In the northern plains of Laizhou City, groundwater quality suffers dual threats from anthropogenic activities: seawater intrusion caused by overextraction of fresh groundwater, and vertical infiltration of agricultur...
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In the northern plains of Laizhou City, groundwater quality suffers dual threats from anthropogenic activities: seawater intrusion caused by overextraction of fresh groundwater, and vertical infiltration of agricultural pol-lutants. Groundwater management requires a comprehensive analysis of both horizontal and vertical pollution in coastal aquifers. In this paper, Intrinsic Aquifer Vulnerability (IAV) was assessed on an integrated scale using two classic IAV models (DRASTIC and GALDIT) separately based on a GIS database. Hydrogeological parameters from two classic IAV models were clustered using affinitypropagation (AP) clusteringalgorithm, and silhouette coefficients were used to determine the optimal classification *** our application, the objects of the AP algorithm are 3320 units divided from the whole study area with 500 m*500 m precision. A comparison of all four outputs in AP-DRASTIC shows that the clustering results of the 4 -classification yielded the best silhouette coefficient of 0.406 out of all four. Cluster 4, which comprises 21% of the area, had relatively low level of groundwater contamination, despite its high level of vulnerability as indicated by the classic DRASTIC index. In the second level of vulnerability Cluster 3, 53.8% of all water samples were found to be contaminated, indicating a greater level of nitrate contamination. With respect to AP-GALDIT, the silhouette coefficient for result 7-classification reaches the highest value of 0.343. There was a high level of vulnerability identified in Clusters 2, 4 and 5 (34.7% of the study area) relating to the classic GALDIT index. The concentration of chloride in all water samples obtained in these areas was extremely high. Groundwater man-agement should be addressed by AP-DRASTIC results on anthropogenic activity/contamination control, and by AP-GALDIT results on groundwater extraction limitation. Overall, this method allows for the evaluation of IAV in other coastal areas on an integrate
Currently, the world is rapidly advancing in terms of the construction of new power systems, and planning suitable distribution network planning while also considering renewable energy has become a hot issue. Based on...
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Currently, the world is rapidly advancing in terms of the construction of new power systems, and planning suitable distribution network planning while also considering renewable energy has become a hot issue. Based on this background, this paper studies the distribution network planning problem. Compared with the traditional planning method, the paper considers the impact of load growth and renewable energy penetration and uses the multi-stage planning method to build the planning model;at the same time, in the scenarios selection, the affinitypropagation (AP) clusteringalgorithm is adopted, which can automatically obtain the number of clusters. Based on the proposed model, an IEEE 33-node is used for simulation. The simulation results show that, compared with the traditional static planning method, the total economic cost of the proposed method is reduced by 4.87% and the wind-solar curtailment rate is reduced by 59.01%;in addition, according to the proposed method, the impact of energy storage equipment and wind-solar permeability on the planning results is studied. It is found that, when considering energy storage, the amount of abandoned wind and light decreases by 22.35% and the total cost first decreases and then increases with the increase in wind-solar permeability, while the total economic cost reaches the minimum at about 40%. The impact of load growth rate on the planning results is also studied. Finally, the generalizability of the proposed method is investigated while using the IEEE 69-node system as an example.
This paper proposes a distributed adaptive robust voltage/var control (DAR-VVC) method in active distribution networks to minimize power loss while keeping operating constraints under uncertainties. The DAR-VVC aims t...
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This paper proposes a distributed adaptive robust voltage/var control (DAR-VVC) method in active distribution networks to minimize power loss while keeping operating constraints under uncertainties. The DAR-VVC aims to coordinate on-load tap changers, capacitor banks and PV inverters in multiple operation stages through a distributed algorithm. To improve efficiency of the distributed algorithm, an affinity propagation clustering algorithm is employed to divide the distribution network by aggregating "the close nodes" together and setting "the far nodes" apart, leading to the network partition where the information exchange between adjacent sub-networks is reduced. Moreover, the virtual load which describes load characteristics of the sub-networks is applied to enhance the boundary conditions. To fully deal with the uncertainties, the proposed DAR-VVC is formulated in a robust optimization model which considers the worst case to guarantee solution robustness against uncertainty realization. Besides, this paper develops an alternating optimization procedure integrating a column-and-constraint generation algorithm and an alternating direction method of multipliers to solve the DAR-VVC problem. The proposed approach is tested on IEEE 33 and IEEE 123 bus distribution test system and numerical simulations verify high efficiency and full solution robustness of the DAR-VVC.
作者:
Li, LuLiang, YanchunLi, TingtingWu, ChunguoZhao, GuozhongHan, XiaosongJilin Univ
Key Lab Symbol Computat & Knowledge Engn Natl Educ Minist Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ
Zhuhai Key Lab Symbol Computat & Knowledge Engn Minist Educ Zhuhai Coll Zhuhai 519041 Peoples R China CNPC
Daqing Oilfield Explorat & Dev Res Inst Daqing Oilfield Personnel Dev Inst Daqing 163000 Peoples R China
It is well known that the classical particle swarm optimization (PSO) is time-consuming when used to solve complex fitness optimization problems. In this study, we perform in-depth research on fitness estimation based...
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It is well known that the classical particle swarm optimization (PSO) is time-consuming when used to solve complex fitness optimization problems. In this study, we perform in-depth research on fitness estimation based on the distance between particles and affinitypropagationclustering. In addition, support vector regression is employed as a surrogate model for estimating fitness values instead of using the objective function. The particle swarm optimization algorithm based on affinitypropagationclustering, the efficient particle swarm optimization algorithm, and the particle swarm optimization algorithm based on support vector regression machine are then proposed. The experimental results show that the new algorithms significantly reduce the computational counts of the objective function. Compared with the classical PSO, the optimization results exhibit no loss of accuracy or stability.
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