A hydrogen-containing integrated energy system optimization scheduling method and related equipment

Abstract

The application relates to the technical field of thermoelectricity cooperation optimization, and discloses a hydrogen-containing comprehensive energy system optimization scheduling method and related equipment, which comprises the following steps: constructing a hydrogen-containing comprehensive energy system framework, establishing operation constraints of various types of energy equipment and coupling constraints among the various types of energy equipment; constructing a hydrogen-containing comprehensive energy system pre-scheduling model based on the operation constraints of the various types of energy equipment and the coupling constraints among the various types of energy equipment; predicting actual carbon emissions according to the hydrogen-containing comprehensive energy system pre-scheduling model, determining carbon quota pre-purchase quantity according to the actual carbon emissions, and obtaining adjusted carbon quota according to the carbon quota pre-purchase quantity; performing dynamic carbon trading based on the adjusted carbon quota, determining the cost of the dynamic carbon trading, and performing rolling optimization on the hydrogen-containing comprehensive energy system to complete hydrogen-containing comprehensive energy system scheduling work; and the hydrogen-containing comprehensive energy system scheduling method can consider user energy use habits and reduce user carbon emission costs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of thermoelectricity collaborative optimization, in particular to a hydrogen-containing integrated energy system optimization scheduling method and related equipment. BACKGROUND

[0002] Integrated energy system (IES) couples electricity, heat, hydrogen and other energy forms into an energy framework, which can realize the complementation of various energy operation characteristics and the multi-time scale transfer of energy, thereby improving energy utilization efficiency and flexible operation capability of the system. Although IES has considerable potential in flexible adjustment, its low-carbon operation is often hindered by increased cost and user habits. Therefore, designing effective energy trading and carbon trading strategies is of great significance to stimulate the carbon emission reduction potential of IES and realize the low-carbon transformation of the energy system.

[0003] The concept of IES originates from the field of combined heat and power, focusing on the collaborative optimization of heat and power systems. With the promotion of energy policy and hydrogen energy technology, hydrogen provides a more feasible and applicable option for IES. Hydrogen integrated energy system (HIES) takes electricity as the core and has long-period and large-capacity hydrogen energy storage support, reducing the limitations of energy cross-time domain transfer. The electricity-to-hydrogen conversion and hydrogen-to-electricity conversion of hydrogen energy storage are performed by different devices, which have higher scheduling flexibility. However, the low-carbon benefits of HIES often contradict its economic benefits. Mainly, the low-carbon operation mode not only has to bear additional carbon trading costs, but also needs to change the original economic operation mode, resulting in an increase in operation cost. In addition, in the existing carbon market mechanism, carbon quotas are allocated to users without distinction of time period according to a certain total amount. This allocation method is top-down, and the adaptability to user's energy consumption mode and diversified decarbonization measures is insufficient. Specifically, the carbon emission demand and carbon emission intensity of users are different in different time periods, causing uneven use of carbon quotas and increasing carbon trading costs. Some carbon reduction measures taken by users, such as connecting renewable energy and configuring energy storage, are also difficult to fully exert their carbon reduction potential under the average carbon quotas.

[0004] In summary, there are three major challenges in integrating the carbon market into HIES. First, it is crucial to strike a balance between carbon emission costs and decarbonization incentives. The main goal of the carbon market is to encourage users to adopt low-carbon operation strategies. A certain carbon trading cost is necessary to promote low-carbon transformation, but excessive cost will reduce the willingness of users to participate in the carbon market. Second, it is necessary to strike a balance between the established energy consumption habits of users and the adoption of low-carbon operation methods. Transitioning to low-carbon operation will inevitably change the existing energy consumption patterns of production and life. It is crucial to strike a balance between minimizing changes to energy consumption patterns and maximizing carbon emission reductions. In addition, hydrogen energy has great potential in improving renewable energy utilization and increasing system flexibility, but as a transitional energy in HIES, its role in the carbon market and low-carbon operation strategy needs further research. SUMMARY

[0005] In order to overcome the defects of the prior art, the purpose of the present application is to provide a hydrogen-containing comprehensive energy system optimization scheduling method to solve the technical problems of the prior art in hydrogen-containing comprehensive energy system scheduling, which is difficult to balance the user's energy consumption habits and reduce the user's carbon emission cost.

[0006] The present application is realized by the following technical solutions:

[0007] In a first aspect, the present application provides a hydrogen-containing comprehensive energy system optimization scheduling method, comprising:

[0008] A hydrogen-containing comprehensive energy system framework is constructed, and operation constraints of each type of energy equipment and coupling constraints between each type of energy equipment are established based on the hydrogen-containing comprehensive energy system framework;

[0009] A hydrogen-containing comprehensive energy system pre-scheduling model is constructed based on the operation constraints of each type of energy equipment and the coupling constraints between each type of energy equipment;

[0010] The actual carbon emission is predicted according to the hydrogen-containing comprehensive energy system pre-scheduling model, the carbon quota pre-purchase quantity is determined according to the actual carbon emission, and the adjusted carbon quota is obtained according to the carbon quota pre-purchase quantity;

[0011] Dynamic carbon trading is carried out based on the adjusted carbon quota, and the cost of dynamic carbon trading is determined;

[0012] The hydrogen-containing comprehensive energy system is rolled and optimized based on the dynamic carbon market, the output of each type of energy equipment of the hydrogen-containing comprehensive energy system is adjusted in real time, and the hydrogen-containing comprehensive energy system scheduling work is completed.

[0013] Preferably, the types of energy equipment include power equipment and heat equipment, wherein the operation constraints of the power equipment are as follows:

[0014]

[0015] wherein, Pgrid is the grid-connected wind or photovoltaic power, Pwind is the available wind or photovoltaic power; Pgas is the output power of the gas turbine, electric boiler, hydrogen power generation device, and electric hydrogen production device, Pmaxgas is the maximum output power of the gas turbine, electric boiler, hydrogen power generation device, and electric hydrogen production device; Pgridtie is the power of the grid tie or electrochemical energy storage, Pmaxgridtie is the upper limit power of the grid tie or electrochemical energy storage;

[0016] The operation constraints of the thermal equipment are as follows:

[0017]

[0018] wherein, Pheat is the thermal power of the electric boiler, gas boiler, hydrogen power generation device, electric hydrogen production device, and gas turbine, Pheatstorage is the thermal power of the heat storage; Pmaxheat is the maximum thermal power of the gas boiler or heat storage; ηheat is the heat production efficiency of the electric boiler, hydrogen power generation device, electric hydrogen production device, and gas turbine, ηgen is the power generation efficiency of the gas turbine, ηheatutilization is the heat utilization rate of the hydrogen power generation device, electric hydrogen production device, and gas turbine;

[0019] The coupling constraints of the electric power equipment and the thermal equipment are as follows:

[0020]

[0021] wherein, Estorage is the energy stored in the electrochemical energy storage, hydrogen energy storage, and heat storage; Pcharge is the charging power of the electrochemical energy storage, hydrogen energy storage, and heat storage, Pdischarge is the discharging power of the electrochemical energy storage, hydrogen energy storage, and heat storage; Cmax is the maximum energy storage capacity of the electrochemical energy storage, hydrogen energy storage, and heat storage.

[0022] Preferably, in the step of constructing the hydrogen-containing comprehensive energy system pre-scheduling model based on the operation constraints of each type of energy equipment and the coupling constraints between each type of energy equipment, the specific process is as follows:

[0023] The Gaussian Copula function is used to construct the source-load joint probability distribution function, the source-load joint probability distribution function of each period is sampled, the source-load time series data of each time interval is obtained according to the inverse transformation of the source-load joint probability distribution function, the source-load time series data is clustered through K-means clustering, and the typical operation scenarios are formed; the probability of each typical operation scenario is calculated to form a scenario set;

[0024] The target of the pre-scheduling model is to minimize the economic operation cost, which includes power cost, fuel cost and maintenance cost; the power cost is measured by the power of the bidirectional tie line and the purchase and sale electricity price, and the purchase and sale electricity fees are distinguished by using an indicator function; the fuel cost is the natural gas cost consumed by the gas turbine and the gas boiler; the maintenance cost includes the maintenance fees of the power, heat and energy storage devices;

[0025] The energy consumption habits of different typical operation scenarios are balanced, a scenario-based expected optimization scheduling method is adopted, the economic scheduling strategy of the hydrogen comprehensive energy system is generated according to the typical scenarios and their probabilities, and the expected system operation cost is minimized.

[0026] Preferably, according to the actual carbon emissions predicted by the hydrogen comprehensive energy system pre-scheduling model, the actual carbon emissions are determined as the basic basis for the demand of the carbon quota pre-purchase amount; and the carbon quota pre-purchase amount is determined by the carbon quota difference and the predicted carbon price;

[0027] The carbon quota difference is calculated by the initial free carbon quota, and when the carbon quota difference is positive, it indicates that there is a surplus of carbon quota, and vice versa, the carbon quota difference is negative, indicating that the carbon quota is insufficient;

[0028] The predicted carbon price includes a fluctuation coefficient and a segmented carbon price;

[0029] The fluctuation coefficient is associated with the carbon quota and the carbon emissions in the market, that is, when the increase of the user's carbon emissions leads to an increase in the market demand for carbon quota, the fluctuation coefficient of the carbon price increases;

[0030] The segmented carbon price takes the transaction volume as the independent variable, and the carbon price changes linearly in different transaction volume ranges according to different transaction volumes;

[0031] The calculation formula of the predicted carbon price is as follows:

[0032] Predicted carbon price = fluctuation coefficient x segmented carbon price.

[0033] Further, in the step of determining the carbon quota pre-purchase amount by the carbon quota difference and the predicted carbon price, the specific determination process is as follows:

[0034] When the carbon quota is insufficient and the predicted carbon price is high, the carbon quota is purchased.

[0035] When the carbon quota is excessive and the predicted carbon price is low, sell the carbon quota

[0036] When the carbon quota is insufficient and the predicted carbon price is low, or when the carbon quota is excessive and the predicted carbon price is high, buy or sell a certain proportion of the initial carbon quota.

[0037] Preferably, the dynamic carbon trading based on the adjusted carbon quota is carried out, and the specific process in the step of determining the cost of dynamic carbon trading is as follows:

[0038] The dynamic carbon price is obtained according to the supply and demand relationship of carbon quota in the market;

[0039] The actual carbon quota difference is determined based on the adjusted carbon quota and the real-time carbon emission, wherein the actual carbon quota difference is equal to the carbon quota trading volume in a carbon trading settlement period;

[0040] The cost of dynamic carbon trading is determined by the carbon quota trading volume and the dynamic carbon price.

[0041] Preferably, the target function and calculation formula of the rolling optimization are as follows:

[0042]

[0043]

[0044]

[0045] In the formula, is the running cost and carbon trading cost of the real-time rolling optimization stage in the k control time domain, is the compensation price of electricity and fuel; is a floor function; j ( k ) represents the k time point of the j control time domain; EX represents the grid tie line, the electric boiler, the electrochemical energy storage, the hydrogen power generation device and the electric hydrogen production device; GX represents the hydrogen energy storage, the heat storage, the gas turbine and the gas boiler; and .

[0046] In the second aspect, the application further provides a hydrogen-containing integrated energy system optimization scheduling system, comprising:

[0047] A system framework modeling module is configured to build a hydrogen-containing integrated energy system framework, and establish operation constraints of each type of energy equipment and coupling constraints between each type of energy equipment based on the hydrogen-containing integrated energy system framework;

[0048] a pre-scheduling model construction module configured to construct a hydrogen-containing comprehensive energy system pre-scheduling model based on operation constraints of each type of energy device and coupling constraints between each type of energy device;

[0049] a carbon quota prediction module configured to predict an actual carbon emission amount according to the hydrogen-containing comprehensive energy system pre-scheduling model, determine a carbon quota pre-purchase amount according to the actual carbon emission amount, and obtain an adjusted carbon quota according to the carbon quota pre-purchase amount;

[0050] a dynamic carbon transaction determination module configured to perform dynamic carbon transaction based on the adjusted carbon quota and determine a cost of the dynamic carbon transaction;

[0051] an optimized scheduling module configured to perform rolling optimization of the hydrogen-containing comprehensive energy system based on the dynamic carbon market, adjust outputs of each type of energy device of the hydrogen-containing comprehensive energy system in real time, and complete scheduling of the hydrogen-containing comprehensive energy system.

[0052] In a third aspect, the present application further provides a mobile terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the hydrogen-containing comprehensive energy system optimized scheduling method as described above when executing the computer program.

[0053] In a fourth aspect, the present application further provides a computer readable storage medium storing a computer program, wherein the computer program is executable on a processor to implement the steps of the hydrogen-containing comprehensive energy system optimized scheduling method as described above.

[0054] Compared with the prior art, the present application has the following beneficial technical effects:

[0055] The present application provides a hydrogen-containing comprehensive energy system optimized scheduling method, which integrates multiple types of energy devices by constructing a hydrogen-containing comprehensive energy system framework, and ensures efficient collaborative work between components in the system and improves energy utilization efficiency and flexibility by explicitly defining operation constraints and coupling constraints of each type of energy device. The hydrogen-containing comprehensive energy system pre-scheduling model constructed based on operation constraints and coupling constraints of each type of energy device can accurately predict the operation state and energy demand of the system. By predicting the actual carbon emission amount and determining the carbon quota pre-purchase amount accordingly, fine-grained management of carbon emissions is achieved. The adjusted carbon quota based on the carbon quota pre-purchase amount is used for dynamic carbon transaction to optimize carbon emission cost. Rolling optimization of the hydrogen-containing comprehensive energy system based on the dynamic carbon market can adjust the outputs of each type of energy device in real time according to the actual situation. This dynamic adjustment capability enables the system to better adapt to changes in energy demand, improving the flexibility and efficiency of energy utilization.

[0056] Further, the application proposes a multi-dimensional joint source-load scenario generation method based on a Gaussian Copula function, which not only excavates the randomness and correlation of power sources such as wind power and photovoltaic power and loads such as power and heat, but also reduces the redundancy of scenarios, enhances the efficiency of pre-scheduling calculation, guarantees the comprehensive coverage of pre-scheduling results on scenarios, improves the effectiveness of system pre-scheduling results, and reduces the calculation complexity.

[0057] Further, the application proposes a carbon quota pre-purchase and real-time trading carbon trading strategy, which can not only reduce the carbon emission cost of energy use through carbon quota pre-purchase, but also encourage users to implement carbon reduction operation measures, including improving the level of renewable energy utilization, increasing low-carbon emission power and heat production equipment, realizing sustainable development of user low-carbon transformation, reducing user carbon emission cost, and promoting user low-carbon energy transformation.

[0058] Further, the application improves the traditional rolling optimization method by considering user energy use habits and carbon reduction demand. In each control time domain, power deviation and carbon quota deviation are optimized at the same time to minimize power compensation cost and carbon quota transaction cost. Compared with the traditional rolling optimization method, the system has higher utilization rate of low-carbon emission power equipment and heat equipment, and more reasonable and sustainable utilization mode of hydrogen energy. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 A flowchart of the hydrogen-containing comprehensive energy system optimization scheduling method in the embodiments of the application is shown in the figure.

[0060] Figure 2 A schematic diagram of the hydrogen-containing comprehensive energy system structure in the embodiments of the application is shown in the figure.

[0061] Figure 3 A schematic diagram of the hydrogen-containing comprehensive energy system source-load joint operation scenario in the embodiments of the application is shown in the figure.

[0062] Figure 4 A schematic diagram of the expected electric-thermal power balance result of the hydrogen-containing comprehensive energy system pre-scheduling in the embodiments of the application is shown in the figure.

[0063] Figure 5 A flowchart of the carbon quota pre-purchase strategy in the embodiments of the application is shown in the figure.

[0064] Figure 6 A schematic diagram of the carbon quota adjustment result of the hydrogen-containing comprehensive energy system in the embodiments of the application is shown in the figure.

[0065] Figure 7 A schematic diagram of the real-time carbon emission and carbon trading result of the hydrogen-containing comprehensive energy system in the embodiments of the application is shown in the figure.

[0066] Figure 8A schematic diagram of real-time electric and thermal power balance results of the hydrogen-containing comprehensive energy system in the embodiment of the present application is shown in the figure.

[0067] Figure 9 A schematic diagram of energy storage capacity changes of electrochemical energy storage, hydrogen energy storage and heat storage in the hydrogen-containing comprehensive energy system in the embodiment of the present application is shown in the figure.

[0068] Figure 10 A schematic diagram of the structure of the optimal scheduling system of the hydrogen-containing comprehensive energy system in the embodiment of the present application is shown in the figure.

[0069] In the figure: 1, system framework modeling module; 2, pre-scheduling model building module; 3, carbon quota prediction module; 4, dynamic carbon trading determination module; 5, optimal scheduling module. DETAILED DESCRIPTION

[0070] In order to enable personnel in the technical field to better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely below in combination with the drawings in the embodiment of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts shall belong to the scope of protection of the present application.

[0071] The purpose of the present application is to provide a hydrogen-containing comprehensive energy system optimal scheduling method to solve the technical problem that the prior art is difficult to balance user energy use habits and reduce user carbon emission costs in hydrogen-containing comprehensive energy system scheduling.

[0072] The present application will be described in further detail below in combination with the drawings:

[0073] Referring to Figure 1 The present application provides a hydrogen-containing comprehensive energy system optimal scheduling method, which comprises:

[0074] Step 1, build a hydrogen-containing comprehensive energy system framework, and establish operation constraints of each type of energy equipment and coupling constraints between each type of energy equipment based on the hydrogen-containing comprehensive energy system framework;

[0075] Specifically, from an energy form perspective, the equipment in a hydrogen-containing integrated energy system includes electrical and thermal equipment. Gas turbines, electric boilers, hydrogen storage (HyS), hydrogen production equipment (P2H), and hydrogen power generation equipment (H2P) all fall under the categories of electrical and thermal equipment. Wind turbines, photovoltaic generators, and electrochemical energy storage (ECS) are electrical equipment. Gas boilers (HS) are thermal equipment. From a contribution perspective, the equipment in the system includes power sources, energy storage, and loads. Wind turbines, photovoltaic generators, gas turbines, and gas boilers are all power sources. ECS, HyS, and HS are energy storage. P2H, H2P, and electric boilers represent energy sources or loads from different energy consumption perspectives. The system's electricity and heat are collected through power buses and heat buses respectively, enabling the interconnection of different equipment.

[0076] The operational model of the hydrogen-containing integrated energy system extends from individual equipment to the overall system construction. The output of various energy devices is subject to upper limits. The grid interconnection lines and ECS (Electronic Control System) exhibit bidirectional power flow characteristics, and their power constraints are symmetrical. The operational constraints of the power equipment are as follows:

[0077] (1)

[0078] In the formula, For grid-connected wind or solar power, Available wind or solar power; This refers to the output power of gas turbines, electric boilers, H2P, and P2H systems. The maximum output power of gas turbines, electric boilers, H2P, and P2H; Power for the power grid interconnect or ECS; This refers to the upper limit power of the grid interconnect or ECS.

[0079] Thermal power equipment utilizes various production methods. Electric boilers are electrically driven, and heating is controlled electrically. Gas-fired boilers are controlled by natural gas input. Heat generation from P2H, H2P, and gas turbines is a byproduct. To improve dispatching flexibility, this portion of heat is not mandatory for full utilization. The power constraints of HS are similar to those of ECS, and the operating constraints of thermal power equipment are as follows:

[0080] (2)

[0081] In the formula, For the thermal power of electric boilers, gas boilers, P2H, H2P, and gas turbines, The thermal power of heat storage; The maximum thermal power of a gas-fired boiler or thermal storage unit; The heat production efficiency of electric boilers, P2H, H2P, and gas turbines; The power generation efficiency of the gas turbine; P2H, H2P, heat utilization rate of gas turbine.

[0082] Unlike HyS, the charging and discharging of ECS and HS are performed by the same device. In order to simplify the operation model of the energy storage system, the charging and discharging power of ECS and HS are defined as follows before establishing the operation model of the HIES energy storage system:

[0083] (3)

[0084] wherein, and are the charging power and discharging power of ECS, respectively; and are the heat storage power and heat release power of HS, respectively.

[0085] The operation model of the hydrogen-containing integrated energy system includes power capacity conversion constraints, charging and discharging asynchronous constraints, capacity cycle constraints, and capacity upper and lower limit constraints, and the specific expressions are as follows:

[0086] (4)

[0087] wherein, is the energy stored in ECS, HyS, and HS. is the charging power of ECS, HyS, and HS, is the discharging power of ECS, HyS, and HS. is the maximum energy storage capacity of ECS, HyS, and HS.

[0088] The electrical and thermal balance equations of the hydrogen-containing integrated energy system are as follows: In particular, the power of ECS and HS is positive, indicating charging, and negative, indicating discharging. The grid tie line power is positive, representing power feeding in.

[0089] (5)

[0090] wherein, P L ( t ) and Q L ( t ) are the electrical load and thermal load of HIES, respectively.

[0091] Step 2: Based on the operation constraints of each type of energy device and the coupling constraints between each type of energy device, a pre-scheduling model of the hydrogen-containing integrated energy system is constructed;

[0092] Specifically, scenarios are the basis of pre-scheduling. Considering the correlation between source loads, a Gaussian Copula function is used to construct the joint probability distribution function of source loads. The joint probability distribution function of each period is sampled. According to the inverse transformation of the joint probability distribution function, the time series data of source loads in each time interval is obtained. The K-means clustering method is used for clustering the sampling results to form typical scenarios. The probability of each scenario is calculated, and a scenario set is obtained, which is as follows:

[0093] (6)

[0094] wherein, SN is the i-th typical scenario, s is the probability of the i-th typical scenario. s p s s

[0095] The objective function of pre-scheduling, i.e. the economic operation cost, includes power cost, fuel cost and maintenance cost. The above costs are basic fees to ensure the normal operation of the system, as shown in formula (7). The optimal scheduling scheme for these costs reflects the energy consumption habits of the system, which is not affected by the carbon market. The system operation cost under this scheduling scheme is the lowest. The introduction of the carbon market will change this energy consumption habit to some extent and increase the operation cost of the system.

[0096] (7)

[0097] wherein, is the operation cost, power cost, fuel cost and maintenance cost under the i-th scenario. s

[0098] The power cost is measured by the power of the bidirectional tie line and the purchase and sale electricity price. The indicator function 1(·) is used to distinguish the purchase and sale electricity fees. The fuel cost takes into account the cost of natural gas consumed by the gas turbine and the gas boiler. The maintenance cost includes the power, heat and energy storage equipment. The power equipment maintenance fee involves wind turbines, photovoltaic generators, P2H and H2P. The heat equipment maintenance fee involves gas boilers and electric boilers. The energy storage equipment maintenance fee involves ECS and HS. The maintenance fee of HyS is borne by H2P and P2H.

[0099] (8)

[0100] (9)

[0101] (10)

[0102] wherein, ​​​​​The electricity purchase and sale price, The fuel price. The unit power maintenance cost of wind power, photovoltaic, gas turbine, P2H, H2P, The unit power maintenance cost of electric boiler, gas boiler, ECS, HS.

[0103] In order to balance the energy consumption habits of different operation scenarios, the target of pre-scheduling is to balance the expected cost of system operation under various scenario probabilities.

[0104] (11)

[0105] The scenario-based HIES pre-scheduling model is composed of objective function (11) and constraints (1)-(5).

[0106] Step 3, according to the actual carbon emission amount predicted by the hydrogen-containing comprehensive energy system pre-scheduling model, the carbon quota pre-purchase amount is determined according to the actual carbon emission amount, and the adjusted carbon quota is obtained according to the carbon quota pre-purchase amount;

[0107] Specifically, the actual carbon emission is the fundamental basis for the pre-purchase demand of carbon quota. The actual carbon emission is predicted according to the pre-scheduling result. The exchange of electricity between the power grid tie line and the hydrogen-containing comprehensive energy system is the main source of carbon emission of electricity. The gas turbine and gas boiler consume external fuel, which is the main source of fuel carbon emission. The carbon emission of electric power and heat equipment is considered to be linearly related to its power generation and heat production. The predicted carbon emission is as follows:

[0108] (12)

[0109] In the formula, is t The predicted carbon emission of the period, is the carbon emission factor of the power grid, gas turbine and gas boiler.

[0110] Based on the initial free carbon quota, the carbon quota difference is further calculated. The initial carbon quota of various devices is different, but it is uniformly distributed in time. If the carbon quota difference is positive, it means that there is a surplus of carbon quota. On the contrary, the negative carbon quota difference means that the carbon quota is insufficient. The carbon quota difference is calculated as follows:

[0111] (13)

[0112] In the formula, is the carbon quota difference in the pre-purchase stage, is the initial carbon quota in the pre-purchase stage.

[0113] Another factor affecting the pre-purchase of carbon quota is the predicted carbon price. The carbon price is composed of two parts: the fluctuation coefficient and the segmented carbon price. The fluctuation of carbon price is related to the carbon quota and carbon emissions in the market. The increase of user carbon emissions leads to an increase in market demand for carbon quota, which in turn drives up the carbon price. Since it is difficult to obtain the carbon emission data of the entire system, the fluctuation coefficient of the predicted carbon price is evaluated according to the operation of the hydrogen-containing integrated energy system. The overall level of the electrical and thermal load of the hydrogen-containing integrated energy system reflects the fluctuation of the carbon price in each period. The fluctuation coefficient of the predicted carbon price is as follows:

[0114] (14)

[0115] wherein, is the fluctuation coefficient of the predicted carbon price.

[0116] The segmented carbon price designed by the present application takes the transaction volume A as the independent variable. According to different transaction volumes, the price increases linearly within different transaction volume ranges. This price linked to the transaction volume perfects the market incentive for low-carbon operation. The segmented carbon price is as follows:

[0117] (15)

[0118] wherein, is the segmented carbon price, α is the interval growth rate, l is the interval length, c 1, c 2, c 3, c 4 is the interval end value. It can be seen that the segmented carbon price is mainly determined by the growth rate and the linear interval, and the upper limit and the lower limit are c 4 and c 1, respectively.

[0119] Based on the fluctuation coefficient and the segmented carbon price, the predicted carbon price formula is as follows:

[0120] (16)

[0121] wherein, is the predicted carbon price.

[0122] The pre-purchase of carbon quota is centralized, and the pre-purchase price does not fluctuate with time, i.e. λ 0 ( t ) = 1. The pre-purchase amount of carbon quota is determined by the carbon quota difference and the predicted carbon price.

[0123] After determining the carbon quota difference, when the carbon quota is insufficient and the predicted carbon price is high, carbon quota is purchased. The amount of carbon quota purchased by each market participant in each time period is capped based on its initial carbon quota. The purchase of carbon quota is shown as follows:

[0124] (17)

[0125] wherein, is the adjusted carbon quota for the i-th market participant after the pre-purchase of carbon quota. i is the carbon quota purchased by the i-th market participant in the t-th time period. t is the maximum purchasable or sellable carbon quota coefficient based on the initial carbon quota. β On the contrary, when the carbon quota is excessive and the predicted carbon price is low, the carbon quota is sold as shown below:

[0126]

[0127] (18)

[0128] wherein, is the adjusted carbon quota for the i-th market participant after the pre-purchase of carbon quota. i is the carbon quota sold by the i-th market participant in the t-th time period. t When the carbon quota is insufficient and the predicted carbon price is low, or when the carbon quota is excessive and the predicted carbon price is high, part of the carbon quota is purchased or sold.

[0129] In the carbon market, excessive holding of carbon quota by participants is not conducive to carbon quota trading, and even destroys market balance. The upper limit of the daily carbon quota holding amount of participants is constrained as shown below:

[0130]

[0131] (19) wherein,

[0132] is the maximum holdable carbon quota coefficient based on the initial carbon quota. ν On the basis of the initial free carbon quota, after experiencing the pre-purchase of carbon quota, the adjusted carbon quota is as follows.

[0133]

[0134] (20)

[0135] wherein, is the adjusted carbon quota after pre-purchase.

[0136] The pre-purchase cost of carbon quota is as follows.

[0137] (21)

[0138] wherein, is the carbon trading cost in the pre-purchase stage.

[0139] ​​The adjustment of carbon quota based on the pre-scheduling result of HIES can optimize the time distribution of carbon quota and meet the carbon emission demand of the system. The independent adjustment of carbon quota can also reduce the impact of carbon market policy changes on the operation of HIES.

[0140] Step 4, dynamic carbon trading based on the adjusted carbon quota, to determine the cost of dynamic carbon trading;

[0141] Specifically, the dynamic carbon price still consists of a segmented carbon price and a fluctuation coefficient. The segmented carbon price is the same as the predicted carbon price. The evaluation of the fluctuation coefficient follows a similar method to the predicted carbon price. According to the short-term load prediction within a day, the electrical load and thermal load of each period are dynamically updated. The dynamic carbon price is shown as follows:

[0142] (22)

[0143] where, is the dynamic carbon price of the k th period, is the fluctuation coefficient of the dynamic carbon price of the k th period.

[0144] Based on the adjusted carbon quota and the real-time carbon emission, the actual carbon quota difference is calculated as follows:

[0145] (23)

[0146] where, is the carbon emission during real-time scheduling, is the carbon quota difference during real-time scheduling.

[0147] In a carbon trading settlement period, the carbon quota trading volume and the actual carbon quota difference are balanced. The relationship between the carbon quota trading volume and the actual carbon quota difference is shown as follows:

[0148] (24)

[0149] where, and are the real-time purchased and sold carbon quotas of the i th market participant in the k th period.

[0150] The settlement of carbon quota is longer than the time scale of real-time scheduling. The t th settlement is the carbon quota from the n ( t -1)+1 to nt period. According to the purchase and sale volume of carbon quota and the dynamic carbon price, the cost of dynamic carbon trading is shown as follows:

[0151] (25)

[0152] wherein, is the carbon trading cost of the real-time scheduling stage.

[0153] Step 5, based on the dynamic carbon market, the hydrogen-containing integrated energy system is rolled and optimized, and the output of each type of energy equipment of the hydrogen-containing integrated energy system is adjusted in real time, and the scheduling work of the hydrogen-containing integrated energy system is completed.

[0154] Specifically, the rolling optimization needs to be carried out under the determined operation boundary. The time sequence power of the power supply and the load is obtained by the ultra-short-term prediction. The control time domain ranges from 1 hour. The optimization step of each optimization period is 1 / n , the k control time domain boundary is as follows.

[0155] (26)

[0156] wherein, SR ( k ) is the operation scenario of the k control time domain.

[0157] The goal of the rolling optimization is to reduce the deviation between the real-time scheduling and the pre-scheduling. These deviations are related to the electric heat power supply output and the real-time carbon emission. The objective function and its calculation method are as follows:

[0158] (27)

[0159] (28)

[0160] (29)

[0161] wherein, is the operation cost and carbon trading cost of the real-time rolling optimization stage in the k control time domain, is the compensation price of the electricity and the fuel; is the floor function; j ( k ) represents the k time point of the j control time domain; EX represents the grid tie line, the electric boiler, the ECS, the H2P and the P2H; GX represents the HyS, the HS, the gas turbine and the gas boiler; and .

[0162] In the rolling optimization, the system operation boundary is constantly updated, and it is meaningless to keep the initial and final energy storage states the same in the control time domain. Therefore, the energy storage operation strategy in the rolling optimization is adjusted as follows:

[0163] (30)

[0164] Through the low-carbon operation strategy framework of the hydrogen-containing integrated energy system composed of pre-scheduling, carbon quota pre-purchase and rolling optimization, the carbon emission cost of users can be effectively reduced, users are encouraged to schedule low-carbon emission energy equipment and adopt low-carbon operation mode, and the low-carbon transformation of the energy system is promoted.

[0165] Embodiment 1

[0166] The embodiment provides a hydrogen-containing integrated energy system optimization scheduling method, and the specific process is as follows:

[0167] The structure diagram of the hydrogen-containing integrated energy system (HIES) is as shown in Figure 2 . The system is configured with 100kW / 200kWh electrochemical energy storage (ECS), 100kW / 400kWh hydrogen energy storage (HyS) and 20kW / 300kWh heat storage (HS). P2H is one of an alkaline electrolytic cell, a proton exchange membrane electrolytic cell or a solid oxide electrolytic cell, and H2P is a hydrogen fuel cell. The upper limit of the tie line power is 200kW. The maximum output of the gas turbine, the electric boiler and the gas boiler is 100kW, 50kW and 50kW respectively. The time interval of the pre-scheduling is 1h, and the time interval of the rolling optimization is 15min (i.e. n = 4). The pre-scheduling and the rolling optimization can be solved by a commercial solver GUROBI or CPLEX.

[0168] Firstly, the Gaussian Copula function is used to generate the source load joint scenario, and then K-means clustering is used to obtain the typical operation scenario. Based on the typical operation scenario, the HIES pre-scheduling is performed.

[0169] The source load joint scenario is as shown in Figure 3 . After clustering, 6 typical scenarios are generated, and the probability is [0.170, 0.144, 0.174, 0.202, 0.160, 0.150]. It can be seen that the wind power and the photovoltaic power, and the electric load and the heat load have significant correlation, and the source load time sequence power matching degree in the same scenario is good.

[0170] The result of the pre-scheduling is as shown in Figure 4The results of the electrical and thermal power balance are shown in FIG. 6. The results of the electrical power balance show that the HIES obtains a large amount of electrical power from the grid in the first 8 hours to power the electric boiler and the ECS. Meanwhile, the electric boiler stores the excess heat in the HS at full power after supporting the thermal load. In the following time period, the ECS and the HS provide power and heat to the HIES to ensure the source-load balance of the system. It can be seen that the HIES completes the energy dispatch from the external grid to the electric boiler and then to the HS to ensure the supply of the thermal load, which improves the flexibility and reliability of the system.

[0171] Secondly, the scope of carbon market participation is determined according to the Figure 5 The carbon quota pre-purchase strategy is shown in FIG. 4. The initial free carbon quota is allocated according to the capacity and actual output of each participant. The initial carbon quotas of the external grid, the gas turbine and the gas boiler are 10 kg / h, 3 kg / h and 1 kg / h, respectively. The initial carbon quota is allocated evenly on an hourly basis. According to the pre-scheduling results, the expected carbon emissions of the HIES are obtained. Due to the linear relationship between carbon emissions and actual output of the equipment, the time distribution of carbon emissions of each participant is similar to the distribution of power output.

[0172] The wind power and photovoltaic power are the power sources in the system, but do not generate carbon emissions in the process of power generation. The grid tie line in the HIES is the only channel to obtain external power and is one of the sources of power carbon emissions. The gas turbine and the gas boiler are the in-system equipment of the HIES, which consume natural gas and generate a large amount of carbon emissions in the process of power generation and heat production. Therefore, in this implementation case, the carbon market involves three participants: the external grid, the gas turbine and the gas boiler.

[0173] The initial free carbon quota is allocated according to the capacity and actual output of each participant. The initial carbon quotas of the external grid, the gas turbine and the gas boiler are 10 kg / h, 3 kg / h and 1 kg / h, respectively. The initial carbon quota is allocated evenly on an hourly basis. According to the pre-scheduling results, the expected carbon emissions of the HIES are obtained. Due to the linear relationship between carbon emissions and actual output of the equipment, the time distribution of carbon emissions of each participant is similar to the distribution of power output.

[0174] The adjustment of the carbon quota not only meets the demand of carbon emissions, but also pursues the minimization of the carbon trading cost. In addition, in order to reduce the risk of the increase of future carbon emission demand, it is also necessary to ensure the rationality of the holding of the carbon quota. The adjusted carbon quota is shown in FIG. 5. The adjusted carbon quota of the external grid is more in line with the actual carbon emissions, but still retains a certain amount of carbon quota as a backup when the carbon emissions of the external grid are 0. The carbon emissions of the gas turbine and the gas boiler are at a high level in some periods. Due to the limitation of the holding amount and the purchase amount of the carbon quota, the adjusted carbon quota still remains relatively stable. Figure 6 Finally, based on the pre-scheduling results and the adjusted carbon quota, the HIES rolling optimization is performed to minimize the power deviation and the carbon quota trading amount.

[0175] The electrical and thermal power balance of the HIES rolling optimization is shown in FIG. 6. The results of the electrical power balance show that the HIES obtains a large amount of electrical power from the grid in the first 8 hours to power the electric boiler and the ECS. Meanwhile, the electric boiler stores the excess heat in the HS at full power after supporting the thermal load. In the following time period, the ECS and the HS provide power and heat to the HIES to ensure the source-load balance of the system. It can be seen that the HIES completes the energy dispatch from the external grid to the electric boiler and then to the HS to ensure the supply of the thermal load, which improves the flexibility and reliability of the system.

[0176] Figure 7 ​The power distribution of the rolling optimization scheduling scheme is similar to that of the pre-scheduling scheme. There are some deviations in the total power output and power distribution on a smaller time scale. In the first 8 hours, the HIES still obtains a large amount of power from the grid. Most of the power is supplied to the electric boiler, and a small amount is supplied to the ECS and the P2H. Compared with the pre-scheduling, the contribution of the HyS to the system power balance is increased. In the following period, the gas turbine and the renewable energy power generation bear most of the power supply. The ECS and the H2P make up a small amount of power shortage.

[0177] The thermal power balance and the electric power balance are cooperatively optimized. The energy accumulation of the HS mainly comes from the electric boiler, which bears the main thermal load in a longer time period. Although the P2H and the H2P generate less power generation heat, they have strong scheduling flexibility on a smaller time scale. The gas turbine bears more than half of the electric load after 9 hours, but it uses its heat only in a large amount after 15 hours. Compared with the pre-scheduling, the difference in energy distribution mainly comes from the reduction of the actual thermal load.

[0178] The real-time carbon trading process with or without carbon quota pre-purchase is compared as shown in FIG. 8. Figure 8 The carbon quota pre-purchase can significantly reduce the amount of real-time carbon trading. The carbon quota pre-purchase is performed to reduce the carbon trading cost, and thus the carbon trading cost under the carbon quota pre-purchase scheme is lower. Table 1 shows the operation cost and the carbon trading cost in three cases: no carbon market is introduced, the carbon market is introduced but no carbon quota pre-purchase is performed, and the carbon market is introduced and the carbon quota pre-purchase is performed. It can be seen that the operation cost and the total cost are the lowest in the case where no carbon market is introduced. In the HIES, the operation and maintenance cost of the low-carbon emission equipment is usually higher. For example, the use of equipment such as the H2P, the P2H, and the electric boiler can enhance the carbon emission reduction benefit of the HIES by supporting the use of high-carbon emission equipment. However, the above equipment has a higher operation and maintenance cost. Without the restriction of the carbon market, the HIES tends to support low-cost, stable, and controllable resources such as the grid, the gas turbine, and the gas boiler, which can lead to uncontrollable carbon emissions. The operation cost and the carbon trading cost in the case where the carbon market is introduced and the carbon quota pre-purchase is performed are 1.7% and 36.6% lower than those in the case where the carbon market is introduced but no carbon quota pre-purchase is performed. On the one hand, the carbon quota pre-purchase reduces the carbon reduction pressure of the system, and the habit of using energy changes less, so the system runs more economically. On the other hand, the carbon quota pre-purchase reduces the unit cost of the carbon quota as a whole, resulting in a decrease in the carbon trading cost.

[0179] Table 1. Operation cost and carbon trading cost of HIES

[0180]

[0181] HyS (Hydrogen Storage) possesses both electrical and thermal power regulation capabilities, offering greater dispatch flexibility than ECS (Electronic Storage System) and HS (Hydrogen Storage). However, HyS has a more complex structure, resulting in higher operation and maintenance costs than ECS and HS, while ECS and HS offer higher energy conversion efficiency. Therefore, HyS has not participated extensively in the economic dispatch of HIES (Hydrogen Energy Systems). The introduction of the carbon market has significantly altered the contribution of HyS to HIES. The dynamics of energy storage capacity are as follows: Figure 9 As shown. In the three scenarios described above, the behavior of electrical and thermal energy storage is similar. Without a carbon market, HyS continuously charges for the first 8 hours. Afterward, the HyS capacity is maintained without external discharge. HyS primarily functions as a power regulator. During the initial 8 hours, grid electricity prices are low. HyS, as the second largest energy storage resource after ECS, continues to charge during this period. In the subsequent period, although there are sufficient hydrogen reserves, due to the high maintenance costs of P2H and the loose power supply situation, hydrogen energy is not converted into electricity on a large scale. With the introduction of a carbon market but without carbon allowance pre-purchase, HyS begins discharging immediately after charging and converts all hydrogen energy into electricity. Due to the constraints of the carbon market and carbon costs, HyS is used extensively to provide electricity and heat for HIES. With the introduction of a carbon market and carbon allowance pre-purchase, hydrogen energy is not fully converted into electricity. Under the adjusted carbon allowance, the carbon reduction pressure on HIES is alleviated, achieving a balance between economic costs and carbon reduction needs.

[0182] In the operation of integrated energy systems, low-carbon operation strategies based on pre-schedule, carbon quota pre-purchase, and rolling optimization enhance the system's utilization of low-carbon energy resources and tap the power regulation potential of low-carbon energy devices such as hydrogen energy storage. Simultaneously, this strategy alleviates the contradiction between the low-carbon transformation of integrated energy systems and changes in energy consumption habits, providing a feasible solution for low-carbon economic operation.

[0183] Example 2

[0184] according to Figure 10 As shown, this embodiment provides an optimized scheduling system for a hydrogen-containing integrated energy system, including:

[0185] System framework construction module 1 is used to construct the framework of a hydrogen-containing integrated energy system, and to establish the operational constraints of various types of energy equipment and the coupling constraints between various types of energy equipment based on the framework of the hydrogen-containing integrated energy system.

[0186] Pre-scheduling model construction module 2 is used to construct a pre-scheduling model for a hydrogen-containing integrated energy system based on the operational constraints of various types of energy equipment and the coupling constraints between various types of energy equipment.

[0187] The carbon quota prediction module 3 is configured to predict the actual carbon emission according to the hydrogen-containing comprehensive energy system pre-scheduling model, determine the carbon quota pre-purchase amount according to the actual carbon emission, and obtain the adjusted carbon quota according to the carbon quota pre-purchase amount.

[0188] The dynamic carbon transaction determination module 4 is configured to perform dynamic carbon transaction based on the adjusted carbon quota, and determine the cost of the dynamic carbon transaction.

[0189] The optimal scheduling module 5 is configured to perform rolling optimization on the hydrogen-containing comprehensive energy system based on the dynamic carbon market, adjust the output of each type of energy equipment of the hydrogen-containing comprehensive energy system in real time, and complete the scheduling of the hydrogen-containing comprehensive energy system.

[0190] Embodiment 3

[0191] The application further provides a mobile terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a hydrogen-containing comprehensive energy system scheduling program.

[0192] The processor implements the steps of the hydrogen-containing comprehensive energy system optimal scheduling method when executing the computer program, for example:

[0193] The system framework construction module 1 is configured to construct a hydrogen-containing comprehensive energy system framework, and establish the operation constraints of each type of energy equipment and the coupling constraints between each type of energy equipment based on the hydrogen-containing comprehensive energy system framework.

[0194] The system framework construction module 1 is configured to construct a hydrogen-containing comprehensive energy system framework, and establish the operation constraints of each type of energy equipment and the coupling constraints between each type of energy equipment based on the hydrogen-containing comprehensive energy system framework.

[0195] The carbon quota prediction module 3 is configured to predict the actual carbon emission according to the hydrogen-containing comprehensive energy system pre-scheduling model, determine the carbon quota pre-purchase amount according to the actual carbon emission, and obtain the adjusted carbon quota according to the carbon quota pre-purchase amount.

[0196] The dynamic carbon transaction determination module 4 is configured to perform dynamic carbon transaction based on the adjusted carbon quota, and determine the cost of the dynamic carbon transaction.

[0197] The optimal scheduling module 5 is configured to perform rolling optimization on the hydrogen-containing comprehensive energy system based on the dynamic carbon market, adjust the output of each type of energy equipment of the hydrogen-containing comprehensive energy system in real time, and complete the scheduling of the hydrogen-containing comprehensive energy system.

[0198] Alternatively, the processor implements the functions of each module in the system when executing the computer program, for example:

[0199] The system framework construction module 1 is configured to construct a hydrogen-containing comprehensive energy system framework, and establish the operation constraints of each type of energy equipment and the coupling constraints between each type of energy equipment based on the hydrogen-containing comprehensive energy system framework.

[0200] A pre-scheduling model construction module 2 is configured to construct a hydrogen-comprising integrated energy system pre-scheduling model based on operation constraints of various types of energy devices and coupling constraints between the various types of energy devices;

[0201] A carbon quota prediction module 3 is configured to predict an actual carbon emission amount based on the hydrogen-comprising integrated energy system pre-scheduling model, determine a carbon quota pre-purchase amount based on the actual carbon emission amount, and obtain an adjusted carbon quota based on the carbon quota pre-purchase amount;

[0202] A dynamic carbon transaction determination module 4 is configured to perform dynamic carbon transaction based on the adjusted carbon quota, and determine a cost of the dynamic carbon transaction;

[0203] An optimized scheduling module 5 is configured to perform rolling optimization of the hydrogen-comprising integrated energy system based on the dynamic carbon market, adjust outputs of the various types of energy devices of the hydrogen-comprising integrated energy system in real time, and complete scheduling of the hydrogen-comprising integrated energy system.

[0204] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the mobile terminal.

[0205] For example, the computer program can be divided into a system framework construction module 1, a pre-scheduling model construction module 2, a carbon quota prediction module 3, a dynamic carbon transaction determination module 4, and an optimized scheduling module 5.

[0206] The specific functions of each module are as follows:

[0207] The system framework construction module 1 is configured to construct a hydrogen-comprising integrated energy system framework, and establish operation constraints of various types of energy devices and coupling constraints between the various types of energy devices based on the hydrogen-comprising integrated energy system framework;

[0208] The pre-scheduling model construction module 2 is configured to construct a hydrogen-comprising integrated energy system pre-scheduling model based on operation constraints of various types of energy devices and coupling constraints between the various types of energy devices;

[0209] The carbon quota prediction module 3 is configured to predict an actual carbon emission amount based on the hydrogen-comprising integrated energy system pre-scheduling model, determine a carbon quota pre-purchase amount based on the actual carbon emission amount, and obtain an adjusted carbon quota based on the carbon quota pre-purchase amount;

[0210] The dynamic carbon transaction determination module 4 is configured to perform dynamic carbon transaction based on the adjusted carbon quota, and determine a cost of the dynamic carbon transaction;

[0211] The optimization scheduling module 5 is used for rolling optimization of the hydrogen-containing comprehensive energy system based on a dynamic carbon market, real-time adjustment of the output of each type of energy equipment of the hydrogen-containing comprehensive energy system, and completion of the scheduling of the hydrogen-containing comprehensive energy system.

[0212] The mobile terminal can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The mobile terminal can include, but is not limited to, a processor and a memory.

[0213] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the mobile terminal, and connects all parts of the mobile terminal through various interfaces and lines.

[0214] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the mobile terminal by running or executing the computer programs and / or modules stored in the memory, and calling data stored in the memory.

[0215] The memory can mainly include a program storage area and a data storage area. The program storage area can store operating systems, at least one application program required by a function (such as a sound playing function, an image playing function, and the like), and the like. The data storage area can store data created according to the use of the mobile terminal (such as audio data, a phone book, and the like), and the like. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0216] Embodiment 4

[0217] The application further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to realize the steps of the hydrogen-containing comprehensive energy system optimization scheduling method.

[0218] If the modules / units integrated in the mobile terminal are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0219] Based on this understanding, all or part of the processes in the above-described method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described aggregated reinforcement learning resource optimization scheduling method. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form.

[0220] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0221] It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0222] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the scheduling of a hydrogen-containing integrated energy system, characterized in that, include: Construct a framework for a hydrogen-containing integrated energy system, and based on this framework, establish operational constraints for various types of energy equipment and coupling constraints between them. A pre-scheduling model for a hydrogen-containing integrated energy system is constructed based on the operational constraints of various types of energy equipment and the coupling constraints between them. The actual carbon emissions are predicted based on the pre-scheduling model of the hydrogen-containing integrated energy system. The carbon allowance pre-purchase amount is determined based on the actual carbon emissions. The adjusted carbon allowance is obtained based on the carbon allowance pre-purchase amount. The actual carbon emissions are the fundamental basis for the demand for carbon allowance pre-purchase; the carbon allowance pre-purchase is determined by the carbon allowance difference and the predicted carbon price. The carbon allowance difference is calculated from the initial free carbon allowance. When the carbon allowance difference is positive, it indicates that there is a carbon allowance surplus; conversely, when the carbon allowance difference is negative, it indicates that there is a carbon allowance shortage. The predicted carbon price includes a volatility coefficient and segmented carbon prices; Among them, the volatility coefficient is related to carbon quotas and carbon emissions in the market. When the increase in user carbon emissions leads to an increase in market demand for carbon quotas, the volatility coefficient of carbon prices increases. The volatility coefficient of carbon prices is predicted based on the operation of the hydrogen-containing integrated energy system. The overall level of the electricity load and heat load of the hydrogen-containing integrated energy system reflects the volatility of carbon prices in each period. Among them, the segmented carbon price uses trading volume as the independent variable, and the carbon price changes linearly within different trading volume ranges according to different trading volumes; The formula for predicting carbon prices is as follows: Predicted carbon price = volatility coefficient × segmented carbon price; Based on the adjusted carbon allowances, dynamic carbon trading is conducted, and the cost of obtaining dynamic carbon trading is determined. Based on the dynamic carbon market, the hydrogen-containing integrated energy system is continuously optimized, and the output of various types of energy equipment in the hydrogen-containing integrated energy system is adjusted in real time to complete the scheduling of the hydrogen-containing integrated energy system. The specific process for determining the cost of dynamic carbon trading based on adjusted carbon quotas is as follows: The dynamic carbon price is obtained based on the supply and demand relationship of carbon quotas in the market. The dynamic carbon price consists of segmented carbon prices and a volatility coefficient. The actual carbon allowance difference is determined based on the adjusted carbon allowance and real-time carbon emissions, wherein the actual carbon allowance difference is equal to the carbon allowance trading volume within a carbon trading settlement cycle. The cost of dynamic carbon trading is determined by the carbon quota trading volume and the dynamic carbon price. The objective function and calculation formula for the rolling optimization are as follows: In the formula, For the real-time scrolling optimization phase in the first k The operating cost of each control time domain; For the real-time scrolling optimization phase in the first k Carbon trading costs in each control time domain; The compensation price for electricity; The price to compensate for fuel; This is the floor function; j ( k ) indicates the first k The first control time domain j A point in time; EX It represents power grid interconnection lines, electric boilers, electrochemical energy storage, hydrogen power generation equipment, and electric hydrogen production equipment; GX Represents hydrogen energy storage, thermal energy storage, gas turbines, and gas-fired boilers; and ; For the first k Dynamic carbon prices for a given period; and The first i Individual market participants k Carbon allowances purchased and sold in real time during specific time periods.

2. The optimized scheduling method for a hydrogen-containing integrated energy system according to claim 1, characterized in that, Energy equipment of various types includes electrical equipment and thermal equipment. The operating constraints for electrical equipment are as follows: In the formula, For grid-connected wind or solar power, Available wind or solar power; This refers to the output power of gas turbines, electric boilers, hydrogen power generation equipment, and electric hydrogen production equipment. This refers to the maximum output power of gas turbines, electric boilers, hydrogen power generation equipment, and electric hydrogen production equipment. For the power of grid interconnects or electrochemical energy storage, The upper limit of the power of grid interconnects or electrochemical energy storage; The operating constraints of thermal equipment are as follows: In the formula, The thermal power of the electric boiler; The heat production efficiency of the electric boiler; This refers to the thermal power of the gas-fired boiler. This represents the maximum thermal power of the gas-fired boiler. The thermal power of hydrogen power generation equipment or electro-hydrogen production equipment; The thermal efficiency of hydrogen power generation equipment or electro-hydrogen production equipment; The heat production efficiency of hydrogen power generation equipment or electro-hydrogen production equipment; This refers to the thermal power of the gas turbine. For the thermal efficiency of the gas turbine; For the heat production efficiency of the gas turbine; The power generation efficiency of the gas turbine; The thermal power of heat storage; This represents the maximum thermal power of the heat storage. The coupling constraints between the electrical equipment and the thermal equipment are as follows: In the formula, Energy stored in electrochemical energy storage, hydrogen energy storage, and thermal energy storage; The charging power for electrochemical energy storage, hydrogen energy storage, and thermal energy storage. The discharge power for electrochemical energy storage, hydrogen energy storage, and thermal energy storage; It represents the maximum energy storage capacity for electrochemical energy storage, hydrogen energy storage, and thermal energy storage.

3. The optimized scheduling method for a hydrogen-containing integrated energy system according to claim 1, characterized in that, The specific process for constructing a pre-scheduling model for a hydrogen-containing integrated energy system based on the operational constraints of various types of energy equipment and the coupling constraints between these types of energy equipment is as follows: The source-load joint probability distribution function is constructed using the Gaussian Copula function. The source-load joint probability distribution function of each period is sampled. According to the inverse transformation of the source-load joint probability distribution function, the source-load time series data of each time interval is obtained. The source-load time series data is clustered by K-means clustering to form typical operating scenarios. The probability of each typical operating scenario is calculated to form a scenario set. The objective of the pre-scheduling model is to minimize the economic operating cost, which includes electricity cost, fuel cost, and maintenance cost. Electricity cost is measured using bidirectional tie-line power and the purchase and sale price of electricity, with an indicator function distinguishing between purchase and sale fees. Fuel cost is the cost of natural gas consumed by the gas turbine and gas boiler. Maintenance cost includes the maintenance expenses for electricity, heat, and energy storage equipment. To balance energy consumption habits under different typical operating scenarios, a scenario-based expectation optimization scheduling method is adopted. Based on typical scenarios and their probabilities, an economic scheduling strategy for hydrogen-containing integrated energy systems is generated to minimize the expected system operating cost.

4. The optimized scheduling method for a hydrogen-containing integrated energy system according to claim 1, characterized in that, The specific determination process for the carbon quota pre-purchase quantity, which is determined by the carbon quota difference and the predicted carbon price, is as follows: Purchase carbon allowances when carbon allowances are insufficient and the predicted carbon price is high. Sell ​​carbon allowances when there is a carbon surplus and the predicted carbon price is low. When carbon allowances are insufficient and the predicted carbon price is low, or when carbon allowances are excessive and the predicted carbon price is high, a certain percentage of the initial carbon allowances will be purchased or sold.

5. An optimized scheduling system for a hydrogen-containing integrated energy system, characterized in that, A method for optimizing the scheduling of a hydrogen-containing integrated energy system as described in any one of claims 1-4 includes: The system framework construction module is used to build a hydrogen-containing integrated energy system framework, and to establish operational constraints and coupling constraints between various types of energy equipment based on the hydrogen-containing integrated energy system framework. The pre-schedule model construction module is used to construct a pre-schedule model for a hydrogen-containing integrated energy system based on the operational constraints of various types of energy equipment and the coupling constraints between various types of energy equipment. The carbon quota prediction module is used to predict the actual carbon emissions based on the pre-scheduling model of the hydrogen-containing integrated energy system, determine the carbon quota pre-purchase amount based on the actual carbon emissions, and obtain the adjusted carbon quota based on the carbon quota pre-purchase amount. The dynamic carbon trading determination module is used to determine the cost of dynamic carbon trading based on the adjusted carbon allowances. The optimization and scheduling module is used to perform rolling optimization of the hydrogen-containing integrated energy system based on the dynamic carbon market, adjust the output of various types of energy equipment in the hydrogen-containing integrated energy system in real time, and complete the scheduling work of the hydrogen-containing integrated energy system.

6. A mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the hydrogen-containing integrated energy system optimization scheduling method as described in any one of claims 1-4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the hydrogen-containing integrated energy system optimization scheduling method as described in any one of claims 1-4.

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