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All aspects of smart grid operation require precise timing technology to improve the timeliness and precision of the network. However, due to the complex and time-varying network environment and the low precision of device timing, the existing timing technology cannot meet the timing precision required by smart grid services. Therefore, according to the multi-armed bandit (MAB) theory and the upper confidence bound (UCB) algorithm, this paper proposes a high-precision timing method for power transmission and distribution park named adaptive timing-aware upper confidence bound (AUCB). The long-term average reward is maximized based on local information and historical decision-making experience. The simulation results show that the proposed high-precision timing method of AUCB in the power transmission and distribution park can effectively improve the device timing precision and realize self-adaptive timing for power system devices.

The construction of intermodal logistics parks in China is developing rapidly, but most of them are in the bad mode of high energy consumption. While polluting the environment, it also affects the cost reduction and efficiency of the whole logistics industry, which is not conducive to the healthy development of the logistics industry. Therefore, under the goals of “carbon peak” and “carbon neutral”, the planning of multimodal logistics parks must seek the development path of low-carbon transformation. This is not only a responsibility, but also a necessary path for the healthy and sustainable development of the logistics industry in the future, which has far-reaching strategic significance. Therefore, based on this background, the paper describes the opportunities and challenges of low-carbon construction of China’s multimodal logistics parks. According to the above analysis, combined with the relevant policies, it puts forward the low-carbon construction path of multimodal logistics parks. It is intended to provide support for the promotion of low-carbon construction of multimodal logistics parks and help the practice of the “carbon peak” and “carbon neutral” strategic goals in practice.

As alternative substances of PBDEs, organophosphate esters (OPEs), an emerging organic pollutant, were increasingly produced and used in many kinds of industries and consumer products. However, OPEs also have various adverse toxic effects. Information on the pollution levels and exposure to OPEs in related industries is still limited. This study presented data on OPE contamination in the soil, leaf, and river water samples from seven typical industrial parks in Southwest China. Total concentration of seven OPEs (Σ 7 OPE) including tri-n-butyl phosphate (TnBP), tris-(2-ethylhexyl) phosphate (TEHP), tris-(2-butoxyethyl) phosphate (TBEP), tris-(2-carboxyethyl) phosphine (TCEP), triphenyl phosphate (TPhP), tris-(1,3-dichloro-2-propyl) ester (TDCPP), and tris-(chlorisopropyl) phosphate (TCPP) in the soil samples (36.2 ~ 219.7 ng/g) and the surrounding river water samples (118.9 ~ 287.7 ng/L) were mostly lower than those in other studies, while the Σ 7 OPE level in the leaves (2053.3 ~ 8152.7 ng/g) was relatively high. There were significant differences in the concentration and distribution of OPEs in the surrounding environment of different industrial parks. TDCPP, TnBP, and TCPP could be used as the characteristic compound in soil samples from auto industrial park, river samples from shoe making industrial park, and leaf samples from logistics park, respectively. The parameter m (the content ratio of chlorinated OPEs to alkyl OPEs) was suggested to distinguish the types of industrial park preliminary. When m ≥ 1, it mainly refers to heavy industries sources such as automobiles, electronics, and machinery, etc. When m <1, it mainly for the light industrial sources such as textile industry, transportation services, and resources processing, etc. For logistics park, furniture park and Wuhou comprehensive industrial park, the volatilization of materials was the main sources of OPEs in the surrounding environment, while more effort was required to strengthen the pollution control and management of the waste water and soil in the pharmacy industrial park, shoe making industrial park and auto industrial park. Risk assessment showed that there was a negligible non-cancer and carcinogenic risk in the soil, while high attention should be paid to the non-cancer risk for children.

International trade connections with COVID-19 impeding the development of the logistics industry in express delivery, the world has become an inseparable part of daily life. To improve protection competency, there is a need for effective research on logistics warehouse fire accident alarms. The goal of this study is to create a novel fire risk evaluation method for fire safety managers in logistics warehouses. The Gustav method is used to convert a plane model to a stereoscopic model. Hazards to construction, hazards to life, and fire rescue competency are all taken into account. The empirical study used JingDong Gu'an logistics park as a case study, and the evaluation results revealed differences in fire risk levels between the two warehouses. The results show that the transmit warehouse had a higher fire risk level than the sorting warehouse. The method describes the total risk of a warehouse fire. It is appropriate for the various types and processes found in modern logistics warehouses. The results of the developed 3D-Dynamic method demonstrate the model's feasibility and practicability even to laypeople with limited professional knowledge.

As new energy integration increases, power grid load curves become steeper. Large logistics parks, with their substantial cooling load, show great peak shaving potential. Leveraging this load while maintaining staff comfort, product quality, and operational costs is a major challenge. This paper proposes a two-stage robust optimization method for large logistics parks to participate in grid peak shaving. First, a Cooling Load’s Economic Contribution (CLEC) index is introduced, integrating the Predicted Mean Vote (PMV) and Sales Pressure Index (SPI). Then, an optimization model is established, accounting for renewable energy uncertainties and maximizing large logistics parks’ participation in peak shaving. Results illustrate that the proposed method leads to a reduction in the peak shaving pressure on the distribution network. Specifically, under the scenario tolerating the maximum potential uncertainty in renewable energy output, the absolute peak-to-valley difference and fluctuation variance of the park’s net load are decreased by 45.82% and 54.59%, respectively. Furthermore, the PMV and the SPI indexes are reduced by 39.12% and 26.36%, respectively. In comparison with the determined optimization method, despite a slight cost increase of 20.06%, the proposed method significantly reduces EDR load shedding by 98.1%.

Automated warehousing and distribution has been an innovation approach to reduce costs and increase efficiency in logistics industry. Taking the intelligent demonstration warehouse in a commercial logistics park in Shandong, China as the background, this paper constructs a platform resource scheduling model under the Automatic Guided Vehicle (AGV) sharing mode to solve the problems of platform allocation and equipment scheduling, and solves it using the simulated annealing algorithm. This paper designs First‐Come, First‐Served (FCFS) rule, platform resource scheduling rules when AGVs are used separately, and platform resource scheduling rules when AGVs are shared, outputting platform operation scheduling schemes. Meanwhile, different numbers of AGVs are scheduled under the AGV sharing mode to validate the model and algorithm. The results show that the platform resource scheduling model proposed in this paper improves the platform utilization rate by 4.4% compared to the traditional FCFS rule, and the latest departure event is advanced by 95 min. The AGV sharing mode can complete vehicle loading tasks in a shorter time and with faster operational efficiency.

Solar power is widely regarded as one of the most promising renewable resources for generating electricity and reducing building energy consumption. Logistics parks, with their low-rise buildings and extensive rooftop areas, offer significant advantages for solar energy utilization via rooftop photovoltaics (PVs). However, limited research has been conducted on the proper operational principles and optimized control strategies for the PV systems of logistics parks, particularly regarding the mismatch between power generation and the loads of various building types under varying climatic conditions. This study proposes four optimal PV operation strategies for large-scale logistics parks across diverse climatic regions, developed using a multi-criteria optimization approach. The strategies optimize the azimuth and tilt angles of PV panels under four adjustment frequencies: annual, semi-annual, seasonal, and monthly. The investigated strategies are validated in a 5500 m2 logistics park, comprising refrigerated storage, warehouses, sorting centers, and other facilities. The results indicate that the proposed strategies outperform conventional fixed-angle approaches, with the monthly adjustment strategy delivering the best performance. Economic costs are reduced by 9.26–17.02%, while self-sufficiency can be improved by 2.00–7.08%. Cold regions with high solar radiation show particularly significant benefits, with self-consumption increasing by 82.44–359.04%. This study provides valuable insights and practical guidelines for optimizing PV system operations in logistics parks, offering enhanced energy efficiency and cost-effectiveness.

Power grid enterprises are changing their function orientation and profit model under the background of new power system reform. Vector autoregressive model used for industrial park to forecast load of distributed energy is created in this paper, which provides the data to the analysis of the investment environment of grid enterprises. We excavate the core influencing factors of grid investment in incremental distribution parks based on the linkage of grid investment influencing factors in system dynamics.

In this study, we used market basket analysis and land accessibility for the layout of MG Steel Logistics Park. Due to the unreasonable cargo space layout, the outbound trucks in the MG Steel Logistics Park detour and transfer, which caused congestion. Using market basket analysis, this research took outbound trucks as the research object to measure the degree of correlation between cargo locations. Then, we measured the convenience of cargo locations by the accessibility and matched it with the steel outbound frequency. We established a model and got the layout allocation plan with the maximum degree of relevance and the minimum difference in cargo-location matching as the target. In the solution algorithm, we use roulette method and the elite retention strategy to improve the genetic algorithm. Finally, we compared the results before and after optimization. We found that the number of truck transfers was reduced by 33%, and the truck staying time was reduced by 21%.

Developer Panattoni is to submit a planning application for a 128,000sq ft warehouse unit in Kent after acquiring an additional 10 acres at its net zero logistics park, G Park Sittingbourne. The real-estate developer said this second phase of development is expected to start in the fourth quarter of this year, with completion due in the third quarter of 2025. Panattoni is already underway with the 645,000sq ft first phase of development at the park, comprising two units of 440,000sq ft and 205,000sq ft. Completion of these is expected in Mar 2025.