Seasonal electricity-hydrogen-gas integrated energy system scheduling method considering time-of-use electricity price
By constructing a seasonal integrated energy system framework of electricity, hydrogen, and gas and a time-of-use electricity pricing mechanism, the problems of multi-energy coordination and energy curtailment in the existing system have been solved, achieving efficient consumption of new energy and low-carbon operation, and reducing system costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHONGQING UNIV OF POSTS & TELECOMM
- Filing Date
- 2026-01-09
- Publication Date
- 2026-04-21
AI Technical Summary
The existing integrated energy system combining electricity, hydrogen, and gas does not incorporate time-of-use pricing as a core control strategy into system dispatching, resulting in the inability to achieve the dispatching goals of multi-energy synergy, seasonal adaptability, and economic efficiency, and also leads to serious energy curtailment issues.
By constructing a seasonal integrated energy system framework of electricity, hydrogen, and gas, and combining time-of-use pricing, equipment constraints, and objective functions, a closed-loop scheduling mechanism is established. The K-Means clustering algorithm is used to process photovoltaic, wind power, and load data to optimize the time-series allocation of energy in the production, storage, and consumption stages.
It has improved the absorption rate of new energy sources, reduced carbon emissions, lowered system costs, and achieved low-carbon operation and economical and efficient dispatch of the integrated energy system.
Smart Images
Figure CN121903294A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of energy dispatching and relates to a seasonal electricity-hydrogen-gas integrated energy system dispatching method that takes into account time-of-use electricity pricing. Background Technology
[0002] As is well known, wind and solar power exhibit significant intermittent and seasonal fluctuations. Wind power output is concentrated in spring, but load demand is low; peak solar power output in summer partially overlaps with peak electricity load; and wind and solar power output declines in autumn and winter while heating load increases. This mismatch in supply and demand timing leads to a surge in power system balance pressure after large-scale grid connection, resulting in prominent seasonal energy curtailment issues. In some regions, wind and solar curtailment rates remain at high levels. How to mitigate the fluctuations of new energy sources and achieve efficient and economical local consumption through a comprehensive energy system has become a core issue urgently needing to be addressed in the current energy transition field.
[0003] While existing research on integrated energy systems involving electricity and hydrogen provides fundamental support for technological development, most studies have not integrated time-of-use pricing as a core control strategy into system scheduling. Only a few have used it as a static cost accounting parameter. These studies have neither combined equipment constraints with the objective function to form a closed-loop scheduling logic, nor have they used time-of-use pricing to guide the temporal allocation of hydrogen energy across production, storage, and consumption. This prevents existing systems from achieving the scheduling goals of multi-energy synergy, seasonal adaptability, and economic efficiency.
[0004] Designing a seasonal electricity-hydrogen-gas integrated energy system dispatching method that takes into account time-of-use pricing has become an urgent problem to be solved in the field of electricity-hydrogen-gas integrated energy system dispatching. Summary of the Invention
[0005] In view of this, the purpose of this invention is to provide a seasonal electricity-hydrogen-gas integrated energy system scheduling method that takes into account time-of-use pricing. By guiding the time-of-use pricing of different seasons, the method can effectively improve the system's absorption of new energy sources, reduce carbon emissions, and lower system costs.
[0006] To achieve the above objectives, the present invention provides the following technical solution: A seasonal electricity-hydrogen-gas integrated energy system dispatching method considering time-of-use pricing includes the following steps: S1: Construct a seasonal integrated energy system framework for electricity, hydrogen and gas. Based on electrolyzers, hydrogen fuel cells, hydrogen storage tanks, methanation reaction pools and carbon capture equipment, as well as the energy input and output from photovoltaic, wind power, electric loads and natural gas companies' energy generation and consumption sites, obtain the energy flow direction between regions, and construct a seasonal integrated energy system framework for electricity, hydrogen and gas. S2: Based on the working principles of electrolyzers, hydrogen fuel cells, methanation reaction tanks, and hydrogen storage tanks, establish mathematical models of the corresponding equipment and establish connections between each piece of equipment and energy source. S3: Classify photovoltaic, wind power, and load data according to season, and cluster the data of each season using the Kmeans algorithm to determine the typical daily data of each season; S4: Set the opening and closing thresholds of the methanation reaction pool based on the hydrogen changes in the hydrogen storage tank, set the large load gap and low-price time-of-use electricity price for different seasons, and use the load gap and low-price time-of-use electricity price discrimination formula to construct a scheduling mechanism with time-of-use electricity price as the core, combined with equipment constraints and the goal of minimizing total cost.
[0007] Furthermore, S1 specifically refers to: A renewable power generation unit using wind and solar energy is constructed. The output power is supplied to the power load unit, and excess energy is fed into an electrolyzer. H2O is introduced into the electrolyzer to generate H2 and O2 through electrolysis. The H2 is then introduced into a hydrogen storage tank for hydrogen storage. The O2 and H2 from the hydrogen storage tank are fed into a hydrogen fuel cell. Through electrochemical reaction, the hydrogen fuel cell outputs additional power and generates H2O. Simultaneously, a carbon capture device is constructed to capture CO2. CO2 and H2 from the hydrogen storage tank are fed into a methanation reactor. The CH4 generated by the reaction is connected to the natural gas company's energy network, realizing the flow and supply of multiple energy sources including electricity, hydrogen, and gas.
[0008] Furthermore, S2 specifically involves: constructing mathematical models of an electrolyzer, a hydrogen fuel cell, a hydrogen storage tank, and a methanation reaction tank based on the seasonal characteristics of the integrated electricity-hydrogen-gas energy system; The hydrogen production capacity of an electrolyzer is determined by its input electrical power, energy conversion efficiency, and the energy-volume conversion relationship of hydrogen. The electrolyzer consumes a certain amount of electrical energy every moment, converting it into hydrogen energy according to the electrolysis efficiency. A fuel cell releases energy by consuming hydrogen, converting the chemical energy of hydrogen into electrical energy according to the fuel cell's efficiency. The required hydrogen consumption is determined by the conversion relationship between the fuel cell's output power, fuel cell efficiency, and hydrogen energy density. A methanation reactor follows the laws of energy conservation and efficiency conversion to convert hydrogen into methane. t The hydrogen storage capacity at any given moment is determined by the hydrogen storage capacity at the previous moment, the hydrogen production capacity at the current moment, and the hydrogen consumption capacity at the current moment; the mathematical expression is:
[0009] In the formula, for The volume of hydrogen gas in the hydrogen storage tank at any given time; for The volume of hydrogen gas in the hydrogen storage tank at any given time; The scheduling time step; for The volumetric rate of hydrogen production in the electrolyzer at any given time; for The rate at which hydrogen is consumed in a fuel cell at any given time; for The rate at which hydrogen is consumed in the methanation reaction at any given moment.
[0010] Furthermore, S3 specifically involves: using the K-Means clustering algorithm to extract typical seasonal days for the electric-hydrogen hybrid energy storage system. The core characteristics and parameter settings of the algorithm are as follows: The K-Means algorithm with partitioned hard clustering is adopted. Based on unsupervised learning logic, it completes the clustering of multi-dimensional time series data of photovoltaic, wind power and load. Through iterative optimization, the sum of squared errors of samples within the cluster to the centroid is minimized, ensuring the homogeneity of time series features of samples within the cluster. Traverse the number of clusters K [2,9] Calculate the silhouette coefficient corresponding to each K value, and select the K value with the highest silhouette coefficient as the optimal number of clusters to avoid the problem of deterioration in clustering effect caused by a fixed number of clusters; Set r=42 to lock the random seed and ensure that the algorithm results can be reproduced; set n=10 to select the globally optimal clustering result through 10 centroid initialization iterations to avoid local optima. After clustering, clusters with a cumulative percentage of 85% are selected based on cluster weights, and a typical day is generated by weighted averaging, taking into account the multi-energy coupling characteristics and data distribution patterns; the clustering process is executed seasonally to adapt to the differences in energy characteristics in different seasons.
[0011] Furthermore, the equipment constraints and total cost minimization objective are as follows: The system net load is:
[0012] The power balance constraints are:
[0013] In the formula, P net ( t The net power of the system is 1. P wind ( t )for t Real-time wind farm output; P pv ( t )for t The actual output of the photovoltaic power station at any given time; P buy ( t )for t Constantly purchasing power from external sources; P fc (t )for t PEMFC output power at any given time; P load ( t )for t Total electrical load of the system at any given time; P elec ( t )for t PEMEL power consumption at all times; P curtail ( t )for t Constantly curtailing wind and solar power; when P net ( t When )≤0, PEMEL can operate, and its input power constraint is:
[0014] The capacity of hydrogen storage tanks is limited to 10% to 90% of their maximum capacity.
[0015] The initiation threshold for the methanation reaction is:
[0016] The shut-off threshold for the methanation reaction is:
[0017] Based on the physical characteristics of the methanation reactor and production scheduling requirements, its operating power range is limited:
[0018] In the formula, y meth ( t The value ) represents the operating status of the methanation reactor. It is 1 when the hydrogen storage tank reaches its start-up threshold, and 0 when it initially falls below or does not reach its shut-down threshold. m,min This is the minimum rated power for the methanation reaction tank; In order to prevent or limit the release of hydrogen and make up for the current load by purchasing electricity or producing hydrogen when the current electricity price is below the threshold, the load gap is predicted to exist in the next 2 to 4 hours and the current hydrogen storage is insufficient, the system will reserve hydrogen energy for future load gaps. Define the system's net gap and future gap criteria; for each time step... t Define the net gap:
[0019] Load gap identification:
[0020] In the formula, D th To set a large load gap; Time-of-use electricity pricing determination:
[0021] In the formula, for t Time-of-use electricity pricing, P buy To set time-of-use electricity price limits; when F ( t )=1 and When the value is 1, hydrogen is reserved in order to cope with the subsequent large load gap in the system, and electricity is purchased from outside to meet the load demand. The objective function of the integrated electric-hydrogen-gas energy system is:
[0022] In the formula, C total C represents the total system cost. buy C is the cost of purchased electricity; elec For the operating cost of the electrolytic cell; C fc For fuel cell operating costs; C st For the operating cost of hydrogen storage tanks; C meth Operating costs of the methanation reactor; C curtail C is the cost of power curtailment penalties; dep This refers to equipment depreciation costs.
[0023] The beneficial effects of this invention are as follows: (1) By constructing a seasonal integrated energy system framework of electricity, hydrogen, and gas, the electrolyzer, hydrogen fuel cell, hydrogen storage tank, methanation reactor, and carbon capture device are coordinated to clarify the flow direction of each energy source, providing a basis for subsequent system model establishment and overall scheduling. This framework can realize the consumption and hydrogen production conversion of surplus wind and solar power, while utilizing the methanation reaction to complete the resource utilization of CO2, significantly improving the new energy consumption rate and achieving the goal of low-carbon system operation.
[0024] (2) Based on the working mechanism of core equipment such as electrolyzers, hydrogen fuel cells, and hydrogen storage tanks, establish mathematical models of the equipment and clarify the relationships between key parameters such as hydrogen production rate, hydrogen consumption rate, and dynamic balance of hydrogen storage. Improve the stability and controllability of system operation.
[0025] (3) The K-Means clustering algorithm is used to process photovoltaic, wind power and load data in different seasons. The number of clusters is optimized by contour coefficient. Multiple centroid iterations are combined to ensure the reliability of clustering results. Highly representative seasonal typical day data are extracted so that the scheduling scheme can accurately match the wind and solar power output patterns and load demand in different seasons and improve the system's adaptability to seasonal fluctuations.
[0026] (4) Integrate time-of-use pricing into system scheduling, and construct a closed-loop scheduling mechanism by combining equipment operation constraints with the goal of minimizing total cost. This addresses the limitation of existing research that treats electricity price only as a static accounting parameter. It increases the absorption of new energy sources, reduces system costs, and achieves low-carbon operation of the integrated energy system.
[0027] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description
[0028] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 A framework diagram for the seasonal scheduling of an integrated electricity-hydrogen-gas energy system, taking into account time-of-use pricing; Figure 2 A framework diagram of a seasonal electricity-hydrogen-gas integrated energy system; Figure 3 A flowchart for the seasonal scheduling of an integrated electricity-hydrogen-gas energy system; Figure 4 A chart showing typical daily data during the spring season; Figure 5 A chart showing typical daily data for the summer season; Figure 6 A chart showing typical daily data for autumn. Figure 7 A chart showing typical daily data during the winter season; Figure 8 Time-of-use electricity pricing chart for spring and autumn seasons; Figure 9 Time-of-use electricity pricing chart for summer and winter; Figure 10 This is a power distribution map for spring. Figure 11 This is a power distribution diagram for summer. Figure 12 A power distribution map for autumn; Figure 13 This is a power distribution map for winter. Figure 14 This is a map showing the gas distribution in the spring system. Figure 15 This is a diagram showing the gas distribution of the system during summer. Figure 16 This is a map showing the gas distribution of the system in autumn. Figure 17This is a map showing the gas distribution of the system during winter. Figure 18 This is a power distribution map for spring. Figure 19 This is a power distribution diagram for summer. Figure 20 A power distribution map for autumn; Figure 21 This is a power distribution map for winter. Figure 22 This is a map showing the gas distribution in the spring system. Figure 23 This is a diagram showing the gas distribution of the system during summer. Figure 24 This is a map showing the gas distribution of the system in autumn. Figure 25 This is a map showing the gas distribution of the system during winter. Detailed Implementation
[0029] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.
[0030] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.
[0031] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.
[0032] Figure 1A framework diagram for the seasonal scheduling of an integrated electricity-hydrogen-gas energy system, taking into account time-of-use pricing; Figure 2 A framework diagram of a seasonal electricity-hydrogen-gas integrated energy system; Figure 3 This is a flowchart of the seasonal scheduling process for the integrated electricity-hydrogen-gas energy system.
[0033] A seasonal electricity-hydrogen-gas integrated energy system dispatching method considering time-of-use pricing includes the following steps: The first part constructs a seasonal integrated energy system framework for electricity, hydrogen, and gas. Based on equipment such as electrolyzers, hydrogen fuel cells, hydrogen storage tanks, methanation reaction pools, and carbon capture, as well as the energy input and output from photovoltaic, wind power, electrical loads, and natural gas companies' energy generation and consumption sites, the energy flow direction between different regions is obtained, and a seasonal integrated energy system framework for electricity, hydrogen, and gas is constructed. The second part establishes mathematical models of the corresponding equipment based on the working principles of electrolyzers, hydrogen fuel cells, methanation reaction tanks, and hydrogen storage tanks, and establishes the connections between each piece of equipment and energy source. The third part categorizes photovoltaic, wind power, and load data by season, and uses the Kmeans algorithm to cluster the data for each season to determine the typical daily data for each season. The fourth part sets the opening and closing thresholds of the methanation reaction pool based on the hydrogen changes in the hydrogen storage tank, sets the large load gap and low-price time-of-use electricity price for different seasons, and uses the load gap and low-price time-of-use electricity price discrimination formula to construct a scheduling mechanism with time-of-use electricity price as the core, combined with equipment constraints and the goal of minimizing total cost.
[0034] In a specific embodiment, we constructed a seasonal integrated energy system framework for electricity, hydrogen, and gas in a certain region, established mathematical models for each device, performed seasonal classification and clustering of photovoltaic, wind power, and load data, and solved the model by combining time-of-use electricity pricing strategies. The steps are as follows: Step 1: Construct a framework for a seasonal integrated electricity-hydrogen-gas energy system; A renewable power generation unit using wind and solar energy is constructed. The output power is supplied to the power load unit, and excess energy is fed into an electrolyzer. Simultaneously, H2O is introduced into the electrolyzer, and the electrolysis reaction driven by electricity generates H2 and O2. The H2 is then introduced into a hydrogen storage tank for hydrogen storage. The O2 and H2 from the hydrogen storage tank are fed into a hydrogen fuel cell. Through an electrochemical reaction, the hydrogen fuel cell outputs supplementary power (connected to the power load unit) and generates H2O. This H2O is then fed back into the electrolyzer for water recycling. A carbon capture device is simultaneously constructed to capture CO2, which, along with the H2 from the hydrogen storage tank, is fed into a methanation reactor. The CH4 generated by the reaction is connected to the natural gas company's energy network. Finally, the power output from the renewable power generation unit and the supplementary power output from the hydrogen fuel cell are combined and supplied to the power load unit. Combined with the CH4 output from the methanation reactor, this achieves the flow and supply of multiple energy sources: electricity, hydrogen, and gas.
[0035] Step 2: Construct mathematical models of the electrolyzer, hydrogen fuel cell, hydrogen storage tank, and methanation reaction tank; The hydrogen production capacity of an electrolyzer is determined by its input electrical power, energy conversion efficiency, and the energy-volume conversion relationship of hydrogen. The electrolyzer consumes a certain amount of electrical energy every moment, converting it into hydrogen energy according to the electrolysis efficiency. A fuel cell releases energy by consuming hydrogen, converting the chemical energy of hydrogen into electrical energy according to the fuel cell's efficiency. The required hydrogen consumption can be obtained from the conversion relationship between the fuel cell's output power, fuel cell efficiency, and hydrogen energy density. A methanation reactor follows the rules of energy conservation and efficiency conversion, converting hydrogen into methane.
[0036] t The hydrogen storage capacity at any given moment is determined by the hydrogen storage capacity at the previous moment, the hydrogen production capacity at the current moment, and the hydrogen consumption capacity at the current moment. The mathematical expression is:
[0037] In the formula, for The volume of hydrogen gas in the hydrogen storage tank at any given time; for The volume of hydrogen gas in the hydrogen storage tank at any given time; The scheduling time step; for The volumetric rate of hydrogen production in the electrolyzer at any given time; for The rate at which hydrogen is consumed in a fuel cell at any given time; for The rate at which hydrogen is consumed in the methanation reaction at any given moment.
[0038] Step 3: Use the K-Means clustering algorithm to extract seasonal typical days for the electric-hydrogen hybrid energy storage system; The K-Means algorithm with partitioned hard clustering is adopted. Based on unsupervised learning logic, it completes the clustering of multi-dimensional time series data of photovoltaic, wind power and load. Through iterative optimization, the sum of squared errors of samples within the cluster to the centroid is minimized, ensuring the homogeneity of time series features of samples within the cluster. Traverse the number of clusters K [2,9] Calculate the silhouette coefficient corresponding to each K value, and select the K value with the highest silhouette coefficient as the optimal number of clusters to avoid the problem of deterioration in clustering effect caused by a fixed number of clusters; Set r=42 to lock the random seed and ensure the algorithm results are reproducible; set n=10 to select the globally optimal clustering result through 10 centroid initialization iterations to avoid local optima. After clustering, clusters with a cumulative percentage of 85% are selected based on cluster weights, and a typical day is generated by weighted averaging, taking into account the multi-energy coupling characteristics and data distribution patterns; the clustering process is executed seasonally to adapt to the differences in energy characteristics in different seasons.
[0039] Step 4: Construct the constraints and objective function of the integrated energy system.
[0040] The system net load is:
[0041] The power balance constraints are:
[0042] In the formula, P net ( t The net power of the system is 1. P wind ( t )for t Real-time wind farm output; P pv ( t )for t The actual output of the photovoltaic power station at any given time; P buy ( t )for t Constantly purchasing power from external sources; P fc ( t )for t PEMFC output power at any given time; P load ( t )for t Total electrical load of the system at any given time; P elec ( t )for t PEMEL power consumption at all times; Pcurtail ( t )for t Constantly curtailing wind and solar power.
[0043] when P net ( t When )≤0, PEMEL can run, and its input power constraint is:
[0044] The capacity of hydrogen storage tanks is limited to 10%-90% of their maximum capacity.
[0045] The initiation threshold for the methanation reaction is:
[0046] The shut-off threshold for the methanation reaction is:
[0047] Based on the physical characteristics of the methanation reactor and production scheduling requirements, its operating power range needs to be limited:
[0048] In the formula, y meth ( t The value ) represents the operating status of the methanation reactor. It is 1 when the hydrogen storage tank reaches its start-up threshold, and 0 when it initially falls below or does not reach its shut-down threshold. m,min This is the minimum rated power for the methanation reaction tank.
[0049] In situations where the current electricity price is below a threshold, a significant load gap is predicted to occur within the next 2-4 hours, and current hydrogen storage is insufficient, the system can choose to block or limit current hydrogen release and compensate for the current load by purchasing electricity or producing hydrogen, thereby reserving hydrogen energy for future significant load gaps.
[0050] Define the system's net gap and future gap criteria. For each time step... t Define net gap
[0051] Load gap identification:
[0052] In the formula, D th To set the maximum load gap for each season.
[0053] Time-of-use electricity pricing determination:
[0054] In the formula, for t Time-of-use electricity pricing, P buy To set time-of-use electricity price limits.
[0055] when F ( t )=1 and When the value is 1, the system retains hydrogen to cope with subsequent large load gaps and purchases electricity from external sources to meet load demands.
[0056] The objective function of the integrated electric-hydrogen-gas energy system is:
[0057] In the formula, C total C represents the total system cost. buy C is the cost of purchased electricity; elec For the operating cost of the electrolytic cell; C fc For fuel cell operating costs; C st For the operating cost of hydrogen storage tanks; C meth Operating costs of the methanation reactor; C curtail C is the cost of power curtailment penalties; dep This refers to equipment depreciation costs.
[0058] The proof is complete.
[0059] Verification example: To verify the effectiveness and superiority of the seasonal electricity-hydrogen-gas integrated energy system scheduling method considering time-of-use pricing described in this invention, a verification example using a certain region is given here.
[0060] Figures 4-7 Typical daily data for seasonal integrated electricity-hydrogen-gas energy systems, including wind power, photovoltaic power, and electricity load, clustered using the K-means algorithm. Figure 8 and Figure 9 This data represents time-of-use electricity prices for different seasons.
[0061] Figures 10-13 This is a comprehensive energy system dispatching method that does not consider time-of-use pricing, taking into account different power outputs in different seasons. Figures 14-17 The seasonal gas distribution data for a comprehensive energy system dispatching method that does not consider time-of-use pricing; Figures 18-21 To consider the different power outputs in different seasons in the integrated energy system dispatching method that takes into account time-of-use pricing; Figures 22-25This study considers the seasonal gas distribution of a comprehensive energy system dispatching method that takes into account time-of-use pricing. A comparison of two schemes reveals that the seasonal electricity-hydrogen-gas integrated energy system dispatching method considering time-of-use pricing achieves the following results: During low-price periods, the load gap is filled by purchasing electricity at a low price and storing hydrogen energy; during high-price periods, fuel cell output is prioritized for supplementary power, with a high degree of matching between fuel cell output and high-price periods. Regarding gas state, the hydrogen storage tank is charged to a safe high level of 80%–90% during low-price periods, and the methanation reactor is precisely matched with hydrogen storage demand, effectively absorbing CO2, reducing carbon emissions, and significantly lowering the total system cost.
[0062] Option 1 is a comprehensive energy system dispatching method that does not consider time-of-use pricing, while Option 2 is a comprehensive energy system dispatching method that does consider time-of-use pricing.
[0063] Table 1 Simulation results of different schemes for seasonal integrated electricity-hydrogen-gas energy systems
[0064] Table 1 shows the cost indicators for the two schemes. The fuel cell operating cost of Scheme 2 is higher than that of Scheme 1 because, under the guidance of time-of-use pricing, Scheme 2 utilizes fuel cell power generation more during periods of high electricity prices to replace expensive externally purchased electricity, thereby increasing the operating time and output of the fuel cells. The methanation operating cost of Scheme 2 is reduced by 25.3%, reflecting its more precise methanation reaction start-up, operating only during periods of hydrogen saturation and when hydrogen consumption is not at high electricity prices. The hydrogen storage tank operating cost is slightly higher in Scheme 2 due to more frequent time-of-use pricing-driven scheduling, leading to greater fluctuations in hydrogen storage capacity. The total electricity purchase cost is reduced by 22.7% in Scheme 2, primarily by rationally controlling the scale of externally purchased electricity during periods of low electricity prices and replacing expensive externally purchased electricity with fuel cell power generation during periods of high electricity prices, significantly reducing electricity purchase expenditure. Ultimately, the total cost of Scheme 2 is 1.04% lower than that of Scheme 1, achieving optimization of system economics.
[0065] Table 2. Simulation results of energy consumption for different schemes of seasonal integrated electricity-hydrogen-gas energy systems.
[0066] Table 2 shows the simulation results of energy consumption for different schemes of the seasonal electricity-hydrogen-gas integrated energy system. Scheme 2 shows a 113.4% increase in fuel cell power generation compared to Scheme 1, reflecting that Scheme 2 converts more hydrogen energy into electricity to replace expensive purchased electricity; the total amount of electricity purchased from the market is reduced by 12.6% in Scheme 2 compared to Scheme 1. In terms of revenue, methane revenue is lower in Scheme 2 than in Scheme 1 because Scheme 2 prioritizes the use of hydrogen energy for electricity replacement during periods of high electricity prices, reducing unnecessary start-ups of the methanation reaction. Although methane revenue is lower, higher overall efficiency is achieved by reducing expensive electricity purchases.
[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A seasonal electricity-hydrogen-gas integrated energy system dispatching method considering time-of-use pricing, characterized in that: Includes the following steps: S1: Construct a seasonal integrated energy system framework for electricity, hydrogen and gas. Based on electrolyzers, hydrogen fuel cells, hydrogen storage tanks, methanation reaction pools and carbon capture equipment, as well as the energy input and output from photovoltaic, wind power, electric loads and natural gas companies' energy generation and consumption sites, obtain the energy flow direction between regions, and construct a seasonal integrated energy system framework for electricity, hydrogen and gas. S2: Based on the working principles of electrolyzers, hydrogen fuel cells, methanation reaction tanks, and hydrogen storage tanks, establish mathematical models of the corresponding equipment and establish connections between each piece of equipment and energy source. S3: Classify photovoltaic, wind power, and load data according to season, and cluster the data of each season using the Kmeans algorithm to determine the typical daily data of each season; S4: Set the opening and closing thresholds of the methanation reaction pool based on the hydrogen changes in the hydrogen storage tank, set the large load gap and low-price time-of-use electricity price for different seasons, and use the load gap and low-price time-of-use electricity price discrimination formula to construct a scheduling mechanism with time-of-use electricity price as the core, combined with equipment constraints and the goal of minimizing total cost.
2. The seasonal electricity-hydrogen-gas integrated energy system dispatching method considering time-of-use pricing as described in claim 1, characterized in that: Specifically, S1 is: A renewable power generation unit using wind and solar energy is constructed. The output power is supplied to the power load unit, and excess energy is fed into an electrolyzer. H2O is introduced into the electrolyzer to generate H2 and O2 through electrolysis. The H2 is then introduced into a hydrogen storage tank for hydrogen storage. The O2 and H2 from the hydrogen storage tank are fed into a hydrogen fuel cell. Through electrochemical reaction, the hydrogen fuel cell outputs additional power and generates H2O. Simultaneously, a carbon capture device is constructed to capture CO2. CO2 and H2 from the hydrogen storage tank are fed into a methanation reactor. The CH4 generated by the reaction is connected to the natural gas company's energy network, realizing the flow and supply of multiple energy sources including electricity, hydrogen, and gas.
3. The seasonal electricity-hydrogen-gas integrated energy system dispatching method considering time-of-use pricing as described in claim 1, characterized in that: Specifically, S2 involves constructing mathematical models of an electrolyzer, hydrogen fuel cell, hydrogen storage tank, and methanation reactor based on the characteristics of a seasonal integrated electricity-hydrogen-gas energy system. The hydrogen production capacity of an electrolyzer is determined by its input electrical power, energy conversion efficiency, and the energy-volume conversion relationship of hydrogen. The electrolyzer consumes a certain amount of electrical energy every moment, converting it into hydrogen energy according to the electrolysis efficiency. A fuel cell releases energy by consuming hydrogen, converting the chemical energy of hydrogen into electrical energy according to the fuel cell's efficiency. The required hydrogen consumption is determined by the conversion relationship between the fuel cell's output power, fuel cell efficiency, and hydrogen energy density. A methanation reactor follows the laws of energy conservation and efficiency conversion to convert hydrogen into methane. t The hydrogen storage capacity at any given moment is determined by the hydrogen storage capacity at the previous moment, the hydrogen production capacity at the current moment, and the hydrogen consumption capacity at the current moment; the mathematical expression is: In the formula, for The volume of hydrogen gas in the hydrogen storage tank at any given time; for The volume of hydrogen gas in the hydrogen storage tank at any given time; The scheduling time step; for The volumetric rate of hydrogen production in the electrolyzer at any given time; for The volumetric hydrogen consumption rate of a fuel cell at any given time; for The rate at which hydrogen is consumed in the methanation reaction at any given moment.
4. The seasonal electricity-hydrogen-gas integrated energy system dispatching method considering time-of-use pricing as described in claim 1, characterized in that: Specifically, S3 involves using the K-Means clustering algorithm to extract typical seasonal days for the electric-hydrogen hybrid energy storage system. The core characteristics and parameter settings of the algorithm are as follows: The K-Means algorithm, which employs partitioned hard clustering, is based on unsupervised learning logic to cluster multi-dimensional time-series data of photovoltaic, wind power, and load. Through iterative optimization, the sum of squared errors from samples within a cluster to the centroid is minimized, ensuring the homogeneity of time-series features of samples within a cluster. Traverse the number of clusters K [2,9] Calculate the silhouette coefficient corresponding to each K value, and select the K value with the highest silhouette coefficient as the optimal number of clusters to avoid the problem of deterioration in clustering effect caused by a fixed number of clusters; Set r=42 to lock the random seed and ensure that the algorithm results can be reproduced; By setting n=10 and performing 10 centroid initialization iterations, the globally optimal clustering result is selected, avoiding local optima. After clustering, clusters with a cumulative percentage of 85% are selected based on cluster weights, and a typical day is generated by weighted averaging, taking into account the multi-energy coupling characteristics and data distribution patterns; the clustering process is executed seasonally to adapt to the differences in energy characteristics in different seasons.
5. The seasonal electricity-hydrogen-gas integrated energy system dispatching method considering time-of-use pricing as described in claim 1, characterized in that: The equipment constraints and total cost minimization objective are as follows: The system net load is: The power balance constraints are: In the formula, P net ( t The net power of the system is 1. P wind ( t )for t Real-time wind farm output; P pv ( t )for t The actual output of the photovoltaic power station at any given time; P buy ( t )for t Constantly purchasing power from external sources; P fc ( t )for t PEMFC output power at any given time; P load ( t )for t Total electrical load of the system at any given time; P elec ( t )for t PEMEL power consumption at all times; P curtail ( t )for t Constantly curtailing wind and solar power; when P net ( t When )≤0, PEMEL can operate, and its input power constraint is: The capacity of hydrogen storage tanks is limited to 10% to 90% of their maximum capacity. The initiation threshold for the methanation reaction is: The shut-off threshold for the methanation reaction is: Based on the physical characteristics of the methanation reactor and production scheduling requirements, its operating power range is limited: In the formula, y meth ( t The value ) represents the operating status of the methanation reactor. It is 1 when the hydrogen storage tank reaches its start-up threshold, and 0 when it initially falls below or does not reach its shut-down threshold. m,min This is the minimum rated power for the methanation reaction tank; In order to prevent or limit the release of hydrogen and make up for the current load by purchasing electricity or producing hydrogen when the current electricity price is below the threshold, the load gap is predicted to exist in the next 2 to 4 hours and the current hydrogen storage is insufficient, the system will reserve hydrogen energy for future load gaps. Define the system's net gap and future gap criteria; for each time step... t Define the net gap: Load gap identification: In the formula, D th To set a large load gap; Time-of-use electricity pricing determination: In the formula, for t Time-of-use electricity pricing, P buy To set time-of-use electricity price limits; when F ( t )=1 and When the value is 1, hydrogen is reserved in order to cope with the subsequent large load gap in the system, and electricity is purchased from outside to meet the load demand. The objective function of the integrated electric-hydrogen-gas energy system is: In the formula, C total C represents the total system cost. buy C is the cost of purchased electricity; elec For the operating cost of the electrolytic cell; C fc For fuel cell operating costs; C st For the operating cost of hydrogen storage tanks; C meth Operating costs of the methanation reactor; C curtail C is the cost of power curtailment penalties; dep This refers to equipment depreciation costs.