To ensure the safe and stable operation of wind farms, it is necessary to monitor the various operation parameters real-time. This paper introduces the Embedded Web Server technology; using DSP+ARM double CPU structure, designs and realizing the wind farm remote monitoring system based on the Embedded Web Server. This system also realizes the dynamic interaction and multiple user authentications for the establishment and improvement of the Boa server. The client can remotely monitor the wind farm's electric parameters and environmental parameters in real-time through the browser. This system fully meets the requirements by field and the remote testing.
With the increasing number of electric vehicles (EVs) in recent years, road congestion is becoming a common phenomenon, which not only prolongs travel time but also causes anxiety for EV users. Therefore, this paper proposes a time-varying shortest path search method for traffic flow (TF) and establishes an EV route planning model based on this method to plan the optimal path for users. Firstly, a convolutional neural network (CNN) is used to predict TF and a new queuing model is established to calculate the charging queuing time. Then, a path planning model considering mid-way charging is established based on the predicted TF data and charging queuing model. Finally, the performance of the proposed method is tested using road network maps of different scales, and a case study on the optimal path of EV with mid-way charging under the minimum objective function is conducted based on a real traffic network. The results show that the proposed time-varying shortest path search method under the TF network can quickly calculate the optimal path and has great potential for solving practical problems.
With the increasing market share of electric vehicles (EVs), optimizing the planning decisions of charging stations becomes critical to support long-distance traveling. Besides, the booming car-sharing industry provides an alternative business model to enhance the utilization of vehicle resources. Recently, several car-sharing enterprises have launched the collaborations with charging station construction companies to construct their own charging facilities, which calls for the co-optimization of charging station planning and scheduling of EVs in car-sharing business. Therefore, this paper develops an optimization model for electric vehicle charging stations placements and type selections, considering routing selection and charging management of an electric vehicle fleet in car-sharing business. Specifically, a stochastic programming framework is exploited to incorporate a variety of operating scenarios, which reflect the uncertainties of riders' requirements, drivers' itineraries, traffic flow information, and electricity prices. The proposed model aims to improve the welfare of all riders and reduce total cost of fulfilling riders' pickup and delivery requirements simultaneously, in which determining the optimal locations and types of charging facilities is required. Moreover, portfolio management is integrated into the optimization model to mitigate investment risk. After a series of reformulation, the proposed optimization model can be recast into a mixed-integer linear programming problem. Using two transportation networks, the effectiveness of the proposed model is validated in numerical studies. It finds that the optimal electric vehicle charging station planning strategy is determined according to the operator's preferences, electricity prices, and installation budgets. Moreover, diversity within operating scenarios yields the positive value-of-stochastic-solution that demonstrates the necessity of implementing stochastic programming.
Non-intrusive load monitoring (NILM) can provide rich power consumption data for home users and power supply companies, which is helpful to use the electricity in an efficient way. This paper proposes a novel NILM method based on quantum particle swarm optimization (QPSO) algorithm. First, the parameters which can represent the load characteristics of household appliances are presented. Then, the detailed NILM method based on QPSO algorithm is described. At last, experiments are carried out on a house with 8 kinds of appliances. Results show that the proposed NILM method is effective, and QPSO algorithm performs better than that of basic PSO algorithm.
In respect to the status quo of pump unit monitoring,virtual instrument,database technology and remote control are used to construct the remote monitoring and testing system of oil pump.The constructed system is capable of signal gathering,data processing,storing and printing,remote monitoring and interference removal.The overall structure and the realization of its function modules are discussed in detail.It can solve such problems in traditional systems as great intensity of labor force,low test efficiency,bad environment and low precision,and it can reduce accident rate and maintenance cost of oil pump units.
With increasing penetration of wind turbines in the utility grid new regulation codes have been issued that require them to have low voltage ride-through capability. In this paper, a passive resistive network, consisting of shunt and series elements are applied at the stator side of DFIG wind turbine is presented. The network is inactive during steady state operation and enabled for short intervals of time during the initiation of voltage sag and recovery events. Computer simulation and experimental results confirming the operation during balanced and unbalanced voltage sags are shown in the paper.
Background: The implementation of Battery Energy Storage Systems (BESSs) and carbon capture units can effectively reduce the total carbon emissions of distribution networks. However, their widespread adoption has been hindered by the high investment costs associated with the BESSs and power generation costs of carbon capture units. Objective: The objective of this paper is to optimize the location and sizing of BESSs in distribution networks that comprise renewable power plants and coal-fired power units with carbon capture systems. The optimization process aims to minimize the grid’s impact from the configuration while maximizing economic cost savings and the benefits of reducing carbon emissions. Methods: A bi-layer optimization model is proposed to determine the configuration of BESSs. The upper layer of the model optimizes the size and operation strategy of the BESSs to minimize the configuration and power generation costs, using YALMIP and CPLEX optimization tools. Carbon emission reduction benefits are considered through deep peak-shaving and carbon tax. The lower layer of the model aims to optimizes the placement of the BESSs to minimize voltage fluctuation and network loss in the power grid. To achieve this, we improved the efficiency of the Nondominated Sorting Genetic Algorithm II (NSGA-II) to update the BESS’s placement. Results: The IEEE33-bus and IEEE118-bus systems were utilized for simulation and comparison in various scenarios. The findings demonstrate that the proposed configuration method can decrease the cost of investment and power generation. Furthermore, it reduces the degree of node voltage fluctuation and network loss in the distribution network. Conclusion: The study reveals that determining the optimal scale of BESSs can mitigate high energy consumption in carbon capture systems and improve the overall performance of power systems that integrate carbon capture technology and renewable power plants.
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