A scheduling method for electric-hydrogen hybrid energy storage system based on real-time hydrogen price

Abstract

The present invention discloses a method for scheduling an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices, and belongs to the field of energy management optimization control. The technical solution is: a method for scheduling an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices, including: constructing a mixed integer programming mathematical model based on the electric-hydrogen hybrid energy storage system; obtaining an initial variable set and a real-time hydrogen price in the electric-hydrogen hybrid energy storage system; constructing an objective function of the mixed integer programming mathematical model with the goal of maximizing the total economic benefit; using a rolling optimization method, with system energy conservation as a constraint, to solve the mixed integer programming mathematical model; obtaining a final variable set based on the solution result and using it to schedule the electric-hydrogen hybrid energy storage system. The beneficial effects of the present invention are: the method for scheduling an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices provided by the present invention can maximize the use of renewable energy, reduce energy costs, and improve the economy and sustainability of the energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management optimization control, and in particular to a scheduling method for an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices. Background Art

[0002] With the continuous development and popularization of renewable energy, such as wind and solar energy, the energy industry is facing new challenges and opportunities. Although renewable energy has advantages such as environmental protection and sustainability, its intermittent nature also brings challenges to the stability of the power grid. With the volatility of solar and wind energy, the power grid needs to be able to flexibly respond to the fluctuations of these energy sources to ensure the reliability and stability of power supply.

[0003] In this context, energy storage technology has become one of the key technologies to solve the volatility of renewable energy. Among them, battery energy storage system is currently the most widely used technology, but its limitations in energy density and life make it not completely applicable in some scenarios. In order to overcome these limitations and further improve the efficiency of energy storage, electric hydrogen hybrid energy storage system came into being. Electric hydrogen hybrid energy storage system uses electricity to electrolyze water into hydrogen and store it. When needed, hydrogen is converted into electricity through fuel cells. This system combines the advantages of battery energy storage system and hydrogen energy technology, with the characteristics of high energy density, long-term storage, zero emissions, etc., which can effectively make up for the shortcomings of battery energy storage system and improve the stability and reliability of the entire energy system.

[0004] Although existing technologies have made some progress in optimizing electric-hydrogen hybrid energy storage systems, they still have some important defects, including ignoring the technical differences between hydrogen energy storage systems and battery energy storage systems and lacking consideration of the real-time market price of hydrogen. These defects limit the performance and economic benefits of the system and require further research and improvement to solve. Specifically:

[0005] 1. The technical differences between hydrogen energy storage systems and battery energy storage systems have been ignored, and a suitable model that reflects the system differences has not been established. This neglect may result in inaccurate optimization strategies and the inability to fully utilize the advantages of electric-hydrogen hybrid energy storage systems.

[0006] 2. Lack of consideration of hydrogen market prices: One of the key aspects of optimizing the electric-hydrogen hybrid energy storage system is to take into account the market price of hydrogen during the period of electricity prices. However, existing studies often ignore this point, which affects the efficiency and economy of the energy storage system. Summary of the invention

[0007] In order to achieve the above-mentioned object of the invention, the present invention provides a method for scheduling an electric-hydrogen hybrid energy storage system based on real-time hydrogen price, comprising the following steps:

[0008] Constructing a mixed integer programming mathematical model based on an electric-hydrogen hybrid energy storage system; the mixed integer programming mathematical model includes a battery energy storage system module and a hydrogen energy storage system module;

[0009] Acquire an initial variable set and a real-time hydrogen price in the electric-hydrogen hybrid energy storage system; the initial variable set includes an initial real-time power and an initial decision variable of each device in the electric-hydrogen hybrid energy storage system;

[0010] Obtaining the total economic benefit of the electric-hydrogen hybrid energy storage system according to the real-time hydrogen price and the initial variable set;

[0011] Taking the maximization of the total economic benefit as the goal, constructing the objective function of the mixed integer programming mathematical model;

[0012] Using a rolling optimization method and taking system energy conservation as a constraint condition, the mixed integer programming mathematical model is solved;

[0013] Obtaining a final variable set according to a solution result of the mixed integer programming mathematical model, wherein the final variable set includes a final real-time power and a final decision variable of each device in the electric-hydrogen hybrid energy storage system;

[0014] The electric-hydrogen hybrid energy storage system is dispatched according to the final variable set.

[0015] Furthermore, the battery energy storage system module includes:

[0016] ,

[0017] ,

[0018] ,

[0019] ,

[0020] ,

[0021] ;

[0022] In the formula, is the charging power of the energy storage battery at time t, is the discharge power of the energy storage battery at time t, is the maximum charging power of the energy storage battery, is the maximum discharge power of the energy storage battery, is the energy storage battery power at time t, is the storage capacity of the energy storage battery, is the power loss rate of the energy storage battery, To optimize the unit interval duration for rolling, is the power state of the energy storage battery at time t, For energy storage batteries ( ) at the moment of charge, is the minimum power state of the energy storage battery, is the maximum power state of the energy storage battery;

[0023] The hydrogen energy storage system modules include:

[0024] ,

[0025] ,

[0026] ;

[0027] In the formula, To increase from time t The quality of hydrogen storage over time, is the hydrogen storage mass at time t, is the hydrogen sales rate at time t, is the hydrogen sales decision variable at time t, is the electrolysis production rate at time t, is the electrolytic production decision variable at time t, is the fuel cell consumption rate at time t, is the fuel cell consumption decision variable at time t, is the maximum hydrogen storage mass, is the hydrogen charging state at time t, is the minimum hydrogen charging state, This is the maximum hydrogen charge state.

[0028] Furthermore, the objective function is:

[0029] ;

[0030] ;

[0031] ;

[0032] In the formula, Y is the total economic benefit, To optimize the profit in the total dynamic hydrogen energy market within the rolling start time, To optimize the total operating cost within the rolling start time, is the profit in the dynamic hydrogen energy market at time t, is the operating cost at time t, n is the total number of rolling optimization unit intervals, is the average photovoltaic electricity cost, is the average electricity cost of wind power, is the grid electricity cost at time t, The average electricity cost for energy storage batteries is is the average electricity cost of the electrolytic cell, The average electricity cost for hydrogen fuel cells is is the photovoltaic power at time t, is the wind power at time t, is the power purchased by the power grid at time t, is the energy storage battery power at time t, is the electrolytic cell power at time t, is the hydrogen fuel cell power at time t, is the real-time hydrogen price at time t, is the hydrogen sales volume at time t, is the hydrogen sales decision variable at time t.

[0033] Furthermore, the final variable set obtained by solving the mixed integer programming mathematical model is:

[0034] ;

[0035] In the formula, is the photovoltaic power at time t, is the wind power at time t, is the power purchased by the power grid at time t, is the energy storage battery power at time t, is the electrolytic cell power at time t, is the hydrogen fuel cell power at time t, is the hydrogen sales decision variable at time t, is the electrolytic production decision variable at time t, is the fuel cell consumption decision variable at time t.

[0036] Furthermore, the energy conservation of the system is:

[0037] ;

[0038] In the formula, is the photovoltaic power at time t, is the wind power at time t, is the power purchased by the power grid at time t, is the energy storage battery power at time t, is the electrolytic cell power at time t, is the hydrogen fuel cell power at time t, is the system electrical load power at time t.

[0039] Furthermore, the steps of solving the mixed integer programming mathematical model using the rolling optimization method are as follows:

[0040] Step S1: setting input data of the mixed integer programming mathematical model;

[0041] Step S2: Initialize the energy storage battery power state and hydrogen charging state, set the current time t and the rolling optimization end time T;

[0042] Step S3: Set the rolling optimization time period to , solving the mixed integer programming mathematical model;

[0043] Step S4: Set ; and determine whether the termination condition is met: , Optimize the unit interval duration for rolling;

[0044] If the termination condition is not met, repeat step S3;

[0045] If the termination condition is met, the solution ends.

[0046] Furthermore, the input data includes the initial variable set and the real-time hydrogen price.

[0047] Furthermore, in step S2, when the current time t is 0 and the rolling optimization end time T is 24, the rolling optimization unit interval duration is For 1 hour.

[0048] Furthermore, the total number of rolling optimization unit intervals is:

[0049] ;

[0050] Where n is the total number of rolling optimization unit intervals, T is the end time of rolling optimization, and t is the current time. Optimize the unit interval duration for scrolling.

[0051] A storage medium includes a stored program. When the program is running, the device where the storage medium is located is controlled to execute an electric-hydrogen hybrid energy storage system scheduling method based on real-time hydrogen prices.

[0052] An electronic device includes a processor and a memory, wherein the memory stores computer-readable instructions, and the processor is used to run the computer-readable instructions. When the computer-readable instructions are run, a method for scheduling an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices is executed.

[0053] The beneficial effects of the present invention are:

[0054] 1. The present invention considers the time difference of scheduling optimization: The scheduling method fully considers the time difference of hydrogen market price, that is, the real-time hydrogen price, especially the price changes during peak hours and valley hours. By purchasing hydrogen for storage during low-price periods and converting the stored hydrogen into electricity for sale during high-price periods, the time difference value of hydrogen is maximized, and the economic benefits of the energy storage system are improved.

[0055] 2. The present invention uses a combination of mixed integer programming and rolling optimization to achieve scheduling optimization, which can more accurately describe and solve the complex scheduling problems of the electric-hydrogen hybrid energy storage system including a battery energy storage system and a hydrogen energy storage system, thereby improving the accuracy and feasibility of the scheduling method.

[0056] 3. The present invention improves the economy and sustainability of the system. The scheduling method of the present invention can reduce energy costs and maximize the use of renewable energy resources. By reasonably scheduling the operation of the energy storage system, higher economic benefits can be achieved based on real-time hydrogen price fluctuations, thereby promoting the sustainable development of the energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flow chart of the present invention using a rolling optimization method to solve a mixed integer programming mathematical model;

[0058] Figure 2 Comparison curves of SOHC under different strategies. DETAILED DESCRIPTION

[0059] In order to clearly illustrate the technical features of this solution, this solution is described below through a specific implementation method.

[0060] See also Figure 1 The embodiment of the present invention provides a method for scheduling an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices, comprising the following steps:

[0061] Construct a mixed integer programming mathematical model based on the electric-hydrogen hybrid energy storage system; the mixed integer programming mathematical model includes a battery energy storage system module and a hydrogen energy storage system module;

[0062] Obtaining an initial variable set and a real-time hydrogen price in the electric-hydrogen hybrid energy storage system; the initial variable set includes the initial real-time power and initial decision variables of each device in the electric-hydrogen hybrid energy storage system;

[0063] Obtain the total economic benefits of the electric-hydrogen hybrid energy storage system based on the real-time hydrogen price and the initial variable set;

[0064] Taking the maximum total economic benefit as the goal, the objective function of the mixed integer programming mathematical model is constructed;

[0065] The rolling optimization method is used to solve the mixed integer programming mathematical model with the system energy conservation as the constraint condition;

[0066] A final variable set is obtained according to the solution result of the mixed integer programming mathematical model, and the final variable set includes the final real-time power and final decision variables of each device in the electric-hydrogen hybrid energy storage system;

[0067] The electric-hydrogen hybrid energy storage system is dispatched according to the final variable set.

[0068] Taking into full account the technical differences between battery energy storage and hydrogen energy storage, in the mixed integer programming mathematical model constructed based on the electric-hydrogen hybrid energy storage system, the battery energy storage system module and the hydrogen energy storage system module are established separately, which can fully utilize the advantages of the electric-hydrogen hybrid energy storage system and realize precise optimization scheduling strategy; scheduling the electric-hydrogen hybrid energy storage system according to the final variable set can effectively allocate the power of each device within each rolling optimization unit interval duration ∆t, and achieve the scheduling goal of the highest total economic benefit; introducing real-time hydrogen prices can adjust the scheduling strategy in time according to changes in time and market demand for hydrogen, thereby ensuring the efficiency and economy of the electric-hydrogen hybrid energy storage system, which plays a vital role in incentivizing the effective use of storage resources and optimizing grid operations; the use of rolling optimization ensures global optimality and reduces the cumulative error from the prediction, thereby aiming to improve the accuracy and reliability of the optimization process.

[0069] Lithium-ion batteries are widely used in energy storage systems due to their high energy density, long life, strong power capacity and low self-discharge rate. In battery energy storage systems, important parameters include the state of charge (SOC) and the storage capacity ( , in kWh). In order to simplify the optimization process, the energy storage battery power of the battery energy storage system at time t ( ) is converted according to the following equations (1)-(4). In order to accurately calculate the operating cost, the charging power of the energy storage battery at time t ( ) and the discharge power of the energy storage battery at time t ( ) are distinguished to ensure that each power value is positive. Equation (1) prevents simultaneous charging and discharging. The recursive formula for the battery energy storage system is defined in equation (5) taking into account the power loss rate (a) of the energy storage battery. The state of charge of the energy storage battery at time t is is constrained within its operating range as shown in Equation (6).

[0070] The battery energy storage system modules include:

[0071] (1)

[0072] (2)

[0073] (3)

[0074] (4)

[0075] (5)

[0076] (6)

[0077] In the formula, is the charging power of the energy storage battery at time t, is the discharge power of the energy storage battery at time t, is the maximum charging power of the energy storage battery, is the maximum discharge power of the energy storage battery, is the energy storage battery power at time t, is the storage capacity of the energy storage battery, is the power loss rate of the energy storage battery, To optimize the unit interval duration for rolling, is the power state of the energy storage battery at time t, For energy storage batteries ( ) at the moment of charge, is the minimum power state of the energy storage battery, is the maximum power state of the energy storage battery;

[0078] The complex hydrogen storage and transportation network is a vital link between production and demand. Therefore, when modeling shared hydrogen storage, i.e., hydrogen energy storage systems, it is necessary to fully consider the challenges of addressing hydrogen pricing. Hydrogen prices, i.e., real-time hydrogen prices, fluctuate around standard market prices. The hydrogen storage mass at time t can be expressed as equation (7). Similar to the recursive form of the battery energy storage system, the recursive formula of the hydrogen energy storage system is given by the hydrogen sales rate at time t ( ), the electrolysis production rate at time t ( ) and the fuel cell consumption rate at time t ( ) and other factors. It is worth noting that their corresponding decision variables ( , , ) is simplified to a 0-1 decision variable, specifically, Indicates the start and stop of electrolytic production at time t (0 means stop electrolytic production, 1 means start electrolytic production), Indicates the start and stop of fuel cell consumption at time t (0 means stop fuel cell consumption, 1 means start fuel cell consumption), Indicates whether hydrogen sales are carried out at time t (0 means no hydrogen sales, 1 means hydrogen sales). If the operation is carried out within time period t, the corresponding decision variable value is 1, otherwise it is 0. The hydrogen state of charge (SOHC) is defined as the ratio of the hydrogen storage mass to the maximum hydrogen storage mass of the hydrogen energy storage system ( ), then the hydrogen charging state at time t is It is expressed as equation (8). It is also constrained by the operating range, as shown in equation (9).

[0079] The hydrogen energy storage system modules include:

[0080] (7)

[0081] (8)

[0082] (9)

[0083] In the formula, To increase from time t The quality of hydrogen storage over time, is the hydrogen storage mass at time t, is the hydrogen sales rate at time t, is the hydrogen sales decision variable at time t, is the electrolysis production rate at time t, is the electrolytic production decision variable at time t, is the fuel cell consumption rate at time t, is the fuel cell consumption decision variable at time t, is the maximum hydrogen storage mass, is the hydrogen charging state at time t, is the minimum hydrogen charging state, This is the maximum hydrogen charge state.

[0084] While constructing the mixed integer programming mathematical model based on the electric-hydrogen hybrid energy storage system, some constraints are also specified for the subsequent use of the rolling optimization method to solve the mixed integer programming mathematical model. The constraints include: the charging / discharging power of the energy storage battery, the energy storage battery state of charge, the hydrogen storage quality, and the hydrogen charging state, providing a basis for subsequent optimization problems. The charging / discharging power of the storage battery is equation (1)-equation (4), the energy storage battery state of charge is equation (5), the energy storage battery state of charge is equation (6), the hydrogen storage quality is equation (7), and the hydrogen charging state is equation (8) and equation (9). In addition, when using the rolling optimization method to solve the mixed integer programming mathematical model, another constraint is also included: system energy conservation; system energy conservation is equation (10):

[0085] (10)

[0086] In the formula, is the photovoltaic power at time t, is the wind power at time t, is the power purchased by the power grid at time t, is the energy storage battery power at time t, is the electrolytic cell power at time t, is the hydrogen fuel cell power at time t, is the system electrical load power at time t.

[0087] In one preferred embodiment, the process of solving the mixed integer programming mathematical model using the rolling optimization method is to effectively The power of each device is allocated to achieve the scheduling goal with the highest total economic benefit. Therefore, the final variable set obtained by solving the mixed integer programming mathematical model is:

[0088] (11)

[0089] In the formula, is the photovoltaic power at time t, is the wind power at time t, is the power purchased by the power grid at time t, is the energy storage battery power at time t, is the electrolytic cell power at time t, is the hydrogen fuel cell power at time t, is the hydrogen sales decision variable at time t, is the electrolytic production decision variable at time t, is the fuel cell consumption decision variable at time t.

[0090] The final variable set is a combination of continuous variables and integer variables, where the real-time power of each device (including photovoltaic, wind power, grid power purchase, energy storage battery, electrolyzer and hydrogen fuel cell) is a continuous variable. The real-time power of each device includes , , , , , That is, they are the real-time power of photovoltaic power, wind power, power purchased from the power grid, power of energy storage batteries, power of electrolyzers, and power of hydrogen fuel cells. The real-time decision value of each decision variable (hydrogen sales decision variable, electrolysis production decision variable, fuel cell consumption decision variable) is an integer variable. is 0 or 1, specifically, Indicates the start and stop of electrolytic production at time t (0 means stop electrolytic production, 1 means start electrolytic production), Indicates the start and stop of fuel cell consumption at time t (0 means stop fuel cell consumption, 1 means start fuel cell consumption), Indicates whether hydrogen is sold at time t (0 means no hydrogen sales, 1 means hydrogen sales). By solving the mixed integer programming mathematical model, the final variable set is obtained, that is, the final variable set includes the real-time power of each device (including photovoltaic, wind power, power grid purchase, energy storage battery, electrolyzer and hydrogen fuel cell), and the optimal combination of each decision variable, to achieve the goal of the highest total economic benefit.

[0091] In the mixed integer programming mathematical model constructed based on the electric hydrogen hybrid energy storage system, the total economic benefit is affected by the operating cost of the system and the profit of hydrogen trading, that is, the profit in the dynamic hydrogen energy market. The objective function in the present invention takes the maximum total economic benefit as the scheduling target. The software currently used for commercial solutions is mainly to achieve the "miniaturization" goal, that is, to minimize the solution value. When it is necessary to solve the "large-scale" goal, it is necessary to take a negative value as a whole when constructing the objective function. Therefore, those skilled in the art can understand that when constructing the objective function with the maximum total economic benefit as the scheduling target, the present invention needs to add a negative sign as a whole: that is, the objective function is:

[0092] (12)

[0093] The operating costs are:

[0094] (13)

[0095] Profits in the dynamic hydrogen energy market are:

[0096] (14)

[0097] In the formula, Y is the total economic benefit, To optimize the profit in the total dynamic hydrogen energy market within the rolling start time, To optimize the total operating cost within the rolling start time, is the profit in the dynamic hydrogen energy market at time t, is the operating cost at time t, n is the total number of rolling optimization unit intervals, is the average photovoltaic electricity cost, is the average electricity cost of wind power, is the grid electricity cost at time t, The average electricity cost for energy storage batteries is is the average electricity cost of the electrolytic cell, The average electricity cost for hydrogen fuel cells is is the photovoltaic power at time t, is the wind power at time t, is the power purchased by the power grid at time t, is the energy storage battery power at time t, is the electrolytic cell power at time t, is the hydrogen fuel cell power at time t, is the real-time hydrogen price at time t, is the hydrogen sales volume at time t, is the hydrogen sales decision variable at time t.

[0098] The profit gained from hydrogen trading is quantified by equation (14), which takes into account the real-time hydrogen price under time-of-use electricity prices and represents the financial benefits associated with buying and selling hydrogen under market bidding conditions.

[0099] The steps of solving the mixed integer programming mathematical model using the rolling optimization method are:

[0100] Step S1: setting input data of the mixed integer programming mathematical model; the input data includes an initial variable set and a real-time hydrogen price.

[0101] Step S2: Initialize the energy storage battery power state and hydrogen charging state, set the current time t and the rolling optimization end time T;

[0102] Step S3: setting the rolling optimization time period to [t, T], and solving the mixed integer programming mathematical model;

[0103] The objective function of the mixed integer programming mathematical model is equation (12):

[0104] ;

[0105] The final variable set for solving the mixed integer programming mathematical model, that is, the decision vector, is equation (11):

[0106] ;

[0107] The constraints of the mixed integer programming mathematical model are equations (1) to (10):

[0108] ,

[0109] ,

[0110] ,

[0111] ,

[0112] ,

[0113] ,

[0114] ,

[0115] ,

[0116] ,

[0117] ;

[0118] It is worth noting that in formula (5)

[0119]

[0120] When step S3 is first executed, The value of is the value of the energy storage battery state of charge SOC initialized in step 2. In the subsequent rolling optimization process, All are energy storage batteries in ( ) The power status at the moment;

[0121] Step S4: Set ; and determine whether the termination condition is met: ;

[0122] If the termination condition is not met, repeat step S3;

[0123] If the termination condition is met, the solution ends.

[0124] In order to solve the multi-time period optimization problem, the embodiment of the present invention adopts a rolling optimization method. Preferably, in this solution, the mixed integer programming mathematical model based on the electric-hydrogen hybrid energy storage system is solved using a rolling optimization method within one day, and the rolling optimization unit interval time is set to is 1 (hour), the current time is set to 0 (hour), and the rolling optimization end time T is 24 (hour). Therefore, the total solution process is 24 time periods per day (according to the formula ), that is, the total number of rolling optimization unit intervals n=24. At each discrete time point, the rolling optimization unit interval is recalibrated from that moment until the end of the day. Each time the mixed integer programming mathematical model is solved, only the time length of the first rolling optimization unit interval is used. The decision vectors within are executed, and the subsequent intervals are ignored. That is, in this case, less than 1 hour, and one day of optimization is completed. This approach ensures the global optimality of the solution and mitigates the accumulated errors from the predictions, thereby aiming to improve the accuracy and reliability of the optimization process.

[0125] Verification Example

[0126] This verification example adopts the scheduling method of the electric-hydrogen hybrid energy storage system based on real-time hydrogen prices. The rolling optimization method is used to solve the mixed integer programming mathematical model based on the electric-hydrogen hybrid energy storage system within one day, and the rolling optimization unit interval duration is set. is 1 (hour), the current time is set to 0 (hour), and the rolling optimization end time T is set to 24 (hour). In order to evaluate the impact of real-time hydrogen prices, a comparative analysis of hydrogen storage status is conducted. Figure 2 As shown in Figure 2, the hydrogen production strategy of the hydrogen energy storage system remains consistent before and after considering the real-time hydrogen price. However, significant changes are observed in the hydrogen sales strategy, highlighting the impact of real-time hydrogen prices on system operation and energy utilization.

[0127] In addition, according to the objective function:

[0128] ;

[0129] Table 1 Economic analysis under different strategies

[0130] (Unit: Yuan)

[0131] Strategy Total operating costs Profits in the Total Dynamic Hydrogen Energy Market Total economic benefits No consideration of real-time hydrogen prices 7507.43 6317.00 1190.43 Considering real-time hydrogen prices 7508.51 6070.88 1437.63

[0132] On that day, the total operating costs, profits in the total dynamic hydrogen energy market and total economic benefits without considering the real-time hydrogen price, as well as the total economic benefits within the starting time of the rolling optimization, profits in the total dynamic hydrogen energy market within the starting time of the rolling optimization, and total operating costs within the starting time of the rolling optimization after considering the real-time hydrogen price are shown in Table 1.

[0133] The results show that incorporating real-time hydrogen prices can improve the profitability of hydrogen trading and that considering real-time hydrogen prices can improve the economic feasibility of the overall power system.

[0134] A storage medium includes a stored program. When the program is running, the device where the storage medium is located is controlled to execute an electric-hydrogen hybrid energy storage system scheduling method based on real-time hydrogen prices.

[0135] An electronic device includes a processor and a memory, wherein the memory stores computer-readable instructions, and the processor is used to run the computer-readable instructions. When the computer-readable instructions are run, a method for scheduling an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices is executed.

[0136] The system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0137] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0138] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for dispatching an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices, characterized in that: The following steps are involved: Constructing a mixed integer programming mathematical model based on an electric-hydrogen hybrid energy storage system; the mixed integer programming mathematical model includes a battery energy storage system module and a hydrogen energy storage system module; The battery energy storage system module includes the power of the energy storage battery, the recursive formula of the battery energy storage system and the power state of the energy storage battery; the recursive formula of the battery energy storage system is constructed according to the power state of the energy storage battery, the storage capacity of the energy storage battery, the power of the energy storage battery and the power loss rate of the energy storage battery; The hydrogen energy storage system module includes a recursive formula of the hydrogen energy storage system and a hydrogen charging state; the recursive formula of the hydrogen energy storage system is constructed according to the hydrogen storage quality, hydrogen sales rate, electrolysis production rate, fuel cell consumption rate, hydrogen sales decision variables, electrolysis production decision variables and fuel cell consumption decision variables; Acquire an initial variable set and a real-time hydrogen price in the electric-hydrogen hybrid energy storage system; the initial variable set includes an initial real-time power and an initial decision variable of each device in the electric-hydrogen hybrid energy storage system; Obtaining the total economic benefit of the electric-hydrogen hybrid energy storage system according to the real-time hydrogen price and the initial variable set; Taking the maximization of the total economic benefit as the goal, the objective function of the mixed integer programming mathematical model is constructed; the objective function is: Y cost,t =k PV ·P PV,t +k WT ·P WT,t +C(t)·P grid,t +k BESS ·P BESS,t +k ele · P ele,t +k fuel ·P fuel,t ; In the formula, Y is the total economic benefit, To optimize the profit in the total dynamic hydrogen energy market within the rolling start time, Y is the total operating cost within the rolling optimization start time, profit,t is the profit in the dynamic hydrogen energy market at time t, Y cost,t is the operating cost at time t, n is the total number of rolling optimization unit intervals, k PV is the average photovoltaic electricity cost, k WT is the average electricity cost of wind power, C(t) is the power cost of the grid at time t, k BESS is the average electricity cost of the energy storage battery, k ele is the average electricity cost of the electrolytic cell, k fuel is the average electricity cost of hydrogen fuel cells, P PV,t is the photovoltaic power at time t, P WT,t is the wind power at time t, P grid,t is the power purchased by the power grid at time t, P BESS,t is the energy storage battery power at time t, P ele,t is the electrolytic cell power at time t, P fuel,t is the hydrogen fuel cell power at time t, λ t is the real-time hydrogen price at time t, is the hydrogen sales volume at time t, is the hydrogen sales decision variable at time t; Using a rolling optimization method and taking system energy conservation as a constraint condition, the mixed integer programming mathematical model is solved; The final variable set is obtained according to the solution result of the mixed integer programming mathematical model, and the final variable set includes the final real-time power and final decision variables of each device in the electric-hydrogen hybrid energy storage system; the final variable set obtained by solving the mixed integer programming mathematical model is: In the formula, is the electrolytic production decision variable at time t, is the fuel cell consumption decision variable at time t; The electric-hydrogen hybrid energy storage system is dispatched according to the final variable set.

2. The method for dispatching an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices according to claim 1, characterized in that: The battery energy storage system module comprises: SOC min ≤SOC t ≤SOC max ; In the formula, is the charging power of the energy storage battery at time t, is the discharge power of the energy storage battery at time t, is the maximum charging power of the energy storage battery, is the maximum discharge power of the energy storage battery, P BESS,t is the energy storage battery power at time t, size BESS is the storage capacity of the energy storage battery, a is the power loss rate of the energy storage battery, Δt is the duration of the rolling optimization unit interval, SOC t is the state of charge of the energy storage battery at time t, SOC t-Δt SOC is the state of charge of the energy storage battery at time (t-Δt). min SOC is the minimum state of charge of the energy storage battery. max It is the maximum power state of the energy storage battery.

3. The method for dispatching an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices according to claim 1, characterized in that: The hydrogen energy storage system module comprises: SOHC t =n t / n max ×100%, SOHC min ≤SOHC t ≤SOHC max ; Where n t+Δt To increase the hydrogen storage mass within Δt time from time t, Δt is the duration of the rolling optimization unit interval, n t is the hydrogen storage mass at time t, is the hydrogen sales rate at time t, is the hydrogen sales decision variable at time t, is the electrolysis production rate at time t, is the electrolytic production decision variable at time t, is the fuel cell consumption rate at time t, is the fuel cell consumption decision variable at time t, n max For maximum hydrogen storage mass, SOHC t is the hydrogen charging state at time t, SOHC min For minimum hydrogen charge state, SOHC max This is the maximum hydrogen charge state.

4. The method for dispatching an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices according to claim 1, characterized in that: The energy conservation of the system is: P PV,t +P WT,t +P grid,t -P BESS,t -P ele,t +P fuel,t -P load,t =0; Where P PV,t is the photovoltaic power at time t, P WT,t is the wind power at time t, P grid,t is the power purchased by the power grid at time t, P BESS,t is the energy storage battery power at time t, P ele,t is the electrolytic cell power at time t, P fuel,t is the hydrogen fuel cell power at time t, P load,t is the system electrical load power at time t.

5. The method for dispatching an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices according to any one of claims 1 to 4, characterized in that: The steps of solving the mixed integer programming mathematical model using the rolling optimization method are as follows: Step S1: setting input data of the mixed integer programming mathematical model; Step S2: Initialize the energy storage battery power state and hydrogen charging state, set the current time t and the rolling optimization end time T; Step S3: setting the rolling optimization time period to [t, T], and solving the mixed integer programming mathematical model; Step S4: let t=t+Δt; and determine whether the termination condition is met: Tt<Δt, Δt is the duration of the rolling optimization unit interval; If the termination condition is not met, repeat step S3; If the termination condition is met, the solution ends.

6. The method for dispatching an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices according to claim 5, characterized in that: The input data includes the initial variable set and the real-time hydrogen price.

7. The method for dispatching an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices according to claim 5, characterized in that: In the step S2, when the current time t is 0, the rolling optimization end time T is 24, and the rolling optimization unit interval duration Δt is 1 hour.

8. The method for dispatching an electric-hydrogen hybrid energy storage system based on real-time hydrogen prices according to claim 1, characterized in that: The total number of rolling optimization unit intervals is: Where n is the total number of rolling optimization unit intervals, T is the end time of rolling optimization, t is the current time, and Δt is the duration of the rolling optimization unit interval.

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