Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.

FaultMonitoring Strategy for PV System Based on I-V Feature Library

Long-term use in challenging natural conditions is possible for photovoltaic modules,which are extremely prone to failure.Failure to diagnose and address faults in Photovoltaic(PV)power systems in a timely manner can cause permanent damage to PV modules and,in more serious cases,fires.Therefore,research into photovoltaic module defect detection techniques is crucial for the growth of the photovoltaic sector as well as for maintaining national economic prosperity and ensuring public safety.Considering the drawbacks of the current real-time and historical data-based methods for monitoring distributed PV systems,this paper proposes a method for monitoring PV systems at the module or string level that can be achieved by monitoring only electrical signals.The approach doesn’t need a lot of tests to get the operational data of PV modules beforehand and only requires theoretical feature libraries of PV modules through panel parameter calculations.The present operating conditions and the open-circuit and short-circuit faults can be precisely identified by comparing the observed open-circuit voltage and short-circuit current with the corresponding data in the theoretical feature library.After that,by comparing the measured maximum power point voltage and current with the corresponding data in the theoretical feature library through the threshold method,aging and shadowing faults can be accurately determined.Experimental testing was done to see whether the suggested method was effective.The results show that the proposed technique is able to diagnose open-circuit faults,short-circuit faults,aging faults,and shadowing faults with shadow occlusion above 20%.

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.

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids.In this paper,a multi-timescale optimal scheduling model is established for interconnected data centers(IDCs)based on model predictive control(MPC),including day-ahead optimization,intraday rolling optimization,and intraday real-time correction.The day-ahead optimization stage aims at the lowest operating cost,the rolling optimization stage aims at the lowest intraday economic cost,and the real-time correction aims at the lowest power fluctuation,eliminating the impact of prediction errors through coordinated multi-timescale optimization.The simulation results show that the economic loss is reduced by 19.6%,and the power fluctuation is decreased by 15.23%.

An estimation framework of regional rooftop photovoltaic potential based on satellite remote sensing images

This paper proposes a comprehensive framework for estimating the regional rooftop photovoltaic(PV)potential.The required rooftop information is extracted from Gao Fen-7 satellite images.In particular,the rooftop area is obtained using a semantic segmentation network.The azimuth and inclination angles are calculated based on the digital surface model.In addition,to improve the accuracy of the economic evaluation,buildings are divided into commercial and industrial buildings and residential buildings.Based on the difference in the roof inclination,the rooftops can be divided into flat roofs,on which the PV panels are installed with the optimal inclination angle,and sloped rooftops,on which the PV panels are installed in a lay-flat manner.The solar irradiation on the plane-of-array is calculated using the isotropic sky translocation model.Then,the available installed capacity and generation potential of the rooftop PV is obtained.Finally,the net present value,dynamic payback period,and internal rate of return are used to evaluate the economic efficiency of the rooftop PV project.The proposed framework is applied in the Da Xing district of Beijing,China,with a total area of 546.84 km^(2).The results show that the rooftop area and available installed capacity of PV are 25.63 km^(2)and 1487.45 MWp,respectively.The annual rooftop PV generation potential is 2832.23 GWh,with significant economic returns.

A Dual Arm Complementary Hybrid Modulation Strategy Combining NL-SPWM for MMC Applications

In the medium voltage direct current(MVDC)transmission system,a small number of MMC sub modules will reduce the power quality.In this paper,based on the research background of Photovoltaic Medium VoltageDirect Current(PV-MVDC)system,aHybrid Modulation Strategy based on theDecoupledDouble SynchronousReference Frame(DDSRF)control strategy is proposed.The dual armcomplementary hybrid modulation combining nearest-level-SPWM(NL-SPWM)can keep the number of SMs in the ON state constant.Then,the corresponding voltage sharing control algorithm of sub module(SM)is introduced.Through theoretical calculation,the modulation strategy can be found to stabilize the DC voltage and reduce the harmonic content.A32-level MMC systemhas been developed to verity that the proposed hybrid modulation strategy and its SM voltage sharing algorithm have the advantages of restraining circulating current and maintaining capacitor voltage balance.

Optimal Allocation of Comprehensive Resources for Large-Scale Access of Electric Kiln to the Distribution Network

With the significant progress of the“coal to electricity”project,the electric kiln equipment began to be connected to the distribution network on a large scale,which caused power quality problems such as low voltage,high harmonic distortion rate,and high reactive power loss.This paper proposes a two-stage power grid comprehensive resource optimization configuration model.A multi-objective optimization solution based on the joint simulation platform of Matlab and OpenDSS is developed.The solution aims to control harmonics and optimize reactive power.In the first stage,a multi-objective optimization model is established to minimize the active network loss,voltage deviation,and equipment cost under the constraint conditions of voltage margin,power factor,and reactive power compensation capacity.Furthermore,the first stage uses a particle swarm optimization(PSO)algorithm to optimize the location and capacity of both series and parallel compensation devices in the distribution network.In the second stage,the optimal configuration model of the active power filter assumes the cost of the APF as the objective function and takes the harmonic voltage content rate,the total voltage distortion rate,and the allowable harmonic current as the constraint conditions.The proposed solution eliminates the harmonics by uniformly configuring active filters in the distribution network and centrally control harmonics at the system level.Finally,taking the IEEE33 distribution network as the object and considering the change of electric furnace permeability in the range of 20%–50%,the simulation results show that the proposed algorithm effectively reduces the distribution network’s loss,its harmonic content and significantly improve its voltage.

Analysis of Power Quality Problems in Large-Scale Application of Air-Source Heat Pump

With the implementation of electric energy alternatives,the large-scale application of electric energy substitution represented by air-source heat pumps has replaced traditional coal-fired heating,which is beneficial for the environment and alleviates air pollution.However,the large-scale application of airsource heat pumps has brought power quality problems such as voltage sags,harmonic pollution,and three-phase imbalance to the distribution network.This paper studies the fixed-frequency and variablefrequency air-source heat pump,introduces its working principle,analyzes the mechanism of its power quality problem.Moreover,the paper establishes a simulation model for the fixed-frequency heat pump and variable-frequency heat pump to connect to the distribution network.This research mainly studies the impact of large-scale fixed-frequency heat pumps on the depth of voltage sags in the distribution network and the impact of large-scale variable-frequency heat pumps on the harmonic content of the distribution network under different penetration rates and uses measured data to verify the reliability of the simulation results.This paper uses experimental data for the first time to verify the real power quality problems of large-scale heat pumps,which can provide a reference for determining the power quality standards for heat pumps connected to the power grid.At the same time,it can also provide a reference for the power quality management of the distribution network that is actually connected to electric heating.

Research on Phase-Shifted Full-Bridge Circuit Based on Frequency and Phase-Shift Synthesis Modulation Strategy

The full-bridge converters usually use transformer leakage inductance and parallel resonant capacitors to achieve smooth current commutation and soft switching functions,which can easily cause problems such as energy leakage and significant duty cycle loss.This paper designs a novel full-bridge zero-current(FB-ZCS)converter with series resonant capacitors and proposes a frequency and phase-shift synthesis modulation(FPSSM)control strategy based on this topology.Compared with the traditional parallel resonant capacitor circuit,the passive components used are significantly reduced,the structure is simple,and there is only a slight energy loss.By controlling the charging time of the capacitor,it can be achieved without additional switches or auxiliary circuits.The automatic control of capacitor energy based on input current addresses the low efficiency of the traditional control strategies.This paper introduces its principle in detail and verifies it through simulation.Finally,an experimental prototype was built further to demonstrate the feasibility of the theory through experiments.The module can be applied to a photovoltaic DC collection system using input parallel output series(IPOS)cascade to provide a new topology for large-scale,long-distance DC transmission.

MPC-based interval number optimization for electric water heater scheduling in uncertain environments

In this paper,interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling.First of all,interval numbers are used to describe uncertain parameters including hot water demand,ambient temperature,and real-time price of electricity.Moreover,the traditional thermal dynamic model of electric water heater is transformed into an interval number model,based on which,the day-ahead load scheduling problem with uncertain parameters is formulated,and solved by interval number optimization.Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices.Furthermore,the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day.Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand,ambient temperature,and real-time price of electricity,enabling customers to flexibly adjust electric water heater control strategy.

Distributionally robust optimization of home energy management system based on receding horizon optimization

This paper investigates the scheduling strategy of schedulable load in home energy management system(HEMS)under uncertain environment by proposing a distributionally robust optimization(DRO)method based on receding horizon optimization(RHO-DRO).First,the optimization model of HEMS,which contains uncertain variable outdoor temperature and hot water demand,is established and the scheduling problem is developed into a mixed integer linear programming(MILP)by using the DRO method based on the ambiguity sets of the probability distribution of uncertain variables.Combined with RHO,the MILP is solved in a rolling fashion using the latest update data related to uncertain variables.The simulation results demonstrate that the scheduling results are robust under uncertain environment while satisfying all operating constraints with little violation of user thermal comfort.Furthermore,compared with the robust optimization(RO)method,the RHO-DRO method proposed in this paper has a lower conservation and can save more electricity for users.