Intelligent Computing in Smart Grid and Electrical Vehicles

Wind power is influenced by multivariable, which usually shows complex nonlinear dynamics. Therefore the wind power is hardly described and traced by single variable prediction model; the precision of which decreases while it contains uncorrelated or redundant variables. The approach is proposed to reconstruct the phase space of multivariable time series and then predicate wind power. First, the delay time of single variable time series is selected by mutual information entropy, and then the embedding dimension of phase space is extended by the false nearest neighborhood method, which can eliminate the redundancy of reconstructed phase space from low space to high space. Then, the vector is utilized as input to predicate the wind power using the radial basis function neural network. Simulation of wind predication of Shanghai wind farms, show that the proposed method can describe the nonlinear system by less variables, and improve the precision and sensitivity of prediction.

In order to achieve the detection for the fault diagnosis of the wind turbine generator bearing, firstly, the transformation of the wavelet packet is adopted to decompose the vibration signal into several layers, and denoise and reconstruct it. Secondly, this paper takes the combination of the wavelet node energy and the characteristic parameters of the denoised signal both in the time and frequency domain as the input feature vector to BP neural network with the function of self- determining hidden layer neurons. Finally, the results of the fault diagnosis are regarded as the output. The experimental data demonstrate that this method can effectively diagnose the fault types of the wind turbine generator bearing.

This paper proposes a multi-objective chance-constrained optimization approach for allocating wind generation and photovoltaic. For reflecting the uncertainty and relevance among wind speed, solar radiation and load, and accelerating the computation, a kind of state selection strategy based on statistical methods is proposed. To evaluate profits from distributed generation more comprehensively, three indices are introduced into the objective function, namely cost index, power loss index and voltage deviation index. To balance the relationship between risk and return, the chance-constrained optimization is adopted. In the process of solution, firstly the novel method integrating the differential evolution for multi-objective optimization and dynamic non-dominated sorting is proposed to get a set of the Pareto-optimal solutions, after that fuzzy multi-attribute decision making method based on information entropy is adopted to select the best compromise solution from the Pareto-optimal solutions. The case studies show that the proposed optimal model is rational, and the algorithm is effective.

Electric vehicles (EVs) are becoming more popular and have gained better customer acceptance in the past few years due to the improved performances, such as high acceleration rate and long driving distance from a single charging. Recent research also shows some promising benefits from integrating EVs with power grid. One of these is to use EV batteries as distributed energy storage. As a result, the excessive electricity generated from renewable resources can be stored in EVs and release to the power grid when needed. However, compared to traditional Nickel-cadmium and lead-acid batteries, Li-ion battery only can be operated in a narrow window, and needs to be properly monitored, managed and protected. This issue becomes severe when it is deployed for large applications, such as EVs and centralised electricity storage, where a large number of Li-Ion cells are interconnected to provide sufficient voltage and current. The solution mainly relies on a robust and efficient battery management system (BMS). This paper presents a brief review on the features of BMS, followed by a practical guide on selecting a commercial BMS from the market and designing a custom BMS for better control of functionalities. A Lithimate Pro BMS from Elithion is used to demonstrate the effectiveness of BMS in managing and protecting Li-Ion cells during the charge and discharge phases.

Because of the particularity of liquid cargo transported by liquid cargo ships, traditional ship stability calculation method is not satisfied enough, especially in the respects of matching degree and calculation precision. In order to solve these problems, this paper proposes an optimal method for stability calculation of sea-going liquid cargo ship. Corrections of trimming and free surface are considered, besides, new data preprocessing is adopted. The simulation is demonstrated that this method could satisfy the stability calculation requirements of these liquid cargo ships with good expandability.

Magneto thermal free convection of air in a square enclosure under a non-uniform magnetic field provided by a permanent magnet is numerically studied. The results show that the natural convection of air in the square enclosure under magnetic field is quite different from that under the gravity field. The local value of Nusselt number under the magnetic field can reach to a much higher value than the maximum local value under the gravity field. Relatively uniform distribution of Nusselt number can be obtained along the cold wall of the enclosure under the magnetic field. A permanent magnet with high magnetic energy product with

B

r

reaches to 3.5 Tesla can plays a comparative role on the average Nusselt number compared with that under the gravity environment.

As to the problems of over-moving in local mean decomposition method (LMD), it is proposed to use the cubic Hermite interpolation to get the envelope curve and local mean curve. Instead of moving mean method, it will improve the over moving effect, improving the speed of LMD shifting effectively. Wind power series can be decomposed into different series by improved LMD, and then artificial neural network (ANN) is used to forecast power by each component. The total wind power prediction result is obtained through reconstructing at last. Case study shows that the prediction accuracy has significantly been improved by comparing with other models.

The uncertainty analysis of power predictive results is very important to the dispatching of wind power. For the shortcomings of traditional methods that determine the confidence probability, this paper proposes a new method to determine the confidence probability based on independent component analysis (ICA) and conditional probability theory. According to the new method, the power independent influence events set can be obtained from ICA, and the problem of determining the confidence probability can be transformed into the problem of determining unconditional probability and conditional probability whose objectives are several independent influence events. The method is clear and easy to be resolved, which fully takes into account occurrence conditions of the objective power and the original content. The simulation results show the confidence probability result obtained by the new method has more realistic sense and scientific guidance value.

This paper presents a high precision simulation model for single phase heated tubes of power plant boilers. The model takes into account not only the dynamic process of the working fluid velocity, but also the coupling effect of the enthalpy-temperature channel and the pressure-flux channel of working fluid. By simulations of a heated tube of the rear superheater of a 600 MW controlled circulation boiler, the validity and feasibility of the model is verified. The present model can be integrated into virtual power plant simulation platforms for better debugging advanced power plant control systems, e.g. the automatic plant startup and shutdown control system (APS).

Mankind is facing the twin challenges of sustainable energy supply and climate change due to the greenhouse gases emissions. Reducing the energy consumption has been given a significant role in addressing these problems. Although modern control technologies have been successfully applied in many engineering systems, PID (Proportional-Integral-Derivative) control and its variants are still the most common control techniques due to their simple control design principle, especially in the power electronics control field. However, little has been done so far to explore how the system performance and control energy are related to each other through the control parameter settings. This paper takes the voltage droop (P) control design in voltage source converters (VSCs) system as the research background to investigate the parameter optimization of droop controllers. The simulation results reveal that there exists a better trade-off between the system performance and control energy consumption through a proper choice of the controller parameters.

In order to make the control of interconnected grid frequency have better load adaptability and maintain the economic stability and reliable operation of the grid, a design method of multivariable predictive controller and its algorithm are proposed. They are based on the state-space model of the interconnected grid while considering the regional capacity constraint. To solve the problem with the control relying on

K

(the frequency error coefficient) in most such questions, the quadratic programming (QP) solving is employed. And so the optimal control is ensured even when there is interconnected grid support. According to the modeling and simulating of the two-area grid frequency control system, the proposed predictive algorithm is compared with the conventional algorithm via PI regulator in single area and dual zone load step disturbance, random white noise disturbance and model mismatch, etc. The results indicate that the proposed predictive control algorithm can significantly improve the recovery performance of the grid system frequency.

This paper presents the mothed to establish the simulation model of the internal feedback motor based on the dynamic model of the internal feedback winding motor, which fills the gaps in the simulik module library. The method makes use of the S Function in the tool-list of matlab/simulink to complete the simulation model. Simulation cases and field tests are used to testify the model, and it’s found that the simulation results correspond to the data of field tests. Therefore the model which is established by the way, can be used correctly.

For promotion of solar energy popularization, study on effect of purchase price can facilitate proper product decisions making. In this paper, an agent based model is built to simulate the households’ dynamic adoption process of solar energy. With varying purchase price, scenario analysis is taken to investigate the relevant market share changes. Random forest is used to measure the effect of purchase price by data collected from the model changes. The results show that impacts of purchase price differ with different types of energy using by households. By energy subsidy, product with higher purchase price can still attract market share of solar energy effectively. Thus, promotion strategies should be variable according to local using conditions of solar energy in residential consumer market.

In order to improve the convergence speed and precision of particle swarm optimization (PSO) and quantum PSO (QPSO), inspired by the idea of quantum physics, a new improved QPSO algorithm named double QPSO (DQPSO) is presented. The particle’s encoding mechanism and the evolutionary search strategy are quantized in DQPSO algorithm, in which the evolution equation of the velocity vector is abandoned, thus the evolution equation is easier, and less parameter are used that makes the algorithm easier to control. Several benchmark multi-modal functions are used to test the proposed DQPSO algorithm, which verified that the new algorithm is superior to standard PSO and QPSO in search capabilities. Then, DQPSO is successfully used to the identification of a thermal system with pure time-delay and non-minimum phase. Finally, the algorithm is applied to the transfer function identification of thermal system based on field operation data.

A method using OPNET for the energy efficiency analysis is proposed to save the energy cost of the wireless meter reading system in this paper. Firstly, a node energy module is generated to reveal the energy consumption accurately. Then, a channel noise is added to the network to generate the practical model. Finally, OPNET simulation is conducted to analyze the influence of a super frame structure on energy consumption by comparing the performance of the system. In comparison with other methods, the energy consumption of the system is minimized. Simulation results show that a longer lifetime of the node is acquired in the proposed method which can be well applied in the real meter reading system.

In modern semiconductor manufacturing facilities maintenance strategies are increasingly shifting from traditional preventive maintenance (PM) based approaches to more efficient and sustainable predictive maintenance (PdM) approaches. This paper describes the development of such an online PdM module for the endpoint detection system of an ion beam etch tool in semiconductor manufacturing. The developed system uses optical emission spectroscopy (OES) data from the endpoint detection system to estimate the RUL of lenses, a key detector component that degrades over time. Simulation studies for historical data for the use case demonstrate the effectiveness of the proposed PdM solution and the potential for improved sustainability that it affords.

The multi-cooling system has a decentralized control structure. Practice and simulation show that the temperature controllers located in the cold rooms tend to synchronize, which decreases the storage quality and reduces lifetime of the compressors. The paper takes the supermarket refrigeration system, a typical multi-cooling system, as example, and investigates the temperature synchronous phenomenon. The thermal load of the surrounding air in the display cases are proposed as an important factor, which dramatically influences behaviors of the system. By adopting different thermal loads through adjusting air speeds of the air curtains, the system operates in the de-synchronization situation and achieves better performances. All simulations are based on a simulation model of the supermarket refrigeration system in Matlab/Simulink. Ideas and results in the paper are not limited into the supermarket refrigeration system and can be extended to other multi-cooling systems.

In recent years, renewable energy resources have drawn a lot of attention worldwide in developing a more sustainable society. Among various forms of renewable energies, wind power has been recognized as one of the most promising ones in many countries and regions including Northern Ireland and Ireland according to the National Renewable Energy Action Plans (NREAPs). However, due to the variability nature of wind power, the wind generation forecasting hours even days ahead proves to be imperative to enhance the flexibility of the operation and control of real-time power systems. In this paper, a variant Gaussian Process employing only nearby measured wind power data is proposed to make short term prediction of the overall wind power production for the whole island of Ireland. Multi Gaussian Process submodels are developed, and the model capability in reflecting the variability and uncertainty in the wind generation system is enhanced. In such method, local data could be utilized more efficiently and computation complexity is reduced at the same time. The forecasting results have been verified in comparison with standard Gaussian Process and persistence model, and improvements can be observed in terms of the model complexity and prediction accuracy. Moreover, a recently proposed teaching-learning based optimization algorithm (TLBO) is applied to build the Gaussian model, and simulations show its faster convergence speed and better global searching capability.

High penetration of electric vehicles (EVs) in power system will significantly bring a large number of load and storage capacities. Different competition modes of EVs will have different effects on electricity markets. In this paper, Cournot competition equilibrium models of electricity market with different competition modes of EVs are developed and the theoretical analysis of market prices become leveled during off-peak and peak period is made. Numerical examples are presented to verify the validity of the theoretical analysis and the effectiveness of the proposed models. It is shown that EVs fleet can smooth market prices curve with arbitrage charging and discharging, especially when EVs fleet acts as a price-taker, it can reach the effect of ”peak load shifting”. In addition, the battery depreciation costs will largely affect the price-smoothing effect.

This paper studies the plug-in electric vehicles charging/discharging mode under the intelligent power grid in Shanghai with the objective of minimizing the total mean square of load curve of charging and discharging electricity. Considering constrains on battery capacity, electricity power and available time, an electric vehicles charging/discharging optimization model is build for power grid in Shanghai. Based on the parasitic and anti-parasitic behaviors in the nature, we propose a Novel Quantum Particle Swarm Optimization (NQPSO) to solve the problem. Two populations - host group and parasitic group are generated to dynamically changing the population size within the parasitic mechanism so as to improve the population genetic. A quantum particle encoding method is designed according the characteristics of the problem. Finally we apply NQPSO upon instances to explore the performance of our algorithm, and the results have showed the computational evidence for its effectiveness.

Fatigue testing plays a more and more important role in modern industry. In order to obtain the general features and the optimal algorithms of closed-loop control systems of the electro-hydraulic fatigue testing machines, a mathematical model of servo electro-hydraulic fatigue testing machine based on xPC real-time control system is developed and simulated in Simulink. In this model, the compound control system with feed-forward (speed feed-forward and acceleration feed-forward) is introduced. And then the static and dynamic loading testing for both position and force control are done to get the control laws and strategies. The research and results not only reveal the control laws and suitable control strategies of the fatigue-testing machine but also prove that the system has high control accuracy, fast signal processing and response ability. It can also realize the position and force control of the fatigue testing machines perfectly.

In this paper, an iterative learning control algorithm was proposed for improving the permanent magnet linear motor (PMLM) velocity tracking performance under iteration-varying desired trajectories. A high-order internal model (HOIM) was utilized to describe the variation of desired trajectories in the iteration domain. By incorporating the HOIM into P-type ILC, the convergence of tracking error can be guaranteed. The rigorous proof was presented to show that the system error converge well. The simulation results indicate that the proposed high-order internal models based approach yields a good performance and achieves perfect tracking.

Use of electron beam welding method for welding dissimilar materials of nitrogen steel and armor steel is proposed in this article. Used high energy electron beam as heat source, by means of welding experiment, the welding performance, joint microstructure, hardness which all had been tested, experimental results show that main organization of the weld zone is dendrite, main components between dendrite is low melting phase and impurities, hardness of the weld zone is higher than two kinds of steels, heat affected zone of armor steel is highest. Experiments had also established a visual collection system, image of electron beam welding of two dissimilar steel had been collected and processed, which can provide a reference for future research in this area.

In this paper, we propose a hybrid algorithm aimed at optimally synthesizing reversible Toffoli circuits in terms of the quantum cost for 4-bit and 5-bit reversible benchmarks. The hybrid algorithm alternates a variable-length evolutionary process with a heuristic factor subtraction algorithm based on Positive Polarity Reed Muller (PPRM) expansion. Further more, the variable length evolutionary algorithm employs a new constraint solving method, which introduces a trade-off factor to control a pair of contradictions: the decreasing of constraint violation and the increasing of quantum cost. The experimental results show that the hybrid algorithm outperforms existing combinations of a definite synthesis approach and a post-optimization method on some commonly used 4-bit and 5-bit benchmarks in point of quantum cost, and obtain some better results than the best known ones.

Adopting piezoelectric material as sensors and actuators, active vibration control of a cantilever beam is studied in this paper based on Linear Quadratic Regulator (LQR). Firstly, the actuator equation, sensor equation, and the vibration equation is constructed, and then the vibration equation is converted to modal state equation using modal analysis method. Secondly, the optimal control law is given by LQR method, with the detailed control flow. Finally, the active vibration control simulation is done for the vibration suppression of a piezoelectric beam. The results show that the control performance for the step response of the first and second vibration modal is good, as well as the coupled modal of the first two modal. And the effectiveness of the proposed LQR method is verified.

Optical incremental encoders are extensively for position measurements used in servo control systems. The position measurements suffer from quantization errors. Acceleration is common obtained by numerical differentiation, which will largely amplify quantization errors. In order to improve the performance of acceleration estimations, an adaptive window algorithm is employed. It maximizes the accuracy of acceleration according to the window size. Besides, velocity is estimated by the mixed time and frequency method, which is utilized to estimate acceleration. Finally, Validity of the acceleration estimation is verified by the experiments.

An improved algorithm is proposed to estimate the SOC of the lithium-ion. Based on the combination of the open-circuit voltage and the Ampere-Hour integral measurements, aimed at the long standing time and error accumulation, improved the algorithm. Assessed manganese acid lithium battery, obtained the relationship between VOC and SOC and the optimal parameter, we can estimate the open-circuit voltage of the lithium battery in a short time by the model parameters, which greatly reduces the open circuit voltage method of incubation time. When charging or discharging the battery, compute the variation of the SOC through Ampere–Hour integral. Experiment confirmed the accuracy of the algorithm. This algorithm can accurately estimate the SOC thus has a certain reference value for the research of lithium battery management system.

The paper firstly discusses the EtherCAT real-time Ethernet technology in detail, including operating principle, communication protocol and superior performance of EtherCAT Ethernet , synchronization, high-speed and so on. To show how to build up a master system based on configuration software TwinCAT and how to design a slave system considering the features of application, the methods of developing systems based on EtherCAT technology are proposed. Finally, a plate loading and unloading system based on EtherCAT technology is designed to obtain a faster response speed and higher synchronization accuracy. The system has realized high-speed remote data transmission and high-precision velocity control for Multi-axis which the traditional fieldbus can’t achieve.

We consider the distributed power control problem of distributed generators(DGs) in smart grid. In order to ensure the aggregated power output level to be desirable, a group of DGs with local and directed communications are expected to operate at the specified same ratio of their maximal available power output. To that end, the non-cooperative game is introduced and the DGs are modeled as self-interested game players. A new game model, termed state based weakly acyclic game, is developed to specify decision making architecture for each DGs, and at the point of the equilibrium of the game, the global objective of the power control problem can be achieved through autonomous DGs that are capable of making rational decisions to optimize their own payoff functions based on the local and directed information from other DGs. The validness of the proposed methodology is verified in simulation.

Wind power is projected to play an important role in the current and future power systems. To integrate offshore wind farms to the existing onshore grid, voltage source converters (VSCs) based high voltage direct current (HVDC) transmission have drawn considerable interests from researchers and industries. As the most important variable in a DC system, DC voltage indicates the power balance between different terminals thus must be maintained in a safe range. To distribute transmitted wind power among distinct onshore AC grids, different operation modes need to be considered. This paper proposes to interoperate the backstepping DC voltage control method between various operation modes. DC cable dynamics are included in the DC voltage controller design to eliminate the effect of DC transmission line. Simulation is carried out in Matlab/Simulink to verify the backstepping enhanced DC voltage control method. It is shown that the transient stability can be improved with the backstepping method.

Large capacity supercritical unit automatic power plant startup and shutdown (APS) control technology has been a research hotspot in the field of plant automation. According to characteristics of general supercritical unit start-up process, this paper introduces development process of supercritical unit APS stimulative simulation based on virtual DCS, including structure design, breakpoint design, interface configuration, development of functional groups and establishment of database, etc., which realizes auto startup process ranging from boiler ignition to full capacity, effectively improves production efficiency and makes significant reference value for practical application.

Economic emission dispatch (EED) in the power system is a non-linear constrained multi-objective optimization problem. In this paper, a new chaotic optimization algorithm for solving this complex problem is proposed. Two forms of logistic maps and marginal analysis for optimization are used in the proposed algorithm. The simulation results obtained by the proposed algorithm are compared with those of chaotic optimization algorithm and other approaches reported in recent literatures. The comparison results demonstrate the effectiveness of the proposed algorithm in solving the multi-objective EED problem.

This paper presents a direct explicit method of the fingerprint positioning for indoor wireless network. In data collection, for the purpose of a reliable and stable signal, a feedback filter is added to the sampler. In positioning phase, the location clustering technique is used to exclude invalid reference points. Then a matching algorithm based on RSSI correlation coefficient is proposed, which can improve positioning accuracy. The example in the paper illustrates the effectiveness of the proposed positioning scheme.

A novel method of closed-loop test simulation for power plant AVC system on RTDS(Real Time Digital Simulator) is presented. The proposed closed-loop test simulation plat includes real time digital simulator models, power plant AVC system and RTU. System models include system plat model and central control system model which is built upon components library on RTDS. The proposed test method will test the AVC system in all system conditions and because of the use of RTDS,the test can be both accurate and credible.A real case is tested to verify the validity of the proposed method.

This paper introduces a method for three-level inverter operating at low switching frequency and presents a novel current harmonic optimum pulsewidth modulation strategy. Combining the selected harmonic elimination PWM (SHEPWM) with the current harmonic minimum PWM (CHMPWM), the switching angles can not only eliminate the selected harmonic but also reduce the current harmonic. The three-level inverter output voltage is analyzed using fourier series and the expression of the total harmonic current distortion(THD

i

) rate is calculated. The THD

i

is regarded as the objective function, with the voltage fundamental amplitude is equal to the reference voltage amplitude and the other harmonic amplitude is zero as nonlinear constraints. Then the switching angle is solved by calculating the THD

i

. After researching its basic principle and the solution of the switching angle, results of the switching angle at 280Hz switching frequency are verified by using MATLAB software.

As one of the most important for the energy-saving and emission-reduction measures, regional energy is expected to contribute much to the development a low carbon society in the local area due its benefits including the rational use of energy, using energy scientifically, comprehensive energy and integrated energy. The so-called regional energy interchanging system, is established by forming some energy communities which can generate and consume energy simultaneously, and are connected with each other through local micro electricity and heat grid. In the interchanging process, along with the improvement of energy use efficiency, additional issue will appear especially the fairness between end-users. Therefore, in this study, as a classic theory for dealing with the profit allocation problems, cooperative game has been employed for the analysis of profit allocation among the users within a regional energy interchanging system. According to the simulation results of a case study, the proposed method has been approved to be good solution for the profit allocation for the users in the energy interchanging system, realizing a perfect combination of efficiency and fairness between different users.

Electric vehicles (EV) can become integral part of a smart grid because instead of just consuming power they are capable of providing valuable services to power systems. To integrate EVs smoothly into the power systems, a multi-agents system (MAS) with hierarchical organization structure is proposed in this paper. The proposed MAS system consists of three types of agents: distribution system operator agent (DSO agent), electric vehicle fleet operator agent (EV FO agent or alternatively called virtual power plant agent) and EV agent. A DSO agent belongs to the top level of the hierarchy and its role is to manage the distribution network safely by avoiding grid congestions and using congestion prices to coordinate the energy schedule of VPPs. VPP agents belong to the middle level and their roles are to manage the charge periods of the EVs. EV agents sit in the bottom level and they represent EV owners and operate the charging behaviour of EVs. To simulate this collaborative (all agents contribute to achieving an optimized global performance) but also competitive environment (each agent will try to increase its utilities or reduce its costs), a multi-agent platform was developed to demonstrate the coordination between the interacting agents.

A novel fault line section locating method of non-solidly grounded system based on Prony relative entropy theory was proposed in this paper. Firstly, piecewise Prony algorithm was used to fit the transient zero-sequence current signal of each detection point in the first T/20 cycle after fault occurs; secondly, transient zero-sequence dominant components were extracted and the relative entropy values of adjacent detection points were computed; lastly, the fault section was located by use of the feature that the transient zero-sequence currents from the same side of fault point possess high similarity while the opposite side of fault point possess low similarity. Simulation results verify the validity and accuracy of the method in this paper.

PV fouling detecting system based on neural network and fuzzy logic is proposed. Comparing with traditional methods, the proposed method is rapid adaptive and universal to all PV power station. Neural network is used to predict the maximum power point (MPP) of a PV module under any lighting conditions. Then fuzzy logic rule is used to identify the fouling condition according to the result from neural network prediction. The experiment shows that the neural network can precisely predict the MPP under any lighting environment and the fuzzy logic rules can precisely identify the fouling condition of PV modules.

Active distribution network (ADN) is an indispensable content of smart distribution network under smart grid framework. On the basis of integration modes study, an economy optimal model of DG integration from a DSO perspective is proposed and two DG integration indexes are defined, respectively, integration ratio and GC ratio. The hourly sequential model is adopted to simulate load and DG uncertainties and a method of DG optimal integration based on stochastic optimal power flow(S-OPF) is presented. Monte Carlo Simulation aiming to get expected value and variance of network parameter precisely is used in stochastic power flow. The influence of integration indexes on economy in two scenarios is analyzed and some useful conclusions are achieved.

Wind farm outputs have the features of intermittence and variability which impose a significant impact on the operation of power systems. In this paper, Latin hypercube sampling (LHS) and reduction technique is used to simulate the 24h power output of a wind farm. Then a model of unit commitment (UC) with predicted wind power (UCW) is established, and a harmony search (HS) with arithmetic crossover operation (ACHS) is employed for solving this problem. The results are analyzed in detail, which assess the impact of wind power on UC and demonstrate that ACHS is practicable for UCW problem with comparison with other proposed HS methods.

In the deregulated electricity market, the generation company (GenCo) can sell electricity power through several trading choices such as bilateral contracts and the spot market. These trading choices have different risk characteristics. Especially, the risk faced by the GenCo in the spot market trading is extremely large. To seek the maximum profits and the minimum risk simultaneously, the GenCo should allocate its generation capacity among these trading choices reasonably. A risk management method based on the information-gap decision theory (IGDT) is proposed to evaluate different generation asset allocation strategies under serious uncertainty of spot market prices. An information-gap model is used to describe the volatility of spot market prices around the forecasted prices. Robustness of the decisions against low spot prices is evaluated using a robustness model and windfall higher profit due to unpredicted higher prices is modeled using an opportunity function. Numerical simulation is used to illustrate the proposed method.

Lane departure warning system plays an important role in driver assistance systems. The proposed algorithm assumes that lanes are always the straight lines and whole algorithm is based on Hough transform. Due to the complexity of urban traffic scenes, false lane detections are highly caused by warning lines and signs whose shapes and colors are similar to the lane boundary. In this study, we improve the accuracy of the lane detection base on Hough, a score function based on the width between left and right lanes is proposed to obtain reliable lane detect results on urban traffic scene. Meanwhile, a list of candidate lanes is constructed at the least of execution time. Experiments under various scenes showed that the proposed lane detection method can work robustly in the real-time.

By producing some form of excitation signal, the new-style narrow-band frequency-sweep magnetic descaling instrument forms the electromagnetic field inside the pipeline, so as to achieve the effect of descaling, while the power amplification part of the instrument generally has the disadvantage of poor reliability. This paper puts forward two methods, shortening the distance between driving signal and the gate of transistor, and reducing the number of via holes, to reduce parasitic parameters. Meanwhile, measure the temperature of transistors with a thermistor directly, when the temperature exceeds the threshold, the adjustable rectifier is shut down automatically to protect the H bridge and enhance the reliability of the magnetic descaling instrument. Experiments show that the reliability of modified magnetic descaling instrument has been greatly improved by using the methods proposed.

The magnetic force pumps are widely used in place of petro-chemical industry, drugs manufacture and treatments for heart disease for their compact structure and reliable working. The design of the electromagnetic devices requires accurate calculation of the field parameters. Taking the bigger airgap and regular airgap boundary condition of the radial field permanent magnet coupler into consideration, analytical method is employed to solve the governing partial differential equations in the article. The electromagnetic field distribution for the inner rotor and outer rotor are obtained respectively by the analytical method, thus the torque is calculated and torque-angle curve is drawn.

According to the distribution of information flow in the integrated EV station, the detailed parameters of the station and car terminals can be obtained through the monitoring and control system. Radio frequency identification is used to collect data from battery system of electric vehicles, combined with IOT (internet of things) and GPS, the information of batteries can be acquired quickly and accurately. Moreover, the state of batteries can be diagnosed and faults be handled timely. Based on the collection of above parameters of the integrated station and the grid, the grid-connected control strategy is proposed according to the grid state and energy flow of the integrated station. The experimental result suggests that the grid-connected control strategy based on information fusion can help to implement peak load shifting effectively and timely, and provide energy flow support when the grid is in a heavy-loaded state so it can restore to its normal state.

An experimental economics (EE) method is used to analyze the influences of subjective willingness on the development of the electric vehicle (EV) industry. It is difficult to run large-scale EE-based simulations and to support decision optimizations due to the limited number of qualified human participants and the incomparability among repeated trials. Taking the customers’ willingness to buy EVs as an example, this paper extracts multi-layer correlation information from a limited number of questionnaires and builds a multi-agent model to match the probabilistic distributions of multi-responder behaviors, for the purpose of reflecting the truly statistic information embedded from the questionnaires. The vraisemblance of both the model and the algorithm is validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results. Based on the work presented in this paper, the influence of a key factor on the EV development can therefore be analyzed by using a simulation platform with mixed inputs from agents modelled in this paper and human participants.

Due to cell-to-cell variations in battery pack, it is hard to manage cells of the battery pack safely and effectively. As a result, the battery pack performance is rapidly degraded, which in turn spread the differences in individual cells. Ambient temperature is a significant factor that influences characteristics of lithium-ion battery and cells variations in the pack. This paper tries to put effort on researching the temperature characteristics of cells series battery pack. The battery model parameters identification tests are designed to analyze the inconsistency characteristics of cells (such as open circuit voltage (OCV), ohmic and polarization resistances, and polarization capacitance) under various ambient temperatures. The results indicate that ohmic and polarization resistances are most significantly increased as the temperature decreases, while the opposite is true for OCV. The variation of cells inconsistency characteristics is obvious along with the temperature change.

Smart Grid has emerged as the next generation power systems. However, the classical scheduling methods are still not suitable for describing the exact feature of data transmission over smart grid because of its different properties from other conventional power systems. Hence, in this paper, by considering both consumers’ and power supplies’ perspective, different pricing strategies are presented based on user priority and load rate. And the corresponding novel scheduling algorithms are also proposed. The simulation experiments are carried out by comparing the proposed algorithms with other existing scheduling algorithms.

The LiFePO4 batteries are widely used in Electric Vehicle(EV)/Hybrid Electric Vehicle(HEV) because of the high energy and power density. However, high environment temperature could accelerate the aging of batteries, while low temperature could reduce output power capability. Therefore, optimal working temperature for batteries should be determined to maintain good performance in all kinds of tough conditions. In this paper, the optimal working temperature range for batteries is analyzed. The capacity loss model is applied to determine the upper limit. The lower limit is calculated taking available capacity and output power loss into consideration. Simulation and experimental results show that the working temperature range between 10℃ and 40℃ could ensure the performance and available capacity.

A design of on-board V2G charger was designed to meet the requirements of the V2G application. Firstly, the functions of on-board V2G charger were analyzed. Then a two-stage structure including a bidirectional AC-DC converter and a bidirectional DC-DC converter was proposed. Traditional charging mode and V2G mode were been discussed for the proposed charging system. The Dual Active Bridge topology was selected as the DC-DC converter. Moreover, the soft-switching operation over a wide output voltage range was analyzed. In the end, a 3.3 kW on-board V2G charger experimental platform was built. The experimental results demonstrated the feasibility of the proposed design and control methods.

In order to study the harmonics of EV charging stations based on V2G technology, this paper proposed a simulation model considering the structure of the bi-directional charger and the control strategy. To study the variation of THD, the bi-directional charger, the charging station and the traditional 6-pulse rectifier for a contrastive analysis has been made. The author has focused on the charging process and the number of chargers which is in the constant current and constant voltage mode, and then the two factors was combined to make a unified analysis. In the end, a comparative study for bidirectional chargers and traditional chargers has been conducted. The simulation results shows that when in constant voltage charging phase, the THD value increases significantly, with the increasing number of chargers, the THD value tends to stabilize, and if the charging power is higher, the harmonics will be smaller.

To forecast and assess the impact of large-scale electric buses (EBs) to power grid, the aggregating charging load model of EBs cluster is indispensable. As EB’s typical operating, including driving and parking, is a cyclical process and has obvious regularity, a procedural simulation method for aggregating charging load model of EBs cluster based on battery swapping mode is proposed in this paper. With the data come from specific buses lines and other information readily available, the behavior process of each individual in EBs cluster is continuously simulated. Then time and SOC information of battery packs emerges and is recorded. Combined with specific charging control method, corresponding aggregating charging load model becomes available. The proposed method has been verified by simulation on an actual buses line with charging/swapping station. The results show that the proposed method can grasp characteristics of EBs cluster’s charging load under multiple factors, thereby improve the practicality and reliability of modeling.

The large-scale integration of wind power, electric vehicles and air conditioning loads would perhaps produce an adverse combined affect on the voltage stability of power system. It is of great significance to investigate the combined effects of such three factors on power system voltage stability. Firstly, the daily power curve models of the wind farms, electric vehicles and air conditioning loads are studied, and a number of typical daily power curves are given for them. Secondly, the paper analyzes the combined effects of the three factors on power system voltage stability by using different practical daily wind power models. Finally, various electric vehicle charging strategies are simulated to explore their effects in improving the system voltage stability.

As auxiliary equipment for electric vehicle, on-board charger should have high performance on harmonic, power factor and efficiency. But in high-power application, the traditional PFC technology has many restrictions and defects. Therefore, based on the parallel interleaved technique, we designed a power factor correction system suitable for on-board charger of electric vehicle. Experiment results show that the design can effectively reduce the input current ripple and improve the output voltage ripple frequency and the power factor.

One of the major issues in innovative automotive engines is to reduce the energy consumption and pollutant emissions, at the same time, to guarantee a high level of performance indices. To this aim, common rail diesel engines can satisfy strict regulations by enhancing the model-based control of the injection process to increase the combustion efficiency. This paper presents a more accurate model for the electro-injector in common rail diesel engines. The model takes into account the mechanical deformation of relevant parts of the electro-injector and the non-linearity of the fuel flow. Model parameters are then optimized by an evolutionary strategy. Simulation shows that the optimized model can be helpful in predicting the real trend of the injected fuel flow rate when assisted with the experimental data, and in controlling the injection.