CN115115087A - Virtual power plant coordinated scheduling method considering hydrogen fuel automobile and hydrogen energy storage - Google Patents

Abstract

The invention discloses a virtual power plant coordinated scheduling method considering hydrogen fuel automobiles and hydrogen energy storage, which comprises the following steps: calculating the hydrogenation time and the required fuel quantity of the hydrogen energy automobile based on the historical data of the road; constructing a hydrogen energy storage system electrolytic cell, a fuel cell and a hydrogen storage tank model; scene generation is carried out on wind and light output by adopting an autoregressive moving average based model, clustering is carried out by adopting a K-means clustering algorithm, and a plurality of typical scenes are generated; and constructing an objective function of the virtual power plant scheduling model with the aim of maximizing the expected economic benefit. Analyzing the influence of whether hydrogen energy storage participates in the scheduling of the virtual power plant and whether the virtual power plant participates in the rotary standby market, the peak shaving market and the carbon emission right trade on the income and the scheduling result; generating a scene set by typical solar wind light output in summer and winter, substituting the scene set into the model for optimal scheduling, and analyzing the scheduling optimization condition of the virtual power plant.

Description

A coordinated scheduling method for virtual power plants considering hydrogen fuel vehicles and hydrogen energy storage

technical field

The invention relates to the technical field of virtual power plant applications, and in particular to a virtual power plant coordination scheduling method considering hydrogen fuel vehicles and hydrogen energy storage.

Background technique:

Compared with traditional internal combustion engine vehicles, hydrogen vehicles (HV) are expected to significantly reduce greenhouse gas emissions due to their cleanliness and energy-saving features. At the same time, their performance in low temperature environments is better than that of electric vehicles. One of the alternative technologies in the field of future transportation.

However, due to the imperfect construction of the current hydrogen fuel supply system, HV has problems such as high fuel cost and few hydrogen refueling stations, which restricts the popularization and application of HV. Using distributed new energy to generate electricity and electrolyze water to produce hydrogen can ensure the supply of hydrogen fuel while using new energy to consume, which is an effective means to solve this problem. Hydrogen energy is beneficial to storage. Among various energy storage methods, hydrogen energy storage has advantages in terms of energy storage density and energy storage time. In addition, hydrogen energy storage also has the advantages of extensive resources, cleanliness and no pollution. The prepared hydrogen can be used as hydrogen fuel to supply HV, and can also be converted into electric energy and heat energy, becoming a link to support multi-energy complementarity and build a unified energy system.

To sum up, under the technical framework of virtual power plant, based on the operational characteristics of HV commercial vehicles, this paper forecasts the hydrogen consumption of HVs. The scenario method is used to consider the uncertainty of new energy output, combined with the characteristics of hydrogen energy storage. Considering the coordinated scheduling of electricity, heat, hydrogen and other loads, the virtual power plant can participate in the energy market (EM), spinning reserve market (SRM), peak regulation market (PRM) and carbon emissions at the same time With the goal of maximizing the expected revenue of VPP, a coordinated optimization scheduling model is constructed to obtain the maximum revenue of VPP from electrolyzed water for hydrogen production, and to explore the commercial feasibility of electrolyzed water to produce hydrogen.

Purpose of invention

The purpose of the present invention is to provide a virtual power plant coordination scheduling method considering hydrogen fuel vehicles and hydrogen energy storage, comparing whether to consider the influence of hydrogen energy storage on the economy of optimal scheduling of virtual power plants, and considering the uncertainty of new energy output. The impact of virtual power plant scheduling is analyzed, and the impact of virtual power plants' participation in energy market, rotating reserve market, peak shaving market and carbon emission trading on revenue and scheduling results is analyzed to ensure the comprehensiveness and optimality of the scheduling plan.

SUMMARY OF THE INVENTION

The present invention provides a method for coordinating and dispatching a virtual power plant considering hydrogen fuel vehicles and hydrogen energy storage. The system includes electrolyzers, HFCs and hydrogen storage tanks. Among them, wind turbines, photovoltaic power stations, CHP units and external power grids jointly provide the hydrogen energy storage system with electricity for electrolysis of water to produce hydrogen and support the electrical load IL to the outside world. The external natural gas market is The CHP unit provides fuel, and the hydrogen energy storage system supports the hydrogen load outward; the HFC, the CHP unit and the thermal energy storage unit jointly support the heat load; the dispatch method includes the following steps:

Step 1. Construct a prediction model considering the hydrogen load required by the hydrogen fuel vehicle coupled with the road network. The form route of the hydrogen fuel vehicle is the driving route from the hydrogen refueling station to other multiple locations; according to the hydrogen fuel vehicle arriving at hydrogen refueling Station time and remaining fuel amount to get fuel demand;

Step 2. Build a hydrogen energy storage system model including electrolyzer, hydrogen storage tank and hydrogen fuel cell, including models of electrolyzer, hydrogen storage tank and fuel cell equipment as well as the model of the whole system;

Step 3, adopt the autoregressive moving average model and the k-means clustering method to generate the scenery typical output scene set;

Step 4. With the goal of maximizing the expected economic benefits, construct the objective function of the virtual power plant dispatching model, as shown in formula (1):

In formula (1), Is _,i (X ₀ ) and C _s,i (X ₀ ) are the respective operating income and cost index functions under the scenario s, and X ₀ is the optimization variable; the objective function adopts the system operation Set constraints, including the overall operation constraints of the virtual power plant and equipment operation constraints;

Step 5. Generate a scene set based on the wind and light output of typical days in summer and winter, and substitute it into the virtual power plant model for optimal scheduling.

Preferably, step 1 further includes the following sub-steps:

Step S11, calculate the time when the hydrogen fuel vehicle arrives at the hydrogenation location:

和剩余燃料量

则根据式(2) -(4)所示计算方法进行计算：Assuming that the HV is controlled by the aggregator, and the hydrogen refueling station is located at the starting station of the HV line, when the vehicle n returns to the starting station after the kth cycle, the aggregator obtains its arrival time

and remaining fuel

Then calculate according to the calculation method shown in formulas (2)-(4):

为车辆初始时间；D_i,d为 途中第(d-1)个站点至第d个站点的道路距离；△t_d-1为在第(d-1)个站点停车等待 的时间；

为车辆n初始燃料量，采用蒙特卡洛模拟进行仿真，生成初始燃料 随机数；l为车辆路线的距离；△m为HV每公里氢耗量；η为汽车非正常行驶状 态下的能耗系数，包括启动加速以及刹车期间发动机所需的负荷功导致的能量 损失；Among them, v _t is the average speed of vehicles on road i in period t, which is closely related to factors such as road grade, time, and road traffic flow; α ₁ , α ₂ , α ₃ and β are regression parameters and correction coefficients, which change with the road grade ; v ₀ is the design speed of each grade of road; F _t is the traffic flow of the road in the t period; F ₀ is the design traffic flow of the road; D is the number of bus stops in the process of vehicle driving;

is the initial time of the vehicle; D _i,d is the road distance from the (d-1)th station to the dth station on the way; △t _d-1 is the waiting time at the (d-1)th station;

is the initial fuel quantity of vehicle n, which is simulated by Monte Carlo simulation to generate initial fuel random numbers; l is the distance of the vehicle route; △m is the hydrogen consumption per kilometer of the HV; η is the energy consumption coefficient of the vehicle in abnormal driving state , including the energy loss due to the load work required by the engine during start-up acceleration and braking;

Step S12, calculate the total amount of hydrogen load demand for hydrogen fuel vehicles:

When the remaining fuel is less than a certain value, hydrogenation is carried out after arriving at the hydrogenation station, and the judgment of the HV hydrogenation state is shown in formulas (5) and (6):

表示第n辆HV在第k次循环结束的t时段 需要加氢；

为t时段氢燃料汽车的燃料需求总量；N为氢燃料汽车的数量；

为氢燃料汽车的储氢瓶含量。Among them, _mset is the fuel threshold of HV;

Indicates that the nth HV needs to be hydrogenated in the period t at the end of the kth cycle;

is the total fuel demand of hydrogen fuel vehicles in period t; N is the number of hydrogen fuel vehicles;

The content of hydrogen storage tanks for hydrogen fuel vehicles.

Preferably, step 2 further includes the following sub-steps:

Step S21, build an electrolytic cell model:

被表示为 产氢率η_PtH和输入电功率

的乘积，如式(7)所示：Set the hydrogen production rate per unit of electricity of the electrolytic cell η _PtH , then the hydrogen production amount of the electrolytic cell in the period t

is expressed as hydrogen production rate η _PtH and input electric power

The product of , as shown in formula (7):

Step S22, build a hydrogen fuel cell model:

和氢燃料电池温度T_HFC决定，将氢燃料电池的模型表示为如式(8)所示：The power generation and heat generation power of hydrogen fuel cells are determined by the consumption of hydrogen, respectively.

and the hydrogen fuel cell temperature T _HFC , the model of the hydrogen fuel cell is expressed as formula (8):

为总功率；Among them, E _ner is the Nernst voltage; η _act , η _ohm , and η _con are activation polarization, ohmic and concentration loss, respectively; F, M _H2 are Faraday's constant and hydrogen molar mass, respectively; K, S are heat exchange coefficients and exchange area; η _HFCh is the heat production efficiency of the hydrogen fuel cell;

is the total power;

Step S23, build a hydrogen storage tank model:

The hydrogen storage tank stores the hydrogen generated by the electrolyzer, provides hydrogen for the hydrogen fuel cell, refuels the hydrogen fuel vehicle and sells the hydrogen to the market for profit. The quality of the hydrogen in the hydrogen storage tank is expressed as formula (9):

为t时段储氢罐中氢气的质量；

为t时段电解槽的产氢量；

为t时段氢燃料电池的耗氢量；

为t时段HV的氢气需求量；In formula (9),

is the mass of hydrogen in the hydrogen storage tank at time t;

is the hydrogen production of the electrolyzer in period t;

is the hydrogen consumption of the hydrogen fuel cell in period t;

is the hydrogen demand of HV in period t;

Step S24, build a hydrogen energy storage system model:

The overall model of the hydrogen energy storage system is expressed as equation (10):

为氢储能系统整体的输出功率；布尔变量

分别表示t时段 氢储能电解槽和氢燃料电池工作状态。In the formula,

is the overall output power of the hydrogen energy storage system; Boolean variable

respectively represent the working states of the hydrogen energy storage electrolyzer and the hydrogen fuel cell in the t period.

Preferably, step 3 further includes the following sub-steps:

Step S31, determining the white noise based on the mean and standard deviation, that is, the sliding regression mean parameter;

Step S32, based on the reference prediction data, and in line with the principle of minimum residual variance, generate 1,000 scenario data for each load wind power output;

Step S33, based on the improved k-means clustering algorithm, cluster the wind and solar output, and generate several typical operating scenarios, which specifically include: finding the initial data clustering center based on the local density mean clustering algorithm; based on the initial clustering center, The k-means clustering algorithm is used to generate typical running scenarios.

Preferably, step 4 further includes the following sub-steps:

Step S41, establishing a virtual power plant coordination scheduling optimization objective model, and the objective function is expressed as shown in formula (13):

Among them, I _s,i (X ₀ ) and C _s,i (X ₀ ) are the respective operating benefit and cost index functions under the scenario s, and X ₀ is the optimization variable; the meanings of the items are as follows:

(1) Under scenario s, the total revenue of electricity sales of virtual power plants participating in the day-ahead energy market, rotating reserve and peak shaving market is expressed as formula (14):

表示t时段的日前电能量市场电价；

表示t时段的备用容量市 场电价；

和

分别表示t时段的向上调峰和向下调峰电价；

和

分别表示场景s下t时段虚拟电厂的日前电能量出力、备用出力和向上向下调 峰；

表示场景s下t时段虚拟电厂的热出力。

分别表示t时 段的EM、SRM、向上调峰和向下调峰电价；

分别为场景s下t时段风、 光、电解槽实际出力；

分别为CHP机组和HFC参与EM、SRM和向上、向下调峰市场的出力；in,

Indicates the day-ahead electricity market electricity price in period t;

represents the market electricity price of reserve capacity in period t;

and

Respectively represent the up-peak and down-peak electricity prices in the t period;

and

Respectively represent the day-ahead power output, standby output and peak-down peaking of the virtual power plant in the period t under the scenario s;

Indicates the heat output of the virtual power plant in the t period under the scenario s.

Respectively represent the EM, SRM, peak-up and down-peak electricity prices in t period;

are the actual output of wind, light, and electrolytic cell in time period t under scene s, respectively;

The output of CHP unit and HFC participating in EM, SRM and up and down peak markets respectively;

(2) The revenue of virtual power plant heating under scenario s is expressed as formula (15):

分别为CHP机组、HFC和热储能储、放的热 功率；in,

are the thermal power of CHP unit, HFC and thermal energy storage and discharge respectively;

(3) In the scenario s, the revenue from the sale of hydrogen by the virtual power plant is expressed as formula (16):

表示氢气价格；in,

represents the hydrogen price;

(4) The carbon trading income is expressed as formula (17):

为场景s下t时段虚拟电厂的碳排放配额；γ_e、 γ_h分别为CHP机组的供电基准值和供热基准值；F_r为机组供热量修正系数；Among them, C _CM is the carbon trading price;

is the carbon emission quota of the virtual power plant in the t period under the scenario s; γ _e and γ _h are the reference value of power supply and the reference value of heat supply of the CHP unit, respectively; F _r is the correction coefficient of heat supply of the unit;

(5) The operation and maintenance cost of wind and solar power generation in scenario s is expressed as formula (18):

分别为场景s 下t时段风、光实际出力；Among them, k _W and k _PV are the unit operation and maintenance costs of wind and solar power generation respectively;

are the actual output of wind and light in time period t under scene s, respectively;

(6) In the scenario s, the penalty cost of the deviation between the virtual power plant and the dispatch instruction is expressed as formula (19):

表示场景s下t时段EM、向上向下调峰的偏差量；γ_em、γ_ru、γ_rd分别为EM、向上向下调峰的惩罚系数；in,

Represents the deviation amount of EM, upward and downward peak reduction in the t period of scene s; γ _em , γ _ru , and γ _rd are the penalty coefficients of EM and upward and downward peak reduction, respectively;

(7) The cost of CHP unit under scenario s is expressed as formula (20):

表示CHP机组 的电出力；Among them, α ₀ , α ₁ , α ₂ , α ₃ , α ₄ , α ₅ are the energy consumption coefficients of CHP units;

Indicates the electrical output of the CHP unit;

(8) The cost of hydrogen energy storage includes the average investment cost and operation and maintenance cost of electrolyzers, fuel cells and hydrogen storage tanks, which are expressed as equation (21):

表示场景s下t时段电解槽、HFC、储氢罐的运行维护 成本；λ_PtH、λ_HFC、λ_HS分别为电解槽、HFC和储氢罐的单位运行维护成本；

为 场景s下t时段储氢罐储氢质量。in,

represents the operation and maintenance costs of the electrolyzer, HFC, and hydrogen storage tank in the t period of the scenario s; λ _PtH , λ _HFC , and λ _HS are the unit operation and maintenance costs of the electrolyzer, HFC and hydrogen storage tank, respectively;

is the hydrogen storage quality of the hydrogen storage tank in the t period of the scenario s.

Further preferably, the objective function adopts system operation set constraints, including the overall operation constraints of the virtual power plant and the equipment operation constraints, wherein the overall operation constraints of the virtual power plant include: day-ahead power market power balance constraints, reserve capacity constraints, upward adjustment The peak power constraint, the downward peak power constraint and the thermal power balance constraint are expressed as follows:

(1) The power balance constraint of the day-ahead electric energy market is expressed as formula (22):

为上级电网下发的t时段EM电量指令；

分别为场景 s下t时段CHP机组和HFC电能量市场的功率；In the formula,

The EM power command issued by the upper-level power grid in the t period;

are the power of the CHP unit and the HFC power market in the t period of the scenario s, respectively;

(2) The reserve capacity constraint is expressed as Eq. (23):

分别为场景s下t时段CHP机组和HFC在备用容量市场 的备用容量；In the formula,

are the spare capacity of CHP units and HFC in the spare capacity market in the period t under scenario s, respectively;

(3) The up and down peak power constraints are expressed as equation (24):

分别为上级电网下发的t时段向上向下调峰的指令功率；

分别为场景s下t时段CHP机组和HFC在灵活调峰 市场向上向下调峰的功率；In the formula,

are the command power for peaking up and down in t period issued by the upper-level power grid, respectively;

are the powers of CHP units and HFC in the flexible peak shaving market up and down in the t period under scenario s, respectively;

(5) The thermal power balance constraint is expressed as Eq. (25):

为场景s下t时段CHP机组的热 功率；

分别为场景s下t时段热储能的充、放热功率；In the formula, H ^t is the heat load demand of the virtual power plant in period t;

is the thermal power of the CHP unit in the t period under the scenario s;

are the charging and discharging powers of the thermal energy storage in the t period of the scene s, respectively;

The equipment operation constraints include CHP unit output constraints, electrolyzer output and ramp constraints, fuel cell operation and ramp constraints, and hydrogen storage tanks and thermal energy storage constraints, which are respectively expressed as:

(6)) Output constraints of CHP units

The operating interval of the CHP unit is limited to the range of the quadrilateral ABCD, and the boundaries AD, AB, BC, and CD represent the minimum limit of steam injection, the maximum heat rate, the maximum limit of fuel injection, and the maximum limit of output electric power, respectively. Define the corner point, That is, the electrical power and thermal power of the boundary intersection point are (x _k , y _k ); the relationship between the electrical power and thermal power of the CHP unit and the coordinates of the boundary intersection point is expressed as formula (26):

In the formula, M _i is the total number of boundary intersection points of the CHP unit, and the combination coefficient satisfies the constraint equation shown in Eq. (27):

(7) The output of the electrolytic cell and the constraints of climbing are expressed as formulas (28) and (29):

分别为电解槽出力上、下限；R_PtH为电解槽出力爬坡速率约 束；In the formula,

are the upper and lower limits of the output of the electrolytic cell, respectively; R _PtH is the limit of the ramp rate of the output of the electrolytic cell;

(8) The fuel cell operation and climbing constraints are expressed as equations (30), (31), (32):

分别为燃料电池出力上、下限；R_HFC为燃料电池出力爬坡 速率约束；In the formula,

are the upper and lower limits of fuel cell output, respectively; R _HFC is the fuel cell output ramp rate constraint;

(9) The hydrogen storage tank and thermal energy storage constraints are expressed as equations (33)-(39):

分别为储氢的上、下限；

分别为储氢量在一天的 始、末值。

分别为储、放热功率的最大值；布尔变量

分别 表示场景s下t时段储热装置是否储放热，是则置1，否则置0；

分别 为储热容量的上、下限；

分别为储热容量在一天的始、末值。In the formula,

are the upper and lower limits of hydrogen storage, respectively;

are the hydrogen storage capacity at the beginning and end of the day, respectively.

are the maximum values of storage and heat release power, respectively; Boolean variable

Respectively indicate whether the heat storage device stores and releases heat in the t period of the scene s, if it is, it is set to 1, otherwise it is set to 0;

are the upper and lower limits of the heat storage capacity, respectively;

are the beginning and end values of the heat storage capacity in a day, respectively.

Preferably, the virtual power plant is composed of 1 CHP unit, 1 wind farm, 1 photovoltaic power station, 1 electrolysis hydrogen production device, 1 fuel cell, 1 hydrogen storage tank and 1 heat storage device; the The maximum power of the electrolytic hydrogen production device is 5MW, and the ramp rate is 1MW; the maximum power of the fuel cell is 5MW, and the ramp rate is 2MW; the maximum capacity of the hydrogen storage tank is 1000kg, the minimum capacity is 100kg, and the initial capacity is 1000kg. The maximum capacity of the heat storage device is 10MW·h, the minimum capacity is 1MW·h, the initial capacity is 5MW·h, and the heat storage and release efficiency is 0.88.

Description of drawings

In order to illustrate the technical solutions of the present invention more clearly, the following will briefly introduce the accompanying drawings used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, which are common in the art. As far as technical personnel are concerned, other drawings can also be obtained based on these drawings without any creative effort.

Fig. 1 is a virtual power plant system composition diagram provided by the present invention;

Fig. 2 is the hydrogen fuel vehicle operation route map provided by the present invention;

Fig. 3 is the operation interval diagram of CHP unit provided by the present invention;

Fig. 4 is the hydrogen load prediction situation diagram of HV provided by the present invention;

Fig. 5 typical summer day energy market, rotating reserve market and peak shaving market electricity value provided by the present invention;

Fig. 6 typical daily energy market, rotating reserve market and peak shaving market electricity value provided by the present invention;

Fig. 7 wind power and photovoltaic forecast power diagram provided by the present invention;

Fig. 8 scheduling instruction and heat load diagram provided by the present invention;

Fig. 9 is a typical summer day virtual power plant optimization situation diagram provided by the present invention;

Figure 10 is a diagram of the optimization situation of a virtual power plant on a typical day in winter provided by the invention;

Detailed ways:

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

It should be understood by those skilled in the art that the step numbers used in the text are only for the convenience of description, and are not intended to limit the order of execution of the steps. The terms used in the present specification are for the purpose of describing particular embodiments only and are not intended to limit the present invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural unless the context clearly dictates otherwise. The terms "comprising" and "comprising" indicate the presence of the described features, integers, steps, operations, elements and/or components, but do not exclude one or more other features, integers, steps, operations, elements, components and/or the existence or addition of its collection. The term "and/or" refers to and including any and all possible combinations of one or more of the associated listed items.

The present invention provides a virtual power plant coordination scheduling method considering hydrogen fuel vehicles and hydrogen energy storage, which specifically includes the following steps:

S101 Build a hydrogen energy vehicle load prediction model considering the road network, including:

(1) Calculate the time for the hydrogen fuel vehicle to arrive at the hydrogenation site:

和剩余燃料量

其 计算方法为：For HVs, they are assumed to be controlled by the aggregator. The hydrogen refueling station is located at the starting station of the HV line. When the vehicle n returns to the starting station after the k-th cycle, the aggregator obtains its arrival time

and remaining fuel

Its calculation method is:

为车辆初始时间；D_i,d为 途中第(d-1)个站点至第d个站点的道路距离；△t_d-1为在第(d-1)个站点停车等待 的时间；

为车辆n初始燃料量，采用蒙特卡洛模拟进行仿真，生成初始燃料 随机数；l为车辆路线的距离；△m为HV每公里氢耗量；η为汽车非正常行驶状 态下的能耗系数，包括启动加速以及刹车期间发动机所需的负荷功导致的能量 损失。Among them, v _t is the average speed of vehicles on road i in period t, which is closely related to factors such as road grade, time, and road traffic flow; α ₁ , α ₂ , α ₃ and β are regression parameters and correction coefficients, which change with the road grade ; v ₀ is the design speed of each grade of road; F _t is the traffic flow of the road in the t period; F ₀ is the design traffic flow of the road; D is the number of bus stops in the process of vehicle driving;

is the initial time of the vehicle; D _i,d is the road distance from the (d-1)th station to the dth station on the way; △t _d-1 is the waiting time at the (d-1)th station;

is the initial fuel quantity of vehicle n, which is simulated by Monte Carlo simulation to generate initial fuel random numbers; l is the distance of the vehicle route; △m is the hydrogen consumption per kilometer of the HV; η is the energy consumption coefficient of the vehicle in abnormal driving state , including the energy loss due to the load work required by the engine during start-up acceleration and braking.

(2) Calculate the total demand for hydrogen load of hydrogen fuel vehicles:

When the remaining fuel is less than a certain value, hydrogenation is carried out after arriving at the hydrogenation station, and the judgment of the HV hydrogenation state is shown in the following formula:

表示第n辆HV在第k次循环结束的t时段 需要加氢；

为t时段氢燃料汽车的燃料需求总量；N为氢燃料汽车的数量；

为氢燃料汽车的储氢瓶含量。Among them, _mset is the fuel threshold of HV;

Indicates that the nth HV needs to be hydrogenated in the period t at the end of the kth cycle;

is the total fuel demand of hydrogen fuel vehicles in period t; N is the number of hydrogen fuel vehicles;

The content of hydrogen storage tanks for hydrogen fuel vehicles.

S102 Build a hydrogen energy storage system model, the specific contents include:

(1) Electrolyzer Model

可表示为产氢率η_PtH和输入 电功率

的乘积：The hydrogen production rate of the electrolytic cell and the power consumption of the electrolytic cell are approximately in a linear relationship. If the hydrogen production rate per unit of electricity of the electrolytic cell is η _PtH , the hydrogen production rate of the electrolytic cell in the period t is

It can be expressed as hydrogen production rate η _PtH and input electric power

The product of :

(2) Hydrogen fuel cell model

和 氢燃料电池温度T_HFC决定。则氢燃料电池的模型为：When the external factors are known, the power generation and heat generation power of the hydrogen fuel cell are respectively affected by the consumption of hydrogen

and the hydrogen fuel cell temperature T _HFC is determined. Then the model of the hydrogen fuel cell is:

为总功率。Among them, E _ner is the Nernst voltage; η _act , η _ohm , and η _con are activation polarization, ohmic and concentration loss, respectively; F, M _H2 are the Faraday constant and hydrogen molar mass, respectively; η _HFCh is the hydrogen fuel cell heat production efficiency;

is the total power.

(3) Hydrogen storage tank model

The hydrogen storage tank stores the hydrogen generated by the electrolyzer, which can provide hydrogen for hydrogen fuel cells, refuel hydrogen vehicles, and sell hydrogen to the market for profit. The mass of hydrogen in the hydrogen storage tank is:

为t时段储氢罐中氢气的质量；

为t时段电解槽的产氢量；

为t时段氢燃料电池的耗氢量；

为t时段HV的氢气需求量。in,

is the mass of hydrogen in the hydrogen storage tank at time t;

is the hydrogen production of the electrolyzer in period t;

is the hydrogen consumption of the hydrogen fuel cell in period t;

is the hydrogen demand of HV in period t.

(4) Hydrogen energy storage system model

The overall model of the hydrogen energy storage system is:

为氢储能系统整体的输出功率；布尔变量

分别表示t时段 氢储能电解槽和氢燃料电池工作状态。where:

is the overall output power of the hydrogen energy storage system; Boolean variable

respectively represent the working states of the hydrogen energy storage electrolyzer and the hydrogen fuel cell in the t period.

S103 processes the uncertainty of wind and solar output in the power grid based on the scene method, including scene generation and scene reduction.

Based on the autoregressive moving average model, the scene is generated, and a large number of forecasting scenarios including the wind and light output are constructed to describe the uncertainty of the wind and light output. The specific contents include:

Determine the white noise based on the mean and standard deviation, that is, the sliding regression mean parameter;

Based on the benchmark forecast data, and in line with the principle of minimum residual variance, generate 1,000 scenario data for each load wind power output;

Further, based on the improved k-means clustering algorithm, the wind and solar output is clustered to generate several typical operating scenarios, the specific contents include:

Find the initial data cluster center based on the local density mean clustering algorithm;

Based on the initial cluster centers, k-means clustering algorithm is used to generate typical operating scenarios.

S104 establishes the objective function and constraints of the optimal scheduling model.

Specifically, establish a virtual power plant coordination scheduling optimization objective model:

Among them, Is _,i (X ₀ ) and C _s,i (X ₀ ) are the index functions of various operating benefits and costs under the scenario s, respectively, and X ₀ is the optimization variable. The meanings of each are as follows:

(1) Under scenario s, the total revenue from electricity sales of virtual power plants participating in the day-ahead energy market, rotating reserve and peak shaving market:

表示t时段的日前电能量市场电价；

表示t时段的备用容量市 场电价；

和

分别表示t时段的向上调峰和向下调峰电价；

和

分别表示场景s下t时段虚拟电厂的日前电能量出力、备用出力和向上向下调 峰；

表示场景s下t时段虚拟电厂的热出力。

分别表示t时 段的EM、SRM、向上调峰和向下调峰电价；

分别为场景s下t时段风、 光、电解槽实际出力；

分别为CHP机组和HFC参与EM、SRM和向上、向下调峰市场的出力。in,

Indicates the day-ahead electricity market electricity price in period t;

represents the market electricity price of reserve capacity in period t;

and

Respectively represent the up-peak and down-peak electricity prices in the t period;

and

Respectively represent the day-ahead power output, standby output and peak-down peaking of the virtual power plant in the period t under the scenario s;

Indicates the heat output of the virtual power plant in the t period under the scenario s.

Respectively represent the EM, SRM, peak-up and down-peak electricity prices in t period;

are the actual output of wind, light, and electrolytic cell in time period t under scene s, respectively;

It is the output of CHP unit and HFC participating in EM, SRM and up and down peaking market respectively.

(2) The benefits of virtual power plant heating under scenario s:

分别为CHP机组、HFC和热储能储、放的热 功率。in,

are the thermal power of CHP unit, HFC and thermal energy storage and discharge, respectively.

(3) Revenue from the sale of hydrogen by the virtual power plant in scenario s:

表示氢气价格。in,

Indicates the hydrogen price.

(4) Virtual power plants can make profits by selling the emission credits that do not meet the carbon emission quotas. Conversely, when net emissions exceed carbon emission allowances, carbon transaction fees are required to purchase emission allowances. The carbon trading income is expressed as follows:

为场景s下t时段虚拟电厂的碳排放配额；γ_e、 γ_h分别为CHP机组的供电基准值和供热基准值；F_r为机组供热量修正系数。Among them, C _CM is the carbon trading price;

is the carbon emission quota of the virtual power plant in the t period under the scenario s; γ _e and γ _h are the reference value of power supply and the reference value of heat supply of the CHP unit, respectively; F _r is the correction coefficient of heat supply of the unit.

(5) The operation and maintenance cost of wind and solar power generation in scenario s is:

分别为场景s 下t时段风、光实际出力。Among them, k _W and k _PV are the unit operation and maintenance costs of wind and solar power generation respectively;

are the actual output of wind and light in time period t under scene s, respectively.

(6) The penalty cost of the deviation between the virtual power plant and the dispatch instruction in the scenario s:

表示场景s下t时段EM、向上向下调峰的偏差量；γ_em、γ_ru、γ_rd分别为EM、向上向下调峰的惩罚系数。in,

Represents the deviation of EM, up and down peaks in the t period of scene s; γ _em , γ _ru , and γ _rd are the penalty coefficients of EM, up and down peaks, respectively.

(7) The cost of CHP units in scenario s is mainly the fuel cost of natural gas. Its cost expression is as follows:

表示CHP机组 的电出力。Among them, α ₀ , α ₁ , α ₂ , α ₃ , α ₄ , α ₅ are the energy consumption coefficients of CHP units;

Indicates the electrical output of the CHP unit.

(8) The cost of hydrogen energy storage includes the average investment cost and operation and maintenance cost of electrolyzers, fuel cells and hydrogen storage tanks. The cost expression is as follows:

表示场景s下t时段电解槽、HFC、储氢罐的运行维护 成本；λ_PtH、λ_HFC、λ_HS分别为电解槽、HFC和储氢罐的单位运行维护成本；

为 场景s下t时段储氢罐储氢质量。in,

represents the operation and maintenance costs of the electrolyzer, HFC, and hydrogen storage tank in the t period of the scenario s; λ _PtH , λ _HFC , and λ _HS are the unit operation and maintenance costs of the electrolyzer, HFC and hydrogen storage tank, respectively;

is the hydrogen storage quality of the hydrogen storage tank in the t period of the scenario s.

The objective function adopts system operation set constraints, including the overall operation constraints of the virtual power plant and equipment operation constraints.

The system operating set constraints include the overall virtual power plant operating constraints and equipment operating constraints. The overall operation constraints of the virtual power plant include: day-ahead power market power balance constraints, reserve capacity constraints, up-peak power constraints, down-peak power constraints, and thermal power balance constraints.

(1) The power balance constraint of the day-ahead electric energy market

为上级电网下发的t时段EM电量指令；

分别为场景 s下t时段CHP机组和HFC电能量市场的功率。In the formula,

The EM power command issued by the upper-level power grid in the t period;

are the power of the CHP unit and the HFC power market in the t period under the scenario s, respectively.

(2) Reserve capacity constraints

分别为场景s下t时段CHP机组和HFC在备用容量市场 的备用容量。In the formula,

are the spare capacity of CHP units and HFC in the spare capacity market in the t period under scenario s, respectively.

(3) Up and down peak power constraints

分别为上级电网下发的t时段向上向下调峰的指令功率；

分别为场景s下t时段CHP机组和HFC在灵活调峰 市场向上向下调峰的功率。In the formula,

are the command power for peaking up and down in t period issued by the upper-level power grid, respectively;

are the peaking power of CHP units and HFC in the flexible peaking market during t period under scenario s, respectively.

(5) Thermal power balance constraints

为场景s下t时段CHP机组的热 功率；

分别为场景s下t时段热储能的充、放热功率。In the formula, H ^t is the heat load demand of the virtual power plant in period t;

is the thermal power of the CHP unit in the t period under the scenario s;

are the charging and discharging powers of the thermal energy storage in the t period under the scenario s, respectively.

The device operating constraints are:

(6) Output constraints of CHP units

The operating range of the CHP unit is shown in Figure 3. The boundaries AD, AB, BC, and CD represent the minimum limit of steam injection, the maximum heat rate, the maximum limit of fuel injection, and the maximum limit of output electric power, respectively. Define the electric power and thermal power of the corner point (boundary intersection) as (x _k , y _k ).

Thermal and electrical coupling of CHP units, these parameters must be maintained within the feasible operating range. Since any point in the region can be represented by a convex combination of boundary intersections, the relationship between electrical power, thermal power and the coordinates of boundary intersections is expressed as:

In the formula, M _i is the total number of boundary intersection points of CHP units, and the combination coefficient satisfies the following equation:

(7) The output of the electrolytic cell and the restriction of climbing

分别为电解槽出力上、下限；R_PtH为电解槽出力爬坡速率约 束。In the formula,

are the upper and lower limits of the output of the electrolytic cell, respectively; R _PtH is the limit of the ramp rate of the output of the electrolytic cell.

(8) Fuel cell operation and climbing constraints

分别为燃料电池出力上、下限；R_HFC为燃料电池出力爬坡 速率约束。In the formula,

are the upper and lower limits of the fuel cell output, respectively; R _HFC is the fuel cell output ramp rate constraint.

(9) Hydrogen storage tank and thermal energy storage constraints

分别为储氢的上、下限；

分别为储氢量在一天的 始、末值。

分别为储、放热功率的最大值；布尔变量

分别 表示场景s下t时段储热装置是否储放热，是则置1，否则置0；

分别 为储热容量的上、下限；

分别为储热容量在一天的始、末值。In the formula,

are the upper and lower limits of hydrogen storage, respectively;

are the hydrogen storage capacity at the beginning and end of the day, respectively.

are the maximum values of storage and heat release power, respectively; Boolean variable

Respectively indicate whether the heat storage device stores and releases heat in the t period of the scene s, if it is, it is set to 1, otherwise it is set to 0;

are the upper and lower limits of the heat storage capacity, respectively;

are the beginning and end values of the heat storage capacity in a day, respectively.

S105. The hydrogen refueling station provides hydrogen refueling services for 50 HVs, and the required hydrogen is provided by hydrogen energy storage. The parameters of the route are shown in Table 1; the predicted hydrogen fuel required by HV is shown in Figure 4.

Table 1 Parameters of HV and route

The virtual power plant consists of 1 CHP unit, 1 wind farm, 1 photovoltaic power station, 1 electrolysis hydrogen production device, 1 fuel cell, 1 hydrogen storage tank and 1 heat storage device. The maximum power of the electrolysis hydrogen production device is 5MW, and the ramp rate is 1MW; the maximum power of the fuel cell is 5MW, and the ramp rate is 2MW; the maximum capacity of the hydrogen storage tank is 1000kg, the minimum capacity is 100kg, and the initial capacity is 500kg; The maximum capacity of the thermal device is 10MW·h, the minimum capacity is 1MW·h, the initial capacity is 5MW·h, and the heat storage and release efficiency is 0.88; the heat price is 90 yuan/MW·h; the hydrogen price is 50 yuan/kg; Typical daily energy market, rotating reserve market and peak shaving market electricity price forecast values in winter are shown in Figure 5 and Figure 6; wind power and photovoltaic forecast power is shown in Figure 7; superior dispatch instructions and virtual power plant heat demand are shown in Figure 8; Divide, T=24. All relevant calculations were completed on an Intel Core i5-7400 processor 3.00GHz, 8GB memory computer, and MATLAB was used to program and solve the calculation example.

For comparative analysis of the validity and correctness of the evaluation method model introduced in the embodiment of the present invention, the comparative scheme is as shown in Table 2:

Table 2 Three comparison schemes

The above three schemes are used to build the VPP scheduling optimization model for typical days in summer and winter.

Table 3 Comparison of benefits and cost results of the three schemes

Table 4 Comparison of scheduling results of three schemes

Compared with Schemes 1 and 2, which do not consider hydrogen energy storage and do not participate in the peak shaving market, Scheme 3 can not only use hydrogen energy storage equipment to prepare hydrogen, generate electricity and supply heat to users during peak load hours, thereby reducing the deviation from dispatching instructions. Reduce command deviation penalties, increase revenue by supplying fuel to HVs, and increase peak shaving revenue by participating in the peak shaving market.

(1) Scheduling optimization of virtual power plants on typical days in summer

A scene set is generated based on the scenery output of a typical day in summer, and is substituted into the model for optimal scheduling. The scheduling optimization of virtual power plants on typical days in summer is shown in Figure 9.

The scenario set is generated by using the forecast value of wind and solar output on a typical day in summer. The optimal scheduling result of VPP is shown in Figure 9. In the period 1-7, due to the low amount of dispatching instructions, the electrolyzer runs and produces hydrogen; in the period 8-13, with the increase in the amount of dispatching instructions, the electrolyzer stops running at this time, and the HFC starts to supply power from 9:00 , At the same time, VPP began to show the deviation of scheduling instructions at 10:00; in the period of 14-18, as the amount of scheduling instructions decreased, the HFC stopped working at this time and started the electrolyzer; in the period of 19-21, the evening peak led to an increase in the amount of scheduling instructions , HFC works in the period of 19-20, at this time, the electrolytic cell stops running to meet the load demand; in the period of 22-24, the dispatching instruction decreases, the HFC stops working, and the electrolytic cell starts.

(2) Scheduling optimization of virtual power plants on typical days in winter

The scheduling optimization of the virtual power plant on a typical day in winter is shown in Figure 10.

The optimal scheduling results of VPP on typical days in winter are shown in Figure 10. Since the market electricity price and dispatching instructions on a typical day in winter are quite different from those in a typical day in summer, the optimization results are also quite different. Among them, the VPP heat load on a typical day in winter increases significantly, so the heat output of the CHP unit increases significantly. Due to the reduction of power grid dispatch instructions, the power supply of HFC is lower than that of typical days in summer. The power supply is supplied during the period of 10-13, and the electrolyzer operates in the period of 1-7, 15-18 and 21-24. There is no command deviation on the day. The invention considers the joint operation of hydrogen fuel vehicles and hydrogen energy storage, and introduces them into virtual power plants for coordinated scheduling, so as to establish a virtual power plant coordination scheduling method considering hydrogen fuel vehicles and hydrogen energy storage, and provides various benefits and cost indicators. A mathematical calculation model is established, and a typical wind and solar output scene set is generated by the scenario method to consider the uncertainty of wind and solar output. The objective function is to maximize the expected economic benefits of the virtual power plant in the actual operation of the next day. The decision variables include the electrical power and thermal power of the CHP unit, Electrolyzer power, fuel cell electrical power and thermal power, thermal energy storage and release heat power, and hydrogen storage tank quality, etc. A comparative analysis of whether to add hydrogen energy storage equipment and whether to participate in the peak shaving market ensures the reliability and optimality of the plan.

The above are the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the principles of the present invention, several improvements and modifications can be made, and these improvements and modifications may also be regarded as It is the protection scope of the present invention.

Claims (7)

1. A virtual power plant coordination scheduling method considering hydrogen fuel vehicles and hydrogen energy storage, the virtual power plant comprises a wind turbine, a photovoltaic power station, a CHP unit, a hydrogen energy storage system, and a thermal energy storage unit, and the hydrogen energy storage system includes Electrolyzers, HFCs and hydrogen storage tanks, among which wind turbines, photovoltaic power stations, CHP units and external power grids jointly provide the hydrogen energy storage system with electricity for electrolysis of water to produce hydrogen and support electrical load IL to the outside world, and the external natural gas market is CHP units Provide fuel, and the hydrogen energy storage system supports the hydrogen load externally; the HFC, CHP unit and the thermal energy storage unit jointly support the thermal load; it is characterized in that, the scheduling method includes the following steps: Step 1. Construct a prediction model considering the hydrogen load required by the hydrogen fuel vehicle coupled with the road network. The form route of the hydrogen fuel vehicle is the driving route from the hydrogen refueling station to other multiple locations; according to the hydrogen fuel vehicle arriving at hydrogen refueling Station time and remaining fuel amount to get fuel demand; Step 2. Build a hydrogen energy storage system model including electrolyzers, hydrogen storage tanks and hydrogen fuel cells, including models of electrolyzers, hydrogen storage tanks, and fuel cell equipment, as well as models of the overall system; Step 3. Use the autoregressive moving average model and the k-means clustering method to generate a set of typical scenery output scenes; Step 4. With the goal of maximizing the expected economic benefits, construct the objective function of the virtual power plant dispatching model, as shown in formula (1):

In formula (1), Is _,i (X ₀ ) and C _s,i (X ₀ ) are the respective operating income and cost index functions under the scenario s, and X ₀ is the optimization variable; the objective function adopts the system operation Set constraints, including the overall operation constraints of the virtual power plant and equipment operation constraints; Step 5. Generate a scene set based on the wind and light output of typical days in summer and winter, and substitute it into the virtual power plant model for optimal scheduling. 2. a kind of virtual power plant coordination scheduling method considering hydrogen fuel vehicle and hydrogen energy storage according to claim 1, is characterized in that, step 1 further comprises the following sub-steps: Step S11, calculate the time when the hydrogen fuel vehicle arrives at the hydrogenation location: Assuming that the HV is controlled by the aggregator, and the hydrogen refueling station is located at the starting station of the HV line, when the vehicle n returns to the starting station after the kth cycle, the aggregator obtains its arrival time

and remaining fuel

Then calculate according to the calculation method shown in formulas (2)-(4):

Among them, v _t is the average speed of vehicles on road i in period t, which is closely related to factors such as road grade, time, and road traffic flow; α ₁ , α ₂ , α ₃ and β are regression parameters and correction coefficients, which change with the road grade ; v ₀ is the design speed of each grade of road; F _t is the traffic flow of the road in the t period; F ₀ is the design traffic flow of the road; D is the number of bus stops in the process of vehicle driving;

is the initial time of the vehicle; D _i,d is the road distance from the (d-1)th station to the dth station on the way; Δt _d-1 is the waiting time at the (d-1)th station;

is the initial fuel quantity of the vehicle n, which is simulated by Monte Carlo simulation to generate the initial fuel random number; l is the distance of the vehicle route; Δm is the hydrogen consumption per kilometer of the HV; η is the energy consumption coefficient under the abnormal driving state of the vehicle, Including the energy loss caused by the load work required by the engine during start-up acceleration and braking; Step S12, calculate the total amount of hydrogen load demand for hydrogen fuel vehicles: When the remaining fuel is less than a certain value, hydrogenation is carried out after arriving at the hydrogenation station. The judgment of the HV hydrogenation state is shown in formulas (5) and (6):

Among them, _mset is the fuel threshold of HV;

Indicates that the nth HV needs to be hydrogenated in the period t at the end of the kth cycle;

is the total fuel demand of hydrogen fuel vehicles in period t; N is the number of hydrogen fuel vehicles;

The content of hydrogen storage tanks for hydrogen fuel vehicles. 3. a kind of virtual power plant coordination scheduling method considering hydrogen fuel vehicle and hydrogen energy storage according to claim 2, is characterized in that, step 2 further comprises the following sub-steps: Step S21, build an electrolytic cell model: Set the hydrogen production rate per unit of electricity of the electrolytic cell η _PtH , then the hydrogen production amount of the electrolytic cell in the period t

is expressed as hydrogen production rate η _PtH and input electric power

The product of , as shown in formula (7):

Step S22, build a hydrogen fuel cell model: The power generation and heat generation power of hydrogen fuel cells are determined by the consumption of hydrogen, respectively.

and the hydrogen fuel cell temperature T _HFC , the model of the hydrogen fuel cell is expressed as formula (8):

Among them, E _ner is the Nernst voltage; η _act , η _ohm , and η _con are activation polarization, ohmic and concentration loss, respectively; F and M _H2 are the Faraday constant and hydrogen molar mass, respectively; η _HFCh is the hydrogen fuel cell heat production efficiency;

is the total power; Step S23, build a hydrogen storage tank model: The hydrogen storage tank stores the hydrogen generated by the electrolyzer, provides hydrogen for the hydrogen fuel cell, and supplements the fuel for the hydrogen fuel vehicle to obtain profits. The hydrogen quality in the hydrogen storage tank is expressed as formula (9):

In formula (9),

is the mass of hydrogen in the hydrogen storage tank at time t;

is the hydrogen production of the electrolyzer in period t;

is the hydrogen consumption of the hydrogen fuel cell in period t;

is the hydrogen demand of HV in period t; Step S24, build a hydrogen energy storage system model: The overall model of the hydrogen energy storage system is expressed as equation (10):

In the formula,

is the overall output power of the hydrogen energy storage system; Boolean variable

respectively represent the working states of the hydrogen energy storage electrolyzer and the hydrogen fuel cell in the t period. 4. a kind of virtual power plant coordination scheduling method considering hydrogen fuel vehicle and hydrogen energy storage according to claim 3, is characterized in that, step 3 further comprises the following sub-steps: Step S31, determining the white noise based on the mean and standard deviation, that is, the sliding regression mean parameter; Step S32 , generating 1000 scene data of each load wind power output based on the reference prediction data and the principle of minimum residual variance; Step S33, based on the improved k-means clustering algorithm, cluster the wind and solar output, and generate several typical operating scenarios, which specifically include: finding the initial data clustering center based on the local density mean clustering algorithm; based on the initial clustering center, The k-means clustering algorithm is used to generate typical running scenarios. 5. A method for coordinating and dispatching a virtual power plant considering hydrogen fuel vehicles and hydrogen energy storage according to claim 4, wherein step 4 further comprises: A virtual power plant coordination scheduling optimization objective model is established, and the objective function is expressed as formula (13):

Among them, I _s,i (X ₀ ), C _s,i (X ₀ ) are the respective operating benefit and cost index functions under the scenario s, and X ₀ is the optimization variable; the operating benefit and cost index functions are respectively include: (1) Under scenario s, the total revenue of electricity sales of virtual power plants participating in the day-ahead energy market, rotating reserve and peak shaving market is expressed as formula (14):

in,

Represents the day-ahead electric energy market price in t period;

represents the market electricity price of reserve capacity in period t;

and

Respectively represent the up-peak and down-peak electricity prices in the t period;

and

Respectively represent the day-ahead power output, standby output and peak-down peaking of the virtual power plant in the period t under the scenario s;

Indicates the heat output of the virtual power plant in the t period under the scenario s.

Respectively represent the EM, SRM, peak-up and down-peak electricity prices in t period;

are the actual outputs of wind, light, and electrolytic cells in the t period of the scene s, respectively;

The output of CHP unit and HFC participating in EM, SRM and up and down peak markets respectively; (2) The revenue of virtual power plant heating under scenario s is expressed as formula (15):

in,

are the thermal power of CHP unit, HFC and thermal energy storage and discharge respectively; (3) In the scenario s, the revenue from the sale of hydrogen by the virtual power plant is expressed as formula (16):

in,

represents the hydrogen price; (4) The carbon trading revenue is expressed as formula (17):

Among them, C _CM is the carbon trading price;

is the carbon emission quota of the virtual power plant in the period t under the scenario s; γ _e and γ _h are the reference value of power supply and the reference value of heat supply of the CHP unit, respectively; F _r is the correction coefficient of heat supply of the unit; (5) The operation and maintenance cost of wind and solar power generation in scenario s is expressed as formula (18):

Among them, k _W and k _PV are the unit operation and maintenance costs of wind and solar power generation respectively;

are the actual output of wind and light in time period t under scene s, respectively; (6) In the scenario s, the penalty cost of the deviation between the virtual power plant and the dispatch instruction is expressed as formula (19):

in,

Represents the deviation amount of EM, upward and downward peak reduction in the t period of scene s; γ _em , γ _ru , and γ _rd are the penalty coefficients of EM and upward and downward peak reduction, respectively; (7) The cost of CHP unit under scenario s is expressed as formula (20):

Among them, α ₀ , α ₁ , α ₂ , α ₃ , α ₄ , α ₅ are the energy consumption coefficients of CHP units;

Indicates the electrical output of the CHP unit; (8) The cost of hydrogen energy storage in scenario s includes the operation and maintenance costs of electrolyzers, fuel cells and hydrogen storage tanks, and is expressed as equation (21):

in,

represents the operation and maintenance costs of the electrolyzer, HFC, and hydrogen storage tank in the t period of the scenario s; λ _PtH , λ _HFC , and λ _HS are the unit operation and maintenance costs of the electrolyzer, HFC and hydrogen storage tank, respectively;

is the hydrogen storage quality of the hydrogen storage tank in the t period of the scenario s. 6. A virtual power plant coordination scheduling method considering hydrogen fuel vehicles and hydrogen energy storage according to claim 5, wherein the objective function adopts system operation set constraints, including virtual power plant overall operation constraints and equipment operation constraints , wherein the overall operation constraints of the virtual power plant include: day-ahead electric energy market power balance constraints, reserve capacity constraints, up-peak power constraints, down-peak power constraints, and thermal power balance constraints, which are respectively expressed as follows: (1) The power balance constraint of the day-ahead electric energy market is expressed as formula (22):

In the formula,

The EM power command issued by the upper-level power grid in the t period;

are the power of the CHP unit and the HFC power market in the t period of the scenario s, respectively; (2) The reserve capacity constraint is expressed as Eq. (23):

In the formula,

are the spare capacity of CHP units and HFC in the spare capacity market in the period t under scenario s, respectively; (3) The up and down peak power constraints are expressed as equation (24):

In the formula,

are the command power for peaking up and down in t period issued by the upper-level power grid, respectively;

are the powers of CHP units and HFC in the flexible peak shaving market up and down in the t period under scenario s, respectively; (5) The thermal power balance constraint is expressed as Eq. (25):

In the formula, H ^t is the heat load demand of the virtual power plant in period t;

is the thermal power of the CHP unit in the t period under the scenario s;

are the charging and discharging powers of the thermal energy storage in the t period of the scene s, respectively; The equipment operation constraints include CHP unit output constraints, electrolyzer output and ramp constraints, fuel cell operation and ramp constraints, and hydrogen storage tank and thermal energy storage constraints, which are expressed as: (6)) Output constraints of CHP units The operating interval of the CHP unit is limited to the range of the quadrilateral ABCD, and the boundaries AD, AB, BC, and CD represent the minimum limit of steam injection, the maximum heat rate, the maximum limit of fuel injection, and the maximum limit of output electric power, respectively. Define the corner point, That is, the electrical power and thermal power of the boundary intersection point are (x _k , y _k ); the relationship between the electrical power and thermal power of the CHP unit and the coordinates of the boundary intersection point is expressed as formula (26):

In the formula, M _i is the total number of boundary intersection points of the CHP unit, and the combination coefficient satisfies the constraint equation shown in Eq. (27):

(7) The output of the electrolytic cell and the constraints of climbing are expressed as formulas (28) and (29):

In the formula,

are the upper and lower limits of the output of the electrolytic cell, respectively; R _PtH is the limit of the ramp rate of the output of the electrolytic cell; (8) The fuel cell operation and climbing constraints are expressed as equations (30), (31), (32):

In the formula,

are the upper and lower limits of fuel cell output, respectively; R _HFC is the fuel cell output ramp rate constraint; (9) The hydrogen storage tank and thermal energy storage constraints are expressed as equations (33)-(39):

In the formula,

are the upper and lower limits of hydrogen storage, respectively;

are the hydrogen storage capacity at the beginning and end of the day, respectively.

are the maximum values of storage and heat release power, respectively; Boolean variable

Respectively indicate whether the heat storage device stores and releases heat in the t period of the scene s, if it is, it is set to 1, otherwise it is set to 0;

are the upper and lower limits of the heat storage capacity, respectively;

are the beginning and end values of the heat storage capacity in a day, respectively. 7. A virtual power plant coordination scheduling method considering hydrogen fuel vehicles and hydrogen energy storage according to any one of claims 1-6, wherein the virtual power plant consists of 1 CHP unit, 1 wind farm, 1 It consists of a photovoltaic power station, an electrolysis hydrogen production device, a fuel cell, a hydrogen storage tank and a heat storage device; the maximum power of the electrolytic hydrogen production device is 5MW, and the ramp rate is 1MW; the fuel The maximum power of the battery is 5MW, and the ramp rate is 2MW; the maximum capacity of the hydrogen storage tank is 1000kg, the minimum capacity is 100kg, and the initial capacity is 500kg; the maximum capacity of the heat storage device is 10MW·h, and the minimum capacity is 1MW·h, the initial capacity is 5MW·h, and the heat storage and release efficiency is 0.88.

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