Zero-carbon park optimal scheduling method considering variable working condition characteristics of hydrogen energy storage system

Abstract

The invention relates to a zero-carbon park optimal scheduling method considering variable working condition characteristics of a hydrogen energy storage system. The method comprises the following steps: step 1, collecting renewable energy and load data of a zero-carbon park; 2, constructing a zero-carbon park system optimization scheduling model, and establishing a zero-carbon park optimization scheduling objective function and constraint conditions; step 3, considering variable working condition operation characteristics of the hydrogen energy storage system, respectively formulating an efficient hydrogen production strategy of an electrolytic cell, an efficient power generation strategy of a fuel cell and an efficient heating strategy of the electrolytic cell and the fuel cell, and further constructing a multi-module mixed operation model of the hydrogen energy storage system; and step 4, completing optimal scheduling of the zero-carbon park system containing hydrogen energy storage. According to the method, the operation power-efficiency change of the hydrogen energy storage system and the input power and thermoelectric load supply power change of the system at different moments can be fully considered, and the optimal dispatching operation method of the electrolytic bath and the fuel cell equipment in the hydrogen energy storage system is determined.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization and scheduling, and relates to a zero-carbon park optimization and scheduling method, in particular to a zero-carbon park optimization and scheduling method that takes into account the variable operating condition characteristics of a hydrogen energy storage system. Background Art

[0002] As an energy hub, hydrogen energy storage plays a vital role in zero-carbon parks, but the nonlinear characteristics of the hydrogen energy storage system have a great impact on the system operating costs.

[0003] The photovoltaic, wind power generation and thermal power load demands in the zero-carbon park are constantly changing, which will undoubtedly cause the hydrogen energy storage equipment to operate in variable conditions, causing the heat-to-electricity ratio of the hydrogen energy storage system to change, affecting the efficiency of the hydrogen energy storage equipment.

[0004] However, in response to the changing thermal and electric load demands of the system, the general cogeneration unit's "electricity-based heat" or "heat-based electricity" scheduling strategy obviously cannot fully utilize the hydrogen energy storage supply capabilities, resulting in reduced operating efficiency of zero-carbon parks with hydrogen energy storage as the hub, and a significant increase in costs.

[0005] Therefore, it is necessary to propose a zero-carbon park optimization scheduling strategy that takes into account the electric-heat-hydrogen conversion operating characteristics of hydrogen energy storage equipment, to form an efficient hydrogen energy storage zero-carbon park scheduling plan, and to help save energy and reduce emissions.

[0006] After searching, no public documents of the prior art that are identical or similar to the present invention were found. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a zero-carbon park optimization scheduling method that takes into account the variable operating conditions of the hydrogen energy storage system. In view of the impact of the nonlinear operating characteristics of the hydrogen energy storage system on the operating efficiency and cost of the zero-carbon park mentioned in the background technology, the operating power-efficiency changes of the hydrogen energy storage system and the changes in the input power and thermal power supply power of the system at different times are fully considered to determine the optimized scheduling operation method of the electrolyzer and fuel cell equipment in the hydrogen energy storage system.

[0008] The present invention solves the practical problem by adopting the following technical solutions:

[0009] A zero-carbon park optimization scheduling method considering the variable operating characteristics of a hydrogen energy storage system comprises the following steps:

[0010] Step 1: Collect renewable energy and load data of zero-carbon parks;

[0011] Step 2: Based on the energy and load data collected by the park, a zero-carbon park system optimization scheduling model is constructed, and the zero-carbon park optimization scheduling objective function and constraint conditions are established;

[0012] Step 3: Based on the zero-carbon park system optimization scheduling model constructed in step 2, considering the variable operating characteristics of the hydrogen energy storage system, formulate efficient hydrogen production strategies for electrolyzers, efficient power generation strategies for fuel cells, and efficient heating strategies for electrolyzers and fuel cells, and then construct a multi-module hybrid operation model of the hydrogen energy storage system;

[0013] Step 4: The zero-carbon park system optimization scheduling model constructed in step 2 and the hydrogen energy storage system multi-module hybrid operation model constructed in step 3 are combined into an overall optimization scheduling model of the zero-carbon park system, and the overall optimization scheduling model of the zero-carbon park system is solved to complete the optimization scheduling of the zero-carbon park system containing hydrogen energy storage.

[0014] Moreover, the specific method of step 1 is:

[0015] Collect wind power, photovoltaic output data and thermal power load demand data of the zero-carbon park in each time period.

[0016] Moreover, the specific method of step 2 is:

[0017] A zero-carbon park system optimization scheduling model is constructed, and the minimum system daily operation cost is taken as the zero-carbon park optimization scheduling objective function. The system daily operation cost C includes: system operation and maintenance cost, system energy purchase cost and operation penalty cost, as follows:

[0018] minC=C r +C buy +C loss (1)

[0019]

[0020]

[0021]

[0022] Where: r el 、r fc 、r hb are the operation and maintenance cost coefficients of the electrolyzer group, fuel cell group and hydrogen boiler respectively; ω e (t) is the price coefficient of electricity purchased from the power grid during period t; is the hydrogen purchase price coefficient; P buy (t), G buy (t) are the power purchased from the grid and the hydrogen purchased from the outside during period t, respectively; α(t) is the penalty price coefficient for power loss load during period t; β is the penalty coefficient for heat loss load; γ is the penalty coefficient for abandoned solar and wind power; P loss (t), H loss (t) and P cp(t) are the power loss load, heat loss load and abandoned solar and wind power in period t respectively.

[0023] The constraints of the zero-carbon park optimization scheduling objective function include:

[0024] 1) Power balance constraints

[0025] The zero-carbon park system needs to ensure that the internal power is always in a balanced state. Its power generation should be the same as the power consumption at all times. The power balance constraints are as follows:

[0026] P pw (t)-P cp (t)+P buy (t) = P load (t)-P loss (t)+P el (t)-P fc (t) (5)

[0027] Where: P pw (t) is the photovoltaic and wind power generation power in period t.

[0028] 2) Thermal energy balance constraints

[0029] The zero-carbon park energy system needs to ensure that the internal thermal energy is always in a balanced state. The thermal energy balance constraints are as follows:

[0030] H el (t)+H fc (t)+H hb (t) = H load (t)-H loss (t)+H in (t)-H out (t) (6)

[0031] 3) Operation constraints of hydrogen energy storage units

[0032] As energy storage equipment, hydrogen storage tanks and heat storage tanks have minimum and maximum capacity constraints. Hydrogen boilers also have capacity constraints during operation. The constraints are as follows:

[0033] V HST,min ≤V HST (t)≤V HST,max (7)

[0034] H ST,min ≤H ST (t)≤H ST,max (8)

[0035] 0≤H hb (t)≤H hb,max (9)

[0036] Where: V HST,max 、V HST,min are the upper and lower limits of the capacity of the hydrogen storage tank respectively; H ST,max , H ST,min are the upper and lower limits of the energy storage power of the heat storage tank; H hb,max is the maximum output power of the hydrogen boiler.

[0037] 4) Other operating constraints are as follows:

[0038] 0≤H loss (t)≤ψ H,loss H load (t) (10)

[0039] 0≤P loss (t)≤ψ P,loss P load (t) (11)

[0040] 0≤P cp (t)≤ψ cp P pw (t) (12)

[0041] 0≤P buy (t)≤P buy,max (13)

[0042] 0≤G buy (t)≤G buy,max (14)

[0043] Where: H,loss , P,loss are the maximum heat loss and power loss load ratios of the system respectively; ψ cp P is the maximum wind and solar power abandonment ratio of the system; buy,max , G buy,max They are the maximum electricity and hydrogen purchasing power of the system respectively.

[0044] Moreover, the specific steps of step 3 include:

[0045] (1) Based on the zero-carbon park system optimization scheduling model constructed in step 2, considering the variable operating characteristics of the hydrogen energy storage system, an efficient hydrogen production strategy for the electrolyzer and an efficient power generation strategy for the fuel cell are formulated;

[0046] (2) Develop efficient heating strategies for electrolyzers and fuel cells;

[0047] (3) Construct a multi-module hybrid operation mode of the hydrogen energy storage system, and construct a multi-module hybrid operation model of the hydrogen energy storage system based on this mode.

[0048] Moreover, the specific steps of step 3 (1) include:

[0049] ① Draw the efficiency curve of the electric-heat-hydrogen energy conversion of the electrolyzer and fuel cell under the change of input power;

[0050] ② Based on the efficiency change curve of the electric-thermal-hydrogen energy conversion drawn in step ①, the electrolyzer and the fuel cell are modularized to form a corresponding module group;

[0051] The specific method for modularizing the electrolyzer and fuel cell is as follows:

[0052] According to the actual power of the electrolyzer and fuel cell and the system requirements, multiple electrolyzers and fuel cells are combined to form an electrolyzer module group and a fuel cell module group. The input and output power of the module group are the sum of the input and output power of each module in the module group. The specific model is as follows:

[0053]

[0054]

[0055] Where: a and b are the number of electrolyzers and fuel cell modules started in period t, respectively; is the power consumption of the nth electrolyzer module in period t; is the hydrogen production power of the nth electrolyzer module in period t; is the heat generation power of the nth electrolyzer module in period t; is the power generation of the nth fuel cell in period t; is the hydrogen consumption power of the nth fuel cell in period t; is the heat generation power of the nth fuel cell in period t.

[0056] ③ By constructing the Lagrangian extreme value function and optimizing the power distribution between module groups, the hydrogen production efficiency of the electrolyzer and the power generation efficiency of the fuel cell are maximized, thereby completing the formulation of an efficient hydrogen production strategy for the electrolyzer and an efficient power generation strategy for the fuel cell;

[0057] The specific steps include:

[0058] 1) Construct the Lagrange extreme value function as follows:

[0059]

[0060]

[0061]

[0062]

[0063] Where N is the number of modules running in the module group within the t period; Gn (t) is the hydrogen production power or power generation power corresponding to the output of the nth module at time t. When this module group is an electrolyzer, G n (t) represents the hydrogen production power output by the nth electrolyzer module at time t; when this module group is a fuel cell, it corresponds to the power generation power; P n (t) is the input power of the nth module at time t; P(t) is the total input power of the module group at time t; P max The maximum output power of a single module; are the upward and downward climbing powers of the nth module in period t respectively; are the upward and downward climbing power limits of the module respectively; λ is the Lagrange multiplier; is the module group operation constraint, i.e., the constraint in formula (18).

[0064] The constraints in the formula are the operating constraints of the electrolyzer and fuel cell module groups, which correspond to the module group capacity constraint limit and the upward and downward climbing power limit per unit time respectively.

[0065] 2) By constructing the Lagrangian extreme value function, formula (20) is used as the operating constraint in the efficient hydrogen production process of the electrolyzer module group and the efficient power generation process of the fuel cell module group, and applied to the optimized operation process of the zero-carbon park system, thereby completing the formulation of the efficient hydrogen production strategy of the electrolyzer module group and the efficient power generation strategy of the fuel cell module group;

[0066] Moreover, the specific method of step 3 (2) is:

[0067] In the efficient heating process, the power distribution constraints between the electrolyzer and the fuel cell module group are as follows:

[0068] P(t)=(N-1)P max +P N (t) (21)

[0069] P 1 (t) = P 2 (t)=…=P N-1 (t) = P max (twenty two)

[0070] By setting the operating constraints of the electrolyzer and fuel cell module group in equations (21)-(22) and applying them to the optimized operation process of the zero-carbon park system, the formulation of an efficient heating strategy for the electrolyzer and fuel cell module group can be completed;

[0071] Moreover, the specific steps of step (3) of step 3 include:

[0072] ① The multi-module hybrid operation mode of the hydrogen energy storage system is:

[0073] The electrolyzer group adopts a mixed operation mode of "high-efficiency hydrogen production" and "high-efficiency heating". The ratio of the heat load power required by the system to the electric power input by the electrolyzer group at each moment determines whether the electrolyzer group should adopt the "high-efficiency hydrogen production" or "high-efficiency heating" operation mode at that moment;

[0074] Similarly, the fuel cell group adopts a mixed operation mode of "high-efficiency power generation" and "high-efficiency heating". The ratio of the thermal load power to the electrical load power required by the system at each moment determines whether the fuel cell group should adopt the "high-efficiency power generation" or "high-efficiency heating" operation mode at that moment.

[0075] ②Construct a multi-module hybrid operation model of the hydrogen energy storage system:

[0076] Assume that R(t) is the multi-module hybrid operation mode model of the hydrogen energy storage system, which represents the operation mode of the electrolyzer group and the fuel cell group at time t. If R(t) = 1, the electrolyzer group operates in a high-efficiency hydrogen production mode and the fuel cell group operates in a high-efficiency power generation mode at time t; otherwise, both devices operate in a high-efficiency heating mode. The expression of the multi-module hybrid operation mode model R(t) of the hydrogen energy storage system is as follows:

[0077]

[0078] Where: H load (t) is the system heat load demand at time t; P s (t) is the module group variable. When the module group is an electrolytic cell, P s (t) = P el (t); When the module group is a fuel cell, P s (t) = P load (t); k is the optimization variable, and the working mode of the module group at that moment is determined by optimizing the k value.

[0079] Moreover, the specific method of step 4 is:

[0080] Judgment during the solution process If yes, the electrolyzer is operated in high-efficiency hydrogen production mode, otherwise the electrolyzer is operated in high-efficiency heating mode; If so, the fuel cell operates in a high-efficiency power generation mode, otherwise the fuel cell operates in a high-efficiency heating mode;

[0081] Advantages and beneficial effects of the present invention:

[0082] 1. The present invention proposes a zero-carbon park optimization scheduling method that takes into account the variable operating conditions of the hydrogen energy storage system. It is formulated based on the variable operating conditions operation strategy of the hydrogen energy storage system. While ensuring the accuracy of zero-carbon park scheduling, it can fully tap the application potential of hydrogen energy storage in the zero-carbon park and realize the flexible supply of thermal power loads of the hydrogen energy storage system.

[0083] 2. The present invention realizes efficient hydrogen production by electrolyzer and efficient power generation by fuel cell, thereby reducing the energy shortage of the system, effectively reducing carbon dioxide emissions from zero-carbon parks containing hydrogen energy storage, and promoting energy conservation and emission reduction.

[0084] 3. The present invention designs a multi-module hybrid operation mode of the hydrogen energy storage system to support the supply of thermal and electric loads in different proportions in the zero-carbon park, ensure the maximization of the system operation efficiency, and provide strong support for the development of the zero-carbon park. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 A flow chart of the zero-carbon park optimization scheduling method considering the variable operating characteristics of the hydrogen energy storage system;

[0086] Figure 2 This is a characteristic diagram of the electrolytic cell's electric-thermal-hydrogen conversion operation mode of the present invention;

[0087] Figure 3 This is a characteristic diagram of the fuel cell electric-thermal-hydrogen variable operating conditions of the present invention;

[0088] Figure 4 This is a data diagram of an example of renewable energy output power calculation of the present invention;

[0089] Figure 5 This is a data diagram of the heat and electricity load calculation example of the park of the present invention;

[0090] Figure 6 This is a data diagram of a real-time electricity price calculation example of the present invention;

[0091] Figure 7 This is an analysis diagram of various cost changes of the present invention under different load heat-to-electricity ratios;

[0092] Figure 8 This is an analysis chart of the total scheduling cost ratio calculation example between schemes under different load heat-to-electricity ratios of the present invention. DETAILED DESCRIPTION

[0093] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings:

[0094] A zero-carbon park optimization scheduling method considering the variable operating characteristics of the hydrogen energy storage system, such as Figure 1 As shown, the following steps are included:

[0095] Step 1: Collect renewable energy and load data of zero-carbon parks;

[0096] The specific method of step 1 is:

[0097] Collect wind power, photovoltaic output data and thermal power load demand data of the zero-carbon park in each time period.

[0098] Step 2: Based on the energy and load data collected by the park, a zero-carbon park system optimization scheduling model is constructed, and the zero-carbon park optimization scheduling objective function and constraint conditions are established;

[0099] The specific method of step 2 is:

[0100] A zero-carbon park system optimization scheduling model is constructed, and the minimum system daily operation cost is taken as the zero-carbon park optimization scheduling objective function. The system daily operation cost C includes: system operation and maintenance cost, system energy purchase cost and operation penalty cost, as follows:

[0101] minC=C r +C buy +C loss (1)

[0102]

[0103]

[0104]

[0105] Where: r el 、r fc 、r hb are the operation and maintenance cost coefficients of the electrolyzer group, fuel cell group and hydrogen boiler respectively; ω e (t) is the price coefficient of electricity purchased from the power grid during period t; is the hydrogen purchase price coefficient; P buy (t), G buy (t) are the power purchased from the grid and the hydrogen purchased from the outside during period t, respectively; α(t) is the penalty price coefficient for power loss load during period t; β is the penalty coefficient for heat loss load; γ is the penalty coefficient for abandoned solar and wind power; P loss (t), H loss (t) and P cp (t) are the power loss load, heat loss load and abandoned solar and wind power in period t respectively.

[0106] The constraints of the zero-carbon park optimization scheduling objective function include:

[0107] 1) Power balance constraints

[0108] The zero-carbon park system needs to ensure that the internal power is always in a balanced state. Its power generation should be the same as the power consumption at all times. The power balance constraints are as follows:

[0109] P pw (t)-P cp (t)+P buy (t) = P load (t)-P loss (t)+Pel (t)-P fc (t) (5)

[0110] Where: P pw (t) is the photovoltaic and wind power generation power in period t.

[0111] 2) Thermal energy balance constraints

[0112] The zero-carbon park energy system needs to ensure that the internal thermal energy is always in a balanced state. The thermal energy balance constraints are as follows:

[0113] H el (t)+H fc (t)+H hb (t) = H load (t)-H loss (t)+H in (t)-H out (t) (6)

[0114] 3) Operation constraints of hydrogen energy storage units

[0115] As energy storage equipment, hydrogen storage tanks and heat storage tanks have minimum and maximum capacity constraints. Hydrogen boilers also have capacity constraints during operation. The constraints are as follows:

[0116] V HST,min ≤V HST (t)≤V HST,max (7)

[0117] H ST,min ≤H ST (t)≤H ST,max (8)

[0118] 0≤H hb (t)≤H hb,max (9)

[0119] Where: V HST,max 、V HST,min are the upper and lower limits of the capacity of the hydrogen storage tank respectively; H ST,max , H ST,min are the upper and lower limits of the energy storage power of the heat storage tank; H hb,max is the maximum output power of the hydrogen boiler.

[0120] 4) Other operating constraints

[0121] To ensure the effective supply of heat and electricity loads within the system, the system's heat loss load and power loss load need to be controlled within a certain range; to ensure the effective use of clean energy such as wind and light, the wind and light abandonment rate needs to be controlled within a certain range; to ensure that the interaction between the system and external energy is within the transmission limit, the system's electricity purchase power and hydrogen purchase power need to be controlled within a certain range.

[0122] The above constraints are as follows:

[0123] 0≤H loss (t)≤ψ H,loss H load (t) (10)

[0124] 0≤P loss (t)≤ψ P,loss P load (t) (11)

[0125] 0≤P cp (t)≤ψ cp P pw (t) (12)

[0126] 0≤P buy (t)≤P buy,max (13)

[0127] 0≤G buy (t)≤G buy,max (14)

[0128] Where: H,loss , P,loss are the maximum heat loss and power loss load ratios of the system respectively; ψ cp P is the maximum wind and solar power abandonment ratio of the system; buy,max , G buy,max They are the maximum electricity and hydrogen purchasing power of the system respectively.

[0129] Step 3: Based on the zero-carbon park system optimization scheduling model constructed in step 2, considering the variable operating characteristics of the hydrogen energy storage system, formulate efficient hydrogen production strategies for electrolyzers, efficient power generation strategies for fuel cells, and efficient heating strategies for electrolyzers and fuel cells, and then construct a multi-module hybrid operation model of the hydrogen energy storage system;

[0130] The specific steps of step 3 include:

[0131] (1) Based on the zero-carbon park system optimization scheduling model constructed in step 2, considering the variable operating characteristics of the hydrogen energy storage system, formulate an efficient hydrogen production strategy for the electrolyzer and an efficient power generation strategy for the fuel cell;

[0132] The specific steps of step (1) of step 3 include:

[0133] ① Draw the efficiency curve of the electric-heat-hydrogen energy conversion of the electrolyzer and fuel cell under the change of input power;

[0134] ② Based on the efficiency change curve of the electric-thermal-hydrogen energy conversion drawn in step ①, the electrolyzer and the fuel cell are modularized to form a corresponding module group;

[0135] The specific method for modularizing the electrolyzer and fuel cell is as follows:

[0136] According to the actual power of the electrolyzer and fuel cell and the system requirements, multiple electrolyzers and fuel cells are combined to form an electrolyzer module group and a fuel cell module group. The input and output power of the module group are the sum of the input and output power of each module in the module group. The specific model is as follows:

[0137]

[0138]

[0139] Where: a and b are the number of electrolyzers and fuel cell modules started in period t, respectively; is the power consumption of the nth electrolyzer module in period t; is the hydrogen production power of the nth electrolyzer module in period t; is the heat generation power of the nth electrolyzer module in period t; is the power generation of the nth fuel cell in period t; is the hydrogen consumption power of the nth fuel cell in period t; is the heat generation power of the nth fuel cell in period t.

[0140] ③ By constructing the Lagrangian extreme value function and optimizing the power distribution between module groups, the hydrogen production efficiency of the electrolyzer and the power generation efficiency of the fuel cell are maximized, thereby completing the formulation of an efficient hydrogen production strategy for the electrolyzer and an efficient power generation strategy for the fuel cell;

[0141] The specific steps include:

[0142] 2) Construct the Lagrange extreme value function as follows:

[0143]

[0144]

[0145]

[0146]

[0147] Where N is the number of modules running in the module group within the t period; G n (t) is the hydrogen production power or power generation power corresponding to the output of the nth module at time t. When this module group is an electrolyzer, G n (t) represents the hydrogen production power output by the nth electrolyzer module at time t; when this module group is a fuel cell, it corresponds to the power generation power; P n(t) is the input power of the nth module at time t; P(t) is the total input power of the module group at time t; P max The maximum output power of a single module; are the upward and downward climbing powers of the nth module in period t respectively; are the upward and downward climbing power limits of the module respectively; λ is the Lagrange multiplier; is the module group operation constraint, i.e., the constraint in formula (18).

[0148] The constraints in the formula are the operating constraints of the electrolyzer and fuel cell module groups, which correspond to the module group capacity constraint limit and the upward and downward climbing power limit per unit time respectively.

[0149] 2) By constructing the Lagrangian extreme value function, formula (20) is used as the operating constraint in the efficient hydrogen production process of the electrolyzer module group and the efficient power generation process of the fuel cell module group, and applied to the optimized operation process of the zero-carbon park system, thereby completing the formulation of the efficient hydrogen production strategy of the electrolyzer module group and the efficient power generation strategy of the fuel cell module group;

[0150] (2) Develop efficient heating strategies for electrolyzers and fuel cells

[0151] according to Figure 2 and Figure 3 It can be seen that in order to achieve efficient heating of the electrolyzer and fuel cell, both the electrolyzer module group and the fuel cell module group need to operate at maximum power. Therefore, in the efficient heating process, the power distribution constraints between the electrolyzer and the fuel cell module group are as follows:

[0152] P(t)=(N-1)P max +P N (t) (21)

[0153] P 1 (t) = P 2 (t)=…=P N-1 (t) = P max (twenty two)

[0154] By setting the operating constraints of the electrolyzer and fuel cell module group in equations (21)-(22) and applying them to the optimized operation process of the zero-carbon park system, the formulation of an efficient heating strategy for the electrolyzer and fuel cell module group can be completed;

[0155] (4) Construct a multi-module hybrid operation mode of a hydrogen energy storage system, and construct a multi-module hybrid operation model of a hydrogen energy storage system based on the mode;

[0156] In this embodiment, the above-mentioned electrolyzer efficient hydrogen production strategy, fuel cell efficient power generation strategy and electrolyzer and fuel cell efficient heating strategy are respectively applicable to the electrolyzer group efficient hydrogen production and efficient heating, or the fuel cell group efficient power generation and efficient heating. The thermal load of the system is random. In order to effectively reduce the system energy supply cost while meeting the system thermal and electric load, the present invention realizes the effective supply of thermal and electric load in the zero-carbon park by setting a hybrid operation mode of electrolyzer and fuel cell.

[0157] The specific steps of step (3) of step 3 include:

[0158] ① The multi-module hybrid operation mode of the hydrogen energy storage system is:

[0159] The electrolyzer group adopts a mixed operation mode of "high-efficiency hydrogen production" and "high-efficiency heating". The ratio of the heat load power required by the system to the electric power input by the electrolyzer group at each moment determines whether the electrolyzer group should adopt the "high-efficiency hydrogen production" or "high-efficiency heating" operation mode at that moment;

[0160] Similarly, the fuel cell group adopts a mixed operation mode of "efficient power generation" and "efficient heating". The ratio of the thermal load power to the electrical load power required by the system at each moment determines whether the fuel cell group should adopt the "efficient power generation" or "efficient heating" operation mode at that moment.

[0161] ②Construct a multi-module hybrid operation model of the hydrogen energy storage system:

[0162] Assume that R(t) is the multi-module hybrid operation mode model of the hydrogen energy storage system, which represents the operation mode of the electrolyzer group and the fuel cell group at time t. If R(t) = 1, the electrolyzer group operates in a high-efficiency hydrogen production mode and the fuel cell group operates in a high-efficiency power generation mode at time t; otherwise, both devices operate in a high-efficiency heating mode. The expression of the multi-module hybrid operation mode model R(t) of the hydrogen energy storage system is as follows:

[0163]

[0164] Where: H load (t) is the system heat load demand at time t; P s (t) is the module group variable. When the module group is an electrolytic cell, P s (t) = P el (t); When the module group is a fuel cell, P s (t) = P load (t); k is the optimization variable, and the working mode of the module group at that moment is determined by optimizing the k value.

[0165] Step 4: The zero-carbon park system optimization and scheduling model constructed in step 2 and the multi-module hybrid operation model of the hydrogen energy storage system constructed in step 3 are combined into an overall optimization and scheduling model of the zero-carbon park system, and the overall optimization and scheduling model of the zero-carbon park system is solved using the matlab platform with the gurobi commercial solver to complete the optimization and scheduling of the zero-carbon park system with hydrogen energy storage.

[0166] The specific method of step 4 is:

[0167] Judgment during the solution process If yes, the electrolyzer is operated in high-efficiency hydrogen production mode, otherwise the electrolyzer is operated in high-efficiency heating mode; If so, the fuel cell operates in a high-efficiency power generation mode, otherwise the fuel cell operates in a high-efficiency heating mode;

[0168] The present invention is further described below by means of specific examples:

[0169] Taking a zero-carbon park as the simulation object, the park's new energy output power and thermal and electrical load data parameters can be seen Figure 4 and Figure 5 The parameters of the electrolyzer group and fuel cell group in the hydrogen energy storage system are shown in Table 1. The purchase price of electricity adopts the real-time electricity price. The specific purchase price can be seen in Figure 6 The purchase price of hydrogen is 3.2 yuan / m 3 The penalty price for wind and solar power abandonment is 0.2 yuan / kWh. The penalty prices for power loss load and heat loss load are both 10 times the real-time electricity price.

[0170] Table 1 Parameters of electrolyzer group and fuel cell group

[0171]

[0172] Three scheduling schemes are set up to compare and analyze the advantages of the proposed hydrogen energy storage hybrid operation scheduling strategy. The specific scheduling schemes are shown below.

[0173] (1) Option 1 - High-efficiency hydrogen production and power generation strategy for hydrogen energy storage system: Focus on the system power supply. The electrolyzer group in the hydrogen energy storage system adopts a high-efficiency hydrogen production mode, and the fuel cell group adopts a high-efficiency power generation mode.

[0174] (2) Option 2 - High-efficiency heat generation strategy for hydrogen energy storage system: Focusing on the system heat supply, both the electrolyzer group and the fuel cell group in the hydrogen energy storage system operate in a high-efficiency heating mode.

[0175] (3) Scheme 3 - Hybrid operation strategy of hydrogen energy storage system: Taking into account the thermal and electrical load supply of the system, both the electrolyzer group and the fuel cell group in the hydrogen energy storage system are operated in the hybrid mode proposed by the present invention.

[0176] The optimal scheduling costs of the zero-carbon park system under the three schemes are shown in Table 2.

[0177] Table 2 Optimized scheduling costs under each scheme

[0178]

[0179] From the perspective of overall optimization scheduling cost, the multi-module hybrid mode operation scheme of the hydrogen energy storage system proposed in the present invention has the lowest total cost among the three schemes, which is 9.59% lower than that of Scheme 1 and 11.34% lower than that of Scheme 2.

[0180] As can be seen from Table 2, the difference in electricity purchase costs under the three scheduling schemes is the most obvious, and the electricity purchase cost also represents the actual carbon emissions of the system. Calculated based on 0.785kgCO2 per kilowatt-hour of thermal power, the system CO2 emissions under the three scheduling schemes are 396.48kg, 425.02kg and 340.48kg respectively. Therefore, the multi-module hybrid mode operation scheme of the hydrogen energy storage system proposed in the present invention can effectively reduce the system CO2 emissions by 14.12%-19.89%.

[0181] In order to analyze the cost changes in the operation of the zero-carbon park hydrogen energy storage system under different load heat-to-electricity ratios and the differences in the total system cost under different scheduling schemes, the system heat load data is adjusted here. The cost changes of Scheme 3 under different load heat-to-electricity ratios can be seen Figure 7 .

[0182] Depend on Figure 7 It can be seen that when the thermal load ratio is 0-0.4, the hybrid operation scheduling strategy of the hydrogen energy storage device proposed in the present invention can switch between the "high-efficiency hydrogen production" and "high-efficiency heating" modes of the electrolyzer, and switch between the "high-efficiency power generation" and "high-efficiency heating" modes of the fuel cell to meet the thermal load supply. Therefore, when the thermal load ratio is lower than 0.4, the system scheduling costs under Scheme 3 do not change significantly; when the thermal load ratio is 0.4-0.6, due to the increase in thermal load, the hydrogen energy storage device needs to convert more electrical energy into thermal energy to meet the system Thermal load, so the system electricity purchase cost increases, and the total cost also increases accordingly; when the thermal load ratio is 0.6-0.8, in order to meet the system thermal load demand, additional hydrogen energy needs to be purchased to drive fuel cells and hydrogen boilers to produce heat. During this stage, the cost of purchasing electricity and hydrogen increases rapidly; when the thermal load ratio is 0.8-1, the system operating cost and electricity purchase cost no longer increase, while the hydrogen purchase cost is still increasing rapidly, indicating that the hydrogen energy storage equipment in the system has reached the rated power at this time, and the increased amount of hydrogen purchased is completely used to drive the hydrogen boiler to produce heat to meet the system thermal load. Therefore, when the system thermal load ratio is 0-0.8, the hybrid operation scheduling strategy of the hydrogen energy storage equipment proposed in the present invention can effectively meet the system thermal load supply.

[0183] The total cost ratio of Scheme 3 to Schemes 1 and 2 under different load heat-to-electricity ratios can be seen Figure 8 , specific analysis Figure 8 It can be seen that:

[0184] 1) By comparing the changes in the total dispatch cost ratio between Scheme 3 and Scheme 1, it can be seen that when the thermal load ratio is 0-0.2, the system thermal load is relatively small. Scheme 3 will adopt the high-efficiency hydrogen production mode for the electrolyzer group in Scheme 1, and the high-efficiency power generation mode for the fuel cell group. Therefore, at this stage, the costs of Scheme 1 and Scheme 3 are the same. As the thermal load ratio of the system gradually increases, the total cost ratio of Scheme 3 to Scheme 1 shows a trend of first increasing and then decreasing. The reason is that the hybrid operation strategy of Scheme 3 allows the hydrogen energy storage equipment to supply energy to effectively meet the changes in the thermal load of the system, while Scheme 1 only focuses on the supply of electricity, resulting in a high overall energy supply cost. When the thermal load ratio is 0.2-0.6, the total dispatch cost ratio of Scheme 3 to Scheme 1 decreases rapidly. When the thermal load ratio is 0.6-1, the system hydrogen energy storage equipment gradually reaches the rated power. Increasing the thermal load will only lead to an increase in the cost of purchasing hydrogen under the two schemes, resulting in a narrowing of the total dispatch cost gap between Scheme 3 and Scheme 2. Figure 8 It can be seen that when the thermal and electric load ratio of the system changes, the energy supply cost of the scheduling scheme 3 proposed in the present invention is still better than that of scheme 1, and the highest cost is reduced by 15.26% compared with scheme 1.

[0185] 2) By comparing the changes in the proportion of total dispatch costs of Scheme 3 and Scheme 2, it can be seen that as the proportion of system thermal load gradually increases, the total dispatch costs of Scheme 3 and Scheme 2 are closer. The reason is that Scheme 2 focuses more on the supply of system thermal load. Therefore, when the thermal load is less, Scheme 2 can generate less electricity, which needs to be supplemented by purchasing electricity. Therefore, the smaller the proportion of thermal load, the better the energy supply cost of Scheme 3 is than Scheme 2. When the proportion of thermal load gradually increases, Scheme 3 will also focus more on the supply of system thermal load. Finally, under Scheme 3, the hydrogen energy storage equipment will all be operated in the "high-efficiency heating" mode. At this time, the total dispatch costs of Scheme 3 and Scheme 2 are consistent. Therefore, under the change of the proportion of system thermal load, the energy supply cost of the dispatching scheme 3 proposed by the present invention is still better than that of Scheme 2. At the highest point, the cost is reduced by 19.71% compared with Scheme 2.

[0186] It can be seen that the hybrid operation strategy of the hydrogen energy storage system proposed in the present invention can switch the operation mode according to the ratio of the system's thermal and electric loads, effectively supply the system's thermal and electric loads, and reduce the total dispatching cost by 9.59% compared with the hydrogen energy storage system's efficient hydrogen production and power generation strategy, and reduce the total dispatching cost by 11.34% compared with the hydrogen energy storage system's efficient heating strategy, and can reduce the system's CO2 emissions by 19.89% at most. Under different thermal and electric load ratios, its energy supply cost is better than the hydrogen energy storage system's efficient hydrogen production and power generation strategy and efficient heating strategy, and can reduce the total dispatching cost by up to 19.71%.

[0187] It should be emphasized that the embodiments of the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation modes. Any other implementation modes derived by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. A zero-carbon park optimization scheduling method considering the variable operating characteristics of a hydrogen energy storage system, characterized by: The following steps are involved: Step 1: Collect renewable energy and load data of zero-carbon parks; Step 2: Based on the energy and load data collected by the park, a zero-carbon park system optimization scheduling model is constructed, and the zero-carbon park optimization scheduling objective function and constraint conditions are established; Step 3: Based on the zero-carbon park system optimization scheduling model constructed in step 2, considering the variable operating characteristics of the hydrogen energy storage system, formulate efficient hydrogen production strategies for electrolyzers, efficient power generation strategies for fuel cells, and efficient heating strategies for electrolyzers and fuel cells, and then construct a multi-module hybrid operation model of the hydrogen energy storage system; Step 4: The zero-carbon park system optimization scheduling model constructed in step 2 and the hydrogen energy storage system multi-module hybrid operation model constructed in step 3 are combined into an overall optimization scheduling model of the zero-carbon park system, and the overall optimization scheduling model of the zero-carbon park system is solved to complete the optimization scheduling of the zero-carbon park system containing hydrogen energy storage.

2. According to claim 1, a zero-carbon park optimization scheduling method considering the variable operating characteristics of the hydrogen energy storage system is characterized by: The specific method of step 1 is: Collect wind power, photovoltaic output data and thermal power load demand data of the zero-carbon park in each time period.

3. According to claim 1, a zero-carbon park optimization scheduling method considering the variable operating characteristics of the hydrogen energy storage system is characterized by: The specific method of step 2 is: A zero-carbon park system optimization scheduling model is constructed, and the minimum system daily operation cost is taken as the zero-carbon park optimization scheduling objective function. The system daily operation cost C includes: system operation and maintenance cost, system energy purchase cost and operation penalty cost, as follows: my C=C r +C buy +C loss (1) Where: r el 、r fc 、r hb are the operation and maintenance cost coefficients of the electrolyzer group, fuel cell group and hydrogen boiler respectively; ω e (t) is the price coefficient of electricity purchased from the power grid during period t; is the hydrogen purchase price coefficient; P buy (t), G buy (t) are the power purchased from the grid and the hydrogen purchased from the outside during period t, respectively; α(t) is the penalty price coefficient for power loss load during period t; β is the penalty coefficient for heat loss load; γ is the penalty coefficient for abandoned solar and wind power; P loss (t), H loss (t) and P cp (t) are the power loss load, heat loss load and abandoned solar and wind power in period t respectively; The constraints of the zero-carbon park optimization scheduling objective function include: 1) Power balance constraints The zero-carbon park system needs to ensure that the internal power is always in a balanced state. Its power generation should be the same as the power consumption at all times. The power balance constraints are as follows: P pw (t)-P cp (t)+P buy (t)=P load (t)-P loss (t)+P el (t)-P fc (t) (5) Where: P pw (t) is the photovoltaic and wind power generation power in period t; 2) Thermal energy balance constraints The zero-carbon park energy system needs to ensure that the internal thermal energy is always in a balanced state. The thermal energy balance constraints are as follows: H el (t)+H fc (t)+H hb (t)=H load (t)-H loss (t)+H in (t)-H out (t) (6) 3) Operation constraints of hydrogen energy storage units As energy storage equipment, hydrogen storage tanks and heat storage tanks have minimum and maximum capacity constraints. Hydrogen boilers also have capacity constraints during operation. The constraints are as follows: V HST,min ≤V HST (t)≤V HST,max (7) H ST,min ≤H ST (t)≤H ST,max (8) 0≤H hb (t)≤H hb,max (9) Where: V HST,max 、V HST,min are the upper and lower limits of the capacity of the hydrogen storage tank respectively; H ST,max , H ST,min are the upper and lower limits of the energy storage power of the heat storage tank; H hb,max is the maximum output power of the hydrogen boiler; 4) Other operating constraints are as follows: 0≤H loss (t)≤ψ H,loss H load (t) (10) 0≤P loss (t)≤ψ P,loss P load (t) (11) 0≤P cp (t)≤ψ cp P pw (t) (12) 0≤P buy (t)≤P buy,max (13) 0≤G buy (t)≤G buy,max (14) Where: H,loss , P,loss are the maximum heat loss and power loss load ratios of the system respectively; ψ cp P is the maximum wind and solar power abandonment ratio of the system; buy,max , G buy,max They are the maximum electricity and hydrogen purchasing power of the system respectively.

4. A zero-carbon park optimization scheduling method considering the variable operating characteristics of a hydrogen energy storage system according to claim 1, characterized in that: The specific steps of step 3 include: (1) Based on the zero-carbon park system optimization scheduling model constructed in step 2, considering the variable operating characteristics of the hydrogen energy storage system, formulate an efficient hydrogen production strategy for the electrolyzer and an efficient power generation strategy for the fuel cell; (2) Develop efficient heating strategies for electrolyzers and fuel cells; (3) Construct a multi-module hybrid operation mode of the hydrogen energy storage system, and construct a multi-module hybrid operation model of the hydrogen energy storage system based on this mode.

5. A zero-carbon park optimization scheduling method considering the variable operating characteristics of a hydrogen energy storage system according to claim 4, characterized in that: The specific steps of step (1) of step 3 include: ① Draw the efficiency curve of the electric-heat-hydrogen energy conversion of the electrolyzer and fuel cell under the change of input power; ② Based on the efficiency change curve of the electric-thermal-hydrogen energy conversion drawn in step ①, the electrolyzer and the fuel cell are modularized to form a corresponding module group; The specific method for modularizing the electrolyzer and fuel cell is as follows: According to the actual power of the electrolyzer and fuel cell and the system requirements, multiple electrolyzers and fuel cells are combined to form an electrolyzer module group and a fuel cell module group. The input and output power of the module group are the sum of the input and output power of each module in the module group. The specific model is as follows: Where: a and b are the number of electrolyzers and fuel cell modules started in period t, respectively; is the power consumption of the nth electrolyzer module in period t; is the hydrogen production power of the nth electrolyzer module in period t; is the heat generation power of the nth electrolyzer module in period t; is the power generation of the nth fuel cell in period t; is the hydrogen consumption power of the nth fuel cell in period t; is the heat generation power of the nth fuel cell in period t; ③ By constructing the Lagrangian extreme value function and optimizing the power distribution between module groups, the hydrogen production efficiency of the electrolyzer and the power generation efficiency of the fuel cell are maximized, thereby completing the formulation of an efficient hydrogen production strategy for the electrolyzer and an efficient power generation strategy for the fuel cell; The specific steps include: 1) Construct the Lagrangian extreme value function as follows: Where N is the number of modules running in the module group within the t period; G n (t) is the hydrogen production power or power generation power corresponding to the output of the nth module at time t. When this module group is an electrolyzer, G n (t) represents the hydrogen production power output by the nth electrolyzer module at time t; when this module group is a fuel cell, it corresponds to the power generation power; P n (t) is the input power of the nth module at time t; P(t) is the total input power of the module group at time t; P max The maximum output power of a single module; are the upward and downward climbing powers of the nth module in period t respectively; are the upward and downward climbing power limits of the module respectively; λ is the Lagrange multiplier; is the module group operation constraint, i.e., the constraint in formula (18); The constraints in the formula are the operation constraints of the electrolyzer and fuel cell module group, which correspond to the module group capacity constraint limit and the upward and downward ramp power limit per unit time respectively; 2) By constructing the Lagrangian extreme value function, formula (20) is used as the operating constraint in the efficient hydrogen production process of the electrolyzer module group and the efficient power generation process of the fuel cell module group, and is applied to the optimized operation process of the zero-carbon park system, thereby completing the formulation of the efficient hydrogen production strategy of the electrolyzer module group and the efficient power generation strategy of the fuel cell module group.

6. A zero-carbon park optimization scheduling method considering the variable operating characteristics of the hydrogen energy storage system according to claim 4, characterized in that: The specific method of step (2) of step 3 is: In the efficient heating process, the power distribution constraints between the electrolyzer and the fuel cell module group are as follows: P(t)=(N-1)P max +P N (t) (21) P 1 (t)=P 2 (t)=…=P N-1 (t)=P max (22) By setting the operating constraints of the electrolyzer and fuel cell module group in equations (21)-(22) and applying them to the optimized operation process of the zero-carbon park system, an efficient heating strategy for the electrolyzer and fuel cell module group can be formulated.

7. A zero-carbon park optimization scheduling method considering the variable operating characteristics of a hydrogen energy storage system according to claim 4, characterized in that: The specific steps of step (3) of step 3 include: ① The multi-module hybrid operation mode of the hydrogen energy storage system is: The electrolyzer group adopts a mixed operation mode of "high-efficiency hydrogen production" and "high-efficiency heating". The ratio of the heat load power required by the system to the electric power input by the electrolyzer group at each moment determines whether the electrolyzer group should adopt the "high-efficiency hydrogen production" or "high-efficiency heating" operation mode at that moment; Similarly, the fuel cell group adopts a mixed operation mode of "high-efficiency power generation" and "high-efficiency heating". The ratio of the thermal load power to the electrical load power required by the system at each moment determines whether the fuel cell group should adopt the "high-efficiency power generation" or "high-efficiency heating" operation mode at that moment. ②Construct a multi-module hybrid operation model of the hydrogen energy storage system: Assume that R(t) is the multi-module hybrid operation mode model of the hydrogen energy storage system, which represents the operation mode of the electrolyzer group and the fuel cell group at time t. If R(t) = 1, the electrolyzer group operates in a high-efficiency hydrogen production mode and the fuel cell group operates in a high-efficiency power generation mode at time t; otherwise, both devices operate in a high-efficiency heating mode. The expression of the multi-module hybrid operation mode model R(t) of the hydrogen energy storage system is as follows: Where: H load (t) is the system heat load demand at time t; P s (t) is the module group variable. When the module group is an electrolytic cell, P s (t) = P el (t); When the module group is a fuel cell, P s (t) = P load (t); k is the optimization variable, and the working mode of the module group at that moment is determined by optimizing the k value.

8. A zero-carbon park optimization scheduling method considering the variable operating characteristics of a hydrogen energy storage system according to claim 1, characterized in that: The specific method of step 4 is: Judgment during the solution process If yes, the electrolyzer is operated in high-efficiency hydrogen production mode, otherwise the electrolyzer is operated in high-efficiency heating mode; If so, the fuel cell operates in a high-efficiency power generation mode, otherwise the fuel cell operates in a high-efficiency heating mode.

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