multi-infeed short circuit ratio (MISCR). After establishing the hybrid AC/DC receiving end grid planning model with MECs, the backtracking search algorithm (BSA) is used to solve the optimal planning. The effectiveness of the proposed hybrid AC/DC grid planning method with MECs is verified by case studies

A review of intelligent ship marine object detection based on RGB camera

Abstract The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost‐effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi‐feature extraction, multi‐fusion technology, and lightweight detection architecture

A Fault Location Method for Medium Voltage Distribution Network Based on Ground Fault Transfer Device

The arc suppression device based on ground fault transfer (GFT) has been preliminarily applied in the medium voltage distribution network (MVDN). An accurate travelling wave (TW) fault location method is proposed to extend the use of the ground fault transfer device. D-PMU is used as a travelling wave detection tool to record the transient voltage travelling waves of fault grounding and bus active grounding during arc suppression. Then, the faulty section is identified through the time difference of travelling wave arrival at the upstream and downstream measurement points. On this basis, the fault location equations of the arrival time and distance of the upstream travelling wave are established, and an accurate fault location method based on the arrival time difference of the travelling wave is proposed. The simulation model is established by PSCAD/EMTDC, and the results show that the method has high location accuracy, and the absolute error is less than 30 m. It is not affected by the TW velocity, the fault conditions, or the distributed power sources