Scheduling optimization method and system of electricity-hydrogen-heat low-carbon building energy system

Abstract

The invention discloses a dispatching optimization method and system for an electricity-hydrogen-heat low-carbon building energy system, and the method comprises the following steps: building a comprehensive energy supply system model for the interaction of a renewable energy power generation unit, a fuel cell, energy storage equipment and a power grid in the electricity-hydrogen-heat low-carbon building energy system; taking the operation cost minimization of the electricity-hydrogen-heat low-carbon building energy system within 24 hours as an optimization target, and constructing a scheduling optimization model according to the time-of-use electricity price and purchase cost, the equipment operation cost, the fuel cell heat recovery income and the renewable energy source abandoning power punishment cost in the comprehensive energy supply system model; and solving the scheduling optimization model by adopting a genetic algorithm to obtain an optimal scheduling strategy of the electricity-hydrogen-heat low-carbon building energy system. Therefore, time-of-use electricity price, renewable energy source uncertainty, fuel cell heat recovery and load peak and valley characteristics are comprehensively considered, and the overall performance and robustness of the electricity-hydrogen-heat low-carbon building energy system are greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of scheduling optimization of energy systems, and particularly relates to a scheduling optimization method and system of an electric-hydrogen-thermal low-carbon building energy system. BACKGROUND

[0002] With the transformation of global energy structure to low carbonization, the optimized scheduling of building energy systems becomes the key to improve energy efficiency and reduce carbon emissions. Traditional building energy systems mainly rely on power grid power supply and fossil fuel heating, which has problems such as low energy utilization efficiency, high carbon emission, and large influence of operation cost on electricity price fluctuation. In recent years, hydrogen energy as a clean energy carrier, combined with renewable energy, forms an electric-hydrogen-thermal multi-energy complementary system, which can effectively improve the renewable energy consumption rate and reduce the dependence on the power grid.

[0003] However, the scheduling method for the electric-hydrogen-thermal low-carbon building energy system in the related art has certain limitations, which makes the overall performance and robustness of the electric-hydrogen-thermal low-carbon building energy system lower. SUMMARY

[0004] To solve the above technical problems, the present application provides a scheduling optimization method of an electric-hydrogen-thermal low-carbon building energy system, which comprehensively considers time-of-use electricity price, renewable energy uncertainty, fuel cell heat recovery and load peak-valley characteristics, greatly improving the overall performance and robustness of the electric-hydrogen-thermal low-carbon building energy system.

[0005] The technical scheme adopted by the present application is as follows:

[0006] A scheduling optimization method of an electric-hydrogen-thermal low-carbon building energy system, comprising the following steps: establishing a comprehensive energy supply system model of renewable energy generation units, fuel cells, energy storage devices and power grid interaction in the electric-hydrogen-thermal low-carbon building energy system; taking the minimization of the running cost of the electric-hydrogen-thermal low-carbon building energy system within 24 hours as the optimization target, and constructing a scheduling optimization model according to the time-of-use electricity price purchase cost, device running cost, fuel cell heat recovery income and renewable energy power penalty cost in the comprehensive energy supply system model; and solving the scheduling optimization model by using a genetic algorithm to obtain the optimal scheduling strategy of the electric-hydrogen-thermal low-carbon building energy system.

[0007] In an embodiment of the present application, the objective function of the scheduling optimization model is:

[0008]

[0009] Wherein, C grid (t) is the time-of-use electricity price purchase cost, C heat (t) is the fuel cell heat recovery income, C curt(t) is the penalty cost of renewable energy curtailment, C dev is the operation cost of the equipment.

[0010] In an embodiment of the present application, the time-of-use electricity purchase cost C

[0011] C grid (t) = p price,grid (t) · P grid (t) · Δt,

[0012] where p price,grid (t) is the time-of-use electricity price at time t, P grid (t) is the electricity purchase power from the grid at time t, and Δt is the time interval. grid (t) is the time-of-use electricity purchase cost.

[0013] In an embodiment of the present application, the fuel cell heat recovery benefit C

[0014] C heat (t) = μp price,heat (t) · P fuel cell (t) · Δt,

[0015] where μ is the heat production coefficient of the fuel cell, p price,heat (t) is the heat price at time t, P fuel cell (t) is the electricity generation power of the fuel cell at time t.

[0016] In an embodiment of the present application, the penalty cost of renewable energy curtailment C

[0017]

[0018] where is the penalty coefficient of renewable energy curtailment, P pre，t is the predicted renewable energy power, and P WT，t is the renewable energy consumption power.

[0019] In an embodiment of the present application, the operation cost of the equipment C

[0020]

[0021] where C init is the total initial investment cost of the equipment, C rep is the total replacement cost present value of the equipment, i is the discount rate of the equipment, T is the full life cycle of the system, and C om is the current operation and maintenance cost of the equipment.

[0022] A scheduling optimization system of an electricity-hydrogen-heat low-carbon building energy system, comprising: a first establishing module, configured to establish a comprehensive energy supply system model of renewable energy power generation units, fuel cells, energy storage devices and grid interaction in the electricity-hydrogen-heat low-carbon building energy system; a second establishing module, configured to minimize the operation cost of the electricity-hydrogen-heat low-carbon building energy system within 24 hours as an optimization target, and construct a scheduling optimization model according to the time-of-use electricity purchasing cost, the device operation cost, the fuel cell heat recovery income and the renewable energy power penalty cost in the comprehensive energy supply system model; and an obtaining module, configured to solve the scheduling optimization model by using a genetic algorithm to obtain an optimal scheduling strategy of the electricity-hydrogen-heat low-carbon building energy system.

[0023] A computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, when the processor executes the computer program, the scheduling optimization method of the electricity-hydrogen-heat low-carbon building energy system is realized.

[0024] A non-transitory computer readable storage medium, having a computer program stored thereon, the program being executed by a processor to realize the scheduling optimization method of the electricity-hydrogen-heat low-carbon building energy system.

[0025] The beneficial effects of the present application are:

[0026] The present application comprehensively considers the time-of-use electricity price, renewable energy uncertainty, fuel cell heat recovery and load peak-valley characteristics, greatly improving the overall performance and robustness of the electricity-hydrogen-heat low-carbon building energy system. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 The flowchart of the scheduling optimization method of the electricity-hydrogen-heat low-carbon building energy system of the embodiment of the present application;

[0028] Figure 2a The typical daily scenario load change graph of the electricity-hydrogen-heat low-carbon building system in the first random scenario with fuel cell heat recovery working condition of one specific embodiment of the present application;

[0029] Figure 2b The typical daily scenario load change graph of the electricity-hydrogen-heat low-carbon building system in the first random scenario without fuel cell heat recovery working condition of one specific embodiment of the present application;

[0030] Figure 2c The typical daily scenario load change graph of the electricity-hydrogen-heat low-carbon building system in the second random scenario with fuel cell heat recovery working condition of one specific embodiment of the present application;

[0031] Figure 2d A typical daily scenario load variation diagram of an electric-hydrogen-thermal low-carbon building system in a second random scenario fuel cell heat recovery working condition according to an embodiment of the present application;

[0032] Figure 3 A comparative analysis diagram of scheduling optimization results of different random scenarios with or without fuel cell heat recovery working conditions;

[0033] Figure 4 A block schematic diagram of a scheduling optimization system of an electric-hydrogen-thermal low-carbon building energy system according to an embodiment of the present application; DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0035] Figure 1 A flowchart of a scheduling optimization method of an electric-hydrogen-thermal low-carbon building energy system according to an embodiment of the present application.

[0036] As shown in the figure, the scheduling optimization method of the electric-hydrogen-thermal low-carbon building energy system according to an embodiment of the present application can include the following steps: Figure 1 S1, a comprehensive energy supply system model of renewable energy power generation units, fuel cells, energy storage devices and grid interaction in the electric-hydrogen-thermal low-carbon building energy system is established.

[0037] In an embodiment of the present application, the specific division standard of peak, flat and valley electricity price periods can be determined according to the government time-of-use electricity price policy document, and the clustering algorithm is used to analyze the user's comprehensive electricity load curve combined with the historical electricity consumption data of the electric-hydrogen-thermal low-carbon building energy system, to accurately identify the peak, flat and valley period distribution of the load.

[0038]

[0039] ​Specifically, first, the historical electricity data of the collected electricity-hydrogen-heat low-carbon building energy system is standardized to obtain the comprehensive electricity load curve of all electricity users. Then, the data of the comprehensive electricity load curve is clustered by clustering algorithm to classify all electricity users according to the electricity load characteristics, obtain the average electricity load curve of each type of electricity user, and divide the peak, flat and valley periods of the comprehensive electricity load curve of all electricity users to obtain the unified peak, flat and valley periods. Then, the peak-valley period load difference, peak-flat period load difference and flat-valley period load difference of the average electricity load curve of each type of electricity user are analyzed to obtain the demand response degree of each type of electricity user, and the influence degree of each type of electricity user on the comprehensive electricity load is obtained by calculating the load rate and peak-valley load difference change of the average electricity load curve of each type of electricity user after electricity transfer, and the electricity influence degree of each type of electricity user is obtained after minimum standardization. Finally, the time-of-use electricity price adjustment mode of each type of electricity user is determined according to the demand response degree and electricity influence degree, and the adjustment principle is that the peak-valley electricity price difference is inversely proportional to the demand response degree of each type of electricity user and proportional to the electricity influence degree.

[0040] In a specific embodiment of the present application, the peak-valley period of the electricity-hydrogen-heat low-carbon building energy system, the time-of-use electricity price period and the load distribution can be as shown in Table 1, and the electricity cost of different power sources and the heat production cost of fuel cells can be as shown in Table 2.

[0041] Table 1 Peak-valley period, time-of-use electricity price period and load distribution

[0042] Period Time distribution (hours) Load (kW) Valley period 2～6，10～14 3～8 Flat period 22～2，6～10 10～14 Peak period 15～22 17～20

[0043] Table 2 Energy unit price of different energy sources

[0044]

[0045]

[0046] Further, after the time period is determined, a comprehensive energy supply system model including a renewable energy power generation unit, a fuel cell (power generation and heat recovery), an energy storage device (such as an electric / thermal energy storage device) and a grid interaction can be established, wherein key dispatching operation variables such as renewable energy power generation, fuel cell power generation, heat recovery, grid power purchase, etc. can be set, a multi-energy coupling optimization framework is constructed, and a complete mathematical representation basis is provided for subsequent construction of a dispatching optimization model.

[0047] Specifically, in an embodiment of the present application, the time-of-use electricity price purchase cost can be realized by dynamically associating the electricity price period with the grid power purchase power to realize fine cost accounting, that is, the time-of-use electricity price purchase cost can be generated by the following formula:

[0048] C grid (t)=p price,grid (t)·P grid (t)·Δt,

[0049] wherein p price,grid (t) is the time-of-use electricity price at time t, P grid (t) is the electricity purchase power from the grid at time t, Δt is the time interval, C grid (t) is the time-of-use electricity purchase cost.

[0050] In an embodiment of the present application, the waste heat generated by the fuel cell power generation can be supplied to the user through a heat exchanger, and the corresponding heat generation amount is determined according to whether the fuel cell is used or not, so that the waste heat of the fuel cell can be recovered at a corresponding heat price and used as part of the cost reduction, i.e., the fuel cell heat recovery income can be generated by the following formula:

[0051] C heat (t)=μp price,heat (t)·P fuel cell (t)·Δt,

[0052] wherein μ is the heat generation coefficient of the fuel cell, p price,heat (t) is the heat price at time t, P fuel cell (t) is the power generation power of the fuel cell at time t.

[0053] In an embodiment of the present application, the abandoned renewable energy power penalty cost can be generated by the following formula:

[0054]

[0055] wherein is the abandoned renewable energy penalty coefficient, P pre，t is the predicted power of the renewable energy, P WT，t is the renewable energy consumption power.

[0056] In an embodiment of the present application, the equipment operation cost can be generated by the following formula:

[0057]

[0058] wherein C init is the total initial investment cost of the equipment, C rep is the total replacement cost present value of the equipment, i is the discount rate of the equipment, T is the full life cycle of the system, C om is the current operation and maintenance cost of the equipment.

[0059] S2, taking the minimum operation cost of the electric-hydrogen-thermal low-carbon building energy system in 24 hours as the optimization target, constructing a scheduling optimization model based on the time-of-use electricity purchasing cost, equipment operation cost, fuel cell heat recovery income and renewable energy power penalty cost in the comprehensive energy supply system model.

[0060] Specifically, the minimum operation cost of the electric-hydrogen-thermal low-carbon building energy system in 24 hours can be taken as the optimization target, and a scheduling optimization model can be constructed based on the time-of-use electricity purchasing cost, equipment operation cost, fuel cell heat recovery income and renewable energy power penalty cost in the comprehensive energy supply system model, with the power supply power constraint, power balance constraint, energy storage device energy and charge-discharge energy constraint as the constraint condition. In an embodiment of the present application, the objective function of the scheduling optimization model is:

[0061]

[0062] Wherein, C grid (t) is the time-of-use electricity purchasing cost, C heat (t) is the fuel cell heat recovery income, C curt (t) is the renewable energy power penalty cost, C dev is the equipment operation cost.

[0063] S3, the genetic algorithm is used to solve the scheduling optimization model to obtain the optimal scheduling strategy of the electric-hydrogen-thermal low-carbon building energy system.

[0064] Specifically, the genetic algorithm is used to solve the multivariable and multi-constraint optimization problem in the above-mentioned embodiment, that is, the scheduling operation variable is coded as a gene sequence, the reciprocal of the total operation cost is taken as the fitness function, and the tournament selection strategy and adaptive crossover and mutation operators are used for iterative optimization. The genetic algorithm can efficiently search for the global optimal solution in a complex solution space, and finally output the optimal scheduling strategy considering the time-of-use electricity price response, renewable energy consumption and waste heat utilization.

[0065] Therefore, the present application realizes significant economic benefits, environmental benefits and operation benefits through the electric-hydrogen-thermal multi-energy collaborative optimization scheduling. In terms of economic benefits, the time-of-use electricity purchasing period is optimized through the time-of-use electricity price strategy, effectively reducing the dependence on high-priced electricity in peak period, and the fuel cell heat recovery utilization significantly reduces the heating cost. In terms of environmental benefits, the hydrogen energy storage and clean power generation replace traditional fossil energy, significantly reducing carbon emissions and improving the renewable energy consumption rate. In terms of system operation benefits, the random scenario optimization method can be used to enhance the ability of the system to cope with the renewable energy output fluctuation and load demand uncertainty, and the intelligent optimization of the genetic algorithm ensures the global optimality of the scheduling strategy, finally realizing the efficient, low-carbon and economic operation of the building energy system.

[0066] In a specific embodiment of the present invention, in order to describe the operation and scheduling of the system under different scenarios, two randomly generated scenarios are considered, and in order to distinguish the scheduling situations under the conditions of fuel cell heat recovery and non-recovery, the two random scenarios are respectively analyzed with the recovery of fuel cell waste heat and the non-recovery of fuel cell waste heat as the working conditions. Among them, the optimization process takes the total operating cost of 24 hours as the objective function, and adopts genetic algorithm for iterative optimization to obtain the optimal power configuration scheme hour by hour under different scenarios. For the working condition of considering fuel cell waste heat recovery, the operating variables are photovoltaic power generation, fuel cell power generation, grid power purchase and fuel cell heat recovery; for the working condition without considering fuel cell heat recovery, the operating variables are photovoltaic power generation, fuel cell power generation and grid power purchase. It should be noted that under the working condition of considering fuel cell heat recovery, the operating cost of this part in the objective function is expressed as income, which can also be understood as a part of cost reduction.

[0067] Therefore, after optimized scheduling, the 24-hour scheduling optimization of the electric-hydrogen-heat low-carbon building system under four working conditions with and without considering fuel cell heat recovery in two random scenarios was obtained. Figures 2a-2d As shown. And, Figure 3 It also intuitively shows the changing trends of the number of iterations and the total 24-hour operating cost under the four working conditions.

[0068] Specifically, Figure 2a and 2b They represent the operating conditions of the first random scenario with and without considering fuel cell heat recovery, Figure 2c and 2drespectively represent the working conditions of considering and not considering fuel cell heat recovery under the second random scenario. As can be seen from the figure, for the scenario of photovoltaic existing in daytime, the influence of whether considering fuel cell heat recovery is not great, that is, for different load demands in daytime, photovoltaic is basically fully utilized. It is not difficult to see from the operation cost that the cost of light abandonment will also double the cost related to photovoltaic. Taking the two working conditions under the first random scenario as an example, photovoltaic power generation is from 7 to 17, and the load is from 1.35 to 10 kW. With the increase of photovoltaic power generation, from 10 to 13, the consumption of photovoltaic is also reduced, because the power grid purchase cost at this time is less than twice the photovoltaic power generation cost (including light abandonment cost), and for the non-photovoltaic period of 18 to 6, the difference between whether the fuel cell performs heat recovery gradually appears. For the power consumption peak period, due to the increase of load demand, if the heat recovery of the fuel cell is not considered, a part of the load will still use the fuel cell to meet the demand. And with the entering of the power consumption flat peak and valley period, the power grid purchase cost is lower than the fuel cell power generation cost, so the power grid purchase is preferred. When considering the heat recovery of the fuel cell, the operation cost of the system will be reduced every hour, so more consideration is given to using fuel cell power generation to meet the load demand of the user.

[0069] Figure 3 The scheduling optimization results of the working conditions of whether considering fuel cell heat recovery under different random scenarios are compared and analyzed in detail. Specifically, in the iterative optimization calculation of 24-hour system operation cost, it is obvious that the number of iterations of the optimized iteration considering the heat recovery of the fuel cell is increased compared with that without considering the heat recovery of the fuel cell. This is because when responding to different load demands, the power grid purchase cost, the fuel cell power generation cost and the heat recovery cost need to be constantly weighed. In addition, for the first random scenario (i.e. scenario 1), the 24-hour total operation cost considering and not considering the heat recovery of the fuel cell is 91.52 ￥ and 121.55 ￥ respectively, which is reduced by 30.03%. And for the second random scenario (i.e. scenario 2), the 24-hour total operation cost considering and not considering the heat recovery of the fuel cell is 92.27 ￥ and 120.37 ￥ respectively, which is reduced by 28.10%. Therefore, the present application optimizes the power purchase period through time-of-use pricing strategy, effectively reduces the dependence on high-priced electricity in peak period, and significantly reduces the heating cost by combining fuel cell waste heat recovery.

[0070] In summary, according to the scheduling optimization method of the electricity-hydrogen-heat low-carbon building energy system, the comprehensive energy supply system model of the renewable energy generation unit, the fuel cell, the energy storage device and the power grid interaction in the electricity-hydrogen-heat low-carbon building energy system is established, the scheduling optimization model is constructed according to the time-of-use electricity price purchasing cost, the equipment operation cost, the fuel cell heat recovery income and the renewable energy power penalty cost in the comprehensive energy supply system model, the genetic algorithm is used to solve the scheduling optimization model, and the optimal scheduling strategy of the electricity-hydrogen-heat low-carbon building energy system is obtained. Therefore, the time-of-use electricity price, the renewable energy uncertainty, the fuel cell heat recovery and the load peak-valley characteristics are comprehensively considered, and the overall performance and robustness of the electricity-hydrogen-heat low-carbon building energy system are greatly improved.

[0071] Corresponding to the scheduling optimization method of the electricity-hydrogen-heat low-carbon building energy system, the application further provides a scheduling optimization system of the electricity-hydrogen-heat low-carbon building energy system.

[0072] As shown in Figure 4 , the scheduling system of the electricity-hydrogen-heat low-carbon building energy system can include a first establishing module 100, a second establishing module 200 and an obtaining module 300.

[0073] The first establishing module 100 is configured to establish a comprehensive energy supply system model of the renewable energy generation unit, the fuel cell, the energy storage device and the power grid interaction in the electricity-hydrogen-heat low-carbon building energy system; the second establishing module 200 is configured to construct a scheduling optimization model according to the time-of-use electricity price purchasing cost, the equipment operation cost, the fuel cell heat recovery income and the renewable energy power penalty cost in the comprehensive energy supply system model, with the minimization of the operation cost of the electricity-hydrogen-heat low-carbon building energy system within 24 hours as the optimization target; and the obtaining module 300 is configured to solve the scheduling optimization model by using a genetic algorithm, so as to obtain the optimal scheduling strategy of the electricity-hydrogen-heat low-carbon building energy system.

[0074] In an embodiment of the application, the objective function of the scheduling optimization model is:

[0075]

[0076] C grid (t) is the time-of-use electricity price purchasing cost, C heat (t) is the fuel cell heat recovery income, C curt (t) is the renewable energy power penalty cost, and C dev is the equipment operation cost.

[0077] In one embodiment of the present invention, the first establishing module 100 is specifically configured to generate the time-of-use electricity purchase cost using the following formula:

[0078] C grid (t) = p price,grid (t)·P grid (t)·Δt,

[0079] Among them, p price,grid (t) is the time-of-use electricity price during period t, P grid (t) is the power purchased from the grid during period t, Δt is the time interval, C grid (t) is the electricity purchase cost at time-of-use electricity price.

[0080] In one embodiment of the present invention, the first establishing module 100 is specifically configured to generate the fuel cell heat recovery benefit using the following formula:

[0081] C heat (t)=μp price,heat (t)·P fuelcell (t)·Δt,

[0082] Where μ is the heat generation coefficient of the fuel cell, p price,heat (t) is the heat price during period t, P fuel cell (t) is the power generation of the fuel cell during period t.

[0083] In one embodiment of the present invention, the first establishing module 100 is specifically configured to generate the penalty cost for curtailing renewable energy power using the following formula:

[0084]

[0085] in, is the penalty coefficient for abandoning renewable energy, P pre，t Forecast power for renewable energy, P WT，t Absorbing renewable energy power.

[0086] In one embodiment of the present invention, the first establishing module 100 is specifically configured to generate the equipment operating cost using the following formula:

[0087]

[0088] Among them, C init is the total initial investment cost of the equipment, C rep is the present value of the total replacement cost of the equipment, i is the equipment discount rate, T is the full life cycle of the system, C om The current operation and maintenance cost of the equipment.

[0089] It should be noted that details not disclosed in the scheduling optimization system of the electric-hydrogen-thermal low-carbon building energy system of the embodiments of the present application are referred to the details disclosed in the scheduling optimization method based on the electric-hydrogen-thermal low-carbon building energy system described above, and will not be described here in detail.

[0090] According to the scheduling optimization system of the electric-hydrogen-thermal low-carbon building energy system of the embodiments of the present application, the first establishing module establishes a comprehensive energy supply system model of the renewable energy power generation unit, the fuel cell, the energy storage device and the grid interaction in the electric-hydrogen-thermal low-carbon building energy system, and the second establishing module takes the minimum operation cost of the electric-hydrogen-thermal low-carbon building energy system within 24 hours as the optimization target, constructs a scheduling optimization model according to the time-of-use electricity purchase cost, the device operation cost, the fuel cell heat recovery income and the renewable energy power penalty cost in the comprehensive energy supply system model, and solves the scheduling optimization model by using the genetic algorithm through the obtaining module to obtain the optimal scheduling strategy of the electric-hydrogen-thermal low-carbon building energy system. Therefore, the time-of-use electricity price, the renewable energy uncertainty, the fuel cell heat recovery and the load peak-valley characteristics are comprehensively considered, and the overall performance and robustness of the electric-hydrogen-thermal low-carbon building energy system are greatly improved.

[0091] Corresponding to the above-mentioned embodiments, the present application also proposes a computer device.

[0092] The computer device of the embodiments of the present application comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the scheduling optimization method of the electric-hydrogen-thermal low-carbon building energy system of the above-mentioned embodiments is realized.

[0093] According to the computer device of the embodiments of the present application, the time-of-use electricity price, the renewable energy uncertainty, the fuel cell heat recovery and the load peak-valley characteristics are comprehensively considered, and the overall performance and robustness of the electric-hydrogen-thermal low-carbon building energy system are greatly improved.

[0094] Corresponding to the above-mentioned embodiments, the present application also proposes a non-transitory computer readable storage medium.

[0095] The non-transitory computer readable storage medium of the embodiments of the present application stores a computer program, and when the processor executes the program, the scheduling optimization method of the electric-hydrogen-thermal low-carbon building energy system described above is realized.

[0096] According to the non-transitory computer readable storage medium of the embodiments of the present application, the time-of-use electricity price, the renewable energy uncertainty, the fuel cell heat recovery and the load peak-valley characteristics are comprehensively considered, and the overall performance and robustness of the electric-hydrogen-thermal low-carbon building energy system are greatly improved.

[0097] In the description of the application, the terms "first", "second", "third", etc. are used only for descriptive purposes and do not connote or imply relative importance or a specific order. Thus, features identified as "first", "second", etc. can implicitly or explicitly include one or more of the features identified with those terms. The term "plurality" means two or more, unless expressly specified otherwise.

[0098] In the present application, unless specifically defined otherwise, the terms "mounting", "connected", "connecting", "fixed", "fixedly connected", etc. should be construed broadly, for example, they can be fixed connection, detachable connection, or integral; they can be mechanical connection, electrical connection, or both; they can be direct connection, or indirect connection via an intermediate medium; they can be internal connection between two elements, or interaction between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0099] In the present application, unless specifically defined otherwise, the first feature "on" or "under" the second feature can be direct contact between the first and second features, or indirect contact between the first and second features through an intermediate medium. Moreover, the first feature "above", "over" and "on" the second feature can be directly above or obliquely above the second feature, or simply indicate that the first feature is higher than the second feature in horizontal height. The first feature "below", "under" and "under" the second feature can be directly below or obliquely below the second feature, or simply indicate that the first feature is lower than the second feature in horizontal height.

[0100] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in the present application and the features of the different embodiments or examples, without contradiction.

[0101] In addition, each function unit in each embodiment of the present application can be integrated in one processing module, or each unit can exist physically separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware, or in the form of software function module. When the integrated module is realized in the form of software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0102] Although the embodiments of the present application have been shown and described above, it should be understood by those ordinary skilled in the art that the above embodiments are exemplary and cannot be understood as limiting the present application, and those ordinary skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A scheduling optimization method for an electricity-hydrogen-heat low-carbon building energy system, characterized in that: The following steps are involved: Establishing a comprehensive energy supply system model for the electricity-hydrogen-heat low-carbon building energy system, including renewable energy generation units, fuel cells, energy storage devices, and grid interactions; Taking minimizing the 24-hour operating cost of the electricity-hydrogen-heat low-carbon building energy system as the optimization goal, a scheduling optimization model is constructed based on the time-of-use electricity purchase cost, equipment operating cost, fuel cell heat recovery income, and penalty cost of abandoned renewable energy power in the integrated energy supply system model; A genetic algorithm is used to solve the scheduling optimization model to obtain the optimal scheduling strategy of the electricity-hydrogen-heat low-carbon building energy system.

2. The scheduling optimization method of the electricity-hydrogen-heat low-carbon building energy system according to claim 1 is characterized in that: The objective function of the scheduling optimization model is: Among them, C grid (t) is the electricity purchase cost at the time-of-use electricity price, C heat (t) is the fuel cell heat recovery benefit, C curt (t) is the penalty cost for abandoning renewable energy power, C dev The operating cost of the equipment.

3. The scheduling optimization method of the electricity-hydrogen-heat low-carbon building energy system according to claim 2 is characterized in that: The electricity purchase cost of the time-of-use electricity price is generated by the following formula: C grid (t)=p price,grid (t)·P grid (t)·Δt, Among them, p price,grid (t) is the time-of-use electricity price during period t, P grid (t) is the power purchased from the grid during period t, Δt is the time interval, C grid (t) is the cost of purchasing electricity at the time-of-use electricity price.

4. The scheduling optimization method of the electricity-hydrogen-heat low-carbon building energy system according to claim 3 is characterized in that: The fuel cell heat recovery benefit is generated by the following formula: C heat (t)=μp price,heat (t)·P fuel cell (t)·Δt, Wherein, μ is the heat generation coefficient of the fuel cell, p price,heat (t) is the heat price during period t, P fuel cell (t) is the power generation of the fuel cell during time period t.

5. The scheduling optimization method of the electricity-hydrogen-heat low-carbon building energy system according to claim 4 is characterized in that: The penalty cost for curtailing renewable energy power is generated by the following formula: in, is the penalty coefficient for abandoning renewable energy, P pre，t Forecast power for renewable energy, P WT，t Absorbing renewable energy power.

6. The scheduling optimization method of the electricity-hydrogen-heat low-carbon building energy system according to claim 5 is characterized in that: The equipment operating cost is generated by the following formula: Among them, C init is the total initial investment cost of the equipment, C rep is the present value of the total replacement cost of the equipment, i is the equipment discount rate, T is the full life cycle of the system, C om The current operation and maintenance cost of the equipment.

7. A scheduling optimization system for an electricity-hydrogen-heat low-carbon building energy system, characterized in that: include: A first establishment module, the first establishment module is used to establish a comprehensive energy supply system model of renewable energy power generation units, fuel cells, energy storage equipment and grid interaction in the electricity-hydrogen-heat low-carbon building energy system; A second establishment module is used to establish a scheduling optimization model based on the time-of-use electricity purchase cost, equipment operation cost, fuel cell heat recovery income and renewable energy power abandonment penalty cost in the integrated energy supply system model, with the minimization of the operating cost of the electricity-hydrogen-heat low-carbon building energy system within 24 hours as the optimization goal; An acquisition module is used to solve the scheduling optimization model using a genetic algorithm to obtain the optimal scheduling strategy for the electricity-hydrogen-heat low-carbon building energy system.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the scheduling optimization method of the electric-hydrogen-heat low-carbon building energy system according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the scheduling optimization method of the electric-hydrogen-heat low-carbon building energy system according to any one of claims 1 to 6 is implemented.

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