Optimal urban EV charging station site selection and capacity determination considering comprehensive benefits of vehicle-station-grid

This paper presents an optimization model for the location and capacity of electric vehicle(EV)charging stations.The model takes the multiple factors of the“vehicle-station-grid”system into account.Then,ArcScene is used to couple the road and power grid models and ensure that the coupling system is strictly under the goal of minimizing the total social cost,which includes the operator cost,user charging cost,and power grid loss.An immune particle swarm optimization algorithm(IPSOA)is proposed in this paper to obtain the optimal coupling strategy.The simulation results show that the algorithm has good convergence and performs well in solving multi-modal problems.It also balances the interests of users,operators,and the power grid.Compared with other schemes,the grid loss cost is reduced by 11.1%and 17.8%,and the total social cost decreases by 9.96%and 3.22%.

SSABC:a super-peer selection algorithm based on capacity

Combining the characteristics of peer-to-peer （P2P） and grid, a super-peer selection algorithm--SSABC is presented in the distributed network merging P2P and grid. The algorithm computes nodes capacities using their resource properties provided by a grid monitoring and discovery system, such as available bandwidth, free CPU and idle memory, as well as the number of current connections and online time. when a new node joins the network and the super-peers are all saturated, it should select a new super-peer from the new node or joined nodes with the highest capacity. By theoretical analyses and simulation experiments, it is shown that super-peers selected by capacity can achieve higher query success rates and shorten the average hop count when compared with super-peers selected randomly, and they can also balance the network load when all super-peers are saturated. When the number of total nodes changes, the conclusion is still valid, which explains that the algorithm SSABC is feasible and stable.

Joint User Scheduling and Antenna Selection in Distributed Massive MIMO Systems with Limited Backhaul Capacity

Massive MIMO systems offer a high spatial resolution that can drastically increase the spectral and/or energy efficiency by employing a large number of antennas at the base station(BS).In a distributed massive MIMO system,the capacity of fiber backhaul that links base station and remote radio heads is usually limited,which becomes a bottleneck for realizing the potential performance gain of both downlink and uplink.To solve this problem,we propose a joint antenna selection and user scheduling which is able to achieve a large portion of the potential gain provided by the massive MIMO array with only limited backhaul capacity.Three sub-optimal iterative algorithms with the objective of sumrate maximization are proposed for the joint optimization of antenna selection and user scheduling,either based on greedy fashion or Frobenius-norm criteria.Convergence and complexity analysis are presented for the algorithms.The provided Monte Carlo simulations show that,one of our algorithms achieves a good tradeoff between complexity and performance and thus is especially fit for massive MIMO systems.

Neighbour Peer Selection Scheme Based on Effective Capacity for Mobile Peer-to-Peer Streaming

For Peer-to-Peer (P2P) streaming services in mobile networks, the selection of appropriate neighbour peers from candidate peers with demanding data is an important approach to improve Quality-of-Service (QoS). This paper proposes a novel Effective Capacity Peer Selection (ECPS) scheme based on effective capacity. In the ECPS scheme, the neighbour peer selection problem was modeled using the Multiple Attribute Decision Making (MADM) theory, which considered multiple factors of candidate peers, including Signal to Interference and Noise Ratio (SINR), residency time, power level, security, moving speed, and effective capacity. This model could increase the suitability of ECPS for wireless mobile environments. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to solve the MADM problem and identify the preferred neighbour peers. Simulation results show that the ECPS scheme can improve the network throughput, reduce packet delay by about 82%, and almost double the packet delivery ratio of the mobile P2P streaming service.

Two-Stage Planning of Distributed Power Supply and Energy Storage Capacity Considering Hierarchical Partition Control of Distribution Network with Source-Load-Storage

Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.

Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.

Power allocation and mode selection methods for cooperative communication in the rectangular tunnel

For the multipath fading on electromagnetic waves of wireless communication in the confined areas,the rectangular tunnel cooperative communication system was established based on the multimode channel model and the channel capacity formula derivation was obtained.On the optimal criterion of the channel capacity,the power allocation methods of both amplifying and forwarding(AF) and decoding and forwarding(DF) cooperative communication systems were proposed in the limitation of the total power to maximize the channel capacity.The mode selection methods of single input single output(SISO) and single input multiple output(SIMO) models in the rectangular tunnel,through which the higher channel capacity can be obtained,were put forward as well.The theoretical analysis and simulation comparison show that,channel capacity of the wireless communication system in the rectangular tunnel can be effectively enhanced through the cooperative technology;channel capacity of the rectangular tunnel under complicated conditions is maximized through the proposed power allocation methods,and the optimal cooperative mode of the channel capacity can be chosen according to the cooperative mode selection methods given in the paper.

Microporous Organic Nanotube Networks from Hyper Cross-linking Core-shell Bottlebrush Copolymers for Selective Adsorption Study

We report a synthesis of microporous organic nanotube networks（MONNs） by a combination of hyper cross-linking and molecular templating of core-shell bottlebrush copolymers. The intrabrush and interbrush cross-linking of polystyrene（PS） shell layer in the core-shell bottlebrush copolymers led to the formation of micropores and large-sized nanopores（meso/macrospores） in MONNs, respectively, while selective removal of polylactide（PLA） core layer generated mesoporous tubular structure. The size of PLA-templated mesoporous cores and porous structure both at micro-and meso-scale could be controlled by simple tuning of the ratio of core/shell or the PLA core fraction in the bottlebrush precursors. Moreover, the resultant MONNs showed a highly selective adsorption capacity for the positively charged dyes on the basis of multi-porosity and carboxylate group-rich structure. In addition, MONNs also exhibited effective performance in size-selective adsorption of biomacromolecules. This work represents a new avenue for the preparation of MONNs and also provides a new application for molecular bottlebrushes in nanotechnology.