CN114819573A

CN114819573A - Day-ahead scheduling method and device of new energy system and computer equipment

Info

Publication number: CN114819573A
Application number: CN202210402094.XA
Authority: CN
Inventors: 胡亚平; 顾慧杰; 赵化时; 彭超逸; 聂涌泉
Original assignee: China Southern Power Grid Co Ltd
Current assignee: China Southern Power Grid Co Ltd
Priority date: 2022-04-18
Filing date: 2022-04-18
Publication date: 2022-07-29

Abstract

The application relates to a day-ahead scheduling method and device of a new energy system and computer equipment. The method comprises the following steps: acquiring initial operation information of a new energy system, and respectively updating a first energy conversion model of the hydrogen production and energy storage system and a second energy conversion model of the pumping and storage power station based on the initial operation information of the new energy system; the first energy conversion model is determined based on the working parameters of the electrical hydrogen production energy storage system; the second energy conversion model is determined for unit configuration based on the pumped storage power station; generating a wind-solar output model of the wind-solar power station according to the installed capacity of the wind-solar power station; based on the wind-solar output model, the updated first energy conversion model and the updated second energy conversion model, taking the day-ahead scheduling constraint as a constraint condition, and solving an optimal solution for a day-ahead scheduling objective function to obtain a day-ahead scheduling plan; the day-ahead scheduling objective function is constructed by maximizing the output electric quantity of the new energy system. The method can improve the energy utilization efficiency and meet the load requirement.

Description

Day-ahead scheduling method and device of new energy system and computer equipment

Technical Field

The application relates to the technical field of renewable energy sources, in particular to a day-ahead scheduling method and device of a new energy system and computer equipment.

Background

With the development of renewable energy, hydrogen is used as a green, zero-carbon, high-density and multifunctional energy carrier, has great application potential in the fields of chemical industry, traffic, municipal administration and the like which are difficult to be electrified, is expected to reduce carbon emission in the field of terminal energy consumption, and supports the construction of a 'zero-carbon' terminal energy system. The new energy hydrogen production is used for preparing hydrogen by the water electrolysis hydrogen production technology, the electric power for preparing hydrogen by water electrolysis comes from clean energy, and the hydrogen preparation process is clean and low-carbon, so that the dominant pattern of hydrogen preparation by the traditional fossil energy is broken, and the hydrogen preparation method plays an important role in realizing low-carbon of the hydrogen preparation mode.

Meanwhile, clean energy represented by wind and light has been rapidly developed in recent years. However, under the influence of natural factors such as air pressure, temperature, sunrise and sunset, wind-solar power generation has strong uncertainty and intermittent isochronous sequence characteristics, the fluctuation amplitude of the wind-solar power generation will increase along with the leap-type increase of installed capacity, the randomness of the power supply side is greatly increased, the reliable sending and consumption of new energy is seriously influenced, and the supply of flexible resources needs to be increased. Considering the clean requirements of an energy system, the proportion of conventional coal-electricity installations is gradually reduced in the future, large-scale consumption of new energy is seriously challenged under the condition of lacking of standby support provided by large-scale conventional coal-electricity, and energy storage and rapid power regulation means suitable for high-proportion and large-scale new energy grid connection are needed.

With the progress of energy storage technology, large-scale energy storage power stations built in the future will play a very important role in the absorption of wind and light energy sources. The pumped storage power station and the electric hydrogen production are used as two energy storage modes with development potential, besides the energy storage function, the active power balance between a load and a power supply can be realized, the rapid power tracking and adjusting capability is realized, and the dynamic balance between intermittent energy supply and relative stability load requirements is maintained.

However, the current new energy system scheduling method or the traditional method has the problems of low energy utilization efficiency and the like.

Disclosure of Invention

In view of the above, it is necessary to provide a method, an apparatus, a computer device, a computer readable storage medium, and a computer program product for day-ahead scheduling of a new energy system, which can improve energy utilization efficiency.

In a first aspect, the present application provides a method for scheduling a new energy system in the day ahead, where the method includes:

acquiring initial operation information of a new energy system, and respectively updating a first energy conversion model of the hydrogen production and energy storage system and a second energy conversion model of the extraction and storage power station based on the initial operation information of the new energy system; the first energy conversion model is determined based on the working parameters of the electrical hydrogen production energy storage system; the second energy conversion model is determined for unit configuration based on the pumped storage power station;

generating a wind-solar output model of the wind-solar power station according to the installed capacity of the wind-solar power station;

based on the wind-solar output model, the updated first energy conversion model and the updated second energy conversion model, taking the day-ahead scheduling constraint as a constraint condition, and solving an optimal solution for a day-ahead scheduling objective function to obtain a day-ahead scheduling plan; the day-ahead scheduling constraints comprise operation constraints of an electric hydrogen production energy storage system, operation constraints of a pumped storage power station, wind-solar output constraints and operation constraints of a new energy system; the day-ahead scheduling objective function is constructed by maximizing the output electric quantity of the new energy system.

In one embodiment, the initial operation information of the new energy system comprises initial energy storage of a hydrogen storage device of the hydrogen production energy storage system and initial capacity of an upper reservoir of the pumped storage power station.

In one embodiment, the operating parameters of the electrical hydrogen production energy storage system comprise the rated power of the electrolytic hydrogen production device, the conversion efficiency of the electrolytic hydrogen production device and the storable capacity of the hydrogen storage device; the unit configuration of the extraction and storage power station comprises the installed capacity of the extraction and storage power station and the number of extraction and storage units.

In one embodiment, the operational constraints of the electrical hydrogen production energy storage system include electrolytic hydrogen production device power constraints, electrolytic hydrogen production device standby constraints, and hydrogen storage device capacity constraints;

the pumping power station operation constraints comprise pumping unit output constraints, pumping unit working condition constraints, pumping unit standby constraints and reservoir operation constraints;

the wind and light output constraint comprises wind and light unit output constraint and abandoned wind and light quantity constraint;

the new energy system operating constraints include new energy system power balance constraints and intra-day hydrogen load demand constraints.

In one embodiment, the day-ahead scheduling plan comprises a planned power consumption of the hydrogen-production energy storage system, a planned pumping power consumption of the pumped storage power station, a planned water discharging power generation power of the pumped storage power station, and a planned output of the wind-solar power station.

In one embodiment, the step of generating the wind-solar power output model of the wind-solar power plant according to the installed capacity of the wind-solar power plant comprises the following steps:

obtaining a preset relation between predicted wind and light power and a predicted error of wind and light output according to the installed capacity of the wind and light power station;

randomly sampling by adopting a Monte Carlo method to generate an original scene set of the wind-solar output prediction error;

processing an original scene set of the wind-solar output prediction error by adopting a Gaussian mixture clustering scene division method to obtain a typical scene set of the wind-solar output prediction error; the typical scene set comprises the occurrence probability of each scene and the wind light output prediction error under each scene;

obtaining a wind-solar output scene set used for describing wind-solar available power uncertainty based on a preset relation and a typical scene set; the wind and light output scene set comprises the occurrence probability of each scene and the wind and light available power under each scene; the wind-solar available power is the sum of the predicted wind-solar power and the predicted wind-solar output error.

In a second aspect, the application further provides a day-ahead scheduling device of the new energy system. The device comprises:

the system comprises an initial operation information acquisition module, a first energy conversion module and a second energy conversion module, wherein the initial operation information acquisition module is used for acquiring initial operation information of the new energy system and respectively updating the first energy conversion model and the second energy conversion model based on the initial operation information of the new energy system; the first energy conversion model is determined based on the working parameters of the electrical hydrogen production energy storage system; the second energy conversion model is determined for unit configuration based on the pumped storage power station;

the wind-solar power generation module is used for generating a wind-solar power generation model of the wind-solar power station according to the installed capacity of the wind-solar power station;

the day-ahead scheduling plan output module is used for solving an optimal solution for a day-ahead scheduling objective function to obtain a day-ahead scheduling plan by taking the day-ahead scheduling constraint as a constraint condition of the day-ahead scheduling objective function based on the wind-solar output model, the updated first energy conversion model and the updated second energy conversion model; the day-ahead scheduling constraints comprise operation constraints of an electric hydrogen production energy storage system, operation constraints of a pumped storage power station, wind-solar output constraints and operation constraints of a new energy system; the day-ahead scheduling objective function is constructed by maximizing the output electric quantity of the new energy system.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method described above when executing the computer program.

In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.

In a fifth aspect, the present application further provides a computer program product. The computer program product comprises a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method.

According to the day-ahead scheduling method and device of the new energy system, the computer equipment, the storage medium and the computer program product, the first energy conversion model of the hydrogen production and energy storage system and the second energy conversion model of the pumping and storage power station are respectively updated based on the initial operation information of the new energy system by acquiring the initial operation information of the new energy system; generating a wind-solar output model of the wind-solar power station according to the installed capacity of the wind-solar power station; based on the wind-solar output model, the updated first energy conversion model and the updated second energy conversion model, the day-ahead scheduling constraint is used as a constraint condition, an optimal solution is obtained for a day-ahead scheduling objective function, a day-ahead scheduling plan is obtained, the effect of demand response can be fully exerted, resource waste is avoided, the hydrogen load demand is met, the maximum generated energy is realized, and the utilization efficiency of energy can be improved.

Drawings

FIG. 1 is a schematic diagram of a new energy system architecture in one embodiment;

FIG. 2 is a schematic flow chart illustrating a method for day-ahead scheduling of a new energy system according to an embodiment;

FIG. 3 is a schematic diagram of energy conversion of an electrical hydrogen production energy storage system in one embodiment;

FIG. 4 is a schematic diagram of the operation of the pumped storage power plant in one embodiment;

FIG. 5 is a graph of wind-solar predictions for various time periods for Zhang-North data in one embodiment;

FIG. 6 is a graph of wind-solar predictions for various time periods for health care data in one embodiment;

FIG. 7 is a graph of the wind-light available contribution for different scenarios of Zhang Bei data in one embodiment;

FIG. 8 is a graph illustrating the wind-light available contribution from different scenarios of the health care data in one embodiment;

FIG. 9 illustrates simulation results for different modes of operation in one embodiment;

FIG. 10 illustrates the energy supply of the new energy system operating as planned for operation mode D in one embodiment;

fig. 11 shows the energy supply of the energy system operating according to scenario 3 in operation mode D according to an exemplary embodiment;

FIG. 12 is a diagram illustrating the optimization results for different wind and photovoltaic prediction accuracies in one embodiment;

FIG. 13 is a diagram illustrating optimization results for different configurations of the pumping power station capacity in one embodiment;

FIG. 14 is a graph illustrating the optimization results for different numbers of transmission sets in one embodiment;

FIG. 15 is a graph illustrating the optimization results for different hydrogen load requirements in one embodiment;

FIG. 16 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

At present, the pumped storage power station cannot be developed and constructed without limit under the influence of natural conditions such as geography, water sources and the like, and is difficult to undertake all adjustment tasks after large-scale new energy grid connection; limited by the current technical level, the hydrogen-electricity conversion efficiency is extremely low and less than 30% when the electricity hydrogen production is combined with the fuel cell for power generation, and the efficient utilization of energy is difficult to realize. Demand side response is an important measure for coping with the contradiction between uncertainty of new energy power generation and real-time balance of power supply and demand from a demand side. Most of traditional demand side load responses only consider electric load demand responses, but cannot consider the coupling relation between electricity and other forms of energy, the effect of demand responses is not fully played, and a large development space exists. The water electrolysis hydrogen production technology has the characteristics of high dynamic response rate and wide power regulation range, and can safely and reliably carry out water electrolysis hydrogen production under the condition of unstable electric energy, so that new energy hydrogen production is expected to be taken as a flexible demand response resource to directly participate in system regulation, and the negative influence of new energy grid connection on system operation is relieved.

The method for scheduling the new energy system in the day ahead provided by the embodiment of the application can be applied to the new energy system shown in fig. 1. The new energy system comprises a wind-light power station, a pumping storage power station and an electrical hydrogen production energy storage system which are connected, and is a wind-light-hydrogen-zero carbon storage power system. Considering the intermittence and randomness of wind and light and the limiting factors of a Pumped Storage Power Station (PS) and an electric Hydrogen production System (EHS), Hydrogen is used as a terminal energy source in the field difficult to electrify, and Hydrogen produced by a new energy source is used as a demand side response resource and the Pumped Storage power station to share the power regulation task so as to realize green power grid connection. Specifically, the electric hydrogen production and energy storage system supplies power through a wind-solar power station and a pumping and storage power station, the electrolytic hydrogen production device sends the produced hydrogen into a hydrogen storage device through a compression device for storage, and the hydrogen storage device provides hydrogen energy for hydrogen load; the pumped storage power station can be powered by a wind and light power station.

In one embodiment, as shown in fig. 2, the present application provides a method for scheduling a new energy system in the day ahead, the method including:

step 210, acquiring initial operation information of the new energy system, and updating a first energy conversion model of the hydrogen production and energy storage system and a second energy conversion model of the extraction and storage power station respectively based on the initial operation information of the new energy system; the first energy conversion model is determined based on the working parameters of the electrical hydrogen production energy storage system; the second energy conversion model is determined for unit configuration based on the pumped storage power station;

specifically, the electrical hydrogen production energy storage system can comprise an electrolytic hydrogen production device, a compression device and a hydrogen storage device; the first energy conversion model includes the following processes: the electric hydrogen production energy storage system converts electric energy into hydrogen energy through the electrolytic hydrogen production device, and stores the hydrogen energy obtained by electrolysis in the hydrogen storage device through the compression device;

in some examples, the electrolytic hydrogen production apparatus may be an electrolytic cell; the compression device may be a compressor; the hydrogen storage device may be a hydrogen storage tank. As shown in fig. 3, the first energy conversion model may be determined using the following equation:

in the formula, t is time;

is the electricity consumption power of the electrolytic hydrogen production device; eta _P2H The conversion coefficient for electrolytic hydrogen production (for example, the conversion efficiency of the energy conversion link for electrolytic hydrogen production);

hydrogen energy flowing into the hydrogen storage device; m _t Hydrogen energy storage capacity of the hydrogen storage device; o is _t The amount of hydrogen energy provided to the hydrogen storage device to the hydrogen load is dependent on factors such as the capacity of the hydrogen storage tank, the degree of wind power enrichment, and the price of the delivered hydrogen energy.

Further, as shown in fig. 4, the pumped storage power station is a pumped storage power station; the pumped storage power station is a hydropower station which pumps water to an upper reservoir by utilizing electric energy in the low ebb period of the electric load and discharges water to a lower reservoir to generate electricity in the peak period of the electric load. The unit configuration of the extraction and storage power station comprises the installed capacity of the extraction and storage power station and the number of extraction and storage units.

In some examples, the number of pumped storage units includes a conventional constant speed unit number and a variable speed constant frequency unit number. The second energy conversion model may be determined using the following equation:

in the formula (I), the compound is shown in the specification,

the power consumption of the pumping and storage unit under the pumping working condition is realized;

generating power for the pumping storage unit under the water drainage working condition; eta _pum The average water quantity conversion coefficient under the water pumping working condition is obtained; eta _gen The average electric quantity conversion coefficient under the water discharging working condition is obtained;

is the volume of water injected into the upper reservoir;

is the volume of water flowing out of the upper reservoir; v _t The water storage capacity of the upper reservoir; n is a radical of _H The number of the pumping and storage units.

Step 220, generating a wind-solar output model of the wind-solar power station according to the installed capacity of the wind-solar power station;

specifically, the wind and light power station comprises a wind power plant and/or a photovoltaic power station; obtaining a preset relation between the predicted wind and light power and the predicted error of wind and light output according to the installed capacity of the wind and light power station; the wind-solar output model can be a wind-solar output scene set comprising the occurrence probability of each scene and the wind-solar available power under each scene; the wind-light output scene set can be obtained by adopting a Monte Carlo (Monte Carlo) method random sampling and Gaussian Mixture clustering (GMM) scene division method; the wind-solar available power is the sum of the predicted wind-solar power and the predicted wind-solar output error. It should be noted that the Monte Carlo method, also called random sampling or statistical test method, can truly simulate the actual physical process, is a calculation method based on probability and statistical theory method; gaussian mixture clustering is a probabilistic clustering method that assumes that all data samples are generated from a mixture of multiple mixture multivariate gaussian distributions.

In some examples, the monte carlo method is adopted for random sampling, and an original scene set of the wind-solar output prediction error is generated; processing an original scene set of the wind-solar output prediction error by adopting a Gaussian mixture clustering scene division method to obtain a typical scene set of the wind-solar output prediction error; the typical scene set comprises the occurrence probability of each scene and the wind light output prediction error under each scene; obtaining a wind-solar output scene set used for describing wind-solar available power uncertainty based on a preset relation and a typical scene set; the wind and light output scene set comprises the probability of each scene and the wind and light available power under each scene.

Step 230, based on the wind-solar output model, the updated first energy conversion model and the updated second energy conversion model, taking the day-ahead scheduling constraint as a constraint condition, and solving an optimal solution for a day-ahead scheduling objective function to obtain a day-ahead scheduling plan; the day-ahead scheduling constraints comprise operation constraints of an electric hydrogen production energy storage system, operation constraints of a pumped storage power station, wind-solar output constraints and operation constraints of a new energy system; the day-ahead scheduling objective function is constructed by maximizing the output electric quantity of the new energy system.

Specifically, the day-ahead scheduling objective function is to ensure that the new energy system can effectively complete a scheduling plan (for example, the actual green power grid-connected power is equal to the planned power and meets the total hydrogen load demand in a day) in any wind and light output scene set, and the green power grid-connected electric quantity of the new energy system is maximized, that is, under the condition that the new energy system is ensured to have enough regulation capacity to generate electricity according to a preset plan, green electric energy is transmitted outwards as much as possible, so that the comprehensive benefit of the new energy system is maximum. The day-ahead scheduling plan can be obtained by adopting a mixed integer linear programming method to obtain an optimal solution.

In some examples, the system is allowed to reasonably curtail in operation, given that under certain scenarios the cost of providing backup, energy storage service may be higher than the benefit of fully admitting wind-solar. The day-ahead scheduling objective function is constructed by adopting the following formula:

in the formula, t is time; n is a radical of _V The number of photovoltaic power stations; n is a radical of _W The number of wind farms; n is a radical of _H The number of the pumping and storage units; p _v,t Planned output of the photovoltaic power station at v time t;

electrical power consumed for the electrolytic hydrogen production apparatus;

the generated power of the pumping storage unit under the water drainage working condition is obtained. The IBM CPLEX12.5 solver can be used to solve the day-ahead scheduling objective function described above.

The application provides a day-ahead scheduling method of a new energy system which considers electricity to hydrogen and has high wind and light permeability, and provides reference for the solution of the problems of clean energy transformation and comprehensive new energy development and utilization. The new energy system takes wind and light as an energy source of the system, so that cleanness and low carbon of energy supply and consumption are guaranteed; the new energy hydrogen production serves as flexible hydrogen load, and the energy form of demand side response is expanded; the hydrogen is used as a terminal energy source in the field difficult to electrify, the new energy hydrogen production is used as a demand side response resource to share the power regulation task with the extraction and storage power station, the defects of low hydrogen-electricity conversion efficiency and limited development scale of the extraction and storage power station are overcome, and the energy utilization efficiency is improved while the system is ensured to have enough regulation capacity to generate electricity according to a preset plan. According to the method and the device, the initial operation information of the new energy system is obtained, and the first energy conversion model of the hydrogen production and energy storage system and the second energy conversion model of the extraction and storage power station are respectively updated based on the initial operation information of the new energy system; generating a wind-solar output model of the wind-solar power station according to the installed capacity of the wind-solar power station; based on the wind-solar output model, the updated first energy conversion model and the updated second energy conversion model, the day-ahead scheduling constraint is used as a constraint condition, an optimal solution is obtained for a day-ahead scheduling objective function, a day-ahead scheduling plan is obtained, the effect of demand response can be fully exerted, resource waste is avoided, the hydrogen load demand is met, the maximum generated energy is realized, and the utilization efficiency of energy can be improved.

Specifically, the wind-solar output prediction error is generally considered to be subject to a mean value of 0 and a standard deviation of δ _t Generating a model for simulating the uncertainty of wind-solar output by normal distribution; further, wind and light output prediction error scenes are generated and reduced, a large number of wind and light power prediction error scenes can be obtained through a Monte Carlo random sampling technology, then a plurality of original scenes are reduced by using a scene division method based on Gaussian mixed clustering, and a group of typical scene sets containing error power and probability information are obtained; describing the uncertainty of the wind and light available power, regarding the wind and light available power as the sum of the predicted output and the prediction error, and combining the predicted power to obtain a group of wind and light output scene sets.

In some examples, when the predicted lead time is within 24h, a model is generated that models the wind/solar power uncertainty using the following equation:

δ _t ＝εP _f，t +0.02Q _cap

in the formula, delta _t The normal distribution standard deviation of the wind and light output prediction error is obtained; epsilon is a parameter for representing the precision of the prediction error, and the smaller the epsilon value is, the higher the prediction precision is; p _f，t In order to predict the wind-light power, the wind-light power is one or two of wind power and light power;Q _cap the wind and light power station is one or two of a wind power station and a photovoltaic power station.

A typical set of scenes is { (P) _Δ，t，1 ，π ₁ )，(P _Δ，t，2 ，π ₂ )，…，(P _Δ，t，ω ，π _ω )，(P _Δ，t，W ，π _W ) In which P is _Δ，t，ω Predicting errors for wind and light output; pi _ω Is the probability of scene occurrence; w is the number of scenes of a typical scene set scene; the scene set of wind and light output is { (P) _t，i ，π ₁ )，(P _t，2 ，π ₂ )，…，(P _t，ω ，π _ω )，(P _t，W ，π _W ) In the formula, P _t，ω Available power for wind and light; p _t，ω ＝P _f，t +P _Δ，t，ω 。

In one embodiment, the operating parameters of the electrical hydrogen production energy storage system comprise the rated power of the electrolytic hydrogen production device, the conversion efficiency of the electrolytic hydrogen production device and the storable capacity of the hydrogen storage device; the unit configuration of the pumping power station comprises the installed capacity of the pumping power station and the number of pumping units.

Specifically, the working parameters of the electrical hydrogen production energy storage system are detailed in the formula of the first energy conversion model, and the unit configuration of the pumped storage power station is detailed in the formula of the second energy conversion model, which is not described herein again.

the new energy system operation constraints include new energy system power balance constraints and intra-day hydrogen load demand constraints.

Specifically, the power constraint of the electrolytic hydrogen production device comprises the following formula:

in the formula, t is time; omega is a scene;

the upper limit of the electric power consumption for hydrogen production of the electrolytic hydrogen production device;

the lower limit of the electric power consumption for hydrogen production of the electrolytic hydrogen production device;

the amount of the electrolytic hydrogen production device is adjusted for hydrogen production at a moment t scene omega;

the amount of the hydrogen is prepared for the electrolytic hydrogen production device under the condition of hydrogen production at the moment t scene omega;

the state mark of the electrolytic hydrogen production device at time t is 1, which indicates that the device is in a corresponding state, 0 indicates that the device is not in a corresponding state, and the device can indicate two operation states of hydrogen production and idle operation.

The standby constraint of the electrolytic hydrogen production device comprises the following formula:

in the formula (I), the compound is shown in the specification,

the available capacity of the upper spare is provided for the new energy system at the moment t under the condition of hydrogen production by the electrolytic hydrogen production device;

the available capacity is provided for the system at the moment t under the condition of hydrogen production by the electrolytic hydrogen production device;

the hydrogen storage device capacity constraint includes the following equation:

M _min ≤M _t ≤M _max

M _min ≤M _t,ω ≤M _max

M ₀ ＝M _end

M _0,ω ＝M _end,ω

0≤O _t

0≤O _t,ω

in the formula, M _t The hydrogen energy reserve of the hydrogen production device is electrolyzed at the moment t; m _t,ω The hydrogen energy storage capacity of the hydrogen production device is electrolyzed at the moment t scene omega; m is a group of _min The minimum stored hydrogen energy of the electrolytic hydrogen production device; m _max Storing the maximum hydrogen energy for the electrolytic hydrogen production device; m ₀ For the initial stage of an electrolytic hydrogen production plantHydrogen energy storage of the segment; m _end Hydrogen energy reserve for the end-of-term period of the electrolytic hydrogen plant; m _0,ω Hydrogen energy reserve for the initial period of the electrolytic hydrogen production device under the scene omega; m _end,ω The hydrogen energy storage capacity of the electrolytic hydrogen production device at the end period under the scene omega; o is _t The planned value of hydrogen energy directly transmitted to the outside by the electrolytic hydrogen production device at the moment t; o is _t,ω The hydrogen energy directly conveyed to the outside by the hydrogen production device is electrolyzed under the time t scene omega.

The output constraint of the pumping unit comprises the following formula:

in the formula (I), the compound is shown in the specification,

the minimum pumping power allowed by the pumping storage unit k;

the maximum pumping power allowed by the pumping storage unit k;

the minimum generating power allowed by the pumping unit k;

the maximum generating power allowed by the pumping storage unit k;

actual standby adjusting quantity of the pumping and storage unit k under the condition of pumping water in a scene omega at a moment t;

the actual standby use amount of the pumping and storage unit k under the condition of pumping water under the scene omega at the moment t is obtained;

actual standby adjusting quantity of the pumping storage unit k under the omega water drainage working condition of the scene t at the moment;

the actual standby adjusting amount of the pumping storage unit k under the omega water drainage working condition of the scene t at the moment is obtained;

marking the state of the pumping and storage unit k under the condition of pumping at the moment t;

and (4) marking the state of the pumping storage unit k under the water discharge working condition at the moment t.

The pumping unit operating condition constraints include the following:

namely, the pumping and storing unit k can only be in one of the working conditions of pumping, draining or idling at the same time; the pumping unit of the pumping power station can not simultaneously have two working conditions of pumping and discharging water.

The backup constraints for the pump bank include the following formula:

the pumping power adjustment quantity of the constraint pumping storage unit k during real-time operation does not exceed the reserved spare capacity, and the method comprises the following steps:

the water discharge (power generation) power adjustment quantity of the constraint pumping storage unit k does not exceed the reserved spare capacity during real-time operation, and the method comprises the following steps:

in the formula (I), the compound is shown in the specification,

providing an upper spare capacity for the pumping unit k to the system at the moment t under the pumping working condition;

providing a lower spare capacity for the pumping unit k to the system at the moment t under the pumping working condition;

providing an upper spare capacity for the system of the pumping unit k at the moment t under the water drainage working condition;

and the lower spare capacity is provided for the system by the pumping unit k at the moment t under the water discharge working condition.

The reservoir operation constraint comprises the following formula:

V _min ≤V _t ≤V _max

V _min ≤V _t,ω ≤V _max

and, guarantee to draw under the arbitrary scene in each scheduling cycle and hold the power station reservoir water storage volume and realize the cycle circulation to guarantee the long-term sustainable operation of system, have:

V ₀ ＝V _end

V _0,ω ＝V _end,ω

in the formula, V _t The water storage capacity of the upper reservoir at the moment t; v _t,ω The water storage capacity of an upper reservoir of the pumped storage power station under the situation omega of time t; v _min The minimum water storage capacity of the upper reservoir; v _max The maximum water storage capacity of the upper reservoir; v ₀ The water storage capacity of the upper reservoir in the initial period; v _end The water storage capacity of the upper reservoir at the end period; v _0,ω The water storage capacity of the upper reservoir of the pumping power station in the initial period under the scene omega is obtained; v _end,ω The water storage amount of the upper reservoir end period of the pumped storage power station under the scene omega is shown.

The wind and light unit output constraint comprises the following formula:

in the formula (I), the compound is shown in the specification,

the installed capacity of the wind farm j;

the installed capacity of the photovoltaic power station v; p _j,t The available power of the wind power plant j at the moment t; p _v,t The available power of the photovoltaic power station v at the moment t;

the curtailment energy constraint comprises the following formula:

0≤S _j,t,ω ≤P _j,t,ω

and, the amount of abandoned light should not be greater than the photovoltaic available power:

0≤S _v,t,ω ≤P _v,t,ω

in the formula, P _j,t,ω The available power of the wind power plant j under the scene omega at the moment t is obtained; s _j,t,ω The wind power abandoning rate of the wind power plant j under the scene omega at the moment t; s _v,t,ω The light abandoning amount of the photovoltaic power station v under the scene omega at the moment t is shown; p _v,t,ω The available power of the photovoltaic power station v at the moment tset omega is obtained.

The new energy system power balance constraint is the electric power balance constraint of the new energy system, and the wind and light randomness can be effectively responded and a power generation plan can be completed by controlling the electricity power of the electricity hydrogen production energy storage system, adjusting the pumping (energy storage) power and the drainage (power generation) power of the pumping and storage power station and abandoning the wind and light means in any wind and light output scene, so that the schedulability of the wind and light resources of the system can be improved. The new energy system power balance constraint includes the following formula:

the constraint of the hydrogen load demand in the day requires that the total amount of hydrogen energy conveyed outwards in the period of the new energy system should meet the hydrogen load demand. The intra-day hydrogen load demand constraints include the following:

Specifically, the day-ahead scheduling plan can coordinate and optimize the electricity and hydrogen production processes including day-ahead power generation-hydrogen production plan, reserve capacity, pumped storage-discharged power generation power, abandoned wind and light electricity and the like in a short period.

Specifically, a first energy conversion model of the hydrogen production energy storage system is updated based on initial energy storage of a hydrogen storage device of the hydrogen production energy storage system; and updating a second energy conversion model of the pumped storage power station based on the initial capacity of the upper reservoir of the pumped storage power station.

In some examples, the day-ahead scheduling method of the new energy system may be examined by taking a north-tensioning zero-carbon power system demonstration project as an example. The total capacity of new energy access in Zhangbei and health and protection places is 7500MW, and the capacity ratio of new energy access in the two places is 2: 1, wind power and photovoltaic access capacity ratio is 1.5: 1, energy storage configuration 1200 MW. The pumping and storage power station comprises 2 conventional constant-speed units and 2 variable-speed constant-frequency units, the rated capacity of each unit is 300MW, the pumping-power generation power of the variable-speed units can be continuously adjusted in a power interval of 10% -100%, the power of the constant-speed units can be adjusted only under the power generation working condition, and the average water quantity/electric quantity conversion coefficients under the power generation-pumping working condition of the pumping and storage units are 999m ³ /(MW. h) and 780m ³ /(MW. h), initial upper reservoir volume 2.00X 10 ⁷ m ³ Minimum storage capacity of 1.06X 10 ⁷ m ³ Maximum storage capacity 4.38X 10 ⁷ m ³ . The rated power of the electrolytic cell is 300MW, the minimum operating power is 10% of the rated power, the electricity-hydrogen energy conversion efficiency is 80%, the upper limit of the stored hydrogen energy is 3000MW.h, the lower limit is 1000MW.h, and the initial stored energy is 2000 MW.h. The hydrogen load is 2000MW.h, the wind and light prediction graphs of the north-opening data and the health and insurance data are respectively shown in fig. 5 and 6, 10 output scenes are obtained by using the scene generation reduction technology, and the wind and light available output graphs of the north-opening data and the health and insurance data under different scenes are respectively shown in fig. 7 and 8, assuming that the wind and light prediction precision parameters epsilon are all equal to 0.2, and are 0.2.

And solving a day-ahead scheduling objective function which considers electricity-to-hydrogen and contains high wind and light permeability according to the method.

1) Influence of the operating mode on the simulation results

The same example was chosen to compare the following 4 operating modes to verify the effectiveness of the process.

The operation mode A is as follows: hydrogen production by new energy does not participate in the real-time adjustment process, and a pumping storage power station is not used;

operation mode B: hydrogen production from new energy participates in the real-time adjustment process, and a pumping storage power station is not used;

the operation mode C is as follows: hydrogen production by new energy does not participate in the real-time regulation process, and a pumping storage power station is provided;

operation mode D: the new energy hydrogen production participates in the real-time adjustment process, and a pumping storage power station is provided.

Fig. 9 shows the green power on-line and power off of the system in the four different operation modes. Compared with the operation mode A, the operation mode B has the advantages of quick power response and wide adjustment range of the hydrogen-electricity-hydrogen-production energy storage system, and the flexibility of system operation is improved by introducing a demand response mode, so that the green power grid electricity quantity is improved by 3.08%, and the electricity discard quantity is reduced by 23.55%; the operation mode C introduces a pumped storage power station to consume redundant wind and light on the basis of the operation mode A, and tracks the rapid change of wind and light output through the rapid change of the pumped storage-water discharge power generation working condition and the rapid adjustment of the output, so that the green power grid-connected electric quantity is improved by 9.36 percent, and the electricity discard quantity is reduced by 80.12 percent; the operation mode D is the day-ahead scheduling method of the new energy system, and on the basis of the operation mode A, an electric hydrogen production energy storage system serving as a demand side response resource and a pumped storage power station with energy storage and regulation functions are introduced at the same time, so that the green power grid electricity quantity is increased by 10.31%, and the electricity discard quantity is reduced by 85.38%. Therefore, the day-ahead scheduling method of the new energy system can enable the electric quantity fed back to the power grid by the new energy system to be more and the abandoned wind and light to be less, and has the best comprehensive benefit compared with other three modes.

2) Analysis of energy supply conditions of wind-light power station, extraction and storage power station and electric hydrogen production energy storage system in operation mode D

The energy supply condition of the new energy system in the operation mode D is analyzed, the output of the wind-light power station, the extraction and storage power station and the electric hydrogen production energy storage system is shown in the figure 10 when the power generation is carried out according to the plan, and the positive value represents the power generation, and the method comprises the following steps: wind power planned output, photovoltaic planned output and planned water discharge power generation output of a pumped storage power station; negative values indicate power consumption, including: the planned power consumption of the hydrogen and electricity production energy storage system and the planned water pumping power consumption of the pumping and storage power station are shown by curves, and the curves represent the planned online electric quantity of the new energy system. When the wind power field/photovoltaic power station operates in real time to output power according to the scene 3, the output power and output power adjustment change conditions of the wind-solar power station, the extraction and storage power station and the hydrogen production and energy storage system are shown in fig. 11, and the power generation part comprises: considering the actual wind power grid-connected power after wind abandoning, considering the actual photovoltaic grid-connected power after light abandoning, and considering the actual water drainage power generation output of the pumped storage power station after output adjustment; the power consuming part includes: the actual power consumption of the hydrogen production and energy storage system after the output adjustment is considered, the actual pumping power consumption of the pumping and energy storage power station after the output adjustment is considered, and the curve represents the actual online electric quantity of the new energy system under the scene 3.

The output of the wind-light scene 3 deviates from the planned output, and the electric quantity of the wind-light super-generation part is consumed by the electric hydrogen production energy storage system to increase the hydrogen production power of the electrolyzed water, the pumped water power consumption power of the pumped storage power station or the water discharge power generation power, and the abandoned wind and light together; the wind-light undergeneration part is supplemented by reducing the hydrogen production power of the electrolyzed water by the electric hydrogen production energy storage system, reducing the water pumping power consumption power of the pumping and storage power station or increasing the water discharging power generation power due to the output fluctuation. The time sequence internet surfing electric quantity planned value and the actual value curve in the graph in fig. 10 and fig. 11 are completely coincided, and the fact that the day-ahead scheduling method of the new energy system can guarantee that the system can effectively cope with wind-light randomness and complete a power generation plan in any wind-light output scene is proved, and the schedulability of wind-light power generation of the system is improved.

3) Effect of different objective functions on optimization results

The day-ahead scheduling objective function is to maximize the green internet power of the new energy system on the basis of ensuring that the new energy system can effectively complete a scheduling plan in any wind-light uncertain set, but not to minimize the wind-light abandoning quantity or maximize the wind-light utilization rate, because the latter cannot ensure the effective utilization of new energy, the cost for providing standby and energy storage for wind-light absorption under certain scenes is possibly higher than the benefit brought by completely accepting the part of wind-light. The calculation example is the optimization result under different objective functions, and is shown in the following table:

although the minimum wind-solar rejection target requires less wind-solar rejection, this portion of the wind-solar energy that is more dissipated increases the demand for spare capacity for which the pumped storage power station also draws/discharges more volume of water. The calculation shows that 8901MW & h for upper spare, 6759MW & h for lower spare and 1959039m for cumulative water pumping/discharging are required to complete the target ³ And the goals of minimizing wind and light abandonment are fulfilled by 10548MW & h for upper standby, 8619MW & h for lower standby and 6642351m for cumulative water pumping/discharging ³ All were increased. The conversion of energy between the pumping and storage power station for providing energy storage and standby service and putting into operation is accompanied with loss, because the electric energy consumed under the pumping working condition is larger than the electric energy generated by consuming water with the same volume, the consumed wind and light are wasted in a new energy system in an energy loss mode, and no substantial contribution is made to the increase of the on-grid electric quantity. Therefore, compared with the minimized wind and light abandoning target, the target has more internet power, and can more effectively utilize wind and light resources.

4) Influence of different wind and light prediction precisions on optimization result

In order to test the influence of the wind and light prediction precision on the optimization result of the day-ahead scheduling method of the new energy system, the value of epsilon is gradually increased, and the obtained result is shown in fig. 12. Epsilon is a parameter that characterizes the accuracy of the prediction error, with smaller values indicating higher accuracy of the prediction.

With the improvement of the wind and light output prediction precision, the system abandons wind and light electric quantity and tends to be down, and the green power grid electric quantity tends to be up. The wind and light output uncertainty degree is reduced along with the improvement of the prediction precision, the requirements of the system on energy storage and standby regulation services are reduced, the corresponding energy loss is reduced, and more green electric energy is fed back to the power grid. Therefore, the improvement of the wind and light prediction technology and the improvement of the prediction precision are an effective means for improving the comprehensive benefit.

5) Influence of different pumped storage power station capacity configurations on optimization results

In order to analyze the influence of the scale of the extraction and storage power station on the comprehensive benefits of the system, different extraction and storage power station capacity configuration schemes are compared, an example is simulated, and the obtained result change curve is shown in fig. 13.

The green electric online electric quantity of system presents the trend that tends to be stable after obviously increasing earlier along with the increase of storage power station installed capacity, and the system abandons the scene electric quantity and presents the trend that tends to be stable after obviously reducing earlier along with the increase of storage power station installed capacity, and the reason lies in: the installed capacity of the pumped storage power station is initially increased, so that the system has energy storage capacity, the adjusting capacity is further improved, redundant wind and light can be consumed, and the wind and light output change can be tracked; when the capacity reaches 1000MW, the operation benefit brought by the pumped storage power station is close to saturation, if the operation benefit is continuously increased, the operation benefit is limited by energy loss, storage capacity and other conditions, the green power grid-connected electricity quantity increase is small, and the abandoned wind and light electricity quantity reduction is small. Therefore, for a certain wind and light installed capacity, the capacity of the pumped storage power station for improving the comprehensive benefits of the system has an upper limit, and therefore the optimal configuration should be considered in terms of capacity when new energy and stored energy are in coordinated operation.

6) Influence of different speed change units on comparison optimization results

In order to analyze the influence of the occupation ratio of the variable speed units in all types of units of the extraction and storage power station on the optimization result, under the same extraction and storage installed capacity configuration, the number of the variable speed units in the extraction and storage power station is gradually increased, and 4 conditions are set in total, and the obtained result is shown in fig. 14.

It can be seen that with the increase of the number of the speed changing units, the wind and light electric quantity discarded by the system is in a descending trend, and the green power grid electric quantity is in an ascending trend. The wind and light output random fluctuation is better responded, the wind and light resources are further utilized, and the comprehensive benefit is better.

7) Effect of different Hydrogen load requirements on optimization results

By setting different hydrogen loads, whether an optimal hydrogen load demand value exists is checked, so that the electricity-hydrogen comprehensive energy (the sum of the green electricity grid electricity quantity and the hydrogen load energy) transmitted outside by the system is maximum. The hydrogen load demand was set to 0 to 2400MW · h, and the results are shown in FIG. 15.

It can be known from the graph that the green electricity grid electricity quantity shows a trend of firstly slowly reducing and then obviously reducing along with the increase of the hydrogen load, and the electricity-hydrogen comprehensive energy shows a trend of firstly gradually increasing and then gradually reducing towards the maximum value along with the increase of the hydrogen load, because: the initial increase of hydrogen load enables the system to have demand side response capability, the system regulation capability is enhanced, more wind and light feedback power grids can be consumed, at the moment, the grid electricity quantity is slowly reduced (the increased hydrogen load is provided by the hydrogen production energy storage system, the electricity is consumed, so the electricity quantity is reduced), the variation amplitude is smaller than the hydrogen energy increase amplitude, and the electricity-hydrogen comprehensive energy is in an increasing trend; when the hydrogen load is increased to be about 900MW & h, the benefit brought by load demand response is close to saturation, the grid electricity quantity is obviously reduced, the variation amplitude of the grid electricity quantity is basically equal to the hydrogen energy increase amplitude, and the electricity-hydrogen comprehensive energy reaches the maximum value; the hydrogen load is continuously increased, because the demand response cannot bring new gain to the system, energy loss exists during electricity-hydrogen conversion, the grid electricity quantity is rapidly reduced, the change range is larger than the increase range of hydrogen energy, and the electricity-hydrogen comprehensive energy is in a descending trend. It can be seen that for a given system, there is an optimum hydrogen load to maximize the combined energy of electricity and hydrogen, and the decision maker should consider the selection of the value of the externally supplied hydrogen load.

It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in a strict order unless explicitly stated in the application, and may be performed in other orders. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the application also provides a day-ahead scheduling device of the new energy system, which is used for realizing the day-ahead scheduling method of the new energy system. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so that specific limitations in the embodiments of the future scheduling device for one or more new energy systems provided below can refer to the limitations on the future scheduling method for the new energy system in the foregoing, and details are not repeated herein.

In one embodiment, the application further provides a day-ahead scheduling device of the new energy system. The device comprises:

In one embodiment, the wind-solar power generation module is further configured to obtain a preset relationship between the predicted wind-solar power and the predicted wind-solar power prediction error according to the installed capacity of the wind-solar power station; randomly sampling by adopting a Monte Carlo method to generate an original scene set of the wind-solar output prediction error; processing an original scene set of the wind-solar output prediction error by adopting a Gaussian mixture clustering scene division method to obtain a typical scene set of the wind-solar output prediction error; the typical scene set comprises the occurrence probability of each scene and the wind light output prediction error under each scene; obtaining a wind-solar output scene set used for describing wind-solar available power uncertainty based on a preset relation and a typical scene set; the wind and light output scene set comprises the occurrence probability of each scene and the wind and light available power under each scene; the wind-solar available power is the sum of the predicted wind-solar power and the predicted wind-solar output error.

All or part of each module in the day-ahead scheduling device of the new energy system can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.

In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 16. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing a first energy conversion model, a second energy conversion model, a wind-solar output model, a day-ahead scheduling constraint, a day-ahead scheduling objective function and day-ahead scheduling plan data of the new energy system. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of day ahead scheduling of a new energy system.

Those skilled in the art will appreciate that the architecture shown in fig. 16 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, the present application also provides a computer-readable storage medium. The computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.

In one embodiment, the present application also provides a computer program product. The computer program product comprises a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A day-ahead scheduling method of a new energy system, the method comprising:

acquiring initial operation information of the new energy system, and respectively updating a first energy conversion model of the hydrogen production and energy storage system and a second energy conversion model of the extraction and storage power station based on the initial operation information of the new energy system; the first energy conversion model is determined based on the working parameters of the electrical hydrogen production and energy storage system; the second energy conversion model is determined for the unit configuration based on the pumped storage power station;

2. The method of claim 1, wherein the initial operational information of the new energy system comprises an initial energy storage of a hydrogen storage device of the electrohydrogen-producing energy storage system and an initial upper reservoir capacity of the pumped storage power plant.

3. The method of claim 2, wherein the operating parameters of the electrical hydrogen production energy storage system include hydrogen production device power rating, hydrogen production device conversion efficiency, and hydrogen storage device storable capacity; the unit configuration of the extraction and storage power station comprises the installed capacity of the extraction and storage power station and the number of extraction and storage units.

4. The method of claim 3, wherein the electrical hydrogen production energy storage system operational constraints include an electrolytic hydrogen production plant power constraint, an electrolytic hydrogen production plant standby constraint, and a hydrogen storage plant capacity constraint;

5. The method of claim 1, wherein the day-ahead scheduling plan comprises a planned power consumption of the electrical hydrogen production energy storage system, a planned pumped power consumption of the pumped storage power plant, a planned pumped water power generation power of the pumped storage power plant, and a planned outtake of the wind and light power plant.

6. The method of any one of claims 1 to 5, wherein the step of generating the wind-solar power generation model of the wind-solar power plant from the installed capacity of the wind-solar power plant comprises:

obtaining a preset relation between the predicted wind-solar power and the predicted wind-solar output error according to the installed capacity of the wind-solar power station;

randomly sampling by adopting a Monte Carlo method to generate an original scene set of the wind-light output prediction error;

processing the original scene set of the wind-solar output prediction error by adopting a Gaussian mixture clustering scene division method to obtain a typical scene set of the wind-solar output prediction error; the typical scene set comprises the occurrence probability of each scene and the wind-solar output prediction error under each scene;

obtaining a wind-light output scene set used for describing wind-light available power uncertainty based on the preset relation and the typical scene set; the wind-solar output scene set comprises the occurrence probability of each scene and the wind-solar available power under each scene; the wind-solar available power is the sum of the predicted wind-solar power and the wind-solar output prediction error.

7. A day-ahead scheduling apparatus of a new energy system, the apparatus comprising:

the initial operation information acquisition module is used for acquiring initial operation information of the new energy system and respectively updating a first energy conversion model and a second energy conversion model based on the initial operation information of the new energy system; the first energy conversion model is determined based on the working parameters of the electrical hydrogen production and energy storage system; the second energy conversion model is determined for the unit configuration based on the pumped storage power station;

a day-ahead scheduling plan output module, configured to use a day-ahead scheduling constraint as a constraint condition of a day-ahead scheduling objective function based on the wind-solar output model, the updated first energy conversion model, and the updated second energy conversion model, and solve an optimal solution for the day-ahead scheduling objective function to obtain a day-ahead scheduling plan; the day-ahead scheduling constraints comprise operation constraints of an electric hydrogen production energy storage system, operation constraints of a pumped storage power station, wind-solar output constraints and operation constraints of a new energy system; and the day-ahead scheduling objective function is constructed by maximizing the output electric quantity of the new energy system.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.

9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.