CN112615387B

CN112615387B - Energy storage capacity configuration method, device, computer equipment and readable storage medium

Info

Publication number: CN112615387B
Application number: CN202011519177.4A
Authority: CN
Inventors: 鲁宗相; 乔颖; 李梓丘; 马慧远
Original assignee: Tsinghua University; State Grid Corp of China SGCC; State Grid Beijing Electric Power Co Ltd
Current assignee: Tsinghua University; State Grid Corp of China SGCC; State Grid Beijing Electric Power Co Ltd
Priority date: 2020-12-21
Filing date: 2020-12-21
Publication date: 2023-06-23
Anticipated expiration: 2040-12-21
Also published as: CN112615387A

Abstract

The application relates to an energy storage capacity configuration method, an energy storage capacity configuration device, computer equipment and a readable storage medium. In the energy storage capacity configuration method, a comprehensive benefit optimization function of the energy storage system is obtained according to power supply benefits of the energy storage system which is operated in a typical cycle, hydrogen supply benefits of hydrogen load, power supply benefits of offshore oil field load, hydrogen supply benefits of gas turbines in offshore oil fields and construction and maintenance cost. The capacity configuration optimization model includes a comprehensive revenue optimization function and capacity configuration constraints. The energy storage capacity configuration method obtains capacity configuration information of the energy storage system by solving a capacity configuration optimization model of the energy storage system, and configures the capacity in the energy storage system according to the capacity configuration information so as to maximize benefits. In addition, the energy storage capacity allocation method fully considers the influence of offshore oil field load and the power part of the gas turbine in the offshore oil field, and the capacity allocation of the energy storage system is better.

Description

Energy storage capacity configuration method, device, computer equipment and readable storage medium

Technical Field

The present disclosure relates to the field of power technologies, and in particular, to a method and apparatus for configuring an energy storage capacity, a computer device, and a readable storage medium.

Background

The offshore wind power-hydrogen energy system takes an offshore wind power cluster as a core, takes a light hydrogen system for manufacturing and storing of an offshore platform as a flexible adjusting unit, and can continuously provide electric energy/hydrogen energy to offshore industry nearby. After flexible regulation and control, the on-site maximum utilization of the offshore wind energy resource is realized, and the abundant wind energy can be injected into an onshore power grid in a friendly mode. How to perform capacity allocation in an offshore wind power-hydrogen energy system to maximize the benefits is a problem to be solved.

Disclosure of Invention

Accordingly, it is necessary to provide an energy storage capacity allocation method, an apparatus, a computer device, and a readable storage medium, in order to solve the problem of how to maximize the benefit of capacity allocation in an offshore wind power-hydrogen energy system.

An energy storage capacity configuration method, comprising:

obtaining power supply benefits to a power grid, hydrogen load power supply benefits, offshore oilfield load power supply benefits, hydrogen supply benefits to gas turbines in offshore oil fields and construction maintenance costs of the energy storage system which are operated typically, and obtaining a comprehensive benefit optimization function of the energy storage system according to the power supply benefits to the power grid, the hydrogen load power supply benefits, the offshore oilfield load power supply benefits, the hydrogen supply benefits to the gas turbines in offshore oil fields and the construction maintenance costs of the energy storage system.

And obtaining the capacity configuration constraint condition of the energy storage system. And obtaining a capacity configuration optimization model of the energy storage system according to the comprehensive benefit optimization function and the capacity configuration constraint condition.

And solving a capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system.

And configuring the capacity in the energy storage system according to the capacity configuration information.

In one embodiment, the step of obtaining a comprehensive benefit optimization function of the energy storage system based on power supply benefits to the grid, hydrogen supply benefits to the hydrogen load, power supply benefits to the offshore oilfield load, hydrogen supply benefits to gas turbines in the offshore oilfield, and construction maintenance costs of the energy storage system for a typical weekly operation comprises:

the annual operating gain of the energy storage system is derived from the grid power supply gain, the hydrogen load power supply gain, the offshore oilfield load power supply gain, and the gas turbine power supply gain for the offshore oilfield for typical weekly operation.

And obtaining the comprehensive benefit optimization function according to the annual average operation benefit and the construction maintenance cost.

In one embodiment, the step of deriving the annual average operating benefit of the energy storage system from the grid power supply benefit, the hydrogen load power supply benefit, the offshore oilfield load power supply benefit, and the gas turbine power supply benefit in the offshore oilfield for a typical weekly operation comprises:

Maximum operating revenue is derived from the grid power revenue, the hydrogen load power revenue, the offshore oilfield load power revenue, and the gas turbine power revenue for the offshore oilfield operation of the energy storage system at a typical cycle.

And obtaining the annual average operation benefit according to the maximum operation benefit.

In one embodiment, the capacity configuration constraint includes: an offshore wind power active balance constraint formula, an offshore oilfield load active balance constraint formula, a gas turbine maximum output constraint formula, a gas turbine minimum output constraint formula, a hot standby constraint formula, a gas turbine fuel constraint formula, an electrolytic cell power constraint formula, a fuel cell power constraint formula, an electrolytic cell and fuel cell efficiency constraint formula, a hydrogen load demand constraint formula or a gas storage tank volume constraint formula.

In one embodiment, the energy storage capacity configuration method further includes:

and obtaining the power supply income of the power grid according to the offshore wind power direct internet surfing power, the fuel cell power generation internet surfing power, the internet surfing electricity price and the power transmission loss coefficient.

And obtaining the hydrogen supply benefits for the hydrogen load according to the hydrogen amount of the hydrogen load and the selling price of the hydrogen.

and obtaining the power supply income of offshore oilfield loads according to offshore wind power supply offshore load power, offshore load power supply of fuel cells and offshore load electricity price.

obtaining the hydrogen supply benefit of the gas turbine in the offshore oil field according to the hydrogen amount supplied to the gas turbine, the selling price of the natural gas and the ratio of the unit volume heat value of the hydrogen to the natural gas.

In one embodiment, the step of solving the capacity configuration optimization model of the energy storage system with the capacity configuration information as a target, to obtain the capacity configuration information of the energy storage system includes:

and solving a capacity configuration optimization model of the energy storage system by adopting a particle swarm algorithm with the capacity configuration information as a target to obtain optimal capacity configuration information of the energy storage system, and taking the optimal capacity configuration information as the capacity configuration information of the energy storage system.

An energy storage capacity configuration device comprises a first acquisition module, a second acquisition module, a solving module and a configuration module.

The first obtaining module is used for obtaining power supply income of the energy storage system to the power grid, hydrogen load supply income, offshore oil field load supply income, hydrogen supply income of the gas turbine in the offshore oil field and construction maintenance cost, and obtaining a comprehensive income optimization function of the energy storage system according to the power supply income of the energy storage system to the power grid, the hydrogen load supply income of the energy storage system, the offshore oil field load supply income, the hydrogen supply income of the gas turbine in the offshore oil field and the construction maintenance cost.

The second obtaining module is used for obtaining the capacity configuration constraint condition of the energy storage system and obtaining a capacity configuration optimization model of the energy storage system according to the comprehensive benefit optimization function and the capacity configuration constraint condition.

The solving module is used for solving the capacity configuration optimization model of the energy storage system with the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system.

The configuration module is used for configuring the capacity of the energy storage system according to the capacity configuration information.

A computer device comprising a memory, a processor, the memory having stored thereon a computer program executable on the processor, the processor implementing the steps of the energy storage capacity configuration method according to any of the embodiments described above when the computer program is executed.

A readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the energy storage capacity configuration method as described in any of the embodiments above.

According to the energy storage capacity configuration method, a comprehensive benefit optimization function of the energy storage system is obtained according to power supply benefits to a power grid, hydrogen supply benefits to hydrogen loads, power supply benefits to offshore oil field loads, hydrogen supply benefits to gas turbines in offshore oil fields and construction maintenance cost of the energy storage system which is operated in a typical cycle. The capacity configuration optimization model includes the comprehensive revenue optimization function and the capacity configuration constraints. The energy storage capacity configuration method obtains capacity configuration information of the energy storage system by solving a capacity configuration optimization model of the energy storage system, and configures the capacity in the energy storage system according to the capacity configuration information so as to maximize benefits. In addition, the energy storage capacity allocation method fully considers the influence of offshore oilfield load and a power part of a gas turbine in offshore oilfield, and the capacity allocation of the energy storage system is better.

Drawings

In order to more clearly illustrate the technical solutions of embodiments or conventional techniques of the present application, the drawings required for the descriptions of the embodiments or conventional techniques will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.

FIG. 1 is a flow chart of the energy storage capacity configuration method provided in one embodiment of the present application;

FIG. 2 is a flow chart of the energy storage capacity configuration method provided in one embodiment of the present application;

FIG. 3 is a flow chart of the energy storage capacity configuration method provided in one embodiment of the present application;

FIG. 4 is a flow chart of the particle swarm algorithm provided in an embodiment of the present application;

FIG. 5 is a graph of the distribution of hydrogen energy of the energy storage capacity configuration method provided in one embodiment of the present application;

fig. 6 is a parameter diagram used for configuration optimization of the energy storage capacity configuration method according to an embodiment of the present application.

Detailed Description

In order to make the above objects, features and advantages of the present application more comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is, however, susceptible of embodiment in many other ways than those herein described and similar modifications can be made by those skilled in the art without departing from the spirit of the application, and therefore the application is not limited to the specific embodiments disclosed below.

The numbering of the components itself, e.g. "first", "second", etc., is used herein only to divide the objects described, and does not have any sequential or technical meaning. The terms "coupled" and "connected," as used herein, are intended to encompass both direct and indirect coupling (coupling), unless otherwise indicated. In the description of the present application, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," etc. indicate or refer to an orientation or positional relationship based on that shown in the drawings, merely for convenience of description and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present application.

In this application, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.

Referring to fig. 1, an embodiment of the present application provides a method for configuring an energy storage capacity, including:

s100, obtaining power supply benefits to a power grid, hydrogen load supply benefits, offshore oilfield load supply benefits, hydrogen supply benefits to gas turbines in offshore oilfields and construction maintenance costs of the energy storage system which are operated in a typical cycle, and obtaining a comprehensive benefit optimization function of the energy storage system according to the power supply benefits to the power grid, the hydrogen load supply benefits, the offshore oilfield load supply benefits, the hydrogen supply benefits to the gas turbines in the offshore oilfield and the construction maintenance costs of the energy storage system.

The energy storage system comprises an offshore wind power-hydrogen energy system. The offshore wind power-hydrogen energy system comprises an offshore wind power system, an electrolytic tank, a hydrogen storage tank, a fuel cell, a gas turbine, offshore load and other equipment.

Offshore wind power systems are used to convert wind energy into electrical energy. The electrolyzer is used for converting electric energy into hydrogen energy. The hydrogen storage tank is used for storing hydrogen energy. Fuel cells are used to convert hydrogen energy into electrical energy. Gas turbines are used to convert hydrogen into electrical energy. The offshore load uses the electrical energy to do work.

The electric energy generated by the offshore wind power system is used for being directly connected to a power grid, outputting to an electrolytic tank or outputting to an offshore load.

The hydrogen energy produced by the electrolyzer may be stored in a hydrogen storage tank, output to a hydrogen load, output to a fuel cell or gas turbine. The hydrogen load refers to the direct selling of hydrogen through a pipeline, but can also be other loads consuming hydrogen (excluding fuel cells and gas turbines).

The power supply income of the power grid refers to the income of a wind power system or a fuel cell in an energy storage system for supplying power to the power grid. The benefit of supplying hydrogen to the hydrogen load refers to the benefit of supplying hydrogen to the hydrogen load by an electrolytic tank or a hydrogen storage tank in the energy storage system. The power supply income of offshore oilfield loads refers to the income of a wind power system or a fuel cell in an energy storage system for supplying power to a power grid. The hydrogen supply benefit to the gas turbine in the offshore oil field refers to the benefit of the hydrogen storage tank for supplying hydrogen to the gas turbine.

In one embodiment, the construction maintenance costs include an annual average investment cost and an annual average operation maintenance cost of the energy storage system.

The wind power output (offshore wind power output) of a typical week and the offshore oilfield load profile of a typical week can represent annual wind power output and offshore oilfield load conditions, respectively. A typical circumference may be one or more.

S200, obtaining capacity configuration constraint conditions of the energy storage system. And obtaining a capacity configuration optimization model of the energy storage system according to the comprehensive benefit optimization function and the capacity configuration constraint condition.

And S300, solving a capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system.

In one embodiment, the energy storage system includes an electrolyzer, a hydrogen storage tank, and a fuel cell, and the capacity configuration information of the energy storage system includes capacity information of the electrolyzer, capacity information of the hydrogen storage tank, and capacity information of the fuel cell.

In one embodiment, the capacity information of the electrolyzer comprises a maximum power of the electrolyzer. The capacity information of the hydrogen storage tank includes a maximum volume of the hydrogen storage tank. The capacity information of the fuel cell includes a maximum power of the fuel cell.

S400, configuring the capacity in the energy storage system according to the capacity configuration information.

The step of configuring the capacity in the energy storage system according to the capacity configuration information comprises the step of selecting the electrolytic cell, the hydrogen storage tank and the fuel cell under the capacity information of the hydrogen storage tank to configure the energy system.

According to the energy storage capacity configuration method, a comprehensive benefit optimization function of the energy storage system is obtained according to power supply benefits of the energy storage system, hydrogen supply benefits of the hydrogen load, power supply benefits of the offshore oilfield load, hydrogen supply benefits of the gas turbine in the offshore oilfield and the construction maintenance cost, wherein the power supply benefits of the energy storage system are obtained according to typical weekly operation. The capacity configuration optimization model includes the comprehensive revenue optimization function and the capacity configuration constraints. The energy storage capacity configuration method obtains capacity configuration information of the energy storage system by solving a capacity configuration optimization model of the energy storage system, and obtains optimized capacity configuration information.

In addition, the energy storage capacity allocation method fully considers the influence of offshore oilfield load and a power part of a gas turbine in offshore oilfield, and the capacity allocation of the energy storage system is better.

Referring also to fig. 2, in one embodiment, the step of obtaining a comprehensive benefit optimization function of the energy storage system in step S100 from power grid power benefits, hydrogen load power benefits, offshore oilfield load power benefits, gas turbine power benefits and construction maintenance costs of the energy storage system for typical weekly operations includes:

s110, obtaining annual average operation benefits of the energy storage system according to the power supply benefits to the power grid, the hydrogen load power supply benefits to the hydrogen load, the power supply benefits to the offshore oil field load and the hydrogen supply benefits to the gas turbines in the offshore oil field, which are operated in a typical cycle.

And S120, obtaining the comprehensive benefit optimization function according to the annual average operation benefit and the construction maintenance cost.

The comprehensive benefit optimization function is as follows:

R＝max(Z-AC _CAP -C _OM )

wherein R is the maximum annual average net benefit. Z is annual average operation income of the energy storage system, and is obtained through operation optimization.

AC _CAP Is the annual average investment cost of the system. C (C) _OM Is the annual operating maintenance cost of the system.

Table 1 shows the meanings of the symbols related to the present application.

TABLE 1

The initial investment cost of the energy storage system is as follows:

wherein C is _i (i=1, 2, 3) is the initial investment costs of the electrolyzer, the fuel cell, and the hydrogen storage tank, respectively. C (C) ₁ Is the initial investment cost of the electrolytic tank. C (C) ₂ Is the initial investment cost of the fuel cell. C (C) ₃ Is the initial investment cost of the hydrogen storage tank. C (C) _CAP For the initial investment costs (costs invested in construction period).

The annual average investment cost is respectively as follows:

r is the annual percentage of the age, and 0.05 is taken; m is the operation period, which is 20 years.

C ₁ ＝P _elymax e _ely

C ₂ ＝P _fcmax e _fc

C ₃ ＝P _tanmax e _tan

The annual average operation maintenance cost is as follows:

C _OM ＝aC _ely +bC _fc +cC _tan

wherein a is the annual average operation maintenance cost of the electrolytic cell accounting for the percentage of the initial investment; b is the annual operating maintenance cost of the fuel cell accounting for the percentage of the initial investment; c is the annual operating maintenance cost of the hydrogen storage tank accounting for the percentage of the initial investment.

In one embodiment, a, b and c are all 0.05.

Referring also to fig. 3, in one embodiment, the step of obtaining annual average operating returns of the energy storage system from the grid power supply returns, the hydrogen load hydrogen supply returns, the offshore oilfield load power supply returns, and the gas turbine hydrogen supply returns for the offshore oilfield in S110 for a typical weekly operation includes:

And S111, optimizing the operation of the energy storage system in a typical week to obtain the power supply benefit to the power grid, the hydrogen supply benefit to the hydrogen load, the power supply benefit to the offshore oil field load and the hydrogen supply benefit to the gas turbine in the offshore oil field, thereby obtaining the maximum operation benefit.

The formula for maximum operating benefit for a typical week is:

wherein n represents the number of typical weeks. In (In) _n Representing a maximum weekly operational benefit of the plurality of weekly operational benefits for the nth week for the capacity configuration of the set of energy storage systems. K represents the total revenue of the capacity configuration of a set of energy storage systems for N weeks of operation.

The objective function of the weekly operation returns is:

where T is the time point, one point is taken every 1 hour, and a total of t=168 points are counted for one week. In (In) _n A plurality of Zhou Yun representing a capacity configuration of a set of energy storage systems corresponding to week nThe largest of the row benefits is the largest week run benefit. Because of the multiple parameters and the multiple boundary conditions, the multiple parameters can take different parameter values in the boundary conditions to obtain multiple parameter groups. Corresponding to capacity configuration of a group of energy storage systems, a plurality of parameter groups can obtain a plurality of weekly operation benefits after being brought to the right of the equal sign of a formula, in _n Representing the maximum value In the benefits of multiple weeks of operation, and obtaining In through optimization _n 。

And S112, obtaining the annual average operation benefit according to the maximum operation benefit.

The annual average operation income formula is as follows:

where K represents the sum of the maximum operating total gains for a plurality of typical cycles for the capacity configuration of the energy storage system of a group. Z is the annual operating gain.

The step of S111 includes:

s1111, obtaining power supply benefits to the power grid, hydrogen supply benefits to the hydrogen load, power supply benefits to offshore oil field load, hydrogen supply benefits to gas turbines in the offshore oil field and capacity configuration constraint conditions of the energy storage system, which correspond to capacity configuration information of a group of energy storage systems and are operated every typical cycle;

s1112, obtaining a formula of maximum operation benefit brought into the weekly operation benefit according to the power supply benefit to the power grid, the hydrogen load power supply benefit to the hydrogen load, the offshore oilfield load power supply benefit and the offshore oilfield gas turbine hydrogen supply benefit corresponding to each typical week, and obtaining the weekly operation benefit of each typical week;

and S1113, superposing the week operation profits of each typical week to obtain the operation total profits.

And step 1114, obtaining a plurality of parameter sets according to the capacity configuration constraint condition of the energy storage system, executing the step 1111-step 1113 corresponding to each set of parameters to respectively calculate a plurality of running total benefits corresponding to the capacity configuration information of the energy storage system, and obtaining the maximum value in the running total benefits corresponding to the capacity configuration of the energy storage system, namely the maximum running benefit K of the typical week corresponding to the capacity configuration of the energy storage system.

S1114 can be solved by a GUROBI solver, and after the step S112, the energy storage capacity configuration method further comprises:

and according to the annual average operation income formula, configuring a corresponding annual average operation income formula of the capacity configuration of the group of energy storage systems. The capacity configuration of the multiple sets of energy storage systems corresponds to multiple annual operating benefits. And then the net gains of a plurality of years can be obtained according to the comprehensive gain optimization function.

And taking the maximum value of the annual average net benefits, namely the maximum annual average net benefit R, and finding the capacity configuration of a group of energy storage systems corresponding to the maximum annual average net benefit R.

In one embodiment, the capacity configuration constraint includes: an offshore wind power active balance constraint formula, an offshore oilfield load active balance constraint formula, a gas turbine maximum output constraint formula, a gas turbine minimum output constraint formula, a hot standby constraint formula, a gas turbine fuel constraint formula, an electrolytic cell power constraint formula, a fuel cell power constraint formula, an electrolytic cell and fuel cell efficiency constraint formula, a hydrogen load demand constraint formula or a gas storage tank volume constraint formula. The capacity configuration constraint condition is a boundary condition and is used for ensuring that the energy storage system can normally operate.

The constraints are as follows:

(1) Active balance constraint of offshore wind power

P _wnet，t +P _ely，t +P _wload，t ＝P _w，t

(2) Active balance constraint for offshore oilfield load

The total output (total power) of the N gas turbines cannot be greater than the demand of offshore oilfield loads, in order to prevent the unreasonable situation that the gas turbines generate electricity to be sent out to the shore.

(3) Maximum and minimum output constraint for gas turbine

I _i，t P _gtmin，i ≤P _gti，t ≤I _i，t P _gtmax，i

I _i，t When=1, it represents that the gas turbine is started in the t period; i _i，t When=0, it represents that the gas turbine is shut down during the period t.

(4) Hot standby constraint

The total standby capacity of the system is provided by the gas turbine (h takes 0.05):

(5) Fuel containment for gas turbine

To the right of equation (5) is the volume of natural gas converted from hydrogen consumed by the gas turbine at time t.

(6) Cell power constraint

m _t P _elymin ≤P _ely,t ≤P _elymax m _t

P in the formula _elymin ＝0.15P _elymax ；m _t =1 represents cell start-up; m is m _t =0 represents cell shutdown.

(7) Fuel cell power constraint

0≤P _fc1,t ≤P _fcmax

0≤P _fc2,t ≤P _fcmax

P _fc1,t +P _fc2,t ≤P _fcmax

(8) Efficiency constraints for electrolysis cells and fuel cells

P _ely,t ＝η _ely V _he,t

P _fc1,t ＝η _fc V _hf1,t

P _fc2,t ＝η _fc V _hf2,t

(9) Hydrogen load demand constraints

0≤V _hsell,t ≤V _hload,t

(10) Volume constraint of gas storage tank

V _h,t+1 ＝V _h,t +V _he,t Δt-V _hf1,t Δt-V _hf2,t Δt-V _hgt,t Δt-V _hsell,t Δt

0≤V _h,t ≤V _hmax

s010, obtaining the power supply income of the power grid according to the offshore wind power direct internet surfing power, the fuel cell power generation internet surfing power, the internet surfing electricity price and the power transmission loss coefficient.

The formula of the power supply income of the power grid is as follows:

t is expressed as time point, one point is taken every 1 hour, and a total of t=168 points is counted for one week.

s020, obtaining the hydrogen supply income to the hydrogen load according to the hydrogen amount of the hydrogen load and the selling price of the hydrogen.

The formula for supplying hydrogen to hydrogen load is as follows:

and S030, obtaining the power supply income of offshore oilfield loads according to offshore wind power supply offshore load power, offshore load power supply by a fuel cell and offshore load electricity price supply.

The formula for supplying power to offshore oilfield loads is as follows:

s040, obtaining the hydrogen supply income of the gas turbine in the offshore oil field according to the hydrogen amount supplied to the gas turbine, the selling price of the natural gas and the ratio of the unit volume heat value of the hydrogen to the natural gas.

The formula of hydrogen supply benefits to the gas turbine in the offshore oil field is as follows:

wherein mu _htc About 10.659/35.544; mu (mu) _loss Taking 0.04; k (K) _e2 ＝0.35K _c 。

In one embodiment, the step of solving the capacity configuration optimization model of the energy storage system with the capacity configuration information as a target in S300 to obtain the capacity configuration information of the energy storage system includes:

The core of the particle swarm algorithm for solving the optimal solution can be characterized as a process for searching the optimal solution through cooperation and information sharing among individuals in the population.

Referring also to FIG. 4, in one embodiment, a method ofIn the process of solving the optimal solution by the particle swarm algorithm, input target parameters comprise: the capacity information x1 of the electrolytic cell, the capacity information x2 of the fuel cell, and the capacity information x3 of the hydrogen storage tank. In this example, m groups are used. And various costs can be obtained according to the capacity. The cost includes: annual average investment cost of electrolyzer C1, annual average investment cost of fuel cell C2, annual average investment cost of hydrogen storage tank C3, total annual average operation maintenance cost C _OM . M groups x1, x2, x3 are respectively input into the lower optimization model. The input auxiliary parameters include: inputting hydrogen selling price, internet surfing electricity price, offshore load electricity price and natural gas selling price; wind power output at each typical week (corresponding to P _w,t Different from week to week) and offshore field load curves for each typical week; hydrogen production efficiency of the electrolytic cell and power generation efficiency of the fuel cell. These values may also be constant values when the energy storage system determines.

The process of solving the optimal solution by adopting the particle swarm algorithm comprises the following steps:

s1, inputting a group of capacity configuration information into a particle swarm algorithm model to obtain the maximum operation benefit of a typical cycle.

S2, obtaining annual average operation benefits according to the maximum operation benefits.

S3, obtaining annual average net benefit according to annual average operation benefit, annual average operation maintenance cost and annual average investment cost.

And S4, calculating a plurality of groups of capacity configuration information according to the S1-S3 to obtain a plurality of annual average net benefits, and obtaining the maximum value of the annual average net benefits to obtain the maximum annual average net benefits.

The process of solving the optimal solution by adopting the particle swarm optimization comprises two-stage optimization: optimizing corresponding to a set of capacity configuration information obtains the maximum operation benefit of a typical week. And optimizing corresponding to multiple groups of capacity configuration information to obtain the maximum annual average net benefit.

After the process of solving the optimal solution by the particle swarm algorithm is finished, the obtained output result can be the optimal capacity configured by the energy storage system, namely the capacity of the electrolytic tank, the capacity of the hydrogen storage tank and the capacity of the fuel cell.

The process of solving the optimal solution is actually a unit combination problem, and the solution is performed by using a Gurobi solver. The cycle operation profits of each typical cycle can be obtained corresponding to a set of target parameters (capacity information of the electrolytic tank, capacity information of the hydrogen storage tank and capacity information of the fuel cell), and thus, the total operation profits of N typical cycles can be obtained.

In one embodiment, the particle swarm algorithm further comprises:

n weeks of profit were equivalent to 1 year profit, and the cost was combined to get m groups of annual average net profits.

And taking the annual average net profit as an adaptive value of the particle swarm, and judging whether the optimal adaptive value meets the precision condition.

And outputting the optimal capacity if the precision condition is met. If not, the individual extremum and the population extremum are updated and the position and velocity of all particles are updated.

The parameters of the specific settings of the particle swarm algorithm include: the particle swarm size, the particle swarm value range, the maximum iteration algebra, the maximum circulation times and the accuracy conditions.

In a specific embodiment, the particle population is sized to 25, i.e., 25 (x 1, x2, x 3) capacity in a generation. The size of the particle swarm affects the accuracy of the speed of the calculation convergence. Too large a scale is too slow to calculate, too small a scale converges without being necessarily accurate, and this value is chosen empirically.

The range of values of the particle swarm refers to the range of values of x1, x2 and x 3. The range of values of the particle swarm influences whether to converge or not and the speed of convergence. If the optimal value is not within the range of values, the optimization result will appear on the boundary. The smaller the value range is, the faster the convergence speed is. This range is also chosen mainly by manual experience.

The maximum iteration algebra is set to 30. The calculation was stopped by looping to 30 generations. The choice of the maximum iteration algebra is mainly related to the complexity of the problem, and in the above embodiment, the accuracy can be achieved within 30 generations of the problem.

And the maximum circulation times refer to how many successive generations of calculation are carried out, when the overall optimal value change is smaller than the precision condition, the calculation is stopped after convergence is indicated. In one embodiment, set to 8, the calculation is stopped when 8 consecutive generations of optimal fitness value change are less than the precision condition.

The accuracy condition is related to the value of an adaptive value function, the order of magnitude of the adaptive value in this problem being 10 ⁷ The accuracy was set to 1000. I.e. the continuous 8 generations of optimal adaptation value changes less than one thousandth, the problem converges.

The particle swarm algorithm does not need to be manually updated, and the parameters automatically updated by the function comprise individual extremum, total extremum and the position and speed of particles:

individual extremum: optimal fitness (annual net profit) in different algebra for each particle.

All extremum: optimal adaptation values in different algebra of 25 particles.

Position and velocity of particles: iterative updating of the value of each particle (x 1, x2, x 3) is achieved, bringing (x 1, x2, x 3) towards the extremum position.

It should be understood that, although the steps in the flowchart of fig. 4 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in FIG. 4 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed need to be sequential, but may be performed in turn or alternately with at least some of the other steps or sub-steps of other steps. For specific limitations of the energy storage capacity configuration device, reference may be made to the above limitation of the energy storage capacity configuration method, and no further description is given here. The modules in the energy storage capacity configuration device in the computer equipment can be fully or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, the operation and configuration optimization method of the offshore wind power-hydrogen energy combined system is verified by utilizing the output and load data of a certain wind power plant, and the specific application method is as follows:

the online electricity price and gas turbine parameters are the same in the operation and configuration optimization. Internet electricity price K _e1,t Peak to valley electricity prices were used as shown in table 2.

TABLE 2

Type(s)	Time period of	Electricity price (Yuan/kWh)
			Peak time	8:00-12:00，15:00-22:00	0.891
At ordinary times	6:00-8:00，12:00-15:00，22:00-23:00	0.510
			Gu Shi	23:00-24:00，0:00-6:00	0.150

The parameters of 5 gas turbines used as backup for offshore loads are shown in Table 3.

TABLE 3 Table 3

Gas turbine numbering	P _gtmin,i (MW)	P _gtmax,i (MW)	b _i (Nm ³ /MWh)	c _i (Nm ³ /h)
					1-3	3	10.5	320	152.6
4-5	1	5	343	130.7

(1) Offshore wind power-hydrogen energy combined system operation optimization

The other parameters of the run optimization are shown in table 4. Hydrogen demand V _hload,t Set to 24000Nm ³ On day, they were sold at 24 points per day.

TABLE 4 Table 4

Parameters (parameters)	Setting value	Parameters (parameters)	Setting value
				η _ely (kwh/Nm ³ )	4.82	P _elymax (MW)	60
η _fc (kwh/Nm ³ )	1.6	P _fcmax (MW)	20
				K _c (Yuan/Nm) ³ )	3	V _hmax (Nm ³ )	20*1000/0.089
K _h (Yuan/Nm) ³ )	4	K _e2 (Yuan/kWh)	1.05

The power of the electrolytic cell, the fuel cell and the gas turbine in the finally optimized system is shown in fig. 1, and the distribution of hydrogen energy is shown in fig. 5. The parameters adopted for the configuration optimization of the energy storage capacity configuration method are shown in fig. 6. The hydrogen for the gas turbine in fig. 6 is 0. However, when the price of hydrogen is low and direct sales are not cost effective, hydrogen is distributed to the gas turbine. Another situation is that the price of natural gas becomes high, it is not cost-effective for the gas turbine to burn natural gas, and hydrogen is also distributed to the gas turbine.

In a typical week, the operating gain of the system after addition of the electrolyzer-hydrogen storage tank-fuel cell was increased by 75.02 ten thousand yuan compared to the original system.

(2) Offshore wind power-hydrogen energy combined system configuration optimization

The parameters used for configuration optimization are shown in table 5. N is taken as 2, and one big wind week and one small wind week are utilized to replace annual income. The final configuration optimization result is 11.4MW for electrolyzer, 0MW for fuel cell, and 2.6t for hydrogen storage tank.

TABLE 5

Parameters (parameters)	Setting value	Parameters (parameters)	Setting value
				e _ely (Yuan/kw)	8000	η _ely (kwh/Nm ³ )	4.82
e _fc (Yuan/kw)	9000	η _fc (kwh/Nm ³ )	1.6
				e _tan (Yuan/kg)	4800	V _h,load (Nm ³ Day/sky	30000
K _h (Yuan/Nm) ³ )	4	K _c (Yuan/Nm) ³ )	3

The embodiment of the application provides an energy storage capacity configuration device, which comprises a first acquisition module, a second acquisition module, a solving module and a configuration module.

The first obtaining module is used for obtaining power supply income of the energy storage system to the power grid, hydrogen load supply income, offshore oil field load supply income, hydrogen supply income of a gas turbine in an offshore oil field and construction maintenance cost, and obtaining a comprehensive income optimization function of the energy storage system according to the power supply income of the energy storage system to the power grid, the hydrogen load supply income of the energy storage system, the offshore oil field load supply income, the hydrogen supply income of the gas turbine in the offshore oil field and the construction maintenance cost.

In one embodiment, the first acquisition module includes a first acquisition sub-module, a second acquisition sub-module, and a third acquisition sub-module.

The first acquisition submodule is used for obtaining annual operation benefits of the energy storage system according to the power supply benefits to the power grid, the hydrogen load power supply benefits to the hydrogen load, the offshore oil field load power supply benefits and the gas turbine hydrogen supply benefits in the offshore oil field, which are operated in a typical cycle. And obtaining the comprehensive benefit optimization function according to the annual average operation benefit and the construction maintenance cost.

The second obtaining sub-module is used for obtaining the comprehensive benefit optimization function according to the annual average operation benefit and the construction maintenance cost.

The third obtaining submodule is used for obtaining a capacity configuration constraint condition of the energy storage system according to the capacity configuration of the energy storage system and an operation coefficient of the energy storage system in the operation process.

In one embodiment, the first acquisition submodule includes a first step module and a second step module.

The first step module is configured to obtain a cycle operation benefit based on the power grid supply benefit, the hydrogen load supply benefit, the offshore oilfield load supply benefit, and the gas turbine supply benefit in the offshore oilfield for a typical cycle operation.

The second step module is used for obtaining the annual average operation benefit according to the weekly operation benefit.

In one embodiment, the energy storage capacity configuration device further comprises a first benefit module, a second benefit module, a third benefit module, and a fourth benefit module.

The first income module is used for obtaining the income of supplying power to the power grid according to the offshore wind power direct internet surfing power, the fuel cell power generation internet surfing power, the internet surfing electricity price and the power transmission loss coefficient.

The second benefit module is used for obtaining the hydrogen supply benefit to the hydrogen load according to the hydrogen amount of the hydrogen load and the selling price of the hydrogen.

The third profit module is used for obtaining the hydrogen supply profit of the gas turbine in the offshore oil field according to offshore wind power offshore load power supply, offshore load power supply of the fuel cell and offshore load electricity price supply.

The fourth benefit module is used for obtaining the hydrogen supply benefit of the gas turbine in the offshore oil field according to the hydrogen supply load, the selling price of the natural gas and the ratio of the unit volume heat value of the hydrogen and the natural gas.

In one embodiment, the solving module is configured to solve a capacity configuration optimization model of the energy storage system by using a machine learning algorithm with capacity configuration information as a target, so as to obtain capacity configuration information of the energy storage system.

In one embodiment, the solving module is configured to solve a capacity configuration optimization model of the energy storage system by using a particle swarm algorithm with capacity configuration information as a target, obtain optimal capacity configuration information of the energy storage system, and use the optimal capacity configuration information as capacity configuration information of the energy storage system.

The embodiment of the application provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor realizes the steps of the energy storage capacity configuration method according to any embodiment when executing the computer program.

The computer device includes a processor, a memory, a network interface, a display screen, and an input system 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 readable storage medium, an internal memory. The non-volatile readable storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile readable storage media. 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 energy storage capacity configuration. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

The processor, when executing the computer program, performs the steps of:

obtaining power supply income of the energy storage system to the power grid, hydrogen load supply income, offshore oilfield load supply income, gas turbine supply hydrogen income in an offshore oilfield and construction maintenance cost, and obtaining a comprehensive income optimization function of the energy storage system according to the power supply income of the energy storage system to the power grid, the hydrogen load supply income, the offshore oilfield load supply income, the gas turbine supply hydrogen income in the offshore oilfield and the construction maintenance cost.

And acquiring a capacity configuration constraint condition of the energy storage system, and acquiring a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint condition.

An embodiment of the present application provides a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the energy storage capacity configuration method as described in any of the embodiments above.

The computer program when executed by a processor performs the steps of:

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples merely represent a few embodiments of the present application and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.

Claims

1. An energy storage capacity configuration method, comprising:

obtaining power supply benefits to a power grid, hydrogen load power supply benefits, offshore oilfield load power supply benefits, hydrogen supply benefits to gas turbines in offshore oil fields and construction maintenance costs of a typical weekly operation energy storage system, and obtaining a comprehensive benefit optimization function of the energy storage system according to the power supply benefits to the power grid, the hydrogen load power supply benefits, the offshore oilfield load power supply benefits, the hydrogen supply benefits to the gas turbines in the offshore oil fields and the construction maintenance costs of the typical weekly operation energy storage system;

Wherein, the formula of the maximum operation gain of the typical week is as follows:

in the above formula, n is the number of typical weeks, in _n Configuring the maximum weekly operation benefit in a plurality of weekly operation benefits corresponding to the nth week for the capacity of the group of energy storage systems, wherein K is the total operation benefit corresponding to the N weeks for the capacity configuration of the group of energy storage systems;

the objective function of the weekly operation returns is:

in the above formula, t is the time point, in _n Representing the maximum weekly operational benefit of the plurality of weekly operational benefits of a set of energy storage systems, in _n Represents the maximum value in the benefits of multiple weeks of operation, R _e1，t For the total power supply income of the power grid at the t time point, R _h1,t For the total gain of hydrogen supply to the hydrogen load at time t, R _e2,t For the total income of supplying power to the offshore load at the t-th time point, R _h2,t Providing total hydrogen benefit to offshore oil fields at a t-th time point;

the annual average operating income formula is:

wherein, K represents the sum of the maximum operation total benefits of the capacity configuration of the energy storage system of a group corresponding to a plurality of typical weeks, and Z is the annual average operation benefit;

the formula of the power supply income of the power grid is as follows:

in the above formula, t is the time point, R _e1，t Total power supply benefit for power grid at t time point, P _wnet，t Direct internet power for offshore wind power, P _fc1，t Power for fuel cell power generation _loss Is the transmission loss coefficient;

the formula for supplying hydrogen to hydrogen load is as follows:

in the above formula, t is represented as a time point, R _h1, For the total gain of hydrogen supply to the hydrogen load at time t, V _hsell，t K for supplying the hydrogen load hydrogen quantity _h Selling price for hydrogen;

the formula for supplying power to offshore oilfield loads is as follows:

in the above, P _wload，t Supplying offshore wind power with offshore load power, P _fc2，t Supplying offshore load power, K, to a fuel cell _e2 To supply the offshore load electricity price, t is the time point;

in the above, mu _htc V is the ratio of the heating value of hydrogen to the unit volume of natural gas _hgt，t K for supplying the hydrogen to the gas turbine _c Selling price for natural gas;

acquiring capacity configuration constraint conditions of the energy storage system, and acquiring a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint conditions;

solving a capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system;

configuring the capacity in the energy storage system according to the capacity configuration information;

Wherein the energy storage system comprises an offshore wind power-hydrogen energy system; the offshore wind power-hydrogen energy system comprises an offshore wind power system, an electrolytic tank, a hydrogen storage tank, a fuel cell, a gas turbine and an offshore load; the offshore wind power system is used for converting wind energy into electric energy; the electrolytic tank is used for converting electric energy into hydrogen energy; the hydrogen storage tank is used for storing hydrogen energy; the fuel cell is used for converting hydrogen energy into electric energy; the gas turbine is used for converting hydrogen into electric energy; the offshore load uses electric energy to do work; the electric energy generated by the offshore wind power system is used for being directly connected to a power grid, outputting to an electrolytic tank or outputting to an offshore load;

the capacity configuration constraint includes: an offshore wind power active balance constraint formula, an offshore oilfield load active balance constraint formula, a gas turbine maximum output constraint formula, a gas turbine minimum output constraint formula, a hot standby constraint formula, a gas turbine fuel constraint formula, an electrolytic cell power constraint formula, a fuel cell power constraint formula, an electrolytic cell and fuel cell efficiency constraint formula, a hydrogen load demand constraint formula or a gas storage tank volume constraint formula;

the formula of the active balance constraint of the offshore wind power is as follows:

P _wnet,t +P _ely,t +P _wload,t ＝P _w,t

In the above, P _wnet,t Direct internet power for offshore wind power, P _ely,t Wind power consumption for electrolytic cell, P _wload,t Supplying offshore wind power with offshore load power, P _w,t The power is output by offshore wind power;

the offshore oilfield load active balance constraint formula is as follows:

in the above, P _gti,t For the power of the ith gas turbine at the t-th point in time, P _wload,t Supplying offshore wind power with offshore load power, P _fc1,t Power on line for fuel cell power generation, P _load,t Is an offshore oilfield load demand;

the maximum and minimum output constraint formulas of the gas turbine are as follows:

I _i,t P _gtmin,i ≤P _gti,t ≤I _i,t P _gtmax,i

in the above, I _i,t P is the start-stop variable of the gas turbine _gtmin,i For the ith gas turbine minimum power, P _gti,t Power at a t-th time point for an ith gas turbine;

the formula of the hot standby constraint is:

in the above, P _{gt max,i} Maximum output of ith gas turbine, P _gti,t For the power of the ith gas turbine at the t-th point in time, P _load,t For offshore oilfield load demand，I _i,t H is a thermal reserve coefficient, which is a start-stop variable of the gas turbine;

the formula of the fuel constraint of the gas turbine is:

in the above, V _hgt，t Mu for supplying hydrogen to the gas turbine _htc B is the ratio of the heating value of hydrogen to natural gas in unit volume _i First coefficient, P, as a function of gas consumption of the ith gas turbine _gti，t For the power of the ith gas turbine at the t-th point in time c _i Second coefficient as a function of gas consumption of the ith gas turbine, I _i，t Is a gas turbine start-stop variable;

the formula of the power constraint of the electrolytic cell is as follows:

m _t P _elymin ≤p _ely，t ≤p _elymax m _t

in the above, m _t For the start-up and shut-down coefficient of the electrolyzer, P _elymin For minimum power of operation of the electrolyzer, p _ely，t Wind power consumption for electrolytic cell, p _elymax Maximum power for the electrolyzer;

the formula of the fuel cell power constraint is:

0≤P _fc1，t ≤P _fcmax

0≤P _fc2，t ≤P _fcmax

P _fc1，t +P _fc2，t ≤P _fcmax

in the above, P _fc1，t Power on line for fuel cell power generation, P _fcmax For maximum power of fuel cell, P _fc2，t Supplying offshore load power to the fuel cell;

the formulas of the efficiency constraints of the electrolytic cell and the fuel cell are as follows:

P _ely，t ＝η _ely V _he，t

P _fc1，t ＝η _fc V _hf1，t

P _fc2，t ＝η _fc V _hf2，t

in the above, P _fc1，t Power on line for fuel cell power generation, P _fcmax For maximum power of fuel cell, P _fc2，t Supplying offshore load power, p, to a fuel cell _ely，t Wind power consumption, eta for the electrolytic cell _ely For the hydrogen production efficiency of the electrolytic cell, eta _fc For generating efficiency of fuel cell, V _he，t For the hydrogen production speed of the electrolytic tank, V _hf1，t The hydrogen consumption speed for power generation, network connection and hydrogen consumption of the fuel cell is V _hf2，t Load supply and hydrogen consumption speed for power generation of the fuel cell;

the formula of the hydrogen load demand constraint is:

0≤V _hsell，t ≤V _hlood，t

in the above, V _hsell，t To supply the hydrogen load hydrogen amount, V _hlood，t Is the hydrogen demand;

the formula of the volume constraint of the air storage tank is as follows:

V _h，t+1 ＝V _h，t +V _he，t Δt-V _hf1，t Δt-V _hf2，t Δt-V _hgt，t Δt-V _hsell，t Δt

0≤V _h，t ≤V _hmax

in the above, V _h，t V is the volume of hydrogen in the hydrogen storage tank _he，t For the hydrogen production speed of the electrolytic tank, V _hf1，t The hydrogen consumption speed for power generation, network connection and hydrogen consumption of the fuel cell is V _hf2，t Load-supplying hydrogen consumption speed for fuel cell power generation, V _hsell，t To supply the hydrogen load hydrogen amount, V _hmax Is the maximum volume of the hydrogen storage tank.

2. The energy storage capacity allocation method according to claim 1, wherein the step of obtaining the comprehensive benefit optimization function of the energy storage system from power supply benefits to the power grid, hydrogen supply benefits to the hydrogen load, power supply benefits to the offshore oilfield load, hydrogen supply benefits to the gas turbines in the offshore oilfield, and construction maintenance costs of the energy storage system for a typical cycle of operation comprises:

obtaining annual operating returns of the energy storage system from the grid supply returns for typical weekly operation, the hydrogen supply returns for hydrogen loading, the offshore oilfield loading supply returns, and the gas turbine supply returns for offshore oilfield operation;

3. The energy storage capacity allocation method according to claim 2, wherein the step of obtaining annual average operating returns of the energy storage system from the power supply returns to the power grid, the hydrogen supply returns to the hydrogen load, the power supply returns to the offshore oilfield load, and the hydrogen supply returns to the gas turbines in the offshore oilfield for typical weekly operations comprises:

Obtaining a maximum operating benefit from the grid power supply benefit, the hydrogen load power supply benefit, the offshore oilfield load power supply benefit, and the gas turbine power supply benefit in the offshore oilfield for typical weekly operations;

4. The energy storage capacity allocation method according to claim 1, further comprising:

5. The energy storage capacity allocation method according to claim 1, further comprising:

6. The energy storage capacity allocation method according to claim 1, further comprising:

7. The energy storage capacity allocation method according to claim 1, further comprising:

obtaining the hydrogen supply benefit of the gas turbine in the offshore oil field according to the hydrogen supply load, the selling price of the natural gas and the unit volume heat value ratio of the hydrogen and the natural gas.

8. The method of claim 1, wherein the step of solving a capacity configuration optimization model of the energy storage system with the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system includes:

9. The energy storage capacity allocation method according to claim 1, wherein the energy storage system includes an electrolytic cell, a hydrogen storage tank, and a fuel cell, and the capacity allocation information of the energy storage system includes capacity information of the electrolytic cell, capacity information of the hydrogen storage tank, and capacity information of the fuel cell.

10. An energy storage capacity allocation apparatus, comprising:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring power supply benefits of an energy storage system to a power grid, hydrogen load supply benefits, offshore oilfield load supply benefits, hydrogen supply benefits of a gas turbine in an offshore oilfield and construction maintenance costs, and acquiring a comprehensive benefit optimization function of the energy storage system according to the power supply benefits of the energy storage system to the power grid, the hydrogen load supply benefits, the offshore oilfield load supply benefits, the hydrogen supply benefits of the gas turbine in the offshore oilfield and the construction maintenance costs; wherein, the formula of the maximum operation gain of a typical week is:

the objective function of the weekly operation returns is:

the annual average operating income formula is:

the formula of the power supply income of the power grid is as follows:

in the above formula, t is the time point, R _e1，t Total power supply benefit for power grid at t time point, P _wnet，t Is offshore windPower to direct power to internet, P _fc1，t Power for fuel cell power generation _loss Is the transmission loss coefficient;

The formula for supplying hydrogen to hydrogen load is as follows:

in the above formula, t is represented as a time point, R _h1,t For the total gain of hydrogen supply to the hydrogen load at time t, V _hsell，t K for supplying the hydrogen load hydrogen quantity _h Selling price for hydrogen;

the formula for supplying power to offshore oilfield loads is as follows:

the second acquisition module is used for acquiring capacity configuration constraint conditions of the energy storage system and obtaining a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint conditions;

the solving module is used for solving a capacity configuration optimization model of the energy storage system with the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system;

the configuration module is used for configuring the capacity of the energy storage system according to the capacity configuration information;

Wherein the energy storage system comprises an offshore wind power-hydrogen energy system; the offshore wind power-hydrogen energy system comprises an offshore wind power system, an electrolytic tank, a hydrogen storage tank, a fuel cell, a gas turbine and an offshore load; the offshore wind power system is used for converting wind energy into electric energy; the electrolytic tank is used for converting electric energy into hydrogen energy; the hydrogen storage tank is used for storing hydrogen energy; the fuel cell is used for converting hydrogen energy into electric energy; the gas turbine is used for converting hydrogen into electric energy; the offshore load uses electric energy to do work; the electric energy generated by the offshore wind power system is used for being directly connected to a power grid, outputting to an electrolytic tank or outputting to an offshore load; the formula of the active power balance constraint of the offshore wind power is as follows:

P _wnet,t +P _ely,t +P _wload,t ＝P _w,t

the offshore oilfield load active balance constraint formula is:

I _i,t P _gtmin,i ≤P _gti,t ≤I _i,t P _gtmax,i

in the above, I _i,t P is the start-stop variable of the gas turbine _{gt min,i} For the ith gas turbine minimum power, P _gti,t Power at a t-th time point for an ith gas turbine;

the equation for the hot standby constraint is:

in the above, P _{gt max,i} Maximum output of ith gas turbine, P _gti,t For the power of the ith gas turbine at the t-th point in time, P _load,t For offshore oilfield load demand, I _i,t H is a thermal reserve coefficient, which is a start-stop variable of the gas turbine;

the formula for fuel constraint for a gas turbine is:

in the above, V _hgt，t Mu for supplying hydrogen to the gas turbine _htc B is the ratio of the heating value of hydrogen to natural gas in unit volume _i First coefficient, P, as a function of gas consumption of the ith gas turbine _gti，t For the power of the ith gas turbine at the t-th point in time c _t Second coefficient as a function of gas consumption of the ith gas turbine, I _i，t Is a gas turbine start-stop variable;

the formula of the power constraint of the electrolytic cell is as follows:

m _t P _elymin ≤p _ely，t ≤p _elymax m _t

in the above, m _t For the start-up and shut-down coefficient of the electrolyzer, P _elymin For minimum power of operation of the electrolyzer, p _e1y，t Wind power consumption for electrolytic cell, p _elymax Maximum power for the electrolyzer;

the formula for the fuel cell power constraint is:

0≤P _fc1，t ≤P _fcmax

0≤P _fc2，t ≤P _fcmax

P _fc1，t +P _fc2，t ≤P _fcmax

the formula for the efficiency constraints of the electrolyzer and fuel cell is:

P _ely，t ＝η _ely V _he，t

P _fc1，t ＝η _fc V _hf1，t

P _fc2，t ＝η _fc V _hf2，t

the formula for the hydrogen load demand constraint is:

0≤V _hsell，t ≤V _hlood，t

the formula of the volume constraint of the air storage tank is as follows:

0≤V _h，t ≤V _hmax

in the above, V _h，t V is the volume of hydrogen in the hydrogen storage tank _he，t Is made of electrolytic cellHydrogen velocity, V _hf1，t The hydrogen consumption speed for power generation, network connection and hydrogen consumption of the fuel cell is V _hf2，t Load-supplying hydrogen consumption speed for fuel cell power generation, V _hsell，t To supply the hydrogen load hydrogen amount, V _hmax Is the maximum volume of the hydrogen storage tank.

11. A computer device comprising a memory, a processor, the memory having stored thereon a computer program executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the energy storage capacity configuration method of any of claims 1 to 9.

12. A readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the energy storage capacity configuration method of any of claims 1 to 9.