Design scheme for fast charging station for electric vehicles with distributed photovoltaic power generation

The demand for fast charging is increasing owing to the rapid expansion of the market for electric vehicles. In addition, the power generation technology for distributed photovoltaic has matured. This paper presents a design scheme for a fast charging station for electric vehicles equipped with distributed photovoltaic power generation system taking the area with certain conditions in Beijing as an example construction site. The technical indexes and equipment lectotype covering the general framework and subsystems of the charging station are determined by analyzing the charging service demand of fast charging stations. In this study, the layout of the station is developed and the operation benefits of the station is analyzed. The design scheme realizes the design objective of "rationalization, modularization and intelligentization" of the fast charging station and can be used as reference for the construction of a fast charging network in urban area.

Load Balancing-Based Routing Optimization Mechanism for Power Communication Networks

In power communication networks, it is a challenge to decrease the risk of different services efficiently to improve operation reliability. One of the important factor in reflecting communication risk is service route distribution. However, existing routing algorithms do not take into account the degree of importance of services, thereby leading to load unbalancing and increasing the risks of services and networks. A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems. First, the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices, service load, and service characteristics. Second, service weights are determined with modified relative entropy TOPSIS method, and a balanced service routing determination algorithm is proposed. Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.

Demand Responsive Market Decision-Makings and Electricity Pricing Scheme Design in Low-Carbon Energy System Environment

The two-way interaction between smart grid and customers will continuously play an important role in enhan-cing the overall efficiency of the green and low-carbon electric power industry and properly accommodating intermittent renewable energy resources.Thus far,the existing electricity pricing mechanisms hardly match the technical properties of smart grid;neither can they facilitate increasing end users participating in the electri-city market.In this paper,several relevant models and novel methods are proposed for pricing scheme design as well as to achieve optimal decision-makings for market participants,in which the mechanisms behind are com-patible with demand response operation of end users in the smart grid.The electric vehicles and prosumers are jointly considered by complying with the technical constraints and intrinsic economic interests.Based on the demand response of controllable loads,the real-time pricing,rewarding pricing and insurance pricing methods are proposed for the retailers and their bidding decisions for the wholesale market are also presented to increase the penetration level of renewable energy.The proposed demand response oriented electricity pricing scheme can provide some useful operational references on the cooperative operation of controllable loads and renewable energy through the feasible retail and wholesale market pricing methods,and thereby enhancing the development of the low-carbon energy system.

Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance(MVCA)algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently,without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore,MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay(< 100100 ms) and low packet loss(< 5%) can bring little influence to those trajectory planning algorithms that only depend on V2 V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.

A Concept Discussion on Northeast Asia Power Grid Interconnection

Northeast Asia is one of world’s major economic and energy consumption centers.Countries in this region are undergoing rapid economic and social development,and energy security and greenhouse gas emission reduction have become prominent issues.In this region,clean energy resources including hydro,wind,and solar are concentrated in Russia,Mongolia,and Northeast China,whilst electricity load centers are in East China,Korea and Japan.Currently,an energy and electricity model has been developed to project electricity demand through 2030 for each country.Based on the idea of a Global Energy Interconnection,this article proposes to establish a Northeast Asia Grid Interconnection(NEAG),connecting Mongolia,China,Korea,Japan,and Russia with large volumes of transmission lines,in an effort to facilitate optimized resource allocation.The NEAG would be accomplished step by step along with identification of key interconnectors.China is set to play an important role in establishing the NEAG by providing a strong sending grid and necessary technological equipment.Tremendous economic,environmental,and social benefits are expected to be generated by the NEAG.

Demand-side Management Based on Model Predictive Control in Distribution Network for Smoothing Distributed Photovoltaic Power Fluctuations

With the rapid increase of distributed photovoltaic(PV) power integrating into the distribution network(DN), the critical issues such as PV power curtailment and low equipment utilization rate have been caused by PV power fluctuations. DN has less controllable equipment to manage the PV power fluctuation. To smooth the power fluctuations and further improve the utilization of PV, the regulation ability from the demandside needs to be excavated. This study presents a continuous control method of the feeder load power in a DN based on the voltage regulation to respond to the rapid fluctuation of the PV power output. PV power fluctuations will be directly reflected in the point of common coupling(PCC), and the power fluctuation rate of PCCs is an important standard of PV curtailment.Thus, a demand-side management strategy based on model predictive control(MPC) to mitigate the PCC power fluctuation is proposed. In pre-scheduling, the intraday optimization model is established to solve the reference power of PCC. In real-time control, the pre-scheduling results and MPC are used for the rolling optimization to control the feeder load demand. Finally,the data from the field measurements in Guangzhou, China are used to verify the effectiveness of the proposed strategy in smoothing fluctuations of the distributed PV power.

Coarse-to-fine visual autonomous unmanned aerial vehicle landing on a moving platform

Autonomous unmanned aerial vehicle(UAV)landing is a challenging task,especially on a moving platform in an unstructured environment.Under such a scenario,successful UAV landing is mainly affected by poor UAV localization performance.To solve this problem,we propose a coarse-to-fine visual autonomous UAV landing system based on an enhanced visual positioning approach.The landing platform is marked with a specially designed QR code marker,which is developed to improve the landing accuracy when the UAV approaches the landing site.Besides,we employ the you only look once framework to enhance the visual positioning accuracy,thereby promoting the landing platform detection when the UAV is flying far away.The framework recognizes the QR code and decodes the position of a UAV by the corner points of the QR code.Further,we use the Kalman filter to fuse the position data decoded from the QR code with those from the inertia measurement unit sensor.Then,the position data are used for UAV landing with a developed hierarchical landing strategy.To verify the effectiveness of the proposed system,we performed experiments in different environments under various light conditions.The experimental results demonstrate that the proposed system can achieve UAV landing with high accuracy,strong adaptability,and robustness.In addition,it can achieve accurate landing in different operating environments without external real-time kinematic global positioning system(RTK-GPS)signals,and the average landing error is 11.5 cm,which is similar to the landing error when using RTK-GPS signals as the ground truth.

Integrated Optimal Siting and Sizing for VSCHVDC-link-based Offshore Wind Farms and Shunt Capacitors

It is economic and secure to determine the optimal siting and sizing of the offshore wind farms(OWFs)integrated into the AC system through voltage-source converter high-voltage direct current(VSC-HVDC)links.In this paper,an integrated planning model for the VSC-HVDC-link-based OWFs and the capacitors is proposed,where a decomposition technique is presented to solve the proposed mixed-integer nonlinear programming(MINLP)problem and obtain the optimal solution.This model can optimize the siting and sizing of the OWFs to improve the voltage profile and reduce the adverse influence of the reactive power of the OWFs.With the proposed planning model,the total investment costs,operation costs and maintenance costs of the OWFs,VSC-HVDC links,and the capacitors can be minimized.Simulations on the modified IEEE 118-bus system show that the proposed integrated planning model can provide more economic scheme than the independent planning scheme,in which the capacitors are planned after the OWFs.Besides,a series of sensitivity analysis on certain equipment costs are studied to obtain the regular pattern for sizing VSC stations.