Optimized scheduling method for hydrogen-containing comprehensive energy system and related equipment

Abstract

The invention relates to the technical field of thermoelectric collaborative optimization, and discloses a hydrogen-containing comprehensive energy system optimization scheduling method and related equipment, and the method comprises the steps: constructing a hydrogen-containing comprehensive energy system frame, and building operation constraints of various types of energy equipment and coupling constraints among the various types of energy equipment; constructing a hydrogen-containing integrated 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; according to the hydrogen-containing comprehensive energy system pre-scheduling model, predicting to obtain actual carbon emission, according to the actual carbon emission, determining a carbon quota pre-purchase amount, and according to the carbon quota pre-purchase amount, obtaining an adjusted carbon quota; performing dynamic carbon transaction based on the adjusted carbon quota, and determining the cost of the dynamic carbon transaction; according to the hydrogen-containing comprehensive energy system scheduling method, the energy consumption habits of the users are considered in the scheduling of the hydrogen-containing comprehensive energy system, and the carbon emission cost of the users is reduced at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative optimization of heat and power, and specifically to an optimal scheduling method for a hydrogen-integrated energy system and related equipment. Background Technique

[0002] An integrated energy system (IES) couples electricity, heat, hydrogen, and other energy forms into an energy framework, enabling the complementarity of the operating characteristics of multiple energies and the multi-time-scale transfer of energy, thereby improving energy utilization efficiency and the flexible operating ability of the system. Although the IES has considerable potential in flexible regulation, its low-carbon operation is often hindered by increased costs and user habits. Therefore, designing effective energy trading and carbon trading strategies is of great significance for stimulating the carbon emission reduction potential of the IES and realizing the low-carbon transformation of the energy system.

[0003] The concept of the IES originated in the field of combined heat and power, focusing on the collaborative optimization of thermoelectric systems. Driven by energy policies and hydrogen energy technologies, hydrogen provides a more feasible and applicable option for the IES. A hydrogen-integrated energy system (HIES) takes electricity as the core and has long-cycle and large-capacity hydrogen energy storage support, reducing the limitations of energy cross-time-domain transfer. The electro-hydrogen conversion and hydrogen-electric conversion of hydrogen energy storage are performed by different devices, with higher scheduling flexibility. However, the low-carbon benefits of the HIES often conflict with its economic benefits. This is mainly manifested in that 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 operating costs. In addition, in the existing carbon market mechanism, carbon quotas are allocated to users in a certain total amount without distinguishing time periods. This allocation method is top-down and lacks adaptability to the energy consumption patterns of users and diverse decarbonization measures. Specifically, the carbon emission demands and carbon emission intensities of users are different in different time periods, resulting in uneven use of carbon quotas and an increase in carbon trading costs. Some carbon reduction measures taken by users, such as accessing renewable energy and configuring energy storage, are also difficult to fully exert their carbon reduction potential under the averaged carbon quotas.

[0004] In summary, integrating the carbon market into the HIES faces three major challenges. First, it is crucial to balance 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 a necessary means to promote the low-carbon transformation, but excessive costs will reduce users' willingness to participate in the carbon market. Second, it is necessary to balance users' established energy consumption habits and the adoption of low-carbon operation methods. The transition to low-carbon operation will inevitably change the existing energy consumption patterns in production and life. It is crucial to balance minimizing changes to the energy consumption pattern and minimizing carbon emissions. In addition, hydrogen energy has great potential in improving the utilization level of renewable energy and increasing the flexible regulation capacity of the system. However, as a transitional energy source in the HIES, its role in the carbon market and low-carbon operation strategies still need to be further studied. Summary of the Invention

[0005] In order to overcome the defects of the above-mentioned existing technologies, the purpose of the present invention is to provide an optimized scheduling method for a hydrogen-integrated energy system to solve the technical problem that it is difficult to balance users' energy consumption habits and reduce users' carbon emission costs in the scheduling of a hydrogen-integrated energy system in the prior art.

[0006] The present invention is realized through the following technical solutions: In the first aspect, the present invention provides an optimized scheduling method for a hydrogen-integrated energy system, including: Construct a framework for a hydrogen-integrated energy system, and establish operation constraints for various types of energy equipment and coupling constraints between various types of energy equipment based on the framework for a hydrogen-integrated energy system; Construct a pre-scheduling model for a hydrogen-integrated energy system based on the operation constraints of various types of energy equipment and the coupling constraints between various types of energy equipment; Predict the actual carbon emissions according to the pre-scheduling model of the hydrogen-integrated energy system, determine the pre-purchase quantity of carbon quotas according to the actual carbon emissions, and obtain the adjusted carbon quotas according to the pre-purchase quantity of carbon quotas; Conduct dynamic carbon trading based on the adjusted carbon quotas to determine the cost of dynamic carbon trading; Perform rolling optimization on the hydrogen-integrated energy system based on the dynamic carbon market, and adjust the output of various types of energy equipment in the hydrogen-integrated energy system in real time to complete the scheduling work of the hydrogen-integrated energy system.

[0007] Preferably, various types of energy equipment include power equipment and thermal equipment. Among them, the operation constraints of the power equipment are as follows:

[0008] In the formula, is the grid-connected wind power or photovoltaic power, is the available wind power or photovoltaic power; $P_{gt}$, $P_{eb}$, $P_{hg}$, $P_{eh}$ are the output powers of gas turbines, electric boilers, hydrogen power generation equipment, and electrolytic hydrogen production equipment, $P_{gt,max}$, $P_{eb,max}$, $P_{hg,max}$, $P_{eh,max}$ are the maximum output powers of gas turbines, electric boilers, hydrogen power generation equipment, and electrolytic hydrogen production equipment; $P_{grid}$ or $P_{ees}$ is the power of the grid connection line or electrochemical energy storage, $P_{grid,max}$ or $P_{ees,max}$ is the upper limit power of the grid connection line or electrochemical energy storage; The operation constraints of thermal equipment are as follows:

[0009] In the formula, $Q_{eb}$, $Q_{gb}$, $Q_{hg}$, $Q_{eh}$, $Q_{gt}$ are the thermal powers of electric boilers, gas boilers, hydrogen power generation equipment, electrolytic hydrogen production equipment, and gas turbines, $Q_{tes}$ is the thermal power of thermal energy storage; $Q_{gb,max}$ or $Q_{tes,max}$ is the maximum thermal power of gas boilers or thermal energy storage; $\eta_{eb}$, $\eta_{gb}$, $\eta_{hg}$, $\eta_{eh}$, $\eta_{gt}$ are the heat production efficiencies of electric boilers, hydrogen power generation equipment, electrolytic hydrogen production equipment, and gas turbines, $\eta_{gt,e}$ is the power generation efficiency of gas turbines, $\mu_{hg}$, $\mu_{eh}$, $\mu_{gt}$ are the thermal utilization rates of hydrogen power generation equipment, electrolytic hydrogen production equipment, and gas turbines; The coupling constraints of the above-mentioned power equipment and thermal equipment are as follows:

[0010] In the formula, $E_{ees}$, $E_{hes}$, $E_{tes}$ are the energies stored in electrochemical energy storage, hydrogen energy storage, and thermal energy storage; $P_{ees,ch}$, $P_{hes,ch}$, $P_{tes,ch}$ are the charging powers of electrochemical energy storage, hydrogen energy storage, and thermal energy storage, $P_{ees,dch}$, $P_{hes,dch}$, $P_{tes,dch}$ are the discharging powers of electrochemical energy storage, hydrogen energy storage, and thermal energy storage; $E_{ees,max}$, $E_{hes,max}$, $E_{tes,max}$ are the maximum energy storage capacities of electrochemical energy storage, hydrogen energy storage, and thermal energy storage.

[0011] Preferably, in the steps of constructing a pre-scheduling model for a hydrogen-integrated energy system based on the operation constraints of various types of energy equipment and the coupling constraints between various types of energy equipment, the specific process is as follows: Use the Gaussian Copula function to construct the joint probability distribution function of source and load, sample the joint probability distribution function of source and load for each period, obtain the time series data of source and load for each time interval according to the inverse transformation of the joint probability distribution function of source and load, cluster the time series data of source and load by K-means clustering to form typical operation scenarios, and calculate the probability of each typical operation scenario to form a scenario set; Set the goal of the pre-scheduling model to minimize the economic operation cost, and the minimized economic operation cost includes electricity cost, fuel cost, and maintenance cost; among them, the electricity cost is measured by the power of the two-way connection line and the purchase and sale electricity prices, and the indicator function is used to distinguish the electricity purchase and sale costs; the fuel cost is the natural gas cost consumed by gas turbines and gas boilers; the maintenance cost includes the maintenance costs of power, thermal, and energy storage equipment. Balance the energy consumption habits in different typical operation scenarios, adopt a scenario-based expected optimization scheduling method, generate an economic scheduling strategy for the hydrogen-integrated energy system according to the typical scenarios and their probabilities, and minimize the expected system operation cost.

[0012] Preferably, in the step of predicting the actual carbon emissions according to the pre-scheduling model of the hydrogen integrated energy system and determining the pre-purchase amount of carbon quotas according to the actual carbon emissions, the actual carbon emissions are the fundamental basis for the demand of the pre-purchase amount of carbon quotas; the pre-purchase amount of carbon quotas is determined by the carbon quota difference and the predicted carbon price; The carbon quota difference is calculated through the initial free carbon quota. When the carbon quota difference is positive, it means there is a surplus of carbon quotas. On the contrary, when the carbon quota difference is negative, it means there is a shortage of carbon quotas; The predicted carbon price includes a volatility coefficient and a segmented carbon price; Among them, the volatility coefficient is related to the carbon quotas and carbon emissions in the market, that is, when the increase in the user's carbon emissions leads to an increase in the market demand for carbon quotas, the volatility coefficient of the carbon price increases; Among them, the segmented carbon price takes the trading volume as the independent variable, and according to different trading volumes, the carbon price changes linearly within different trading volume ranges; The calculation formula of the predicted carbon price is as follows: Predicted carbon price = volatility coefficient × segmented carbon price.

[0013] Furthermore, in the step of determining the pre-purchase amount of carbon quotas through the carbon quota difference and the predicted carbon price, the specific determination process is as follows; When there is a shortage of carbon quotas and the predicted carbon price is high, purchase carbon quotas; When there is a surplus of carbon quotas and the predicted carbon price is low, sell carbon quotas When there is a shortage of carbon quotas and the predicted carbon price is low, or when there is a surplus of carbon quotas and the predicted carbon price is high, purchase or sell a certain proportion of the initial carbon quotas.

[0014] Preferably, in the step of determining the cost of dynamic carbon trading based on the adjusted carbon quotas, the specific process is as follows: Obtain the dynamic carbon price according to the supply and demand relationship of carbon quotas in the market; Determine the actual carbon quota difference based on the adjusted carbon quotas and the real-time carbon emissions, where the actual carbon quota difference is equal to the carbon quota trading volume within a carbon trading settlement cycle; Determine the cost of dynamic carbon trading through the carbon quota trading volume and the dynamic carbon price.

[0015] Preferably, the objective function and calculation formula for rolling optimization are as follows:

[0016]

[0017]

[0018] In the formula, is the operating cost and carbon trading cost in the k th control time domain during the real-time rolling optimization stage, is the compensation price of electricity and fuel; is the floor function; j ( k ) represents the k th time point in the j th control time domain; EX represents the grid tie line, electric boiler, electrochemical energy storage, hydrogen power generation equipment, and hydrogen production from electricity equipment; GX represents hydrogen energy storage, heat storage, gas turbine, and gas boiler; and .

[0019] In a second aspect, the present invention further provides a hydrogen-integrated energy system optimal scheduling system, including: A system framework construction module, configured to construct a hydrogen-integrated energy system framework, and establish operation constraints of various types of energy equipment and coupling constraints between various types of energy equipment based on the hydrogen-integrated energy system framework; A pre-scheduling model construction module, configured to construct a hydrogen-integrated energy system pre-scheduling model based on the operation constraints of various types of energy equipment and the coupling constraints between various types of energy equipment; A carbon quota prediction module, configured to predict the actual carbon emissions according to the hydrogen-integrated energy system pre-scheduling model, determine the pre-purchase amount of carbon quota according to the actual carbon emissions, and obtain the adjusted carbon quota according to the pre-purchase amount of carbon quota; A dynamic carbon trading determination module, configured to perform dynamic carbon trading based on the adjusted carbon quota, and determine the cost of dynamic carbon trading; An optimal scheduling module, configured to perform rolling optimization on the hydrogen-integrated energy system based on the dynamic carbon market, and adjust the output of various types of energy equipment of the hydrogen-integrated energy system in real time to complete the scheduling work of the hydrogen-integrated energy system.

[0020] In a third aspect, the present invention further provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the hydrogen-integrated energy system optimal scheduling method as described above are implemented.

[0021] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the steps of the above-mentioned optimization scheduling method for a hydrogen-integrated energy system.

[0022] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention provides an optimization scheduling method for a hydrogen-integrated energy system. By constructing a framework for the hydrogen-integrated energy system and integrating various types of energy equipment, and by clarifying the operation constraints and coupling constraints of each type of energy equipment, the efficient collaborative operation among the components within the system is ensured, and the efficiency and flexibility of energy utilization are improved. The pre-scheduling model of the hydrogen-integrated energy system constructed based on the operation constraints and coupling constraints of each type of energy equipment can accurately predict the operation state and energy demand of the system. By predicting the actual carbon emissions and determining the pre-purchase quantity of carbon quotas accordingly, refined management of carbon emissions is achieved. The carbon emissions cost is optimized through dynamic carbon trading based on the adjusted carbon quotas according to the pre-purchase quantity of carbon quotas. The hydrogen-integrated energy system is rolling-optimized based on the dynamic carbon market, and the output of each type of energy equipment can be adjusted in real time according to the actual situation. This dynamic adjustment ability enables the system to better adapt to changes in energy demand, and improves the flexibility and efficiency of energy utilization.

[0023] Furthermore, the present invention proposes a multi-dimensional joint source-load scenario generation method based on the Gaussian Copula function. This method not only explores the randomness and correlation of power sources such as wind power and photovoltaic power, as well as loads such as electricity and heat, but also reduces the redundancy of scenarios. While enhancing the pre-scheduling calculation efficiency, it ensures the comprehensive coverage of the pre-scheduling results for scenarios, improves the effectiveness of the system pre-scheduling results, and reduces the computational complexity.

[0024] Furthermore, the present invention proposes a carbon trading strategy for day-ahead pre-purchase and intra-day real-time trading of carbon quotas. This strategy can not only reduce the carbon emissions cost of energy consumption through pre-purchasing carbon quotas, but also encourage users to implement carbon reduction operation measures, including improving the utilization level of renewable energy, increasing power generation and heat production equipment with low carbon emissions, etc., to achieve the sustainable development of users' low-carbon transformation. At the same time, it reduces the carbon emissions cost of users and promotes the low-carbon energy utilization transformation of users.

[0025] Furthermore, in order to balance the users' energy consumption habits and carbon reduction requirements, the present invention improves the traditional rolling optimization method. In each control time domain, the power deviation and carbon quota deviation are simultaneously optimized to minimize the power compensation cost and carbon quota trading cost. Compared with the traditional rolling optimization method, the system has a higher utilization rate of low-carbon emission power equipment and thermal equipment, and a more reasonable and sustainable utilization mode of hydrogen energy. Description of the Drawings

[0026] Figure 1 It is a flowchart of the optimization scheduling method for the hydrogen - containing integrated energy system in the embodiments of the present invention; Figure 2 It is a schematic structural diagram of the hydrogen - containing integrated energy system in the embodiments of the present invention; Figure 3 It is a schematic diagram of the combined operation scenario of the source and load of the hydrogen - containing integrated energy system in the embodiments of the present invention; Figure 4 It is a schematic diagram of the expected electro - thermal power balance result of the pre - scheduling of the hydrogen - containing integrated energy system in the embodiments of the present invention; Figure 5 It is a flowchart of the carbon quota pre - purchase strategy in the embodiments of the present invention; Figure 6 It is a schematic diagram of the carbon quota adjustment result of the hydrogen - containing integrated energy system in the embodiments of the present invention; Figure 7 It is a schematic diagram of the real - time carbon emission and carbon trading result of the hydrogen - containing integrated energy system in the embodiments of the present invention; Figure 8 It is a schematic diagram of the real - time electro - thermal power balance result of the rolling optimization of the hydrogen - containing integrated energy system in the embodiments of the present invention; Figure 9 It is a schematic diagram of the change in the energy storage capacity of the electrochemical energy storage, hydrogen energy storage, and thermal energy storage in the hydrogen - containing integrated energy system in the embodiments of the present invention; Figure 10 It is a schematic structural diagram of the optimization scheduling system of the hydrogen - containing integrated energy system in the embodiments of the present invention; In the figure: 1. System framework construction module; 2. Pre - scheduling model construction module; 3. Carbon quota prediction module; 4. Dynamic carbon trading determination module; 5. Optimization scheduling module. Detailed implementation manners

[0027] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with 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 of 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.

[0028] The purpose of the present invention is to provide an optimization scheduling method for a hydrogen - containing integrated energy system to solve the technical problem that it is difficult to take into account the user's energy - using habits and reduce the user's carbon emission cost in the scheduling of the hydrogen - containing integrated energy system in the prior art.

[0029] The following will make a further detailed description of the present invention with reference to the accompanying drawings: See Figure 1, the present invention provides an optimized scheduling method for a hydrogen-integrated energy system, including: Step 1, construct a framework for a hydrogen-integrated energy system, and based on the framework of the hydrogen-integrated energy system, establish the operation constraints of various types of energy equipment and the coupling constraints between various types of energy equipment; Specifically, from the perspective of energy forms, the equipment in the hydrogen-integrated energy system includes power equipment and thermal equipment. Gas turbines, electric boilers, hydrogen energy storage (HyS), power-to-hydrogen equipment (P2H), and hydrogen power generation equipment (H2P) belong to both power equipment and thermal equipment. Wind turbines, photovoltaic generators, and electrochemical energy storage (ECS) belong to power equipment. Gas boilers, (HS) belong to thermal equipment. From the perspective of contributions, the equipment in the system includes power sources, energy storage, and loads. Wind turbines, photovoltaic generators, gas turbines, and gas boilers all belong to power sources. ECS, HyS, and HS belong to energy storage. P2H, H2P, and electric boilers belong to energy or loads from different energy consumption perspectives. The electricity and heat in the system are respectively collected through the power bus and the heat bus to realize the interconnection of different equipment.

[0030] Among them, the operation model of the hydrogen-integrated energy system is constructed from the individual equipment to the overall system. The output of various types of energy equipment has an upper limit constraint. The grid connection line and ECS have the characteristics of bidirectional power flow, and their power constraints are symmetric. The operation constraints of power equipment are as follows: (1) In the formula, is the power of grid-connected wind power or photovoltaic power, is the available power of wind power or photovoltaic power; is the output power of gas turbines, electric boilers, H2P, and P2H, is the maximum output power of gas turbines, electric boilers, H2P, and P2H; is the power of the grid connection line or ECS; is the upper limit power of the grid connection line or ECS.

[0031] The production methods of thermal equipment are relatively diverse. Electric boilers are driven by electricity, and heat generation is controlled by electricity. Gas boilers are controlled by natural gas input. The heat generated by P2H, H2P, and gas turbines is a by-product. To improve the flexibility of scheduling, this part of the heat will not be forced to be fully utilized. The power constraint of HS is similar to that of ECS. The operation constraints of thermal equipment are as follows: (2) In the formula, is the thermal power of electric boilers, gas boilers, P2H, H2P, and gas turbines, is the thermal power of heat energy storage; is the maximum thermal power of gas boilers or heat energy storage; are the heat production efficiencies of the electric boiler, P2H, H2P, and gas turbine; is the power generation efficiency of the gas turbine; are the heat utilization rates of P2H, H2P, and the gas turbine.

[0032] Different from HyS, the charging and discharging of ECS and HS are carried out by the same device. To simplify the operation model of the energy storage system, before establishing the operation model of the HIES energy storage system, the charging and discharging powers of ECS and HS are defined as follows: (3) In the formula, and are the charging power and discharging power of ECS respectively; and are the heat storage power and heat release power of HS respectively.

[0033] The operation model of the hydrogen-integrated energy system includes power capacity conversion constraints, charging and discharging asynchronous constraints, capacity cycle constraints, and capacity upper and lower limit constraints. The specific expressions are as follows: (4) In the formula, 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.

[0034] The electrical balance and heat balance equations of the hydrogen-integrated energy system are as follows: In particular, the power of ECS and HS being positive indicates charging, and being negative indicates discharging. The power of the grid connection line being positive represents power feeding in.

[0035] (5) In the formula, P L ( t ) and Q L ( t ) are the electrical load and heat load of HIES respectively.

[0036] Step 2: Construct a pre-scheduling model of the hydrogen-integrated energy system based on the operation constraints of various types of energy equipment and the coupling constraints between various types of energy equipment; Specifically, the scenario is the basis for pre-scheduling. Considering the correlation between the source and load, the Gaussian Copula function is used to construct the joint probability distribution function of the source and load. The joint probability distribution function for each period is sampled. According to the inverse transformation of the joint probability distribution function, the time-series data of the source and load for each time interval are obtained. The K-means clustering method is used to cluster the sampling results to form typical scenarios. The probability of each scenario is calculated, and a set of scenarios is obtained, as shown below: (6) In the formula, SN ( s ) is the s th typical scenario, p ( s ) is the probability of the s th typical scenario.

[0037] The objective function of pre-scheduling, i.e., the economic operation cost, includes electricity cost, fuel cost, and maintenance cost. The above costs are the basic expenses to ensure the normal operation of the system, as shown in formula (7). The optimal scheduling plan for these costs reflects the energy consumption habits of the system and is not affected by the carbon market. The operation cost of the system under this scheduling plan is the lowest. The introduction of the carbon market will, to a certain extent, change this energy consumption habit and increase the operation cost of the system.

[0038] (7) In the formula, is the operation cost, electricity cost, fuel cost, and maintenance cost under the s th scenario.

[0039] The electricity cost is measured by the power of the two-way tie line and the purchase and sale electricity prices. The indicator function 1(·) is used to distinguish the electricity purchase and sale expenses. The fuel cost considers the natural gas cost consumed by gas turbines and gas boilers. The maintenance cost includes electricity, heat, and energy storage equipment. The maintenance cost of electricity equipment involves wind turbines, photovoltaic generators, P2H, and H2P. The maintenance cost of heat equipment involves gas boilers and electric boilers. The maintenance cost of energy storage equipment involves ECS and HS. The maintenance cost of HyS is borne by H2P and P2H.

[0040] (8) (9) (10) In the formula, are the electricity purchase and sale prices, is the fuel price. are the unit power maintenance costs of wind power, photovoltaic, gas turbine, P2H, and H2P, The unit power maintenance cost of electric boilers, gas boilers, ECS, and HS.

[0041] To balance the energy consumption habits in different operating scenarios, the goal of pre-scheduling is the expectation of the system operating cost under various scenario probabilities.

[0042] (11) The scenario-based HIES pre-scheduling model consists of the objective function (11) and constraints (1)-(5).

[0043] Step 3: According to the pre-scheduling model of the hydrogen-integrated energy system, predict the actual carbon emissions, determine the pre-purchase quantity of carbon quotas based on the actual carbon emissions, and obtain the adjusted carbon quotas based on the pre-purchase quantity of carbon quotas; Specifically, the actual carbon emissions are the fundamental basis for the pre-purchase demand of carbon quotas. The actual carbon emissions are predicted based on the pre-scheduling results. The power exchange between the grid connection line and the hydrogen-integrated energy system is the main source of power carbon emissions. Gas turbines and gas boilers consume external fuels, which are the main sources of fuel carbon emissions. The carbon emissions of power and heat equipment are considered to be linearly related to their power generation and heat production. The predicted carbon emissions are as follows: (12) In the formula, is t the predicted carbon emissions in period the carbon emission factors of the grid, gas turbines, and gas boilers.

[0044] Based on the initial free carbon quotas, further calculate the carbon quota difference. The initial carbon quotas of various equipment are different, but they are evenly distributed over time. If the carbon quota difference is positive, it means there is a surplus of carbon quotas. On the contrary, a negative carbon quota difference indicates a shortage of carbon quotas. The carbon quota difference is calculated as follows: (13) In the formula, is the carbon quota difference in the pre-purchase stage, is the initial carbon quota in the pre-purchase stage.

[0045] Another factor affecting the pre-purchase of carbon quotas is the predicted carbon price. The carbon price consists of two parts: the volatility coefficient and the segmented carbon price. The fluctuation of the carbon price is related to the carbon quotas and carbon emissions in the market. The increase in the user's carbon emissions leads to an increase in the market demand for carbon quotas, which in turn pushes up the carbon price. Since it is difficult to obtain the carbon emission data of the entire system, the volatility coefficient of the predicted carbon price is evaluated according to the operating conditions of the hydrogen-integrated energy system. The overall levels of the electrical load and heat load of the hydrogen-integrated energy system reflect the fluctuations of the carbon price in each period. The volatility coefficient of the carbon price is as follows: (14) In the formula, is the fluctuation coefficient for predicting the carbon price.

[0046] The segmented carbon price designed in the present invention takes the trading volume A as the independent variable. According to different trading volumes, the price shows a linear increase within different trading volume ranges. This price linked to the trading volume improves the market's incentive for low-carbon operation modes. The segmented carbon price is as follows: (15) In the formula, is the segmented carbon price, α is the interval growth rate, l is the interval length, c 1, c 2, c 3, c 4 are the interval endpoint values. It can be seen that the segmented carbon price is mainly determined by the growth rate and the linear interval, and the upper and lower limits are c 4 and c 1 respectively.

[0047] Based on the fluctuation coefficient and the segmented carbon price, the formula for predicting the carbon price is as follows: (16) In the formula, is the predicted carbon price.

[0048] The prepurchase of carbon quotas is carried out centrally, and the prepurchase price does not fluctuate with time, that is, λ 0 ( t ) = 1. The prepurchase volume of carbon quotas is jointly determined by the carbon quota difference and the predicted carbon price.

[0049] After determining the carbon quota difference, when the carbon quota is insufficient and the predicted carbon price is high, carbon quotas are purchased. The purchase volume of carbon quotas for each market participant in each time period is capped according to its initial carbon quota. The purchase of carbon quotas is as follows: (17) In the formula, is the carbon quota purchased by the i th market participant for the t time period, β is the maximum purchasable or sellable carbon quota coefficient based on the initial carbon quota.

[0050] On the contrary, when the carbon quota is excessive and the predicted carbon price is low, the carbon quota is sold as follows: (18) In the formula, is the iThe carbon quotas sold by a market participant during t a time period.

[0051] 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, purchase or sell part of the carbon quota.

[0052] In the carbon market, if participants hold excessive carbon quotas, it is not conducive to carbon quota trading and may even disrupt the market balance. The upper limit constraint on the daily carbon quota holdings of participants is as follows: (19) Where, ν is the maximum holdable carbon quota coefficient based on the initial carbon quota.

[0053] Based on the initial free carbon quota, after the carbon quota pre-purchase, the adjusted carbon quota is as follows.

[0054] (20) Where, is the adjusted carbon quota after pre-purchase.

[0055] The pre-purchase cost of the carbon quota is as follows.

[0056] (21) Where, is the carbon trading cost during the pre-purchase stage.

[0057] Adjusting the carbon quota based on the HIES pre-scheduling results can optimize the time distribution of the carbon quota and meet the carbon emission requirements of the system. Independent adjustment of the carbon quota can also reduce the impact of carbon market policy changes on the operation of HIES.

[0058] Step 4, conduct dynamic carbon trading based on the adjusted carbon quota to determine the cost of dynamic carbon trading; Specifically, the dynamic carbon price still consists of the segmented carbon price and the fluctuation coefficient. The segmented carbon price is the same as the predicted carbon price. The evaluation of the fluctuation coefficient follows a method similar to that of the predicted carbon price. According to the intraday short-term load forecast, dynamically update the electricity load and heat load for each time period. The dynamic carbon price is as follows: (22) Where, is the dynamic carbon price for the k th time period, is the fluctuation coefficient of the dynamic carbon price for the k th time period.

[0059] Based on the adjusted carbon quota and the real-time carbon emissions, the actual carbon quota difference is calculated as follows: (23) In the formula, is the carbon emission during the real-time scheduling stage, is the difference in carbon quotas during the real-time scheduling stage.

[0060] Within a carbon trading settlement cycle, the volume of carbon quota transactions is balanced with the actual difference in carbon quotas. The relationship between the volume of carbon quota transactions and the actual difference in carbon quotas is as follows: (24) In the formula, and are respectively the carbon quotas purchased and sold in real time by the i th market participant during the k time period.

[0061] The settlement of carbon quotas has a longer time scale than real-time scheduling. The t th settlement is for the carbon quotas in the time period from n ( t - 1) + 1 to nt . According to the purchase and sale volume of carbon quotas and the dynamic carbon price, the cost of dynamic carbon trading is as follows: (25) In the formula, is the carbon trading cost during the real-time scheduling stage.

[0062] Step 5: Based on the dynamic carbon market, perform rolling optimization on the hydrogen-integrated energy system, and adjust the output of various types of energy equipment in the hydrogen-integrated energy system in real time to complete the scheduling work of the hydrogen-integrated energy system.

[0063] Specifically, the rolling optimization needs to be carried out under the determined operating boundaries. The time-series power of the power supply and load is obtained from ultra-short-term forecasting. The control time domain range is 1 hour. The optimization step size for each optimization cycle is 1 / n , and the boundaries of the k th control time domain are as follows.

[0064] (26) In the formula, SR ([[]] k ) is the operating scenario of the k th control time domain.

[0065] The goal of rolling optimization is to reduce the deviation between real-time scheduling and pre-scheduling. These deviations are related to the output of electric and thermal power supply and real-time carbon emissions. The objective function and its calculation method are as follows: (27) (28) (29) In the formula, is the operating cost and carbon trading cost in the k th control time domain during the real-time rolling optimization stage, is the compensation price of electricity and fuel; is the floor function; j ( k ) represents the k th time point in the j th control time domain; EX represents the grid tie line, electric boiler, ECS, H2P and P2H; GX represents HyS, HS, gas turbine and gas boiler; and .

[0066] In rolling optimization, the system operation boundary is continuously updated, and it is meaningless to keep the initial and ending energy storage states the same within the control time domain. Therefore, the energy storage operation strategy in rolling optimization is adjusted as follows: (30) Through the low-carbon operation strategy framework of the hydrogen-integrated energy system composed of pre-scheduling, carbon quota pre-purchase and rolling optimization, the user's carbon emission cost can be effectively reduced, the user is incentivized to dispatch low-carbon emission energy equipment and adopt low-carbon operation methods, and the low-carbon transformation of the energy system is promoted.

[0067] Example 1 This example provides an optimized scheduling method for a hydrogen-integrated energy system, and the specific process is as follows: The structural schematic diagram of the hydrogen-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 specifically one of an alkaline electrolyzer, a proton exchange membrane electrolyzer or a solid oxide electrolyzer, and H2P is specifically a hydrogen fuel cell. The upper limit of the tie line power is 200kW. The maximum outputs of the gas turbine, electric boiler and gas boiler are 100kW, 50kW and 50kW respectively. The time interval of pre-scheduling is 1h, and the time interval of rolling optimization is 15min (i.e., n = 4). Pre-scheduling and rolling optimization can be solved by commercial solvers GUROBI or CPLEX.

[0068] First, the source-load joint scenario is generated by using the Gaussian Copula function, and then the typical operation scenarios are obtained through K-means clustering. Based on the typical operation scenarios, HIES pre-scheduling is carried out.

[0069] The source-load joint scenario is as shown inFigure 3 As shown, 6 typical scenarios are generated after clustering, with probabilities [0.170, 0.144, 0.174, 0.202, 0.160, 0.150]. It can be seen that there is a significant correlation between wind power and photovoltaic power, as well as between power load and heat load, and the matching degree of the source-load time-series power in the same scenario is relatively good.

[0070] The results of the pre-scheduling are as Figure 4 shown. The power balance of the electric power and heat power shown in the results is based on the scheduling results of 6 typical scenarios, and the expected scheduling results calculated through the probability distribution. In terms of power balance, within the first 8 hours, HIES obtained a large amount of electric power from the power grid to supply the electric boiler and ECS. At the same time, after the electric boiler supported the heat load, it stored the excess heat at full power in the HS. In the following time period, the ECS and HS provided electric power and heat power to HIES to ensure the source-load balance of the system. It can be seen that in order to ensure the heat load supply, HIES completed the energy scheduling from the external power grid to the electric boiler and then to the HS, improving the flexibility and reliability of the system.

[0071] Secondly, determine the scope of participation in the carbon market, and complete the pre-purchase of carbon quotas according to the Figure 5 carbon quota pre-purchase strategy shown.

[0072] Wind power generation and photovoltaic power generation are the power sources within the system, but no carbon emissions are generated during the power generation process. The grid connection line in HIES is the only channel to obtain external power and is also one of the sources of power carbon emissions. Gas turbines and gas boilers are the equipment within the HIES system, consuming natural gas and generating a large amount of carbon emissions during the power generation and heat production processes. Therefore, in this implementation case, the carbon market involves three participants: the external power grid, gas turbines, and gas boilers.

[0073] The initial free carbon quota is allocated according to the production capacity and actual output of the participants. The initial carbon quotas of the external power grid, gas turbines, and gas boilers are 10 kg / h, 3 kg / h, and 1 kg / h respectively. The initial carbon quota is evenly distributed per hour. According to the pre-scheduling results, the expected carbon emissions of HIES are obtained. Due to the linear relationship between carbon emissions and the actual output of the equipment, the time distribution of the carbon emissions of each participant is also similar to the distribution of the power output.

[0074] The adjustment of the carbon quota should not only meet the demand for carbon emissions but also pursue the minimization of the carbon trading cost. In addition, in order to reduce the risk brought by the increasing future carbon emission demand, it is also necessary to ensure the rationality of holding the carbon quota. The adjusted carbon quota is as Figure 6As shown. The adjusted carbon quota of the external power grid is more in line with the actual carbon emissions. However, when the carbon emissions of the external power grid are 0, a certain amount of carbon quota is still reserved as a backup. The carbon emissions of gas turbines and gas boilers are at a relatively high level during certain periods. Limited by the carbon quota holdings and purchases, the adjusted carbon quota still remains relatively stable.

[0075] Finally, based on the pre-scheduling results and the adjusted carbon quota, with the goal of minimizing power deviation and carbon quota trading volume, the HIES rolling optimization is carried out.

[0076] The electric power balance and heat power balance of the HIES rolling optimization are as Figure 7 shown. It can be seen that the power distribution of the rolling optimization scheduling scheme is similar to that of the pre-scheduling scheme. On a relatively small time scale, there are some deviations in the total power output and power distribution. In the first 8 hours, the HIES still obtains a large amount of electricity from the power grid. Most of this electricity is supplied to the electric boiler, and a small part is supplied to the ECS and P2H. Compared with the pre-scheduling, the contribution of the HyS to the system power balance increases. In the subsequent time periods, gas turbines and renewable energy generation undertake most of the power supply. The ECS and H2P make up for a small amount of power shortage.

[0077] The heat power balance and electric power balance are co-optimized. The energy accumulation of the HS mainly comes from the electric boiler and undertakes the main heat load during a relatively long period. Although the generated heat from P2H and H2P is less, they have strong scheduling flexibility on a relatively small time scale. The gas turbine undertakes more than half of the power load after 9 hours, but only uses its heat in large quantities after 15 hours. Compared with the pre-scheduling, the difference in energy distribution mainly comes from the reduction of the actual heat load.

[0078] The comparison of the real-time carbon trading process with or without pre-purchasing carbon quotas is as Figure 8As shown. The pre-purchase of carbon quotas can significantly reduce the real-time carbon trading volume. The pre-purchase of carbon quotas is carried out with the goal of reducing carbon trading costs. Therefore, the carbon trading cost under the carbon quota pre-purchase scheme is relatively low. Table 1 shows the operating costs and carbon trading costs in three cases: without introducing the carbon market, introducing the carbon market but without pre-purchasing carbon quotas, and introducing the carbon market and pre-purchasing carbon quotas. It can be seen that the operating cost and total cost are the lowest without introducing the carbon market. In HIES, the operation and maintenance costs of low-carbon emission equipment are usually higher. For example, the use of equipment such as H2P, P2H, and electric boilers can utilize high-carbon emission equipment, thereby enhancing the carbon emission reduction benefit of HIES. However, the above-mentioned equipment has higher operation and maintenance costs. Without the restriction of the carbon market, HIES tends to support low-cost, stable, and controllable resources such as the power grid, gas turbines, and gas boilers, which may lead to uncontrollable carbon emissions. The operating cost and carbon trading cost in the case of introducing the carbon market and pre-purchasing carbon quotas are 1.7% and 36.6% lower respectively than those in the case of introducing the carbon market but without pre-purchasing carbon quotas. On the one hand, after the pre-purchase of carbon quotas, the carbon reduction pressure of the system decreases, the change in energy consumption habits is less, and the system operation mode is more economical. On the other hand, the pre-purchase of carbon quotas reduces the unit cost of carbon quotas as a whole, resulting in a decrease in carbon trading costs.

[0079] Table 1 Operating Costs and Carbon Trading Costs of HIES

[0080] HyS has both electric power and heat power regulation capabilities, and has higher scheduling flexibility than ECS and HS. However, the structure of HyS is more complex, the operation and maintenance cost is higher than that of ECS and HS, and the energy conversion efficiency of ECS and HS is higher. Therefore, HyS does not participate in the economic dispatch of HIES on a large scale. After the introduction of the carbon market, the contribution of HyS in HIES has changed significantly. The dynamics of the energy storage capacity are as Figure 9As shown in the figure. In the above three cases, the behaviors of electric energy storage and thermal energy storage are similar. In the case without the introduction of a carbon market, HyS conducts continuous charging within the first 8 hours. After that, the capacity of HyS is maintained without external discharge. HyS mainly plays a role in power regulation. In the initial 8 hours, the electricity price of the power grid is very low. As the second-largest power storage resource after ECS, HyS continuously charges during this period. In the subsequent period, although there is sufficient hydrogen reserve, due to the high maintenance cost of P2H and the loose power supply situation, hydrogen energy is not converted into electricity on a large scale again. In the case of introducing a carbon market but without pre-purchasing carbon quotas, HyS starts discharging immediately after charging and converts all hydrogen energy into electricity. Due to the restrictions of the carbon market and carbon costs, HyS is widely used to provide power and heat supply for HIES. In the case of introducing a carbon market and pre-purchasing carbon quotas, hydrogen energy is not completely converted into electricity. Under the adjusted carbon quotas, the carbon emission reduction pressure of HIES is alleviated, and a balance is achieved between economic costs and carbon emission reduction requirements.

[0081] In the operation of the integrated energy system, the low-carbon operation strategy based on pre-scheduling, pre-purchasing of carbon quotas, and rolling optimization enhances the utilization of low-carbon energy resources in the system and explores the potential of power and electricity regulation of low-carbon energy equipment such as hydrogen energy storage. At the same time, this strategy alleviates the contradiction between the low-carbon transformation of the integrated energy system and the change of energy consumption habits, and provides a feasible solution for the operation of the low-carbon economy.

[0082] Embodiment 2 According to Figure 10 As shown in the figure, this embodiment provides an optimized scheduling system for a hydrogen-containing integrated energy system, including: System framework construction module 1, used to construct the framework of the hydrogen-containing integrated energy system, and establish the operation 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; Pre-scheduling model construction module 2, used to construct a pre-scheduling model for the hydrogen-containing integrated energy system based on the operation constraints of various types of energy equipment and the coupling constraints between various types of energy equipment; Carbon quota prediction module 3, used to predict the actual carbon emissions according to the pre-scheduling model of the hydrogen-containing integrated energy system, determine the pre-purchased amount of carbon quotas according to the actual carbon emissions, and obtain the adjusted carbon quotas according to the pre-purchased amount of carbon quotas; Dynamic carbon trading determination module 4, used to conduct dynamic carbon trading based on the adjusted carbon quotas and determine the cost of dynamic carbon trading; Optimized scheduling module 5, used to perform rolling optimization on the hydrogen-containing integrated energy system based on the dynamic carbon market, adjust the output of various types of energy equipment of the hydrogen-containing integrated energy system in real time, and complete the scheduling work of the hydrogen-containing integrated energy system.

[0083] Embodiment 3 The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a hydrogen-integrated energy system scheduling program.

[0084] When the processor executes the computer program, the steps of the above-mentioned hydrogen-integrated energy system optimization scheduling method are implemented, for example: Construct a hydrogen-integrated energy system framework, and establish operation constraints for various types of energy equipment and coupling constraints between various types of energy equipment based on the hydrogen-integrated energy system framework; Construct a pre-scheduling model for the hydrogen-integrated energy system based on the operation constraints of various types of energy equipment and the coupling constraints between various types of energy equipment; Predict the actual carbon emissions according to the pre-scheduling model of the hydrogen-integrated energy system, determine the pre-purchase quantity of carbon quotas according to the actual carbon emissions, and obtain the adjusted carbon quotas according to the pre-purchase quantity of carbon quotas; Conduct dynamic carbon trading based on the adjusted carbon quotas, and determine the cost of dynamic carbon trading; Perform rolling optimization on the hydrogen-integrated energy system based on the dynamic carbon market, and adjust the output of various types of energy equipment in the hydrogen-integrated energy system in real time to complete the scheduling work of the hydrogen-integrated energy system.

[0085] Alternatively, when the processor executes the computer program, the functions of each module in the above system are implemented, for example: System framework construction module 1, which is used to construct a hydrogen-integrated energy system framework, and establish operation constraints for various types of energy equipment and coupling constraints between various types of energy equipment based on the hydrogen-integrated energy system framework; Pre-scheduling model construction module 2, which is used to construct a pre-scheduling model for the hydrogen-integrated energy system based on the operation constraints of various types of energy equipment and the coupling constraints between various types of energy equipment; Carbon quota prediction module 3, which is used to predict the actual carbon emissions according to the pre-scheduling model of the hydrogen-integrated energy system, determine the pre-purchase quantity of carbon quotas according to the actual carbon emissions, and obtain the adjusted carbon quotas according to the pre-purchase quantity of carbon quotas; Dynamic carbon trading determination module 4, which is used to conduct dynamic carbon trading based on the adjusted carbon quotas and determine the cost of dynamic carbon trading; Optimization scheduling module 5, which is used to perform rolling optimization on the hydrogen-integrated energy system based on the dynamic carbon market, and adjust the output of various types of energy equipment in the hydrogen-integrated energy system in real time to complete the scheduling work of the hydrogen-integrated energy system.

[0086] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the mobile terminal.

[0087] For example, the computer program may 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 trading determination module 4, and an optimization scheduling module 5; The specific functions of each module are as follows: The system framework construction module 1 is used to construct a hydrogen-integrated energy system framework and establish the operation constraints of various types of energy devices and the coupling constraints between various types of energy devices based on the hydrogen-integrated energy system framework; The pre-scheduling model construction module 2 is used to construct a pre-scheduling model of the hydrogen-integrated energy system based on the operation constraints of various types of energy devices and the coupling constraints between various types of energy devices; The carbon quota prediction module 3 is used to predict the actual carbon emissions according to the pre-scheduling model of the hydrogen-integrated energy system, determine the pre-purchase amount of carbon quotas according to the actual carbon emissions, and obtain the adjusted carbon quota according to the pre-purchase amount of carbon quotas; The dynamic carbon trading determination module 4 is used to perform dynamic carbon trading based on the adjusted carbon quota and determine the cost of dynamic carbon trading; The optimization scheduling module 5 is used to perform rolling optimization on the hydrogen-integrated energy system based on the dynamic carbon market, adjust the output of various types of energy devices of the hydrogen-integrated energy system in real time, and complete the scheduling work of the hydrogen-integrated energy system.

[0088] The mobile terminal may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The mobile terminal may include, but is not limited to, a processor and a memory.

[0089] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the mobile terminal, and connects various parts of the entire mobile terminal through various interfaces and circuits.

[0090] The memory can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and by invoking the data stored in the memory, the processor realizes various functions of the mobile terminal.

[0091] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as 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 magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0092] Embodiment 4 The present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the scheduling of a hydrogen-containing integrated energy system are realized.

[0093] If the modules / units integrated in the mobile terminal are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0094] Based on such understanding, all or part of the processes in the above methods of the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above aggregation reinforcement learning resource optimization scheduling method can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc.

[0095] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0096] It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0097] 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 them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for optimizing and scheduling a hydrogen-containing integrated energy system, characterized in that: include: Construct a hydrogen-containing integrated energy system framework, and establish operation constraints of various types of energy equipment and coupling constraints between various types of energy equipment based on the hydrogen-containing integrated energy system framework; Construct a pre-dispatching model for a hydrogen-containing integrated energy system based on the operating constraints of various types of energy equipment and the coupling constraints between various types of energy equipment; The actual carbon emissions are predicted based on the pre-dispatch model of the hydrogen-containing integrated energy system, the carbon quota pre-purchase amount is determined based on the actual carbon emissions, and the adjusted carbon quota is obtained based on the carbon quota pre-purchase amount; Conduct dynamic carbon trading based on the adjusted carbon quota and determine the cost of dynamic carbon trading; Based on the dynamic carbon market, the hydrogen-containing integrated energy system is optimized on a rolling basis, the output of various types of energy equipment in the hydrogen-containing integrated energy system is adjusted in real time, and the scheduling of the hydrogen-containing integrated energy system is completed.

2. The method for optimizing and scheduling a hydrogen-containing integrated energy system according to claim 1, characterized in that: Various types of energy equipment include power equipment and thermal equipment. The operating constraints of power equipment are as follows: In the formula, is the wind power or photovoltaic power connected to the grid, is the available wind or photovoltaic power; The output power of gas turbines, electric boilers, hydrogen power generation equipment, and electric hydrogen production equipment. The maximum output power of gas turbines, electric boilers, hydrogen power generation equipment, and electric hydrogen production equipment; is the power of the grid tie line or electrochemical energy storage, The upper limit power of the grid tie line or electrochemical energy storage; The operating constraints of thermal equipment are as follows: In the formula, The thermal power of electric boilers, gas boilers, hydrogen power generation equipment, electric hydrogen production equipment, and gas turbines. is the thermal power of the heat storage; The maximum thermal power of the gas boiler or heat storage; The heat generation efficiency of electric boilers, hydrogen power generation equipment, electric hydrogen production equipment, and gas turbines. is the power generation efficiency of the gas turbine, Thermal utilization rate of hydrogen power generation equipment, electric hydrogen production equipment, and gas turbines; The coupling constraints of the power equipment and thermal equipment are as follows: In the formula, For energy stored in electrochemical energy storage, hydrogen energy storage, and thermal energy storage; Charging power for electrochemical energy storage, hydrogen energy storage, and thermal storage, Discharge power for electrochemical energy storage, hydrogen energy storage, and thermal storage; It is the maximum energy storage capacity for electrochemical energy storage, hydrogen energy storage, and thermal energy storage.

3. The method for optimizing and scheduling a hydrogen-containing integrated energy system according to claim 1, characterized in that: In the step of constructing a pre-scheduling model for a hydrogen-containing integrated energy system based on the operation constraints of various types of energy equipment and the coupling constraints between various types of energy equipment, the specific process is as follows: Use Gaussian Copula function to construct the source-load joint probability distribution function, sample the source-load joint probability distribution function of each period, obtain the source-load time series data of each time interval according to the inverse transformation of the source-load joint probability distribution function, cluster the source-load time series data through K-means clustering to form a typical operation scenario, calculate the probability of each typical operation scenario, and form a scenario set; The goal of the pre-dispatch model is to minimize the economic operation cost, which includes electricity cost, fuel cost and maintenance cost. The electricity cost is measured by the power of the two-way tie line and the purchase and sale price of electricity, and the indicator function is used to distinguish the purchase and sale costs of electricity. The fuel cost is the cost of natural gas consumed by gas turbines and gas boilers. The maintenance cost includes the maintenance costs of electricity, heat and energy storage equipment. The energy consumption habits of different typical operating scenarios are balanced, and a scenario-based expected optimization scheduling method is adopted to generate an economic scheduling strategy for the hydrogen-containing integrated energy system based on typical scenarios and their probabilities to minimize the expected system operating costs.

4. The method for optimizing and scheduling a hydrogen-containing integrated energy system according to claim 1, characterized in that: In the step of predicting the actual carbon emissions according to the pre-scheduling model of the hydrogen integrated energy system and determining the carbon quota pre-purchase amount according to the actual carbon emissions, the actual carbon emissions are the fundamental basis for the demand for the carbon quota pre-purchase amount; the carbon quota pre-purchase amount is determined by the carbon quota difference and the predicted carbon price; The carbon quota difference is calculated based on the initial free carbon quota. When the carbon quota difference is positive, it means there is a surplus of carbon quota. On the contrary, when the carbon quota difference is negative, it means there is a shortage of carbon quota. The predicted carbon price includes a fluctuation coefficient and a segmented carbon price; Among them, the volatility coefficient is related to the carbon quota and carbon emissions in the market, that is, when the increase in user carbon emissions leads to an increase in market demand for carbon quotas, the volatility coefficient of carbon prices increases; Among them, the segmented carbon price takes the trading volume as the independent variable. According to different trading volumes, the carbon price changes linearly within different trading volume ranges; The calculation formula for the predicted carbon price is as follows: Predicted carbon price = volatility coefficient × segmented carbon price.

5. A method for optimizing and scheduling a hydrogen-containing integrated energy system according to claim 4, characterized in that: In the step of determining the carbon quota pre-purchase amount by using the carbon quota difference and the predicted carbon price, the specific determination process is as follows; When carbon allowances are insufficient and the predicted carbon price is high, purchase carbon allowances; Selling carbon allowances when there is excess carbon allowances and the forecasted carbon price is low When carbon allowances are insufficient and carbon prices are forecast to be low, or when carbon allowances are in excess and carbon prices are forecast to be high, a certain proportion of the initial carbon allowances is purchased or sold.

6. The method for optimizing and scheduling a hydrogen-containing integrated energy system according to claim 1, characterized in that: The specific process of the step of performing dynamic carbon trading based on the adjusted carbon quota and determining the cost of dynamic carbon trading is as follows: Get dynamic carbon prices based on the supply and demand relationship of carbon quotas in the market; Determine the actual carbon quota difference based on the adjusted carbon quota and the real-time carbon emissions, where the actual carbon quota difference is equal to the carbon quota 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.

7. The method for optimizing and scheduling a hydrogen-containing integrated energy system according to claim 1, characterized in that: The objective function and calculation formula of the rolling optimization are as follows: In the formula, For the real-time rolling optimization phase, k The operating costs and carbon trading costs of the control time domain are Compensatory prices for electricity and fuel; is the floor rounding function; j ( k ) indicates the k The first control time domain j time point; EX Representing grid tie lines, electric boilers, electrochemical energy storage, hydrogen power generation equipment and electric hydrogen production equipment; GX representing hydrogen energy storage, thermal storage, gas turbines and gas boilers; and .

8. A hydrogen-containing integrated energy system optimization and scheduling system, characterized in that: include: The system framework building module is used to build a hydrogen-containing integrated energy system framework, and to establish operation constraints of various types of energy equipment and coupling constraints between various types of energy equipment based on the hydrogen-containing integrated energy system framework; A pre-dispatch model building module is used to build a pre-dispatch model of a hydrogen-containing integrated energy system based on the operation constraints of various types of energy equipment and the coupling constraints between various types of energy equipment; A carbon quota prediction module is used to predict the actual carbon emissions based on the pre-dispatching 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; A dynamic carbon trading determination module is used to conduct dynamic carbon trading based on the adjusted carbon quota and determine the cost of dynamic carbon trading; The optimization and scheduling module is used to carry out 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 of the hydrogen-containing integrated energy system.

9. 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, the steps of the method for optimizing and scheduling a hydrogen-containing integrated energy system as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing and scheduling a hydrogen-containing integrated energy system as described in any one of claims 1 to 7 are implemented.

Images