New energy accommodation capacity evaluation method and device considering pure electric ship charging load

Abstract

The application discloses a new energy consumption capacity evaluation method and device considering pure electric ship charging load, carries out probability modeling on the charging load of pure electric ships in a basin, obtains total annual load of the whole basin, determines a new energy consumption probability space based on a joint output probability distribution model of wind power and photovoltaic power stations and the total annual load of the whole basin, and determines average abandoned power, abandoned rate, consumption rate and average consumption power of new energy generation according to the new energy consumption probability space, evaluates future new energy consumption capacity of a regional power grid in the basin based on the average abandoned power, abandoned rate, consumption rate and average consumption power, and models, probability simulates and obtains an evaluation result of the new energy consumption capacity of the regional power grid specially for the concentrated investment of pure electric ship load and the regional power grid containing new energy power stations, so that the new energy consumption capacity of the regional power grid in the future basin can be accurately evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy consumption evaluation, and particularly relates to a new energy consumption capacity evaluation method and device considering charging load of pure electric ships. BACKGROUND

[0002] New energy has become an important transformation direction of shipping energy consumption to replace traditional energy. However, the inherent intermittency, anti-peaking characteristics and volatility of new energy represented by photovoltaic and wind power will have a negative impact on the stable operation of the system. On the other hand, the introduction of pure electric ships is another important means of energy saving and emission reduction in the shipping industry. In 2015, the world's first battery-driven ferry, MF Ampere, was put into operation, marking the beginning of the new era of pure electric ships.

[0003] Pure electric ships are usually powered by batteries and have a large charging demand when they are docked. There is a certain degree of matching between the load characteristics and the peak output of new energy. When the new energy sending capacity of the regional power grid in the river basin is insufficient, pure electric ships can be considered as one of the ideal power loads for local consumption of new energy. Therefore, comprehensive analysis of shipping energy and new energy consumption, and accurate evaluation of the new energy consumption capacity of the regional power grid in the future river basin, can help to scientifically formulate the number of pure electric ships and the new energy generation plan, and has a positive significance for promoting new energy generation and consumption, reducing the phenomenon of abandoned wind and light, and realizing mutual benefit and win-win between the power industry and the shipping industry. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a new energy consumption capacity evaluation method and device considering charging load of pure electric ships, which can accurately evaluate the new energy consumption capacity of the regional power grid in the future river basin.

[0005] To solve the above technical problems, the technical scheme adopted by the present application is:

[0006] A new energy consumption capacity evaluation method considering charging load of pure electric ships, comprising the steps of:

[0007] Probabilistic modeling of the charging load of pure electric ships in the river basin to obtain the total annual load of the whole river basin;

[0008] Establishing a joint output probability distribution model of wind power and photovoltaic power stations;

[0009] Determining a new energy consumption probability space based on the joint output probability distribution model of wind power and photovoltaic power stations and the total annual load of the whole river basin, and determining the average abandoned power, abandoned rate, consumption rate and average consumption power of new energy generation according to the new energy consumption probability space;

[0010] Evaluating the future new energy consumption capacity of the regional power grid in the river basin based on the average abandoned power, the abandoned rate, the consumption rate and the average consumption power.

[0011] Further, the charging load of the pure electric ship in the river basin is probabilistically modeled, and the total annual load of the whole river basin is obtained, including:

[0012] Obtain the number of pure electric ships and the number of ports;

[0013] Calculate the probability distribution of the sailing distance of the ships entering each port based on the number of pure electric ships and the number of ports;

[0014] Take the berthing loading and unloading time of the pure electric ship as the charging time, and calculate the probability distribution of the berthing charging time of the pure electric ship;

[0015] Calculate the unit distance power consumption of each ship according to the ship type data of the pure electric ship, and calculate the port charging capacity of the pure electric ship according to the unit distance power consumption and the sailing distance of the pure electric ship in the port;

[0016] Calculate the port charging time of the pure electric ship according to the port charging capacity of the pure electric ship and the actual charging power;

[0017] Obtain the total annual load of the whole river basin based on the probability distribution of the sailing distance of the ships entering each port, the probability distribution of the berthing charging time of the pure electric ship, the port charging capacity of the pure electric ship, and the port charging time of the pure electric ship.

[0018] Further, the calculation of the probability distribution of the sailing distance of the ships entering each port based on the number of pure electric ships and the number of ports includes:

[0019]

[0020]

[0021] In the formula, f s,j (x) represents the probability distribution of the sailing distance of the ships entering port j, x represents the independent variable, σ s,j represents the variance of the lognormal distribution of port j, μ s,j represents the mean of the lognormal distribution of port j, m s,j represents the mean of the sailing distance of the ships entering port j, x i,j represents the sailing distance of the i-th ship when it arrives at the j-th port, N s represents the number of pure electric ships, v s,j represents the variance of the sailing distance of the ships entering port j;

[0022] The calculation of the probability distribution of the berthing charging time of the pure electric ship includes:

[0023]

[0024] wherein f t,j (x) represents the probability distribution of the charging time of the pure electric ship in the jth port, σ t,j represents the variance of the charging time of the pure electric ship in the jth port, μ t,j represents the mean of the charging time of the pure electric ship in the jth port;

[0025] the calculating the unit mileage electricity consumption of each ship according to the ship type data of the pure electric ship and the calculating the port charging quantity of the pure electric ship according to the unit mileage electricity consumption and the port running mileage of the pure electric ship includes:

[0026]

[0027] wherein E Unit,i represents the unit mileage electricity consumption of the ith ship, S max.i represents the endurance mileage in the ship type data of the ith ship, E bat，i represents the battery capacity in the ship type data of the ith ship, E i,j represents the charging quantity of the ith ship entering the jth port, S i,j represents the port running mileage of the pure electric ship;

[0028] the calculating the port charging duration of the pure electric ship according to the port charging quantity of the pure electric ship and the actual charging power includes:

[0029]

[0030] wherein P i,j represents the actual charging power of the ith pure electric ship in the jth port, P ship,i represents the rated charging power of the ith pure electric ship, P charger,j represents the power limit of the charging pile in the jth port, t i,j represents the charging duration of the ith pure electric ship in the jth port;

[0031] the obtaining the total annual load of the whole river basin based on the ship navigation mileage probability distribution of each port, the charging time probability distribution of the pure electric ship, the port charging quantity of the pure electric ship and the port charging duration of the pure electric ship includes:

[0032]

[0033] P j,Σ (t year )=[P j,Σ (t day,1 ) P j,Σ (t day,2 )... P j,Σ (t day,365 )];

[0034]

[0035] where P j (t day ) represents the daily charging load of the jth port, A 1×24,i represents the time series data of the ith pure electric ship charging for one day at the jth port, a i,k represents the charging condition of the ith pure electric ship at the jth port at time k, k = 1, 2, …, 24, t day represents the time within a day, P j,Σ (t day ) represents the daily charging load of the whole basin, P port,Σ (t year ) represents the total annual load of the whole basin, P j,Σ (t year ) represents the annual charging load time series curve, N p represents the number of ports, P j,normal (t year ) represents the conventional load other than charging piles.

[0036] Further, the method for establishing the joint output probability distribution model of wind power and photovoltaic power station comprises the following steps:

[0037] determining the probability density distribution of wind speed and the probability density distribution of illumination intensity;

[0038] determining the output power of wind turbine and the output power of photovoltaic power station based on the probability density distribution of wind speed and the probability density distribution of illumination intensity;

[0039] establishing the joint output probability distribution model of wind power and photovoltaic power station based on the output power of wind turbine and the output power of photovoltaic power station.

[0040] Further, the method for determining the probability density distribution of wind speed and the probability density distribution of illumination intensity comprises the following steps:

[0041]

[0042] where f(v) represents the probability density distribution of wind speed, a1 represents the scale parameter of Weibull distribution, b1 represents the shape parameter of Weibull distribution, v represents wind speed, represents the probability density distribution of illumination intensity, a2 represents the scale parameter of Beta distribution, b2 represents the shape parameter of Beta distribution, S represents illumination intensity, Spvmax represents the maximum illumination intensity, and B(a2, b2) represents the Beta distribution function;

[0043] The determining the wind turbine output power and the photovoltaic power station output power based on the probability density distribution of the wind speed and the probability density distribution of the illumination intensity comprises:

[0044]

[0045] P pv = SAη;

[0046] In the formula, P w represents the wind turbine output power, P r represents the wind turbine rated output, V c represents the wind turbine cut-in wind speed, V r represents the wind turbine rated wind speed, V f represents the wind turbine cut-out wind speed, P pv represents the photovoltaic power station output power, A represents the photovoltaic array irradiation area, and η represents the photoelectric conversion efficiency.

[0047] Further, the establishing the wind power and photovoltaic power station combined output probability distribution model based on the wind turbine output power and the photovoltaic power station output power comprises:

[0048] The wind power and photovoltaic power station combined output probability distribution model is constructed using an Archimedean Copula function based on the wind turbine output power and the photovoltaic power station output power.

[0049] Further, the determining the new energy consumption probability space based on the wind power and photovoltaic power station combined output probability distribution model and the total annual load of the whole flow field comprises:

[0050]

[0051] In the formula, P new,ca (t) represents the new energy consumption probability space, P port,Σ (t) represents the total annual load of the whole flow field, N g represents the number of conventional generator units in the flow field, Z k (t) represents the binary variable of the start-stop operation of the unit, β k represents the minimum output coefficient of the k conventional units, P gen,k (t) represents the rated capacity of the k conventional unit at the t time.

[0052] Further, the determining the average curtailment power, curtailment rate, consumption rate and average consumption power of the new energy power generation according to the new energy consumption probability space comprises:

[0053] The wind power and photovoltaic power station combined output probability distribution model is discretized to obtain a discrete probability distribution matrix of the new energy theoretical output;

[0054] Discretize the new energy consumption probability space to obtain a discrete probability distribution matrix of the new energy consumption probability space;

[0055] Calculate a new energy curtailment power distribution matrix and a new energy consumption power distribution matrix according to the discrete probability distribution matrix of the new energy theoretical output and the discrete probability distribution matrix of the new energy consumption probability space;

[0056] Solve the joint probability of the discrete probability distribution matrix of the new energy theoretical output and the discrete probability distribution matrix of the new energy consumption probability space to obtain a probability distribution matrix of new energy curtailment power and a probability distribution matrix of new energy consumption power;

[0057] Obtain the average curtailment power, curtailment rate, consumption rate and average consumption power of new energy power generation based on the probability distribution matrix of new energy curtailment power and the probability distribution matrix of new energy consumption power.

[0058] Further, the evaluation of the future new energy consumption capacity of the regional power grid in the basin based on the average curtailment power, the curtailment rate, the consumption rate and the average consumption power includes:

[0059] The evaluation of the future pure electric ship operation quantity, pure electric ship route participation, pure electric ship type and new energy power station construction scale of the regional power grid in the basin based on the average curtailment power, the curtailment rate, the consumption rate and the average consumption power.

[0060] In order to solve the above technical problems, another technical solution adopted by the present application is:

[0061] A new energy consumption capacity evaluation device considering pure electric ship charging load, comprising:

[0062] A total annual load determination module is configured to model the charging load of pure electric ships in the basin to obtain the total annual load of the whole basin;

[0063] A model establishment module is configured to establish a wind power and photovoltaic power station joint output probability distribution model;

[0064] A calculation module is configured to determine a new energy consumption probability space based on the wind power and photovoltaic power station joint output probability distribution model and the total annual load of the whole basin, and determine the average curtailment power, curtailment rate, consumption rate and average consumption power of new energy power generation according to the new energy consumption probability space;

[0065] An evaluation module is configured to evaluate the future new energy consumption capacity of the regional power grid in the basin based on the average curtailment power, the curtailment rate, the consumption rate and the average consumption power.

[0066] The beneficial effect of the present application is that the charging load of pure electric ships in the basin is probabilistically modeled, the total annual load of the whole basin is obtained, the new energy consumption probability space is determined based on the joint output probability distribution model of wind power and photovoltaic power station and the total annual load of the whole basin, and the average power of abandoned electricity, the rate of abandoned electricity, the rate of consumption and the average consumption power of new energy generation are determined according to the new energy consumption probability space, the future new energy consumption capacity of the regional power grid in the basin is evaluated based on the average power of abandoned electricity, the rate of abandoned electricity, the rate of consumption and the average consumption power, and the new energy consumption capacity of the regional power grid in the basin is modeled, probabilistically simulated and evaluated to obtain the evaluation result of the new energy consumption capacity, so that the new energy consumption capacity of the future regional power grid in the basin can be accurately evaluated. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 A step flow chart of a new energy consumption capacity evaluation method considering pure electric ship charging load for an embodiment of the present application;

[0068] Figure 2 A structure schematic diagram of a new energy consumption capacity evaluation device considering pure electric ship charging load for an embodiment of the present application;

[0069] Figure 3 A new energy consumption space evaluation schematic diagram in a new energy consumption capacity evaluation method considering pure electric ship charging load for an embodiment of the present application. DETAILED DESCRIPTION

[0070] To explain the technical content, the achieved purposes and effects of the present application in detail, the following will be explained in combination with embodiments and the drawings.

[0071] Please refer to Figure 1 A new energy consumption capacity evaluation method considering pure electric ship charging load, comprising the steps of:

[0072] Probabilistically modeling the charging load of pure electric ships in the basin to obtain the total annual load of the whole basin;

[0073] Establishing a joint output probability distribution model of wind power and photovoltaic power station;

[0074] Determining a new energy consumption probability space based on the joint output probability distribution model of wind power and photovoltaic power station and the total annual load of the whole basin, and determining the average power of abandoned electricity, the rate of abandoned electricity, the rate of consumption and the average consumption power of new energy generation according to the new energy consumption probability space;

[0075] Evaluating the future new energy consumption capacity of the regional power grid in the basin based on the average power of abandoned electricity, the rate of abandoned electricity, the rate of consumption and the average consumption power.

[0076] From the above description, the beneficial effects of the present application are that: the charging load of pure electric ships in the basin is probabilistically modeled, the total annual load of the whole basin is obtained, the new energy consumption probability space is determined based on the joint output probability distribution model of wind power and photovoltaic power station and the total annual load of the whole basin, and the average power of new energy generation, the power rejection rate, the consumption rate and the average consumption power are determined according to the new energy consumption probability space. The future new energy consumption capacity of the regional power grid in the basin is evaluated based on the average power rejection, the power rejection rate, the consumption rate and the average consumption power. The regional power grid with concentrated pure electric ship load and containing new energy power station is modeled and probabilistically simulated to obtain the evaluation result of new energy consumption capacity, so that the new energy consumption capacity of the future regional power grid in the basin can be accurately evaluated.

[0077] Further, the probabilistic modeling of the charging load of pure electric ships in the basin to obtain the total annual load of the whole basin comprises:

[0078] The number of pure electric ships and the number of ports are obtained;

[0079] The sailing mileage probability distribution of the incoming ships of each port is calculated based on the number of pure electric ships and the number of ports;

[0080] The berthing and unloading time of the pure electric ships is taken as the charging time, and the berthing charging time probability distribution of the pure electric ships is calculated;

[0081] The unit mileage power consumption of each ship is calculated according to the ship type data of the pure electric ships, and the port charging capacity of the pure electric ships is calculated according to the unit mileage power consumption and the berthing mileage of the pure electric ships;

[0082] The port charging duration of the pure electric ships is calculated according to the port charging capacity of the pure electric ships and the actual charging power;

[0083] The total annual load of the whole basin is obtained based on the sailing mileage probability distribution of the incoming ships of each port, the berthing charging time probability distribution of the pure electric ships, the port charging capacity of the pure electric ships and the port charging duration of the pure electric ships.

[0084] From the above description, considering that there is a certain internal law between pure electric ship charging and route arrangement, the probabilistic simulation method is used for modeling and solving, and the total annual load of the whole basin is simulated according to the probability distribution of the ship charging behavior, which is more accurate than simulating the load based on random distribution.

[0085] Further, the calculation of the sailing mileage probability distribution of the incoming ships of each port based on the number of pure electric ships and the number of ports comprises:

[0086]

[0087] In the formula, fs,j (x) represents the port j into the port ship sailing mileage probability distribution, x represents the independent variable, σ s,j represents the variance of the lognormal distribution of port j, μ s,j represents the mean of the lognormal distribution of port j, m s,j represents the mean of the port j port ship driving mileage, x i,j represents the driving mileage of the i-th ship to the j-th port, N s represents the number of pure electric ships, v s,j represents the variance of the port j port ship driving mileage;

[0088] The port j port ship unloading time of the pure electric ship is taken as the charging time, and the pure electric ship port charging time probability distribution is calculated.

[0089]

[0090] In the formula, f t,j (x) represents the port j pure electric ship charging time probability distribution, σ t,j represents the variance of the j-th port pure electric ship charging time, μ t,j represents the mean of the j-th port pure electric ship charging time;

[0091] The unit mileage power consumption of each ship is calculated according to the ship type data of the pure electric ship, and the pure electric ship port charging quantity is calculated according to the unit mileage power consumption and the pure electric ship port driving mileage.

[0092]

[0093] In the formula, E Unit,i represents the unit mileage power consumption of the i-th ship, S max.i represents the endurance mileage in the ship type data of the i-th ship, E bat，i represents the battery capacity in the ship type data of the i-th ship, E i,j represents the charging quantity of the i-th ship into the j-th port, S i,j represents the pure electric ship port driving mileage;

[0094] The pure electric ship port charging time is calculated according to the pure electric ship port charging quantity and the actual charging power.

[0095]

[0096] In the formula, P i,j represents the actual charging power of the i-th pure electric ship in the j-th port, P ship,i represents the rated charging power of the i-th pure electric ship, P charger,jP (t) represents the power limit of the jth port charging pile, t i,j P (t) represents the charging duration of the ith pure electric ship at the jth port;

[0097] The total annual load of the entire river basin is obtained based on the probability distribution of the navigation mileage of each port entering ship, the probability distribution of the charging time of the pure electric ship at the port, the charging capacity of the pure electric ship at the port, and the charging duration of the pure electric ship at the port.

[0098]

[0099] P j,Σ (t year )=[P j,Σ (t day,1 ) P j,Σ (t day,2 )... P j,Σ (t day,365 )];

[0100]

[0101] In the formula, P j (t day ) represents the daily charging load of the jth port, A 1×24,i represents the time sequence data of the ith pure electric ship charging at the jth port for one day, a i,k represents the charging condition of the ith pure electric ship at the jth port at k time, k=1,2,…,24, t day represents the time within a day, P j,Σ (t day ) represents the daily charging load of the entire river basin, P port,Σ (t year ) represents the total annual load of the entire river basin, P j,Σ (t year ) represents the annual charging load time sequence curve, N p represents the number of ports, P j,normal (t year ) represents the conventional load other than the charging pile.

[0102] As can be seen from the above description, the conventional load other than the charging pile can be estimated by using a typical power consumption data set of a certain port, and finally the annual charging load time sequence curve is superimposed on the conventional load summed according to the number of ports to obtain the total annual load of the entire river basin, so as to perform subsequent calculation of new energy consumption space.

[0103] Further, the establishment of the wind power and photovoltaic power station joint output probability distribution model comprises:

[0104] determining the probability density distribution of wind speed and the probability density distribution of light intensity;

[0105] determine wind turbine output power and photovoltaic power station output power based on the probability density distribution of the wind speed and the probability density distribution of the light intensity;

[0106] establish a wind power and photovoltaic power station combined output probability distribution model based on the wind turbine output power and the photovoltaic power station output power.

[0107] As can be seen from the above description, because there is a certain coupling relationship between wind and light resources, the output of wind power and photovoltaic power station should not be regarded as independent events, so establishing a wind power and photovoltaic power station combined output probability distribution model is conducive to accurately evaluating new energy consumption capacity.

[0108] Further, the determination of the probability density distribution of the wind speed and the probability density distribution of the light intensity comprises:

[0109]

[0110] In the formula, f(v) represents the probability density distribution of the wind speed, a1 represents the scale parameter of the Weibull distribution, b1 represents the shape parameter of the Weibull distribution, v represents the wind speed, represents the probability density distribution of the light intensity, a2 represents the scale parameter of the Beta distribution, b2 represents the shape parameter of the Beta distribution, S represents the light intensity, Spvmax represents the maximum light intensity, and B(a2, b2) represents the Beta distribution function;

[0111] The determination of the wind turbine output power and the photovoltaic power station output power based on the probability density distribution of the wind speed and the probability density distribution of the light intensity comprises:

[0112]

[0113] P pv = SAη;

[0114] In the formula, P w represents the wind turbine output power, P r represents the rated output of the wind turbine, V c represents the cut-in wind speed of the wind turbine, V r represents the rated wind speed of the wind turbine, V f represents the cut-out wind speed of the wind turbine, P pv represents the photovoltaic power station output power, A represents the irradiation area of the photovoltaic array, and η represents the photoelectric conversion efficiency.

[0115] As can be seen from the above description, when simulating the probability distribution of the theoretical output of new energy, the two-parameter Weibull distribution function is used to express the probability density distribution of the wind speed, and the Beta distribution function is used to describe the probability density distribution of the light intensity, so that the wind turbine output power and the photovoltaic power station output power determined thereby are more reliable.

[0116] Further, the wind power and photovoltaic power station combined output probability distribution model is established based on the wind turbine output power and the photovoltaic power station output power.

[0117] The wind power and photovoltaic power station combined output probability distribution model is constructed based on the wind turbine output power and the photovoltaic power station output power using an Archimedean Copula function.

[0118] As can be seen from the above description, since the probability distributions of wind and light output both have the characteristics of "spike thick tail", the Archimedean Copula function can be used to construct a binary joint distribution, which is more accurate and reliable.

[0119] Further, the new energy consumption probability space is determined based on the wind power and photovoltaic power station combined output probability distribution model and the total annual load of the whole basin.

[0120]

[0121] In the formula, P new,ca (t) represents the new energy consumption probability space, P port,Σ (t) represents the total annual load of the whole basin, N g represents the number of conventional generating units in the basin, Z k (t) represents the binary variable of the start and stop operation of the unit, β k represents the minimum output coefficient of k conventional units, P gen,k (t) represents the rated capacity of the kth conventional unit at time t.

[0122] As can be seen from the above description, the new energy consumption probability space is determined based on the wind power and photovoltaic power station combined output probability distribution model and the total annual load of the whole basin, so as to more efficiently calculate the average curtailment power, curtailment rate, consumption rate and average consumption power of new energy generation in the future.

[0123] Further, the average curtailment power, curtailment rate, consumption rate and average consumption power of new energy generation are determined according to the new energy consumption probability space.

[0124] The wind power and photovoltaic power station combined output probability distribution model is discretized to obtain a discrete probability distribution matrix of new energy theoretical output;

[0125] The new energy consumption probability space is discretized to obtain a discrete probability distribution matrix of the new energy consumption probability space;

[0126] The new energy curtailment power distribution matrix and the new energy consumption power distribution matrix are calculated according to the discrete probability distribution matrix of the new energy theoretical output and the discrete probability distribution matrix of the new energy consumption probability space.

[0127] solving the joint probability of the discrete probability distribution matrix of the new energy theory output and the discrete probability distribution matrix of the new energy consumption probability space, to obtain the probability distribution matrix of the new energy power and the probability distribution matrix of the new energy consumption power;

[0128] Based on the probability distribution matrix of the new energy power and the probability distribution matrix of the new energy consumption power, the average power of new energy generation, the power rate, the consumption rate and the average consumption power are obtained.

[0129] From the above description, it can be seen that the joint output probability distribution model of wind power and photovoltaic power station and the new energy consumption probability space are discretized, which is convenient for unified solution, so as to quickly and accurately calculate the average power of new energy generation, the power rate, the consumption rate and the average consumption power.

[0130] Further, the evaluation of the future new energy consumption capacity of the regional power grid in the basin based on the average power, the power rate, the consumption rate and the average consumption power includes:

[0131] According to the average power, the power rate, the consumption rate and the average consumption power, the number of pure electric ships to be put into operation in the future, the routes of pure electric ships to participate in, the types of pure electric ships and the construction scale of new energy power stations in the regional power grid in the basin are evaluated.

[0132] From the above description, according to the average power, the power rate, the consumption rate and the average consumption power, the number of pure electric ships to be put into operation in the future, the routes of pure electric ships to participate in, the types of pure electric ships and the construction scale of new energy power stations in the regional power grid in the basin are evaluated, so as to scientifically formulate the number of pure electric ships to be put into operation and the construction plan of new energy power generation, promote the consumption of new energy power generation and reduce the phenomenon of abandoned wind and light.

[0133] Please refer to Figure 2 Another embodiment of the present application provides a new energy consumption capacity evaluation device considering the charging load of pure electric ships, comprising:

[0134] The total annual load determination module is used for probabilistic modeling of the charging load of pure electric ships in the basin to obtain the total annual load of the whole basin;

[0135] The model establishment module is used for establishing a joint output probability distribution model of wind power and photovoltaic power station;

[0136] The calculation module is used for determining the new energy consumption probability space based on the joint output probability distribution model of wind power and photovoltaic power station and the total annual load of the whole basin, and determining the average power of new energy generation, the power rate, the consumption rate and the average consumption power according to the new energy consumption probability space;

[0137] The evaluation module is configured to evaluate the future new energy consumption capacity of the regional power grid in the basin based on the average curtailment power, the curtailment rate, the consumption rate, and the average consumption power.

[0138] From the above description, the beneficial effects of the present application are as follows: the charging load of pure electric ships in the basin is probabilistically modeled to obtain the total annual load of the whole basin, the new energy consumption probability space is determined based on the joint output probability distribution model of wind power and photovoltaic power stations and the total annual load of the whole basin, the average curtailment power, the curtailment rate, the consumption rate, and the average consumption power of new energy generation are determined according to the new energy consumption probability space, and the future new energy consumption capacity of the regional power grid in the basin is evaluated based on the average curtailment power, the curtailment rate, the consumption rate, and the average consumption power. Thus, the regional power grid with concentrated pure electric ship load and containing new energy power stations is modeled and probabilistically simulated to obtain the evaluation result of the new energy consumption capacity, so that the new energy consumption capacity of the future regional power grid in the basin can be accurately evaluated.

[0139] The new energy consumption capacity evaluation method and device considering pure electric ship charging load described above can be applied to the regional power grid in the basin, and the following specific embodiments are described:

[0140] Please refer to Figure 1 and Figure 3 , the embodiment one of the present application is:

[0141] A new energy consumption capacity evaluation method considering pure electric ship charging load, comprising the steps of:

[0142] S1, the charging load of pure electric ships in the basin is probabilistically modeled to obtain the total annual load of the whole basin, specifically including S11-S16:

[0143] S11, the number of pure electric ships and the number of ports are obtained.

[0144] S12, the sailing distance probability distribution of each port is calculated based on the number of pure electric ships and the number of ports; assuming that there are N s pure electric ships in the basin, and there are N p ports, the typical data of the carrying route information is counted, and the probability distribution of the sailing distance of each pure electric ship each time it docks is calculated with each port as a reference point. Generally, the sailing distance of pure electric ships is considered to conform to the normal distribution rule, and the maximum likelihood method is used for processing. The sailing distance probability distribution of the port j is described by using the logarithmic normal distribution form lnX~N(μ,δ 2 ), specifically as follows:

[0145]

[0146] In the formula, fs,j (x) represents the port j into the port ship sailing mileage probability distribution, x represents the independent variable, σ s,j represents the variance of the lognormal distribution of port j, μ s,j represents the mean of the lognormal distribution of port j, m s,j represents the mean of the port j port ship driving mileage, x i,j represents the driving mileage of the i-th ship to the j-th port, N s represents the number of pure electric ships, v s,j represents the driving mileage variance of the port j port ship. Wherein, i=1, 2,..., N s ; j=1, 2,..., N p .

[0147] Because pure electric ship port loading and unloading can generally be synchronized with charging, therefore:

[0148] S13, the port loading and unloading time of the pure electric ship is taken as the charging time, and the pure electric ship port charging time probability distribution is calculated, specifically:

[0149]

[0150] In the formula, f t,j (x) represents the pure electric ship charging time probability distribution in port j, σ t,j represents the variance of the charging time of the j-th port pure electric ship, μ t,j represents the mean of the charging time of the j-th port pure electric ship. The charging time is the charging start time.

[0151] S14, according to the ship type data of the pure electric ship, the unit mileage power consumption of each ship is calculated, and the port charging capacity of the pure electric ship is calculated according to the unit mileage power consumption and the pure electric ship port driving mileage, specifically:

[0152]

[0153] In the formula, E Unit,i represents the unit mileage power consumption of the i-th ship, S max.i represents the endurance mileage in the ship type data of the i-th ship, E bat，i represents the battery capacity in the ship type data of the i-th ship, E i,j represents the charging capacity of the i-th ship into the j-th port, S i,j represents the pure electric ship port driving mileage.

[0154] Because the charging time is restricted by the maximum output power of the charging pile and the rated charging power of the battery, therefore:

[0155] S15. Calculate the port charging time for the pure electric vessel based on the port charging amount and actual charging power, specifically as follows:

[0156]

[0157] In the formula, P i,j P represents the actual charging power of the i-th pure electric ship at the j-th port. ship,i P represents the rated charging power of the i-th pure electric ship. charger,j t represents the power limit of the j-th port charging pile. i,j This represents the charging time of the i-th all-electric ship at the j-th port.

[0158] S16. Based on the probability distribution of the voyage distance of ships entering each port, the probability distribution of the charging time of pure electric ships, the charging amount of pure electric ships at ports, and the charging duration of pure electric ships at ports, the total annual load of the entire basin is obtained. Since the daily load fluctuates randomly, it can be averaged after multiple samplings according to the probability distribution. Specifically:

[0159]

[0160] P j,Σ (t year )=[P j,Σ (t day,1 ) P j,Σ (t day,2 ... P j,Σ (t day,365 )];

[0161]

[0162] In the formula, P j (t day ) represents the daily charging load of the j-th port, A 1×24,i This represents the time-series data of the i-th all-electric ship charging at the j-th port for one day, a i,k This represents the charging status of the i-th all-electric ship at time k in port j, where k = 1, 2, ..., 24, t day P represents a time within a day. j,Σ (t day P represents the daily charging load of the entire river basin. port,Σ (t year P represents the total annual load of the entire basin. j,Σ (t year ) represents the annual charging load time-series curve, N p P represents the number of ports. j,normal (t year This indicates the regular load other than the charging pile.

[0163] wherein the definition matrix A j to represent N s The time sequence data of charging of the pure electric ship in the port j, and the step is one hour in the application. j The element a i,k The charging condition of the pure electric ship i in the port j at time k, the element in the matrix A j is assigned according to the following formula, and the element is assigned as P i,j when charging, and is assigned as zero when not charging, and is specifically:

[0164]

[0165] In the formula, T i,j represents the charging start time of the i-th pure electric ship in the j-th port.

[0166] S2, a wind power and photovoltaic power station joint output probability distribution model is established; since there is a certain coupling relationship between wind and light resources, the output of wind power and photovoltaic power station should not be regarded as independent events, so the joint solution based on the positive correlation between wind and light output is needed to obtain the total output of new energy power generation, and the solving time can also be shortened, which specifically includes S21-S23:

[0167] S21, the probability density distribution of wind speed and the probability density distribution of light intensity are determined, which specifically includes:

[0168]

[0169] In the formula, f(v) represents the probability density distribution of wind speed, a1 represents the scale parameter of Weibull distribution, b1 represents the shape parameter of Weibull distribution, v represents wind speed, represents the probability density distribution of light intensity, a2 represents the scale parameter of Beta distribution, b2 represents the shape parameter of Beta distribution, S represents light intensity, Spvmax represents the maximum light intensity, and B(a2, b2) represents the Beta distribution function.

[0170] S22, the wind turbine output power and the photovoltaic power station output power are determined based on the probability density distribution of wind speed and the probability density distribution of light intensity, which specifically includes:

[0171]

[0172] In the formula, P w represents the wind turbine output power, P r represents the rated output of the wind turbine, V c represents the cut-in wind speed of the wind turbine, V r represents the rated wind speed of the wind turbine, V f represents the cut-out wind speed of the wind turbine, and Ppv Ppv represents the output power of the photovoltaic power station, A represents the irradiation area of the photovoltaic array, and η represents the photoelectric conversion efficiency.

[0173] S23, based on the fan output power and the photovoltaic power station output power, a wind power and photovoltaic power station combined output probability distribution model is established; considering that the outputs of both in the actual power grid are not simply summed but have a certain coupling relationship, the probability distribution of the wind and light outputs is observed, both have the characteristics of "spike thick tail", and the Archimedean Copula function can be used to construct a binary joint distribution.

[0174] Wherein, the Archimedean Copula function is a connection function, which stipulates that the joint distribution function of an N-dimensional variable can be composed of the edge distribution of the N variables and a Copula function, the Copula function is used to describe the correlation between variables, and the expression of the Copula function is:

[0175] F(x1,x2,...,x n )=C(F1(x1),F2(x2),...,F n (x n ));

[0176] In the formula, C() represents the Copula connection function, F(x1, x2, …, x n ) represents the joint distribution function of variables, F n (x n ) represents the edge distribution function of a single variable, and x n represents the independent variable of the nth edge distribution function.

[0177] The common Archimedean Copula function form is divided into three kinds, which are Clayton-Copula, Gumble-Copula and Frank-Copula, and the analytical expressions of the binary distribution functions of the three functions are shown in Table 1.

[0178] Table 1 Analytical expression table of binary joint distribution function of wind-light

[0179]

[0180]

[0181] The correlation of wind-light is represented by the rank correlation coefficient τ, and τ=0.1553 is calculated according to the distribution function simulation data, and the calculation formula of τ and θ of the three functions and the corresponding θ value verification are shown in Table 2.

[0182] Table 2 Parameter θ value and wind-light correlation verification table

[0183]

[0184] D1(0) in Table 2 represents the value calculation function of τ, specifically:

[0185] To select the optimal Copula function, the squared Euclidean distance is introduced, specifically:

[0186] Let (x i , y i )(i = 1, 2, …, n) be samples from a two-dimensional population (X, Y), and the squared Euclidean distance is defined as:

[0187]

[0188] In the formula, F(u i , v i ) represents sample data, and F(u i , v i ) represents the mean of sample data.

[0189] The squared Euclidean distance can reflect the goodness of the Copula function when simulating the original data, and the smaller the squared Euclidean distance, the better the fitting performance of the Copula function. The squared Euclidean distances of the above three Copula functions are shown in Table 3.

[0190] Table 3 Comparison of function fitting performance based on squared Euclidean distance

[0191]

[0192]

[0193] Therefore, it is concluded that the Clayton-Copula function is the best choice for constructing the joint output probability distribution model of wind power and photovoltaic power stations.

[0194] Specifically:

[0195] P new (x) = max[(P w (x)) -θ +((P pv (x)) -θ -1) -1 / θ , 0];

[0196] In the formula, P new (x) represents the joint output probability distribution of wind power and photovoltaic power stations, P w (x) represents the output power of the wind turbine, i.e. P w (v), and P pv (x) represents the output power of the photovoltaic power station, i.e. P pv (S).

[0197] S3, determine a new energy consumption probability space based on the wind power and photovoltaic power station joint output probability distribution model and the total annual load of the whole basin, and determine the average curtailment power, curtailment rate, consumption rate and average consumption power of new energy generation according to the new energy consumption probability space, specifically comprising S31-S36:

[0198] As shown in Figure 3 , when the new energy generation output is too high, the load will not be able to consume these energies, in order to ensure the normal operation of the power system, it can only be discarded. Considering that the minimum output of conventional units, that is, the maximum peak shaving margin, the system has the strongest new energy consumption capacity, in order to consume as much new energy as possible, the output of conventional units can be reduced until the minimum technical output (corresponding to the dashed line of "minimum technical output of conventional units" in Figure 3 ) is reached, and the new energy output (light curve in Figure 3 ) is superimposed on this basis to obtain the total system output. The total annual load of the whole basin obtained above (corresponding to the dark curve in Figure 3 ) minus the minimum output of conventional units is the new energy consumption space. Further analysis shows that the total system output is compared with the total annual load of the whole basin, when the former is greater than the latter, the curtailment occurs (corresponding to the shaded part in Figure 3 ), otherwise the new energy can be completely consumed.

[0199] S31, determine a new energy consumption probability space based on the wind power and photovoltaic power station joint output probability distribution model and the total annual load of the whole basin, specifically comprising:

[0200]

[0201] In the formula, P new,ca (t) represents the new energy consumption probability space, P port,Σ (t) represents the total annual load of the whole basin, N g represents the number of conventional power generating units in the basin, Z k (t) represents the binary variable of the start and stop operation of the unit, the value of 1 represents the start of the unit, and 0 represents the stop of the unit, β k represents the minimum output coefficient of k conventional units, P gen,k (t) represents the rated capacity of the kth conventional unit at time t.

[0202] According to the above formula, the time sequence simulation data of the new energy consumption space can be obtained, and then the probability distribution thereof can be obtained.

[0203] S32, discretize the wind power and photovoltaic power station joint output probability distribution model to obtain a discrete probability distribution matrix of the theoretical output of new energy.

[0204] Specifically, the new energy output probability distribution series of data points is extracted with 1 kW as a step, and the new energy output is different from the upper limit of the new energy consumption space, so the total installed capacity P of the system can be taken max As an upper limit to facilitate subsequent calculation between matrices, a P max matrix of 2 rows and 1 column is established new (x i )(i = 0, 1, 2,..., P max ) is discretized for the wind power and photovoltaic power station combined output probability distribution model, which is:

[0205]

[0206] In the formula, P new (x i ) represents the discrete probability distribution matrix of the theoretical output of new energy, x i represents the output power value, f new (x i ) represents the probability corresponding to the output power value, and f new (x i ) is equal to the integral of the output probability of each sub-interval. When the first column value of the matrix is greater than the upper limit P new,max of the new energy power generation output, the probability is 0, and finally the discrete probability distribution corresponding to P max intervals is obtained.

[0207] S33, the new energy consumption probability space is discretized to obtain a discrete probability distribution matrix of the new energy consumption probability space.

[0208] Specifically, the new energy consumption space value is segmented from 0 to P max with 1 kW as a step, the number of new energy consumption space values falling in each region in a unit of time is counted, and the corresponding probability is obtained by dividing the total amount of data. Based on the statistical method, the time series data of the new energy consumption space is converted into a discrete probability distribution P new,ca (y i ), which also uses a (P max ×2) matrix P new,ca (y i )(i = 0, 1, 2,..., P max ) to represent, which is:

[0209]

[0210] In the formula, P new,ca (y i ) represents the discrete probability distribution matrix of the new energy consumption probability space, y i represents the new energy consumption probability space value, f new,ca (y i) represents the probability corresponding to the new energy consumption probability space value, f new,ca (y i ) is equal to the integral of the output probability of each segment interval, and when the value of the first column of the matrix is greater than the maximum value P (new,ca),max of the new energy consumption space, the probability is 0, and finally the discrete probability distribution corresponding to P max is also obtained.

[0211] S34, according to the discrete probability distribution matrix of the new energy theoretical output and the discrete probability distribution matrix of the new energy consumption probability space, the new energy curtailment power distribution matrix P new,ab (z i ) and the new energy consumption power distribution matrix P new,con (m i ) are calculated.

[0212] Specifically, as shown in the figure, the new energy curtailment power P new,ab and the new energy consumption power P new,con at time t can be represented as:

[0213]

[0214] In the above formula, it is represented that when the new energy output is greater than the new energy consumption space, the curtailment power is generated, and the curtailment power value is the difference between the former and the latter. On the contrary, the curtailment power is 0, and the green electricity is completely consumed; the new energy consumption power value is the new energy output and the minimum value of the new energy consumption space.

[0215] S35, the discrete probability distribution matrix of the new energy theoretical output and the discrete probability distribution matrix of the new energy consumption probability space are solved by joint probability, and the probability distribution matrix of the new energy curtailment power and the probability distribution matrix of the new energy consumption power are obtained.

[0216] Specifically, with 1kW as the step, the joint probability of P new,ca (y i ) and P new (x i ) is solved, and the probability distribution matrix f new,ab of the new energy curtailment power and the probability distribution matrix f new,con of the new energy consumption power are obtained as follows:

[0217]

[0218] S36, based on the probability distribution matrix of the new energy curtailment power and the probability distribution matrix of the new energy consumption power, the average curtailment power, curtailment rate, consumption rate and average consumption power of the new energy power generation are obtained, specifically:

[0219]

[0220] In the formula, α represents the average abandoned power of new energy power generation, β represents the abandoned rate of new energy power generation, ξ represents the consumption rate of new energy power generation, γ represents the average consumption power of new energy power generation, z i represents the discrete independent variable of the average abandoned power of new energy power generation, m i represents the discrete independent variable of the average consumption power of new energy power generation.

[0221] S4, based on the average abandoned power, the abandoned rate, the consumption rate and the average consumption power, evaluating the future new energy consumption capacity of the regional power grid in the basin.

[0222] Specifically, according to the average abandoned power, the abandoned rate, the consumption rate and the average consumption power, evaluating the future pure electric ship operation quantity, pure electric ship participating route, pure electric ship type and new energy power station construction scale of the regional power grid in the basin.

[0223] In the planning process, generally, the superior department will definitely limit the quantity of pure electric ships, participating routes and ship types, and according to this, the P port,Σ (t year ) containing the charging load of pure electric ships is determined, while other loads in the regional power grid and conventional generator units (not containing new energy installed capacity growth) will remain unchanged in the future, so the new energy consumption space is basically determined, at this time, adjusting the new energy installed capacity in the planning process will change the probability matrix P new (x i ) and P new,max parameters of new energy output, and then affect the consumption rate of new energy.

[0224] The more the new energy installed capacity is added, the lower the consumption rate is, and a large amount of new energy electric energy is abandoned because it cannot be consumed by the load. As long as the growth speed of the new energy power station scale matches the growth speed of the ship, the new energy consumption rate can be greater than 95%. In summary, the above-mentioned method of the application is helpful for planning the construction scale of the port distributed new energy power station according to the situation of the put-in ship.

[0225] When the new energy installed capacity is unchanged and only the quantity of pure electric ships in operation is changed, the corresponding evaluation index of new energy consumption capacity is shown in Table 4.

[0226] Table 4 Analysis result table of new energy consumption capacity of "ship-shore-grid" system in different scenarios

[0227]

[0228] Further increase the number of pure electric ships in operation and keep the new energy output and conventional load parameters unchanged, the trend of the abandoned electricity rate with the number of ships put into operation can be obtained. When keeping other input variables constant, increasing the number of pure electric ships put into operation by 30 ships as a step (when 120-180 ships are put into operation, the ship type ratio refers to scenario three), it can be seen that the abandoned electricity rate shows a downward trend, and after putting 150 pure electric ships into operation, the abandoned electricity rate of the river basin power grid is very small, and full consumption of new energy can be realized, as shown in Table 5.

[0229] Table 5 Influence trend table of the number of pure electric ships put into operation on the abandoned electricity rate

[0230] Pure electric ship put into operation 30 60 90 120 150 180 Abandonment of electricity rate 6.34％ 5.30％ 3.83％ 1.52％ 0.02％ 0.00％

[0231] Table 6 Influence trend table of new energy installed capacity on the abandoned electricity rate

[0232] New energy installed capacity 7000kW 8000kW 9000kW 10000kW 11000kW Abandonment of electricity rate 6.21％ 10.46％ 14.85％ 19.10％ 26.02％

[0233] When keeping other input variables constant, changing the new energy installed capacity by 1000kW as an increment under the condition of scenario one, the abandoned electricity rate is calculated, and the results are shown in Table 6. It can be seen that the abandoned electricity rate shows a significant upward trend, indicating that the actual port load needs to be considered for distributed new energy planning capacity. Combined with the results of Table 5 and Table 6, during the construction process of the future port river basin power grid, if the number of pure electric ships and the installed capacity of new energy power stations are kept growing synchronously, full consumption of new energy generation can be basically realized, resource waste can be prevented, and the overall economy can be improved.

[0234] Please refer to Figure 2 , embodiment two of the present application is:

[0235] A new energy consumption capacity evaluation device considering pure electric ship charging load, comprising:

[0236] A total annual load determination module is configured to probabilistically model the charging load of pure electric ships in the river basin to obtain the total annual load of the entire river basin.

[0237] A model establishment module is configured to establish a joint output probability distribution model of wind power and photovoltaic power stations.

[0238] A calculation module is configured to determine a new energy consumption probability space based on the joint output probability distribution model of wind power and photovoltaic power stations and the total annual load of the entire river basin, and determine the average abandoned electricity power, the abandoned electricity rate, the consumption rate and the average consumption power of new energy generation according to the new energy consumption probability space.

[0239] An evaluation module is configured to evaluate the future new energy consumption capacity of the regional power grid in the river basin based on the average abandoned electricity power, the abandoned electricity rate, the consumption rate and the average consumption power.

[0240] In summary, the application provides a new energy consumption capacity evaluation method and device considering pure electric ship charging load, which probabilistically models the charging load of pure electric ships in a basin to obtain total annual load of the whole basin, determines a new energy consumption probability space based on a joint output probability distribution model of wind power and photovoltaic power stations and the total annual load of the whole basin, and determines average abandoned power, abandoned rate, consumption rate and average consumption power of new energy generation according to the new energy consumption probability space, and evaluates the future new energy consumption capacity of the regional power grid in the basin based on the average abandoned power, abandoned rate, consumption rate and average consumption power, so as to accurately evaluate the new energy consumption capacity of the regional power grid in the future basin. In addition, considering that there is a certain internal law between pure electric ship charging and route arrangement, the probabilistic simulation method is used for modeling and solving, and the total annual load of the whole basin is simulated according to the probability distribution of the ship charging behavior, which is more accurate than simulating the load based on random distribution. In addition, since there is a certain coupling relationship between wind and light resources, the output of wind power and photovoltaic power stations should not be regarded as independent events, so the joint output probability distribution model of wind power and photovoltaic power stations is established, which is beneficial to accurately evaluate the new energy consumption capacity.

[0241] Those skilled in the art will understand that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming language Java and interpreted scripting language JavaScript.

[0242] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems) and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The functions specified in one flow or multiple flows and / or blocks

[0243] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0244] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0245] Although preferred embodiments of the application have been described herein, substitutions and alterations are possible in view of the disclosure of this application without departing from the spirit and scope of the present application. Therefore, it is intended that the appended claims be interpreted as including all such alternatives and modifications as fall within the true spirit and scope of the present application. What is claimed is:

[0246] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described herein.

Claims

1. A method for assessing the renewable energy absorption capacity considering the charging load of pure electric ships, characterized in that, Including the following steps: Probabilistic modeling of the charging load of pure electric ships in the basin is performed to obtain the total annual load of the entire basin; Establish a probability distribution model for the combined output of wind power and photovoltaic power plants; Based on the combined output probability distribution model of wind power and photovoltaic power stations and the total annual load of the entire basin, the probability space for new energy consumption is determined, and the average curtailment power, curtailment rate, consumption rate and average consumption power of new energy power generation are determined according to the probability space for new energy consumption. The future renewable energy absorption capacity of the regional power grid within the basin is assessed based on the average power curtailment, the power curtailment rate, the absorption rate, and the average power absorption. The probabilistic modeling of the charging load of pure electric ships in the basin yields the total annual load for the entire basin, including: Obtain the number of pure electric ships deployed and the number of ports; Calculate the probability distribution of the voyage distance of ships entering each port based on the number of ships deployed and the number of ports. Using the berthing and loading / unloading times of the pure electric vessel as the charging times, calculate the probability distribution of the berthing and charging times of the pure electric vessel. The unit mileage power consumption of each ship is calculated based on the ship type data of the pure electric ship, and the port charging amount of the pure electric ship is calculated based on the unit mileage power consumption and the port berthing mileage of the pure electric ship. The port charging time of the pure electric ship is calculated based on the port charging amount and actual charging power of the pure electric ship. The total annual load of the entire basin is obtained based on the probability distribution of the voyage distance of ships entering each port, the probability distribution of the charging time of pure electric ships at the port, the charging amount of pure electric ships at the port, and the charging time of pure electric ships at the port. The establishment of the probability distribution model for the combined output of wind power and photovoltaic power plants includes: Determine the probability density distribution of wind speed and the probability density distribution of light intensity; The output power of the wind turbine and the output power of the photovoltaic power station are determined based on the probability density distribution of the wind speed and the probability density distribution of the light intensity. A probability distribution model of the combined output power of wind power and photovoltaic power station is established based on the output power of the wind turbine and the output power of the photovoltaic power station. The characteristic feature is that determining the average curtailment power, curtailment rate, absorption rate, and average absorption power of new energy power generation based on the new energy absorption probability space includes: The combined output probability distribution model of the wind power and photovoltaic power station is discretized to obtain the discrete probability distribution matrix of the theoretical output of new energy. The probability space of new energy consumption is discretized to obtain the discrete probability distribution matrix of the probability space of new energy consumption. Calculate the new energy curtailment power distribution matrix and the new energy absorption power distribution matrix based on the discrete probability distribution matrix of the new energy theoretical output and the discrete probability distribution matrix of the new energy absorption probability space; By jointly solving the discrete probability distribution matrix of the theoretical output of the new energy source and the discrete probability distribution matrix of the probability space of the new energy absorption, the probability distribution matrix of the power curtailment of new energy and the probability distribution matrix of the power absorption of new energy are obtained. Based on the probability distribution matrix of the abandoned power of the new energy source and the probability distribution matrix of the absorbed power of the new energy source, the average abandoned power, abandoned rate, absorbed rate and average absorbed power of the new energy source are obtained.

2. The method for assessing the renewable energy absorption capacity considering the charging load of pure electric ships according to claim 1, characterized in that, The calculation of the probability distribution of the voyage distance of vessels entering each port based on the number of vessels deployed and the number of ports includes: ； ； In the formula, f s,j (x) represents the probability distribution of the voyage distance of ships entering port j, where x represents the independent variable. Let the variance of port j be the log-normal distribution. μ s,j Let represent the mean of the log-normal distribution of port j. m s,j This represents the average mileage traveled by ships calling at port j. x i,j This represents the distance traveled by the i-th ship when it arrives at the j-th port. N s This indicates the number of pure electric ships deployed. v s,j This represents the variance of the mileage traveled by vessels calling at port j. The step of using the berthing and loading / unloading time of the pure electric vessel as the charging time, and calculating the probability distribution of the berthing and charging time of the pure electric vessel includes: ； In the formula, f t,j (x) represents the probability distribution of a pure electric ship charging at port j. Let V be the variance of the charging times of pure electric ships at port j. μ t,j This represents the average charging time of pure electric ships at the j-th port; The calculation of the unit mileage power consumption of each ship based on the ship type data of the pure electric ship, and the calculation of the port charging amount of the pure electric ship based on the unit mileage power consumption and the port berthing mileage of the pure electric ship, include: ； In the formula, E Unit,i This represents the power consumption per unit distance of the i-th ship. S max.i This represents the cruising range in the ship type data of the i-th vessel. This represents the battery capacity in the ship type data of the i-th vessel. E i,j This represents the charging amount of the i-th ship upon entering the j-th port. S i,j This indicates the mileage traveled by a pure electric vessel while docking; The calculation of the port charging time for the pure electric vessel based on the port charging amount and actual charging power includes: ； In the formula, P i,j This represents the actual charging power of the i-th all-electric ship at the j-th port. P ship,i This represents the rated charging power of the i-th all-electric ship. This represents the power limit of the j-th port charging station. t i,j This represents the charging time of the i-th all-electric ship at the j-th port; The total annual load of the entire basin, derived from the probability distribution of the voyage distance of ships entering each port, the probability distribution of the charging time of pure electric ships at ports, the charging amount of pure electric ships at ports, and the charging duration of pure electric ships at ports, includes: ； ； ； ； In the formula, Let A represent the daily charging load of the j-th port. 1×24,i This represents the time-series data of the i-th all-electric ship charging at port j for one day. Let k represent the charging status of the i-th all-electric ship at port j at time k, where k = 1, 2, ..., 24. Indicates a time of day. This represents the daily charging load of the entire river basin. This represents the total annual load of the entire basin. This represents the annual charging load time-series curve. N p Indicates the number of ports. This indicates the regular load other than charging stations.

3. The method for assessing the renewable energy absorption capacity considering the charging load of pure electric ships according to claim 1, characterized in that, The probability density distributions for determining wind speed and light intensity include: ； ； In the formula, This represents the probability density distribution of wind speed. The scale parameter of the Weibull distribution is represented. b 1 represents the shape parameter of the Weibull distribution, and v represents the wind speed. The probability density distribution representing light intensity. The scale parameter representing the beta distribution. b 2 represents the shape parameter of the beta distribution, and S represents the light intensity. S pvmax Indicates the maximum light intensity. Represents the beta distribution function; The determination of wind turbine output power and photovoltaic power plant output power based on the probability density distribution of wind speed and the probability density distribution of light intensity includes: ； ； In the formula, P w Indicates the output power of the fan. P r Indicates the rated output of the fan. V c This indicates the wind speed at which the fan cuts in. V r Indicates the rated wind speed of the fan. V f This indicates the fan cutoff speed. P pv η represents the output power of the photovoltaic power station, A represents the irradiated area of ​​the photovoltaic array, and η represents the photoelectric conversion efficiency.

4. The method for assessing the renewable energy absorption capacity considering the charging load of pure electric ships according to claim 1, characterized in that, The establishment of the probability distribution model for the combined output power of wind power and photovoltaic power station based on the output power of the wind turbine and the output power of the photovoltaic power station includes: Based on the output power of the wind turbine and the output power of the photovoltaic power station, an Archimedes Copula function is used to construct a probability distribution model of the combined output power of the wind power and photovoltaic power station.

5. The method for assessing the renewable energy absorption capacity considering the charging load of pure electric ships according to claim 1, characterized in that, The determination of the probability space for renewable energy absorption based on the combined output probability distribution model of the wind power and photovoltaic power plants and the total annual load of the entire basin includes: ； In the formula, This represents the probability space for the absorption of new energy sources. P port,Σ ( t This represents the total annual load of the entire basin. N g This indicates the number of conventional generator units in the river basin. Z k (t) represents a binary variable indicating the start-up and shutdown of the generator unit. β k This represents the minimum output coefficient of k conventional generating units. P gen,k ( t ) represents the rated capacity of the kth conventional unit at time t.

6. The method for assessing the renewable energy absorption capacity considering the charging load of pure electric ships according to claim 1, characterized in that, The assessment of the future renewable energy absorption capacity of the regional power grid within the basin based on the average curtailed power, the curtailment rate, the absorption rate, and the average absorption power includes: The average power curtailment, the curtailment rate, the absorption rate, and the average power absorption rate are used to assess the future number of pure electric vessels to be put into operation in the regional power grid within the basin, the routes in which pure electric vessels will participate, the types of pure electric vessels, and the scale of new energy power plant construction.

7. A device for assessing the renewable energy absorption capacity considering the charging load of pure electric ships, using the renewable energy absorption capacity assessment method considering the charging load of pure electric ships as described in any one of claims 1-6, characterized in that, include: The total annual load determination module is used to probabilistically model the charging load of pure electric ships in the basin to obtain the total annual load of the entire basin. The model building module is used to build a probability distribution model of the combined output of wind power and photovoltaic power plants. The calculation module is used to determine the probability space of new energy consumption based on the probability distribution model of the combined output of wind power and photovoltaic power stations and the total annual load of the entire basin, and to determine the average curtailment power, curtailment rate, consumption rate and average consumption power of new energy power generation according to the probability space of new energy consumption. The evaluation module is used to assess the future renewable energy absorption capacity of the regional power grid within the basin based on the average curtailed power, the curtailment rate, the absorption rate, and the average absorption power.

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