New energy consumption capability evaluation method and device considering pure electric ship charging load

Abstract

The invention discloses a new energy consumption capability assessment method and device considering the charging load of a pure electric ship, and the method comprises the steps: carrying out the probability modeling of the charging load of the pure electric ship in a watershed, and obtaining the total annual load of the whole watershed; determining 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 watershed, and determining the average power abandoning power, the power abandoning rate, the consumption rate and the average consumption power of new energy power generation according to the new energy consumption probability space; the future new energy consumption capability of the regional power grid in the drainage basin is evaluated based on the average power abandoning power, the power abandoning rate, the consumption rate and the average consumption power, so that modeling and probability simulation are specially carried out on the regional power grid which is intensively input into pure electric ship loads and comprises a new energy power station to obtain an evaluation result of the new energy consumption capability. Therefore, the new energy consumption capability of the regional power grid in the future drainage basin can be accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy consumption assessment, and in particular to a method and device for assessing new energy consumption capacity taking into account the charging load of a pure electric ship. Background Art

[0002] Replacing traditional energy with new energy has become an important transformation direction for shipping energy consumption. However, the inherent intermittent, anti-peak 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 conservation and emission reduction in the shipping industry. In 2015, the world's first battery-driven ferry "MF Ampere" was put into operation, opening a new era of pure electric ships.

[0003] Pure electric ships are usually powered by batteries, and have a large demand for charging when moored at ports. There is a certain degree of matching between their load characteristics and the peak output of new energy. When the new energy transmission capacity of the port basin power grid is insufficient, it can be regarded as one of the more ideal power loads for local consumption of new energy. Therefore, comprehensive analysis of shipping energy consumption and new energy consumption, and accurate assessment of the new energy consumption capacity of regional power grids in the basin in the future, will help to scientifically formulate the number of pure electric ships to be deployed and the construction plan of new energy power generation, which will have positive significance for promoting the consumption of new energy power generation, reducing the phenomenon of wind and solar power abandonment, and achieving mutual benefit and win-win results between the power industry and the shipping industry. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for evaluating the new energy absorption capacity taking into account the charging load of pure electric ships, which can accurately evaluate the new energy absorption capacity of the regional power grid in the future river basin.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for evaluating the new energy consumption capacity considering the charging load of pure electric ships comprises the following steps:

[0007] 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;

[0008] Establish a probability distribution model for the combined output of wind power and photovoltaic power stations;

[0009] 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 river basin, and determine the average abandoned power, abandoned power rate, consumption rate and average consumption power of new energy power generation according to the probability space of new energy consumption;

[0010] The future new energy absorption capacity of the regional power grid in the basin is evaluated based on the average abandoned power, the abandoned power rate, the absorption rate and the average absorption power.

[0011] Furthermore, the probability modeling of the charging load of pure electric ships in the basin to obtain the total annual load of the entire basin includes:

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

[0013] Calculate the probability distribution of the sailing mileage of ships entering each port based on the delivery quantity and the number of ports;

[0014] The time when the pure electric ship docks for loading and unloading is used as the charging time, and the probability distribution of the charging time when the pure electric ship docks is calculated;

[0015] Calculate the unit mileage power consumption of each ship according to the ship type data of the pure electric ship, and calculate the port charging amount of the pure electric ship according to the unit mileage power consumption and the port mileage of the pure electric ship;

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

[0017] The total annual load of the entire river basin is obtained based on the probability distribution of the sailing mileage of the ships entering each port, the probability distribution of the charging time of the pure electric ships at the port, the charging amount of the pure electric ships at the port, and the charging time of the pure electric ships at the port.

[0018] Furthermore, the calculation of the probability distribution of the sailing mileage of ships entering each port based on the delivery quantity and the number of ports includes:

[0019]

[0020]

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

[0022] The method of using the time when the pure electric ship is docked for loading and unloading as the charging time and calculating the probability distribution of the charging time when the pure electric ship is docked includes:

[0023]

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

[0025] The calculating of the unit mileage power consumption of each ship according to the ship type data of the pure electric ship, and the calculating of the port charging amount of the pure electric ship according to the unit mileage power consumption and the port mileage of the pure electric ship includes:

[0026]

[0027] In the formula, E Unit,i represents the power consumption per unit mileage of the i-th ship, S max.i represents the cruising range 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 amount of the i-th ship entering the j-th port, S i,j Indicates the mileage of pure electric ships at port;

[0028] The calculating of the charging time of the pure electric ship at the port according to the charging amount of the pure electric ship at the port and the actual charging power comprises:

[0029]

[0030] Where P i,j represents the actual charging power of the i-th pure electric ship at the j-th port, P ship,i represents the rated charging power of the i-th pure electric ship, P charger,j represents the power limit of the j-th port charging pile, t i,j represents the charging time of the i-th pure electric ship at the j-th port;

[0031] The total annual load of the whole basin obtained based on the probability distribution of the mileage of ships entering each port, the probability distribution of the charging time of the pure electric ships at the port, the charging amount of the pure electric ships at the port, and the charging time of the pure electric ships at the port includes:

[0032]

[0033] P j,Σ (t year )=[P j,Σ (t day,1 ) 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 i-th pure electric ship charging at the j-th port for one day, a i,k represents the charging status of the i-th pure electric ship at the j-th port at time k, k = 1, 2, ..., 24, t day Indicates the time of day, P j,Σ (t day ) represents the daily charging load of the entire 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 timing curve, N p Indicates the number of ports, P j,normal (t year ) represents conventional loads other than charging piles.

[0036] Furthermore, the establishment of a probability distribution model for the combined output of wind power and photovoltaic power stations includes:

[0037] Determine the probability density distribution of wind speed and the probability density distribution of light intensity;

[0038] 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 light intensity;

[0039] A probability distribution model of the combined output of wind power and photovoltaic power station is established based on the wind turbine output power and the photovoltaic power station output power.

[0040] Furthermore, the determination of the probability density distribution of wind speed and the probability density distribution of light intensity includes:

[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 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;

[0043] The determining 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 includes:

[0044]

[0045] P pv = SAη;

[0046] Where P w Indicates the fan output power, P r Indicates the rated output of the fan, V c Indicates the fan cut-in wind speed, V r Indicates the rated wind speed of the fan, V f Indicates the fan cut-off wind speed, P pv 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.

[0047] Furthermore, the establishing of a wind power and photovoltaic power station joint output probability distribution model based on the wind turbine output power and the photovoltaic power station output power includes:

[0048] Based on the wind turbine output power and the photovoltaic power station output power, an Archimedean Copula function is used to construct a probability distribution model for the combined output of wind power and photovoltaic power stations.

[0049] Furthermore, the determination of the probability space for accommodating new energy sources 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 river basin includes:

[0050]

[0051] Where P new,ca (t) represents the probability space of new energy consumption, 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 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.

[0052] Furthermore, determining the average abandoned power, abandoned power rate, absorption rate and average absorption power of renewable energy power generation according to the renewable energy absorption probability space includes:

[0053] Discretizing the probability distribution model of the combined output of wind power and photovoltaic power stations to obtain a discrete probability distribution matrix of the theoretical output of new energy;

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

[0055] Calculate the new energy abandonment power distribution matrix and the 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] Performing joint probability solution on the discrete probability distribution matrix of the theoretical output of the new energy and the discrete probability distribution matrix of the probability space of the new energy consumption, to obtain the probability distribution matrix of the new energy abandoned power and the probability distribution matrix of the new energy consumption power;

[0057] Based on the probability distribution matrix of the new energy abandoned power and the probability distribution matrix of the new energy consumption power, the average abandoned power, abandoned power rate, consumption rate and average consumption power of the new energy power generation are obtained.

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

[0059] According to the average abandoned power, the abandoned power rate, the absorption rate and the average absorption power, the number of pure electric ships put into operation, the routes participated by pure electric ships, the types of pure electric ships and the scale of construction of new energy power stations in the regional power grid in the basin are evaluated in the future.

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

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

[0062] The total annual load determination module is used to perform probabilistic modeling of the charging load of pure electric ships in the basin and obtain the total annual load of the entire basin;

[0063] Model building module, used to establish the probability distribution model of the combined output of wind power and photovoltaic power stations;

[0064] A calculation module, for determining a 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 entire river basin, and determining an average abandoned power, abandoned power 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 used to evaluate the future new energy consumption capacity of the regional power grid in the basin based on the average abandoned power, the abandoned power rate, the consumption rate and the average consumption power.

[0066] The beneficial effects of the present invention are as follows: probabilistic modeling is performed on the charging load of pure electric ships in the basin to obtain the total annual load of the entire basin, the probability space of new energy consumption is determined based on the probability distribution model of the joint output of wind power and photovoltaic power stations and the total annual load of the entire basin, and the average abandoned power, abandoned power rate, absorption rate and average absorption power of new energy power generation are determined according to the new energy absorption probability space, and the future new energy absorption capacity of the regional power grid in the basin is evaluated based on the average abandoned power, abandoned power rate, absorption rate and average absorption power. In this way, modeling and probabilistic simulation are carried out specifically for the regional power grid that concentrates on pure electric ship loads and includes new energy power stations to obtain the evaluation result of the new energy absorption capacity, so as to accurately evaluate the new energy absorption capacity of the regional power grid in the basin in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flowchart of the steps of a method for evaluating the new energy consumption capacity considering the charging load of a pure electric ship according to an embodiment of the present invention;

[0068] Figure 2 This is a structural schematic diagram of a new energy consumption capacity evaluation device considering the charging load of a pure electric ship according to an embodiment of the present invention;

[0069] Figure 3 This is a schematic diagram of new energy consumption space assessment in a new energy consumption capacity assessment method considering the charging load of a pure electric ship according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0071] Please refer to Figure 1 , a method for evaluating the new energy consumption capacity considering the charging load of pure electric ships, comprising the steps of:

[0072] Probabilistic modeling of the charging load of all-electric ships in the basin is performed to obtain the total annual load of the entire basin;

[0073] Establish a probability distribution model for the combined output of wind power and photovoltaic power stations;

[0074] 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 river basin, and determine the average abandoned power, abandoned power rate, consumption rate and average consumption power of new energy power generation according to the probability space of new energy consumption;

[0075] The future new energy absorption capacity of the regional power grid in the basin is evaluated based on the average abandoned power, the abandoned power rate, the absorption rate and the average absorption power.

[0076] From the above description, it can be seen that the beneficial effects of the present invention are: 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, and the probability space of new energy consumption is determined based on the probability distribution model of the joint output of wind power and photovoltaic power stations and the total annual load of the entire basin, and the average abandoned power, abandoned power rate, absorption rate and average absorption power of new energy power generation are determined according to the new energy absorption probability space, and the future new energy absorption capacity of the regional power grid in the basin is evaluated based on the average abandoned power, abandoned power rate, absorption rate and average absorption power. In this way, modeling and probabilistic simulation are carried out specifically for the regional power grid that concentrates on pure electric ship loads and includes new energy power stations to obtain the evaluation results of the new energy absorption capacity, so that the new energy absorption capacity of the regional power grid in the basin in the future can be accurately evaluated.

[0077] Furthermore, the probability modeling of the charging load of pure electric ships in the basin to obtain the total annual load of the entire basin includes:

[0078] Obtain the number of pure electric ships deployed and the number of ports;

[0079] Calculate the probability distribution of the sailing mileage of ships entering each port based on the delivery quantity and the number of ports;

[0080] The time when the pure electric ship docks for loading and unloading is used as the charging time, and the probability distribution of the charging time when the pure electric ship docks is calculated;

[0081] Calculate the unit mileage power consumption of each ship according to the ship type data of the pure electric ship, and calculate the port charging amount of the pure electric ship according to the unit mileage power consumption and the port mileage of the pure electric ship;

[0082] Calculate the charging time of the pure electric ship at the port according to the charging amount of the pure electric ship at the port and the actual charging power;

[0083] The total annual load of the entire river basin is obtained based on the probability distribution of the sailing mileage of the ships entering each port, the probability distribution of the charging time of the pure electric ships at the port, the charging amount of the pure electric ships at the port, and the charging time of the pure electric ships at the port.

[0084] From the above description, it can be seen that there are certain inherent rules between the charging of pure electric ships and the route arrangement. The probability simulation method is used to model and solve the problem. The total annual load of the entire river basin is simulated according to the probability distribution of ship charging behavior, which is more accurate than simulating the load based entirely on random distribution.

[0085] Furthermore, the calculation of the probability distribution of the sailing mileage of ships entering each port based on the delivery quantity and the number of ports includes:

[0086]

[0087] In the formula, fs,j (x) represents the probability distribution of the voyage mileage of ships entering port j, x represents the independent variable, σ s,j represents the lognormal distribution variance of port j, μ s,j represents the mean of the lognormal distribution of port j, m s,j represents the mean mileage of ships calling at port j, x i,j N is the mileage of the i-th ship when it arrives at the j-th port. s represents the number of pure electric ships deployed, v s,j represents the variance of the mileage of ships calling at port j;

[0088] The method of using the time when the pure electric ship is docked for loading and unloading as the charging time and calculating the probability distribution of the charging time when the pure electric ship is docked includes:

[0089]

[0090] In the formula, f t,j (x) represents the probability distribution of the charging time of a pure electric ship at port j, σ t,j represents the variance of the charging time of pure electric ships in the jth port, μ t,j represents the mean value of the charging time of pure electric ships in the jth port;

[0091] The calculating of the unit mileage power consumption of each ship according to the ship type data of the pure electric ship, and the calculating of the port charging amount of the pure electric ship according to the unit mileage power consumption and the port mileage of the pure electric ship includes:

[0092]

[0093] In the formula, E Unit,i represents the unit mileage power consumption of the i-th ship, S max.i represents the cruising range 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 amount of the i-th ship entering the j-th port, S i,j Indicates the mileage of pure electric ships at port;

[0094] The calculating of the charging time of the pure electric ship at the port according to the charging amount of the pure electric ship at the port and the actual charging power comprises:

[0095]

[0096] Where P i,j represents the actual charging power of the i-th pure electric ship at the j-th port, P ship,i represents the rated charging power of the i-th pure electric ship, P charger,jrepresents the power limit of the j-th port charging pile, t i,j represents the charging time of the i-th pure electric ship at the j-th port;

[0097] The total annual load of the whole basin obtained based on the probability distribution of the mileage of ships entering each port, the probability distribution of the charging time of the pure electric ships at the port, the charging amount of the pure electric ships at the port, and the charging time of the pure electric ships at the port includes:

[0098]

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

[0100]

[0101] 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 i-th pure electric ship charging at the j-th port for one day, a i,k represents the charging status of the i-th pure electric ship at the j-th port at time k, k = 1, 2, ..., 24, t day Indicates the time of day, P j,Σ (t day ) represents the daily charging load of the entire 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 timing curve, N p Indicates the number of ports, P j,normal (t year ) represents conventional loads other than charging piles.

[0102] From the above description, it can be seen that the conventional load other than charging piles can be estimated using a typical electricity consumption data set of a port. Finally, the annual charging load time series curve is superimposed on the conventional load summed by the number of ports to obtain the total annual load of the entire river basin, so as to carry out subsequent calculations of the new energy consumption space.

[0103] Furthermore, the establishment of a probability distribution model for the combined output of wind power and photovoltaic power stations includes:

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

[0105] 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 light intensity;

[0106] A probability distribution model of the combined output of wind power and photovoltaic power station is established based on the wind turbine output power and the photovoltaic power station output power.

[0107] From the above description, it can be seen that due to the existence of 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. Therefore, establishing a probability distribution model for the joint output of wind power and photovoltaic power stations is conducive to accurately evaluating the new energy absorption capacity.

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

[0109]

[0110] 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 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;

[0111] The determining 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 includes:

[0112]

[0113] P pv = SAη;

[0114] Where P w Indicates the fan output power, P r Indicates the rated output of the fan, V c Indicates the fan cut-in wind speed, V r Indicates the rated wind speed of the fan, V f Indicates the fan cut-off wind speed, P pv 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.

[0115] From the above description, it can be seen that when simulating the probability distribution of theoretical output of new energy, the two-parameter Weibull distribution function is first used to describe the probability density distribution of wind speed, and then the Beta distribution function is used to describe the probability density distribution of light intensity. The wind turbine output power and photovoltaic power station output power determined in this way are more reliable.

[0116] Furthermore, the establishing of a wind power and photovoltaic power station joint output probability distribution model based on the wind turbine output power and the photovoltaic power station output power includes:

[0117] Based on the wind turbine output power and the photovoltaic power station output power, an Archimedean Copula function is used to construct a probability distribution model for the combined output of wind power and photovoltaic power stations.

[0118] From the above description, we can see that since the probability distributions of wind and solar outputs both have the characteristics of “peaked and thick tails”, the Archimedean Copula function can be used to construct a binary joint distribution, which is more accurate and reliable.

[0119] Furthermore, the determination of the probability space for accommodating new energy sources 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 river basin includes:

[0120]

[0121] Where P new,ca (t) represents the probability space of new energy consumption, 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 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] From the above description, it can be seen that the probability space for new energy consumption is determined based on the probability distribution model of the joint output of wind power and photovoltaic power stations and the total annual load of the entire river basin, so as to more efficiently calculate the average abandoned power, abandoned power rate, absorption rate and average absorption power of new energy power generation in the future.

[0123] Furthermore, determining the average abandoned power, abandoned power rate, absorption rate and average absorption power of renewable energy power generation according to the renewable energy absorption probability space includes:

[0124] Discretizing the probability distribution model of the combined output of wind power and photovoltaic power stations to obtain a discrete probability distribution matrix of the theoretical output of new energy;

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

[0126] Calculate the new energy abandonment power distribution matrix and the 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;

[0127] Performing joint probability solution on the discrete probability distribution matrix of the theoretical output of the new energy and the discrete probability distribution matrix of the probability space of the new energy consumption, to obtain the probability distribution matrix of the new energy abandoned power and the probability distribution matrix of the new energy consumption power;

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

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

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

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

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

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

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

[0135] Model building module, used to establish the probability distribution model of the combined output of wind power and photovoltaic power stations;

[0136] A calculation module, for determining a 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 entire river basin, and determining an average abandoned power, abandoned power rate, consumption rate and average consumption power of new energy power generation according to the new energy consumption probability space;

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

[0138] From the above description, it can be seen that the beneficial effects of the present invention are: 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, and the probability space of new energy consumption is determined based on the probability distribution model of the joint output of wind power and photovoltaic power stations and the total annual load of the entire basin, and the average abandoned power, abandoned power rate, absorption rate and average absorption power of new energy power generation are determined according to the new energy absorption probability space, and the future new energy absorption capacity of the regional power grid in the basin is evaluated based on the average abandoned power, abandoned power rate, absorption rate and average absorption power. In this way, modeling and probabilistic simulation are carried out specifically for the regional power grid that concentrates on pure electric ship loads and includes new energy power stations to obtain the evaluation results of the new energy absorption capacity, so that the new energy absorption capacity of the regional power grid in the basin in the future can be accurately evaluated.

[0139] The above-mentioned method and device for evaluating the new energy consumption capacity considering the charging load of pure electric ships of the present invention can be applied to the regional power grid within the basin, and the following is an explanation through specific implementation methods:

[0140] Please refer to Figure 1 and Figure 3 , Embodiment 1 of the present invention is:

[0141] A method for evaluating the new energy consumption capacity considering the charging load of pure electric ships comprises the following steps:

[0142] S1. 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, including S11-S16:

[0143] S11. Obtain the number of pure electric ships deployed and the number of ports.

[0144] S12, based on the number of deployments and the number of ports, calculate the probability distribution of the voyage mileage of ships entering each port; assuming that N s There are N pure electric ships p The typical data of the shipping route information are counted for each port. Taking each port as a reference point, the probability distribution of the mileage of each pure electric ship at each port can be calculated. In general, it can be considered that the mileage of pure electric ships conforms to the normal distribution law. The maximum likelihood method is used for processing, and the logarithmic normal distribution form lnX~N(μ,δ 2 ) describes the probability distribution of the voyage mileage of ships entering port j, specifically:

[0145]

[0146] In the formula, fs,j (x) represents the probability distribution of the voyage mileage of ships entering port j, x represents the independent variable, σ s,j represents the lognormal distribution variance of port j, μ s,j represents the mean of the lognormal distribution of port j, m s,j represents the mean mileage of ships calling at port j, x i,j N is the mileage of the i-th ship when it arrives at the j-th port. s represents the number of pure electric ships deployed, v s,j represents the variance of the mileage of ships calling at port j. Where i=1,2,...,N s ; j = 1, 2..., N p .

[0147] Since pure electric ships can generally carry out loading and unloading while charging at the port, therefore:

[0148] S13, taking the time of loading and unloading of the pure electric ship at the port as the charging time, and calculating the probability distribution of the charging time of the pure electric ship at the port, specifically:

[0149]

[0150] In the formula, f t,j (x) represents the probability distribution of the charging time of a pure electric ship at port j, σ t,j represents the variance of the charging time of pure electric ships in the jth port, μ t,j represents the average charging time of pure electric ships in the jth port. The charging time is the charging start time.

[0151] S14, calculating the unit mileage power consumption of each ship according to the ship type data of the pure electric ship, and calculating the port charging amount of the pure electric ship according to the unit mileage power consumption and the mileage of the pure electric ship at the port, 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 cruising range 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 amount of the i-th ship entering the j-th port, S i,j Indicates the mileage of pure electric ships during port visits.

[0154] Since the charging time is limited by both the maximum output power of the charging pile and the rated charging power of the battery, therefore:

[0155] S15. Calculate the charging time of the pure electric ship at the port according to the charging amount of the pure electric ship at the port and the actual charging power, specifically:

[0156]

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

[0158] S16. Based on the probability distribution of the mileage of ships entering each port, the probability distribution of the charging time of the pure electric ship at the port, the charging amount of the pure electric ship at the port, and the charging time of the pure electric ship at the port, the total annual load of the whole basin is obtained. Since the daily load has random fluctuations, multiple extractions can be performed according to the probability distribution and then the average is taken, which is specifically:

[0159]

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

[0161]

[0162] 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 i-th pure electric ship charging at the j-th port for one day, a i,k represents the charging status of the i-th pure electric ship at the j-th port at time k, k = 1, 2, ..., 24, t day Indicates the time of day, P j,Σ (t day ) represents the daily charging load of the entire 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 timing curve, N p Indicates the number of ports, P j,normal (t year ) represents conventional loads other than charging piles.

[0163] In which, the definition of matrix A j To represent N s The time series data of a pure electric ship charging at port j, in the present invention, the step length is one hour. j middle element a i,k represents the charging status of pure electric ship i at port j at time k, and the matrix A j The elements in are assigned values ​​according to the following formula. When charging, the element is assigned value P i,j , when not charged, the value is zero, specifically:

[0164]

[0165] Where, T i,j Indicates the charging start time of the i-th pure electric ship at the j-th port.

[0166] S2. Establish a probability distribution model for the combined output of wind power and photovoltaic power stations. 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. Therefore, it is necessary to jointly solve them based on the positive correlation characteristics between wind and light outputs to obtain the total output of new energy power generation, and at the same time shorten the solution time, including S21-S23:

[0167] S21. Determine the probability density distribution of wind speed and the probability density distribution of light intensity, specifically:

[0168]

[0169] 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 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, 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 light intensity, specifically:

[0171]

[0172] Where P w Indicates the fan output power, P r Indicates the rated output of the fan, V c Indicates the fan cut-in wind speed, V r Indicates the rated wind speed of the fan, V f Indicates the fan cut-off wind speed, Ppv 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.

[0173] S23. A probability distribution model of the joint output 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. Considering that the output of the two in the actual power grid is not a simple sum, but has a certain coupling relationship, the probability distribution of wind and light output is observed. Both have the characteristics of "sharp peak and thick tail", and the Archimedean Copula function can be used to construct a binary joint distribution.

[0174] Among them, 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 marginal distribution of these N variables and a Copula function. The Copula function is used to describe the correlation between variables. The expression of the Copula function is:

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

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

[0177] There are three common Archimedean Copula function forms, namely Clayton-Copula, Gumble-Copula and Frank-Copula. The binary distribution function expressions of the three functions are shown in Table 1.

[0178] Table 1 Analytical formula of wind-solar binary joint distribution function

[0179]

[0180]

[0181] The rank correlation coefficient τ is used to represent the correlation between wind and solar power. According to the simulated data of the distribution function, τ = 0.1553 is calculated. The calculation formulas 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-solar correlation verification table

[0183]

[0184] In Table 2, D1(θ) represents the τ value calculation function, specifically:

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

[0186] Assume (x i ,y i )(i=1, 2, ..., n) is a sample 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, F(u i ,v i ) represents the sample data mean.

[0189] The squared Euclidean distance can reflect the quality of the Copula function in simulating the original data. 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] It can be concluded that the Clayton-Copula function is the best choice to construct the probability distribution model of the combined output 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] Where P new (x) represents the probability distribution of the combined output of wind power and photovoltaic power stations, P w (x) represents the fan output power, that is, P w (v), P pv (x) represents the output power of the photovoltaic power station, that is, P pv (S).

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

[0198] like Figure 3 As shown in the figure, when the output of renewable energy is too high, the load will not be able to consume this energy. In order to ensure the normal operation of the power system, it can only be abandoned. Considering that the output of conventional units is the smallest, that is, when the peak-shaving margin is the largest, the system's new energy absorption capacity is the strongest. In order to absorb as much renewable energy as possible, the output of conventional units can be reduced until they can operate at the minimum technical output (corresponding to Figure 3 The “minimum technical output of conventional units” dotted line in the figure) is added with the output of new energy ( Figure 3 The total annual load of the whole basin obtained above (corresponding to the light-colored curve) is obtained. Figure 3 The dark curve in the middle minus the minimum output of conventional units is the new energy consumption space. Further analysis compares the total system output with the total annual load of the entire basin. When the former is greater than the latter, power abandonment occurs (corresponding to Figure 3 The shaded part in the middle), otherwise it can fully absorb new energy.

[0199] S31, determining the probability space for new energy consumption based on the wind power and photovoltaic power station combined output probability distribution model and the total annual load of the whole river basin, specifically including:

[0200]

[0201] Where P new,ca (t) represents the probability space of new energy consumption, 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) is a binary variable representing the start and stop of the unit. Its value is 1 for the start of the unit and 0 for the stop of the unit. 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 spatial time series simulation data of new energy consumption can be obtained, and then its probability distribution can be obtained.

[0203] S32. Discretize the probability distribution model of the combined output of wind power and photovoltaic power stations to obtain a discrete probability distribution matrix of the theoretical output of new energy.

[0204] Specifically, a series of data points of the probability distribution of renewable energy output are extracted with a step size of 1 kW. Since the renewable energy output is different from the upper limit of renewable energy consumption space, the total installed capacity of the system P can be taken as max As an upper limit to facilitate subsequent calculations between matrices, establish a P max Matrix P with 2 rows and 2 columns new (x i )(i=0,1,2,...,P max ) discretizes the probability distribution model of the combined output of wind power and photovoltaic power stations, and obtains:

[0205]

[0206] Where P new (x i ) represents the discrete probability distribution matrix of the theoretical output of new energy, x i Indicates the output power value, f new (x i ) represents the probability corresponding to the output power value, f new (x i ) is equal to the integral of the output probability of each segment interval. When the value of the first column of the matrix is ​​greater than the upper limit of the output of renewable energy power generation P new,max The probability is taken as 0, and the corresponding P is finally obtained. max A discrete probability distribution over an interval.

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

[0208] Specifically, the new energy consumption space value is increased from 0 to P with a step size of 1 kW. max The data is divided into segments, and the number of new energy consumption space values ​​falling in each area per unit time is counted. The corresponding probability can be obtained by dividing it by the total amount of data. Based on the statistical method, the new energy consumption space time series data is converted into a discrete probability distribution P new,ca (y i ), also using a (P max ×2) new,ca (y i )(i=0,1,2,...,P max ) is expressed as:

[0209]

[0210] Where P new,ca (y i ) represents the discrete probability distribution matrix of the probability space of new energy consumption, y i represents the probability space value of new energy consumption, f new,ca (y i) represents the probability corresponding to the value of the probability space of new energy consumption, f new,ca (y i ) is equal to the integral of the output probability of each segment interval. When the value of the first column of the matrix is ​​greater than the maximum value of the new energy consumption space P (new,ca),max When the probability is taken as 0, the corresponding P is finally obtained. max A discrete probability distribution over an interval.

[0211] S34, calculating the new energy abandoned power distribution matrix P 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 new,ab (z i ) and the new energy consumption power distribution matrix P new,con (m i ).

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

[0213]

[0214] The above formula indicates that when the output of new energy is greater than the space for absorbing new energy, abandoned power is generated, and the abandoned power value is the difference between the former and the latter. Otherwise, the abandoned power is 0, and the green electricity is completely absorbed; the power value for absorbing new energy is the minimum value of the output of new energy and the space for absorbing new energy.

[0215] S35. Perform a joint probability solution on the discrete probability distribution matrix of the theoretical output of the new energy and the discrete probability distribution matrix of the probability space of the new energy consumption to obtain the probability distribution matrix of the new energy abandoned power and the probability distribution matrix of the new energy consumption power.

[0216] Specifically, with a step size of 1 kW, for P new,ca (y i ) and P new (x i ) to solve the joint probability, we can get the probability distribution matrix f of the abandoned power of renewable energy new,ab and the probability distribution matrix f of the renewable energy consumption power new,con for:

[0217]

[0218] S36. Based on the probability distribution matrix of the new energy abandoned power and the probability distribution matrix of the new energy absorbed power, the average abandoned power, abandoned power rate, absorbed power rate and average absorbed power of the new energy power generation are obtained, which are specifically:

[0219]

[0220] In the formula, α represents the average abandoned power of renewable energy power generation, β represents the abandoned power rate of renewable energy power generation, ξ represents the consumption rate of renewable energy power generation, γ represents the average consumed power of renewable energy power generation, and z i The discrete independent variable representing the average abandoned power of renewable energy generation, m i A discrete independent variable representing the average power consumption of renewable energy generation.

[0221] S4. Evaluate the future renewable energy absorption capacity of the regional power grid in the river basin based on the average abandoned power, the abandoned power rate, the absorption rate and the average absorption power.

[0222] Specifically, the number of pure electric ships put into operation, the routes participated by pure electric ships, the types of pure electric ships and the scale of construction of new energy power stations in the regional power grid in the basin in the future are evaluated based on the average abandoned power, the abandoned power rate, the absorption rate and the average absorption power.

[0223] During the planning process, the higher authorities will generally clearly define the number of pure electric ships to be deployed, the routes and ship types involved, and determine the P including the charging load of pure electric ships accordingly. port,Σ (t year ), while other loads and conventional generators in the regional power grid (excluding the growth of new energy installed capacity) will remain unchanged in the future, so the new energy consumption space is basically determined. At this time, adjusting the new energy installed capacity during the planning process will change the probability matrix P of new energy output new (x i ) and P new,max Parameters, which in turn affect the consumption rate of new energy.

[0224] The more new energy installed capacity is added, the lower the absorption rate is, and a large amount of new energy electricity is discarded because it cannot be consumed by the load. As long as the growth rate of the scale of new energy power stations matches the growth rate of ships, the new energy absorption rate can be guaranteed to be greater than 95%. In summary, the above method of the present invention is helpful to plan the construction scale of distributed new energy power stations in port areas according to the situation of deployed ships.

[0225] When the installed capacity of new energy remains unchanged and only the number of pure electric ships put into operation changes, the corresponding indicators for evaluating the new energy absorption capacity are shown in Table 4.

[0226] Table 4 Analysis results of new energy absorption capacity of “ship-shore-grid” system under different scenarios

[0227]

[0228] By further increasing the number of pure electric ships in operation and keeping parameters such as new energy output and conventional load unchanged, the trend of power abandonment rate with the number of ships put into operation can be obtained. When other input variables are kept constant and the number of pure electric ships put into operation is increased by 30 ships (the ship type ratio when 120-180 ships are put into operation refers to scenario 3), it can be seen that the power abandonment rate is on a downward trend. After 150 pure electric ships are put into operation, the power abandonment rate of the basin power grid is extremely small, and the full consumption of new energy can be achieved, as shown in Table 5.

[0229] Table 5 Trend of the impact of the number of pure electric ships in operation on the power abandonment rate

[0230] Pure electric ships put into operation 30 60 90 120 150 180 Power abandonment rate 6.34％ 5.30％ 3.83％ 1.52％ 0.02％ 0.00％

[0231] Table 6 Trend of the impact of new energy installed capacity on power abandonment rate

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

[0233] When other input variables are kept constant and the installed capacity of new energy is changed in increments of 1000kW under scenario 1, the power abandonment rate is calculated. The results are shown in Table 6. It can be seen that the power abandonment rate shows a clear upward trend, indicating that the actual port load needs to be considered to plan and determine the capacity of distributed new energy. Combining the results of Tables 5 and 6, in the future construction of the port basin power grid, if the number of pure electric ships and the installed capacity of new energy power stations are allowed to grow synchronously, the full absorption of new energy power generation can be basically achieved, resource waste can be prevented, and overall economic efficiency can be improved.

[0234] Please refer to Figure 2 , Embodiment 2 of the present invention is:

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

[0236] The total annual load determination module is used to perform probabilistic modeling of the charging load of pure electric ships in the basin and obtain the total annual load of the entire basin;

[0237] Model building module, used to establish the probability distribution model of the combined output of wind power and photovoltaic power stations;

[0238] A calculation module, for determining a 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 entire river basin, and determining an average abandoned power, abandoned power rate, consumption rate and average consumption power of new energy power generation according to the new energy consumption probability space;

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

[0240] In summary, the present invention provides a method and device for evaluating the new energy absorption capacity taking into account the charging load of pure electric ships. The charging load of pure electric ships in the basin is probabilistically modeled to obtain the total annual load of the entire basin. The new energy absorption probability space is determined 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. The average abandoned power, abandoned power rate, absorption rate and average absorption power of new energy power generation are determined according to the new energy absorption probability space. The future new energy absorption capacity of the regional power grid in the basin is evaluated based on the average abandoned power, abandoned power rate, absorption rate and average absorption power. This is specifically aimed at areas that concentrate on pure electric ship loads and include new energy power stations. The power grid was modeled and probabilistically simulated to obtain the evaluation results of the new energy absorption capacity, so as to accurately evaluate the new energy absorption capacity of the regional power grid in the basin in the future; in addition, considering that there are certain inherent rules between the charging of pure electric ships and the route arrangement, the probability simulation method was used for modeling and solving, and the total annual load of the entire basin was simulated according to the probability distribution of ship charging behavior, which is more accurate than simulating the load based entirely on random distribution; and, due to the certain coupling relationship between wind and light resources, the output of wind power and photovoltaic power stations should not be regarded as independent events. Therefore, a probability distribution model for the joint output of wind power and photovoltaic power stations is established, which is conducive to accurately evaluating the new energy absorption capacity.

[0241] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt 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.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0242] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0243] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0244] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0245] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0246] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for evaluating the new energy consumption capacity considering the charging load of pure electric ships, characterized in that: Includes 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 stations; 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 river basin, and determine the average abandoned power, abandoned power rate, consumption rate and average consumption power of new energy power generation according to the probability space of new energy consumption; The future new energy absorption capacity of the regional power grid in the basin is evaluated based on the average abandoned power, the abandoned power rate, the absorption rate and the average absorption power.

2. A new energy consumption capacity evaluation method considering the charging load of pure electric ships according to claim 1, characterized in that: The probability modeling of the charging load of pure electric ships in the basin is performed to obtain the total annual load of the entire basin, which includes: Obtain the number of pure electric ships deployed and the number of ports; Calculate the probability distribution of the sailing mileage of ships entering each port based on the delivery quantity and the number of ports; The time when the pure electric ship docks for loading and unloading is used as the charging time, and the probability distribution of the charging time when the pure electric ship docks is calculated; Calculate the unit mileage power consumption of each ship according to the ship type data of the pure electric ship, and calculate the port charging amount of the pure electric ship according to the unit mileage power consumption and the port mileage of the pure electric ship; Calculate the charging time of the pure electric ship at the port according to the charging amount of the pure electric ship at the port and the actual charging power; The total annual load of the entire river basin is obtained based on the probability distribution of the sailing mileage of the ships entering each port, the probability distribution of the charging time of the pure electric ships at the port, the charging amount of the pure electric ships at the port, and the charging time of the pure electric ships at the port.

3. A method for evaluating the new energy consumption capacity considering the charging load of pure electric ships according to claim 2, characterized in that: The calculation of the probability distribution of the sailing mileage of ships entering each port based on the delivery quantity and the number of ports includes: In the formula, f s,j (x) represents the probability distribution of the voyage mileage of ships entering port j, x represents the independent variable, σ s,j represents the lognormal distribution variance of port j, μ s,j represents the mean of the lognormal distribution of port j, m s,j represents the mean mileage of ships calling at port j, x i,j N is the mileage of the i-th ship when it arrives at the j-th port. s represents the number of pure electric ships deployed, v s,j represents the variance of the mileage of ships calling at port j; The method of using the time when the pure electric ship is docked for loading and unloading as the charging time and calculating the probability distribution of the charging time when the pure electric ship is docked includes: In the formula, f t,j (x) represents the probability distribution of the charging time of a pure electric ship at port j, σ t,j represents the variance of the charging time of pure electric ships in the jth port, μ t,j represents the mean value of the charging time of pure electric ships in the jth port; The calculating of the unit mileage power consumption of each ship according to the ship type data of the pure electric ship, and the calculating of the port charging amount of the pure electric ship according to the unit mileage power consumption and the port mileage of the pure electric ship includes: In the formula, E Unit,i represents the unit mileage power consumption of the i-th ship, S max.i represents the cruising range 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 amount of the i-th ship entering the j-th port, S i,j Indicates the mileage of pure electric ships at port; The calculating of the charging time of the pure electric ship at the port according to the charging amount of the pure electric ship at the port and the actual charging power comprises: Where P i,j represents the actual charging power of the i-th pure electric ship at the j-th port, P ship,i represents the rated charging power of the i-th pure electric ship, P charger,j represents the power limit of the j-th port charging pile, t i,j represents the charging time of the i-th pure electric ship at the j-th port; The total annual load of the whole basin obtained based on the probability distribution of the mileage of ships entering each port, the probability distribution of the charging time of the pure electric ships at the port, the charging amount of the pure electric ships at the port, and the charging time of the pure electric ships at the port includes: 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 i-th pure electric ship charging at the j-th port for one day, a i,k represents the charging status of the i-th pure electric ship at the j-th port at time k, k = 1, 2, ..., 24, t day Indicates the time of day, P j,Σ (t day ) represents the daily charging load of the entire 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 timing curve, N p Indicates the number of ports, P j,normal (t year ) represents conventional loads other than charging piles.

4. The method for evaluating the new energy consumption capacity considering the charging load of pure electric ships according to claim 1 is characterized in that: The establishment of a probability distribution model for the combined output of wind power and photovoltaic power stations includes: Determine the probability density distribution of wind speed and the probability density distribution of light intensity; 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 light intensity; A probability distribution model of the combined output of wind power and photovoltaic power station is established based on the wind turbine output power and the photovoltaic power station output power.

5. A method for evaluating the new energy consumption capacity considering the charging load of pure electric ships according to claim 4, characterized in that: The determination of the probability density distribution of wind speed and the probability density distribution of light intensity includes: 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 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; The determining 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 includes: P pv =SAη; Where P w Indicates the fan output power, P r Indicates the rated output of the fan, V c Indicates the fan cut-in wind speed, V r Indicates the rated wind speed of the fan, V f Indicates the fan cut-off wind speed, P pv 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.

6. A method for evaluating the new energy consumption capacity considering the charging load of pure electric ships according to claim 4, characterized in that: The establishing of a wind power and photovoltaic power station joint output probability distribution model based on the wind turbine output power and the photovoltaic power station output power comprises: Based on the wind turbine output power and the photovoltaic power station output power, an Archimedean Copula function is used to construct a probability distribution model for the combined output of wind power and photovoltaic power stations.

7. The method for evaluating the new energy consumption capacity considering the charging load of pure electric ships according to claim 1 is characterized in that: The determination of the probability space for 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 river basin includes: Where P new,ca (t) represents the probability space of new energy consumption, 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 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.

8. The method for evaluating the new energy consumption capacity considering the charging load of pure electric ships according to claim 1 is characterized in that: Determining the average abandoned power, abandoned power rate, absorption rate and average absorption power of renewable energy generation according to the renewable energy absorption probability space includes: Discretizing the probability distribution model of the combined output of wind power and photovoltaic power stations to obtain a discrete probability distribution matrix of the theoretical output of new energy; Discretizing the new energy consumption probability space to obtain a discrete probability distribution matrix of the new energy consumption probability space; Calculate the new energy abandonment power distribution matrix and the 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; Performing joint probability solution on the discrete probability distribution matrix of the theoretical output of the new energy and the discrete probability distribution matrix of the probability space of the new energy consumption, to obtain the probability distribution matrix of the new energy abandoned power and the probability distribution matrix of the new energy consumption power; Based on the probability distribution matrix of the new energy abandoned power and the probability distribution matrix of the new energy consumption power, the average abandoned power, abandoned power rate, consumption rate and average consumption power of the new energy power generation are obtained.

9. The method for evaluating the new energy consumption capacity considering the charging load of pure electric ships according to claim 1 is characterized in that: The evaluation of the future new energy consumption capacity of the regional power grid in the basin based on the average abandoned power, the abandoned power rate, the consumption rate and the average consumption power includes: According to the average abandoned power, the abandoned power rate, the absorption rate and the average absorption power, the number of pure electric ships put into operation, the routes participated by pure electric ships, the types of pure electric ships and the scale of construction of new energy power stations in the regional power grid in the basin are evaluated in the future.

10. A device for evaluating the new energy consumption capacity considering the charging load of pure electric ships, characterized in that: include: The total annual load determination module is used to perform probabilistic modeling of the charging load of pure electric ships in the basin and obtain the total annual load of the entire basin; Model building module, used to establish the probability distribution model of the combined output of wind power and photovoltaic power stations; A calculation module, for determining a 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 entire river basin, and determining an average abandoned power, abandoned power rate, consumption rate and average consumption power of new energy power generation according to the new energy consumption probability space; An evaluation module is used to evaluate the future new energy consumption capacity of the regional power grid in the basin based on the average abandoned power, the abandoned power rate, the consumption rate and the average consumption power.

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