A seasonal scheduling method for an electric-hydrogen hybrid energy storage system

Abstract

The present invention discloses a seasonal scheduling method for an electric-hydrogen hybrid energy storage system, including: constructing 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 seasonal hydrogen energy storage system module; 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 total economic benefits; using a rolling optimization method to solve the mixed integer programming mathematical model with system energy conservation as a constraint; 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: based on the real-time hydrogen price, the seasonal scheduling method of the present invention adds seasonal factors, can adjust the energy charging and discharging strategy of the hydrogen energy storage system according to seasonal changes, reduce energy costs while improving 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 seasonal scheduling method for an electric-hydrogen hybrid energy storage system. Background Art

[0002] With the increasing emphasis on renewable energy worldwide, the power generation of renewable energy such as wind and solar energy has increased year by year. However, the intermittent and unstable nature of these energy sources has brought great challenges to the power grid. In order to ensure the reliability and stability of power supply, energy storage technology has become the key to solving this problem.

[0003] The existing electric-hydrogen hybrid energy storage system uses electricity to electrolyze water into hydrogen and stores it. When needed, the hydrogen is converted into electricity through a fuel cell. This system combines the advantages of battery energy storage systems and hydrogen energy technology, with the characteristics of high energy density, long-cycle storage, and zero emissions. It can effectively make up for the shortcomings of battery energy storage systems 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 have not properly addressed the impact of seasonal fluctuations in new energy on hydrogen storage system scheduling when faced with the seasonal characteristics of new energy. Specifically, existing methods have defects in the following aspects:

[0005] 1. Ignoring seasonal fluctuations: Existing methods often only focus on short-term energy demand and supply when scheduling hydrogen storage systems, ignoring the impact of seasonal fluctuations on renewable energy power generation. This results in the hydrogen storage system being unable to effectively store excess energy during the peak period of seasonal renewable energy power generation, and being unable to meet energy demand during the trough period.

[0006] 2. Static scheduling strategy: Existing methods often adopt static scheduling strategies, which cannot adjust the operating status of the hydrogen storage system according to the real-time situation of seasonal new energy fluctuations. This leads to the lack of flexibility and adaptability of the hydrogen storage system in dealing with seasonal fluctuations.

[0007] 3. Ignoring market factors: Existing methods often fail to fully consider the time differences in hydrogen market prices when scheduling hydrogen storage systems. When seasonal new energy fluctuates greatly, hydrogen market prices will also fluctuate accordingly. If the operation of the hydrogen storage system cannot be adjusted according to market price changes, it will lead to increased energy costs.

[0008] 4. Lack of cross-seasonal optimization: When scheduling hydrogen storage systems, existing methods usually only focus on energy demand and supply within a single season, and lack cross-seasonal optimization strategies. This results in the hydrogen storage system being unable to achieve long-term energy balance and cost optimization when seasonal new energy fluctuates greatly. Summary of the invention

[0009] In order to achieve the above-mentioned object of the invention, the present invention provides a seasonal scheduling method of an electric-hydrogen hybrid energy storage system, comprising the following steps:

[0010] 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 seasonal hydrogen energy storage system module;

[0011] 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;

[0012] 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;

[0013] Taking the maximization of the total economic benefit as the goal, constructing the objective function of the mixed integer programming mathematical model;

[0014] Using a rolling optimization method and taking system energy conservation as a constraint condition, the mixed integer programming mathematical model is solved;

[0015] 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;

[0016] The electric-hydrogen hybrid energy storage system is dispatched according to the final variable set.

[0017] Furthermore, the battery energy storage system module includes:

[0018]

[0019]

[0020] SOC min ≤SOC t ≤SOC max ;

[0021] 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 tis 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 is the maximum power state of the energy storage battery;

[0022] The seasonal hydrogen energy storage system modules include:

[0023]

[0024] SOHC t =n t / n max ×100%,

[0025] SOHC min ≤SOHC t ≤SOHC max ;

[0026] Where n t+Δt To increase the hydrogen storage mass within Δt time from time t, 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 is the maximum hydrogen charging state, is the seasonal adjustment factor.

[0027] Furthermore, the objective function is:

[0028]

[0029] 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 ·

[0030] Pele,t +k fuel ·P fuel,t ;

[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, 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.

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

[0034]

[0035] 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, 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] 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 load power at time t, is the seasonal adjustment factor.

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

[0043] 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;

[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] Further, in 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.

[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, t is the current time, and Δt is the duration of the rolling optimization unit interval.

[0051] A storage medium includes a stored program, which controls a device where the storage medium is located to execute a seasonal scheduling method for an electric-hydrogen hybrid energy storage system when the program is running.

[0052] An electronic device includes a processor and a memory, wherein the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions. When the computer-readable instructions are executed, a seasonal scheduling method for an electric-hydrogen hybrid energy storage system 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. By analyzing historical data and seasonal weather forecasts, the present invention can predict the seasonal changes in renewable energy power generation, and adjust the charging and discharging strategy of the hydrogen storage system accordingly, thereby reducing energy costs and improving the economy and sustainability of the energy system. Different from the static scheduling strategy, the present invention adopts a dynamic scheduling strategy, which can dynamically adjust the operating status of the hydrogen energy storage system according to the real-time renewable energy power generation and market demand, thereby improving the system's response speed and adaptability to seasonal fluctuations.

[0056] 3. The present invention not only focuses on the energy demand and supply in a single season, but also proposes a cross-season optimization strategy. By achieving long-term energy balance and cost optimization under the condition of large seasonal fluctuations in new energy, the operating efficiency and economic benefits of the hydrogen energy storage system are improved.

[0057] 4. 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.

[0058] 5. 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 rationally scheduling the operation of the energy storage system with seasonal factors, 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

[0059] Figure 1 A flow chart of the present invention using a rolling optimization method to solve a mixed integer programming mathematical model;

[0060] Figure 2 Comparison curves of SOHC under different strategies. DETAILED DESCRIPTION

[0061] In order to clearly illustrate the technical features of this solution, this solution is described below through a specific implementation method.

[0062] See also Figure 1 The embodiment of the present invention provides a seasonal scheduling method for an electric-hydrogen hybrid energy storage system, comprising the following steps:

[0063] 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 seasonal hydrogen energy storage system module;

[0064] 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;

[0065] 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;

[0066] Taking the maximum total economic benefit as the goal, the objective function of the mixed integer programming mathematical model is constructed;

[0067] The rolling optimization method is used to solve the mixed integer programming mathematical model with the system energy conservation as the constraint condition;

[0068] 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;

[0069] The electric-hydrogen hybrid energy storage system is dispatched according to the final variable set.

[0070] Taking into full account the technical differences between battery energy storage and hydrogen energy storage, in the mixed integer programming mathematical model based on the electric-hydrogen hybrid energy storage system, the battery energy storage system module and the seasonal hydrogen energy storage system module are established respectively, which can make full use of the advantages of the electric-hydrogen hybrid energy storage system and realize the 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 time Δt, and achieve the scheduling goal with the highest total economic benefit; introducing real-time hydrogen prices can timely adjust the scheduling strategy according to the changes in the demand level of hydrogen in the time and market, so as to ensure the efficiency and economy of the electric-hydrogen hybrid energy storage system, which plays a vital role in stimulating 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, so as to improve the accuracy and reliability of the optimization process. At the same time, the seasonal fluctuations of the hydrogen energy storage system are fully considered and dynamically adjusted according to the real-time situation. Such a strategy can achieve cross-seasonal energy balance and cost optimization, thereby further improving the operating efficiency and economic benefits of the hydrogen energy storage system on the basis of real-time hydrogen prices.

[0071] 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 (size) of the energy storage battery. BESS , in kWh). In order to simplify the optimization process, the energy storage battery power (P BESS,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 SOC of the energy storage battery at time t is t is constrained within its operating range as shown in equation (6).

[0072] The battery energy storage system modules include:

[0073]

[0074] SOC min ≤SOC t ≤SOC max (6)

[0075] 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 The maximum power state of the energy storage battery;

[0076] 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, full consideration must be given to addressing the challenges of hydrogen pricing. The price of hydrogen, i.e., the real-time hydrogen price, fluctuates around the standard market price. The hydrogen storage quality of the hydrogen energy storage system over a time period of Δt can be expressed as equation (7). The hydrogen storage quality at time t is expressed as n t Similar to the recursive form of the battery energy storage system, the recursive formula of the hydrogen energy storage system is composed of the hydrogen sales rate at time t The electrolytic production rate at time t and the fuel cell consumption rate at time t It is worth noting that their corresponding decision variables is simplified to 0-1 decision variables, 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 (n max ), then the hydrogen charging state SOHC at time t is t Expressed as equation (8). SOHC t It is also constrained by the operating range, as shown in equation (9).

[0077] The seasonal hydrogen energy storage system modules include:

[0078]

[0079] SOHC t =n t / n max ×100% (8)

[0080] SOHC min ≤SOHC t ≤SOHC max (9)

[0081] Where n t+Δt To increase the hydrogen storage mass within Δt time from time t, 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 is the maximum hydrogen charging state, is the seasonal adjustment factor.

[0082] It should be noted that the seasonal adjustment factor The calculation of seasonal adjustment factors usually involves the analysis of historical seasonal data and the prediction of future seasonal conditions. For ease of understanding, the following example illustrates how to calculate the seasonal adjustment factor:

[0083] 1. Historical data analysis:

[0084] Collect historical energy demand data, analyze the historical energy demand data, and determine the average value of energy demand in each season (spring, summer, autumn, and winter) and historical renewable energy power generation (such as photovoltaic and wind power).

[0085] 2. Seasonal weather forecast:

[0086] Get weather forecast data for each season in the future, including parameters such as temperature, wind speed, and sunshine time.

[0087] 3. Forecast of renewable energy power generation:

[0088] The weather forecast data for each future season is used to predict the average value of the new energy power generation for each future season.

[0089] This is achieved through statistical models or machine learning algorithms.

[0090] 4. Calculate the seasonal adjustment factor:

[0091] The seasonal adjustment factor may be calculated based on a comparison between the predicted average value of the renewable energy power generation in each future season and the average value of the historical renewable energy power generation, and the calculation formula is:

[0092]

[0093] in, is the average value of the renewable energy power generation predicted within time t, It is the average value of historical renewable energy power generation. When calculating, the corresponding value of the same season is used to calculate the seasonal adjustment factor.

[0094] This ratio represents the relative change of the predicted average power generation in each quarter to the historical average power generation. If the predicted average power generation in each quarter is higher than the historical average power generation level, the seasonal adjustment factor will be greater than 1; if it is lower than the average level, the seasonal adjustment factor will be less than 1.

[0095] 5. Consider changes in demand:

[0096] The seasonal adjustment factor should also take into account changes in energy demand. Therefore, the forecast of energy demand can also be included in the calculation. Specifically, when predicting the power generation of new energy, the forecast of energy demand in each season in the future can be carried out at the same time. The calculation formula is as follows:

[0097]

[0098] Among them, D t is the average energy demand of each quarter predicted within time t, D avg It is the average value of historical energy demand in each season. When calculating, the corresponding value of the same season is used to calculate the seasonal adjustment factor.

[0099] 6.Seasonal adjustment factor correction:

[0100] In actual operation, the seasonal adjustment factor may need to be modified according to the preferences of the system operator to ensure its feasibility and effectiveness in actual operation.

[0101] Specific application scenarios:

[0102] 1. Summer: In summer, due to abundant sunshine and high wind speed, the amount of electricity generated by renewable energy (such as photovoltaic and wind power) is usually higher. This may lead to an increase in excess electricity received by the hydrogen energy storage system, so the seasonal adjustment factor may be set to a higher value to promote more water electrolysis hydrogen production activities, thereby storing more hydrogen. At the same time, since energy demand may be lower in summer, the system may reduce the sale of hydrogen to the market and maintain a higher level of hydrogen storage for use in winter.

[0103] 2. Winter: In winter, renewable energy generation may decrease due to reduced sunshine hours and lower wind speeds. In this case, the seasonal adjustment factor may be set to a lower value to reduce water electrolysis hydrogen production activities and avoid excessive power consumption when energy supply is insufficient. At the same time, due to increased energy demand in winter, the system may increase the amount of hydrogen sold to the market to meet heating and other energy needs.

[0104] 3. Spring and Autumn: Spring and autumn are usually seasonal transition periods, when renewable energy generation is relatively stable and energy demand is also at a medium level. The seasonal adjustment factor may be set to a medium value to balance hydrogen production and sales. The system may maintain a relatively stable hydrogen storage level and adjust hydrogen sales based on real-time energy demand and prices.

[0105] 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, which provide 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):

[0106]

[0107] 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 load power at time t, is the seasonal adjustment factor.

[0108] In one of the preferred embodiments, the process of solving the mixed integer programming mathematical model by the rolling optimization method is to effectively allocate the power of each device within each rolling optimization unit interval time Δt 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:

[0109]

[0110] 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, 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.

[0111] 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 P PV,t , P WT,t , P grid,t , P BESS,t , P ele,t , P fuel,t 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, and δ 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.

[0112] 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:

[0113]

[0114] The operating costs are:

[0115] 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 ·

[0116] P ele,t +k fuel ·P fuel,t (13)

[0117] Profits in the dynamic hydrogen energy market are:

[0118]

[0119] 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, PBESS,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.

[0120] 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.

[0121] The steps of solving the mixed integer programming mathematical model using the rolling optimization method are:

[0122] 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.

[0123] 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;

[0124] Step S3: setting the rolling optimization time period to [t, T], and solving the mixed integer programming mathematical model;

[0125] The objective function of the mixed integer programming mathematical model is equation (12):

[0126]

[0127] The final variable set for solving the mixed integer programming mathematical model, that is, the decision vector, is equation (11):

[0128]

[0129] The constraints of the mixed integer programming mathematical model are equations (1) to (10):

[0130]

[0131] SOC min ≤SOC t ≤SOC max ,

[0132]

[0133] SOHC t =n t / n max ×100%,

[0134] SOHCmin ≤SOHC t ≤SOHC max ,

[0135]

[0136] It is worth noting that in formula (5)

[0137]

[0138] When step S3 is first executed, SOC t-Δt 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, SOC t-Δt They are the power states of the energy storage batteries at the time (t-Δt);

[0139] Step S4: let t=t+Δt; and determine whether the termination condition is met: Tt<Δt;

[0140] If the termination condition is not met, repeat step S3;

[0141] If the termination condition is met, the solution ends.

[0142] In order to solve the multi-time period optimization problem, the embodiment of the present invention adopts a rolling optimization method. Preferably, in this scheme, the rolling optimization method is used to solve the mixed integer programming mathematical model constructed based on the electric hydrogen hybrid energy storage system within one day, and the rolling optimization unit interval duration Δt is set to 1 (hour), the current time is set to 0 (hour), and the rolling optimization end time T is 24 (hours). Therefore, the total solution process, that is, there are 24 time periods in one day (according to the formula n=[(Tt) / Δt]), 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 decision vectors from the first rolling optimization unit interval duration Δt are executed, and the subsequent intervals are ignored. When the rolling optimization duration is less than Δt, that is, less than 1 hour in this case, the optimization of the day ends. This method ensures the global optimality of the solution and reduces the cumulative error from the prediction, thereby aiming to improve the accuracy and reliability of the optimization process.

[0143] Verification Example

[0144] This verification example adopts the seasonal scheduling method of the electric-hydrogen hybrid energy storage system, and uses the rolling optimization method to solve the mixed integer programming mathematical model based on the electric-hydrogen hybrid energy storage system within one day. The rolling optimization unit interval duration Δt is set to 1 (hour), the current time is set to 0 (hour), and the rolling optimization end time T is set to 24 (hours). In order to evaluate the impact of real-time hydrogen prices, a comparative analysis of hydrogen energy 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.

[0145] In addition, according to the objective function:

[0146]

[0147] Table 1 Economic analysis under different strategies (unit: yuan)

[0148] 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

[0149] 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.

[0150] 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.

[0151] Furthermore, after adding the seasonal adjustment factor in actual application, the operation statistics of the hydrogen energy storage system in the new energy and electric hydrogen hybrid energy storage system in different seasons are shown in Table 2:

[0152]

[0153]

[0154] Table 2

[0155] Total output of photovoltaic and wind power:

[0156] Summer: The total output of photovoltaic and wind power is the highest. This may be because there is plenty of sunshine and suitable wind speed in summer, which is conducive to the generation of renewable energy.

[0157] Winter: Although the photovoltaic output is relatively low, the wind power output is higher, which may be due to the higher wind speed in winter, which partially compensates for the decrease in photovoltaic output.

[0158] Spring and Autumn: PV and wind power output is relatively low in these two seasons, which may be because the weather conditions in these seasons are between summer and winter, resulting in lower power generation efficiency of renewable energy.

[0159] Average hydrogen storage state:

[0160] Summer: The average hydrogen energy storage state is the lowest. This may be because the new energy power generation is high in summer and the hydrogen storage system discharges frequently, resulting in a decrease in the energy storage state.

[0161] Spring and Autumn: The average hydrogen energy storage state in these two seasons is relatively high, which may be because the new energy power generation is moderate in these seasons, and the hydrogen storage system can both charge and discharge, maintaining a higher energy storage state.

[0162] Winter: Although wind power output is high, the average hydrogen energy storage state is low, probably because the overall renewable energy generation is still insufficient to maintain a high energy storage state.

[0163] Hydrogen storage throughput:

[0164] Summer: Hydrogen storage throughput is the highest, which is consistent with the high level of renewable energy generation and increased activity of hydrogen storage systems in summer.

[0165] Winter: Although the photovoltaic output is not the highest, the hydrogen energy storage throughput remains at a high level due to the high wind power output.

[0166] Spring and Fall: Hydrogen storage throughput is relatively low in these two seasons, which is consistent with the lower renewable energy generation in these seasons.

[0167] In summary, the above data reflect the impact of changes in renewable energy power generation in different seasons on the operating status and efficiency of the hydrogen energy storage system. In summer and winter, due to the high renewable energy power generation, the activity of hydrogen energy storage increases, but in summer, due to the abundant sunshine, the photovoltaic output is high, making the energy storage state relatively low. In spring and autumn, due to the moderate renewable energy power generation, the hydrogen energy storage system can both charge and discharge, maintaining a high energy storage state. Through such adjustment and scheduling, the adaptability and economy of the hydrogen energy storage system to seasonal fluctuations are improved.

[0168] A storage medium includes a stored program, which controls a device where the storage medium is located to execute a seasonal scheduling method for an electric-hydrogen hybrid energy storage system when the program is running.

[0169] An electronic device includes a processor and a memory, wherein the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions. When the computer-readable instructions are executed, a seasonal scheduling method for an electric-hydrogen hybrid energy storage system is executed.

[0170] 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.

[0171] 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.

[0172] 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 seasonal scheduling method for an electric-hydrogen hybrid energy storage system, 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 seasonal hydrogen energy storage system module; The seasonal hydrogen energy storage system module comprises: In the formula, To increase from time t The quality of hydrogen storage over time, To optimize the unit interval duration for rolling, 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, is the maximum hydrogen charging state, is the seasonal adjustment factor; 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, constructing the objective function of the mixed integer programming mathematical model; The rolling optimization method is used to solve the mixed integer programming mathematical model with the system energy conservation as the constraint condition; the system energy conservation is: ； 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 load power at time t, is the seasonal adjustment factor; 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; Dispatching the electric-hydrogen hybrid energy storage system according to the final variable set; Method for calculating seasonal adjustment factor: Historical data analysis: Collecting historical energy demand data, analyzing the historical energy demand data, and determining the average value of energy demand and historical renewable energy power generation in each season; Seasonal Weather Forecast: Obtain weather forecast data for each season in the future, including temperature, wind speed, and sunshine time parameters; Forecast of renewable energy power generation: Using the weather forecast data for each future season, and using a statistical model or a machine learning algorithm to predict the average value of renewable energy power generation in each future season; Calculate the seasonal adjustment factor: The calculation of the seasonal adjustment factor is based on the comparison between the average value of the predicted renewable energy power generation in each future season and the average value of the historical renewable energy power generation, and the calculation formula is: ; In the formula, is the average value of the renewable energy power generation predicted within time t, It is the average value of historical renewable energy power generation; when calculating, the corresponding value of the same season is taken to calculate the seasonal adjustment factor.

2. The seasonal scheduling method of the electric-hydrogen hybrid energy storage system according to claim 1, characterized in that: The battery energy storage system module comprises: ， ， ， ， ， ； 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 state of charge 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, It is the maximum power state of the energy storage battery.

3. The seasonal scheduling method of the electric-hydrogen hybrid energy storage system according to claim 1, characterized in that: The objective function is: ； ； ； 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 amount of hydrogen sold at time t, is the hydrogen sales decision variable at time t.

4. The seasonal scheduling method of the electric-hydrogen hybrid energy storage system according to claim 1, characterized in that: The final variable set obtained by solving the mixed integer programming mathematical model is: ； 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.

5. The seasonal scheduling method of the electric-hydrogen hybrid energy storage system 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: Set the rolling optimization time period to , solving the mixed integer programming mathematical model; Step S4: Set ; and determine whether the termination condition is met: , Optimize the unit interval duration for rolling; If the termination condition is not met, repeat step S3; If the termination condition is met, the solution ends.

6. The seasonal scheduling method of the electric-hydrogen hybrid energy storage system according to claim 5, characterized in that: The input data includes the initial variable set and the real-time hydrogen price.

7. The seasonal scheduling method of the electric-hydrogen hybrid energy storage system according to claim 5, characterized in that: 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.

8. The seasonal scheduling method of the electric-hydrogen hybrid energy storage system according to claim 3, 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, and t is the current time. Optimize the unit interval duration for scrolling.

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