Real-time scheduling method of hydrogen-electricity hybrid energy storage power system and computer system

Abstract

The invention relates to a real-time scheduling method of a hydrogen-electricity hybrid energy storage power system and a computer system, and belongs to the technical field of power system scheduling. Comprising the following steps: respectively solving a day-ahead scheduling objective function and a real-time scheduling objective function by adopting mixed integer linear programming according to a model and constraint conditions of the hydrogen-electricity hybrid energy storage power system to obtain solving results, and obtaining an optimal solution for guiding real-time scheduling of the system according to the solving results. According to a model of the hydrogen-electricity hybrid energy storage power system and constraint conditions thereof, mixed integer linear programming is adopted to solve a day-ahead scheduling objective function and a real-time scheduling objective function to obtain solving results, and an optimal solution for guiding real-time scheduling of the system is obtained according to the solving results. Under the condition that the new energy power generation and the user load are uncertain, the day-ahead scheduling operation cost of the hydrogen-electricity hybrid energy storage power system is the lowest, and the new energy power generation amount of the hydrogen-electricity hybrid energy storage power system meets the user load requirement in real time.

Description

Technical Field

[0001] The invention relates to a real-time dispatching method and a computer system for a hydrogen-electric hybrid energy storage power system, belonging to the technical field of power system dispatching. Background Art

[0002] As global energy demand continues to grow, the consumption of traditional fossil energy has had a serious impact on the environment, and the limited reserves of traditional fossil energy are difficult to support long-term energy demand. Therefore, human society is seeking cleaner and more sustainable energy to meet energy demand. New energy power generation, especially solar power generation and wind power generation, has become a hot topic in research and application due to its cleanliness and renewability.

[0003] However, there are significant fluctuations and intermittent problems in renewable energy generation, which poses challenges to the stability of the power grid and the continuity of power supply. In order to solve this problem, the concept of integrated energy system came into being. Through the coupling of different energy forms such as heat, electricity, and hydrogen, the integrated energy system achieves energy complementarity and optimal utilization, and improves the economy, flexibility, and reliability of the system.

[0004] Energy storage technology is one of the key solutions for integrated energy systems. Traditional energy storage methods mainly include chemical energy storage (such as lithium batteries), physical energy storage (such as pumped storage) and thermal energy storage. Among them, lithium battery energy storage is widely used due to its fast response speed and high energy density. However, problems such as the cycle life and environmental adaptability of lithium batteries limit its application in large-scale energy storage. Similarly, hydrogen energy, as a clean energy carrier, has attracted attention for its high energy density and flexible storage methods. Fuel cell technology can efficiently convert hydrogen energy into electrical energy, while electrolyzer technology can convert electrical energy into hydrogen energy, realizing the storage and conversion of electrical energy.

[0005] At present, there have been studies combining fuel cells and lithium batteries to form a hybrid energy storage system in order to give full play to their respective advantages and improve the overall performance and economy of the system. However, the existing technology still has deficiencies in system design, energy management, cost optimization, etc., especially in the hybrid energy storage scheduling mode. There is no research and lack of corresponding systematic scheduling methods.

[0006] In order to make full use of new energy, reduce the difficulty of grid connection, and improve the safety and economy of the system, it is necessary to develop more efficient and reliable energy storage technologies. At the same time, it is also necessary to further optimize the scheduling strategy of the integrated energy system to achieve the optimal configuration and utilization of energy. Summary of the invention

[0007] The purpose of the present invention is to provide a real-time scheduling method and computer system for a hydrogen-electric hybrid energy storage power system, so as to solve the problem that the existing hybrid energy storage power system has no corresponding scheduling method, which makes it difficult to dynamically meet user needs in real time when user load and power generation are uncertain.

[0008] To achieve the above object, the solution of the present invention includes:

[0009] A real-time dispatching method for a hydrogen-electric hybrid energy storage power system of the present invention comprises: constructing a model of the hydrogen-electric hybrid energy storage power system and its constraints, constructing a day-ahead dispatching objective function with the lowest day-ahead dispatching operation cost of the system, and constructing a real-time dispatching objective function with the smallest power deviation under the condition that both the new energy generation and the user load are uncertain;

[0010] The real-time scheduling objective function is based on the energy storage strategy that prioritizes lithium batteries, and the real-time scheduling objective function is gradually optimized by setting the optimization step size;

[0011] According to the model and constraints, mixed integer linear programming is used to solve the day-ahead scheduling objective function and the real-time scheduling objective function respectively to obtain the solution results. According to the solution results, the optimal solution for guiding the real-time scheduling of the system is obtained, so that when both renewable energy power generation and user load are uncertain, the day-ahead scheduling operation cost of the system is the lowest and the renewable energy power generation of the system can meet the user load demand in real time.

[0012] Furthermore, the solution result includes a first result of solving the day-ahead scheduling objective function and a second result of solving the real-time scheduling objective function, and the intersection of the first result and the second result is taken as the optimal solution.

[0013] Furthermore, the day-ahead scheduling objective function is:

[0014]

[0015] In the formula, min(f cost ) is the day-ahead scheduling objective function, C buy (t) is the cost of purchasing electricity from the power grid per hour, t∈[0,24],

[0016] is the hourly operating cost of each device in the system that generates operating costs, C DR Compensation cost for system demand response, C pun Punishment for abandoning wind and solar power in the system's renewable energy.

[0017] Furthermore, the real-time scheduling objective function is:

[0018] min(f Δ (t))=min(ΔP real (t)+α·ΔHreal (t))

[0019] In the formula, min(f Δ (t)) is the real-time scheduling objective function, ΔP real (t) is the system power deviation of the current optimization step, ΔH real (t) is the system thermal energy difference of the current optimization step, α is the proportionality coefficient, and the proportionality coefficient is the ratio of the average thermal energy cost obtained by the day-ahead dispatch to the average electric energy cost obtained by the day-ahead dispatch.

[0020] Furthermore, the optimization step size is set according to the step size of collecting power data.

[0021] Furthermore, the model of the hydrogen-electric hybrid energy storage power system includes an energy storage system model, a new energy power station model and a load end model; the energy storage system model includes a fuel cell model, an electrolytic cell model, a hydrogen storage tank model and a lithium battery model, and the new energy power station model includes a photovoltaic power station model and / or a wind power station model.

[0022] Furthermore, the constraints of the hydrogen-electric hybrid energy storage power system include a hydrogen energy balance equation, an electric energy balance equation, a thermal energy balance equation, a real-time optimized electric energy balance equation and a real-time optimized thermal energy balance equation.

[0023] Further, the fuel cell model is:

[0024]

[0025] In the formula, H SOFC (t) is the heat generated by the fuel cell, P SOFC (t) is the energy generated by the fuel cell, V SOFC (t) is the hydrogen energy consumed by the fuel cell, ΔH is the calorific value of hydrogen, is the efficiency of releasing heat during the fuel cell reaction. It is the efficiency of converting the chemical energy of fuel cell gas into electrical energy;

[0026] The electrolytic cell model is:

[0027]

[0028] Where P ec (t) is the energy required for the electrolytic cell to work; H ec (t) is the heat absorbed by the electrolytic cell during operation; It is the efficiency of heat consumption during the electrolytic cell reaction; It is the efficiency of converting electrical energy into gas chemical energy during the electrolytic cell reaction;

[0029] The hydrogen storage tank model is:

[0030]

[0031] -V SSmax ≤V SS (t)-V SS (t-1)≤V SSmax

[0032] Where V SS (t) is the hydrogen energy stored in the hydrogen storage tank at the current moment, C VSS is the hydrogen tank capacity, V SSmax Maximum power for charging / discharging the hydrogen tank, V SS (t-1) is the hydrogen energy stored in the hydrogen storage tank at the previous moment;

[0033] The lithium battery model is:

[0034]

[0035] Where P LB (t) is the electrical energy stored in the lithium battery; P LB (t-1) is the electrical energy stored in the lithium battery at the previous moment; is the charge level of the lithium battery; is the discharge capacity of the lithium battery; For lithium battery charging efficiency, is the discharge efficiency of lithium battery; Maximum power for charging lithium batteries, The maximum discharge power of the lithium battery;

[0036] The photovoltaic power station model is:

[0037] P PV (t) = PV·α PV (t) rand PV

[0038] Where P PV (t) is the amount of electricity generated by the photovoltaic power station, PV is the planned capacity of the photovoltaic power station system, α PV (t) is the photovoltaic power generation capacity factor, rand PV is the fluctuation coefficient of photovoltaic power generation during real-time scheduling;

[0039] The wind power station model is:

[0040] P Wind (t) = Wind·α Wind (t) rand Wind

[0041] Where P Wind (t) is the amount of electricity generated by the wind power station, Wind is the planned capacity of the wind power station system, αWind (t) is the wind power capacity factor, rand Wind is the wind power generation fluctuation coefficient during real-time scheduling;

[0042] The load end model includes a heat pump model, which is:

[0043] H HP (t) = P HP (t)·η HP

[0044] In the formula, H HP (t) is the heat generated by the heat pump, P HP (t) is the power consumption of the heat pump, η HP is the heat pump electrical heat conversion efficiency.

[0045] Furthermore, the hydrogen energy balance equation is:

[0046] V SOEC (t)+V SS (t-1) = V SOFC (t)+V SS (t)

[0047] Where V SOEC (t) is the hydrogen energy produced by the electrolytic cell, V SS (t-1) is the hydrogen energy stored in the hydrogen storage tank at the previous moment, V SOFC (t) is the hydrogen energy consumed by the fuel cell, V SS (t) is the hydrogen energy stored in the hydrogen storage tank;

[0048] The electric energy balance equation is:

[0049]

[0050] Where P PV (t) is the amount of electricity generated by the photovoltaic power station; P Wind (t) is the amount of electricity generated by the wind power station, P SOFC (t) is the energy generated by the fuel cell, P grid (t) is the amount of electricity purchased by the system from the grid per hour, is the discharge capacity of the lithium battery, P SOEC (t) is the energy consumption of the electrolytic cell, P load (t) is the electrical load, is the charge capacity of the lithium battery, P HP (t) is the energy consumption of the electric heat pump;

[0051] The heat balance equation is:

[0052] H SOFC (t)+H HP (t) = HSOEC (t)+H load (t)

[0053] In the formula, H SOFC (t) is the heat generated by the fuel cell, H HP (t) is the heat produced by the electric heat pump, H SOEC (t) is the heat consumed by the electrolytic cell, H load (t) is the system heat load;

[0054] The real-time optimized power balance equation is:

[0055]

[0056] In the formula, The amount of electricity generated by photovoltaic power plants optimized for real-time dispatch; The amount of electricity generated by wind power plants optimized for real-time dispatch, P SOFC (t) is the energy generated by the fuel cell operation, It is the amount of electricity purchased from the power grid every hour by the real-time dispatch optimization system. is the lithium battery discharge capacity optimized by real-time scheduling, P SOEC (t) is the working energy consumption of the electrolytic cell, It is the electric load optimized by real-time dispatch. Optimized lithium battery charging capacity for real-time scheduling, It is the real-time dispatch optimization of the working energy consumption of the electric heat pump;

[0057] The real-time optimized thermal energy balance equation is:

[0058]

[0059] In the formula, H SOFC (t) is the heat generated by the operation of the fuel cell, is the heat produced by the electric heat pump with real-time scheduling optimization, H SOEC (t) is the heat consumed by the electrolytic cell, It is the system heat load optimized by real-time scheduling.

[0060] A computer system of the present invention includes a processor, and the processor is used to execute a computer program to implement the steps of the real-time scheduling method of the hydrogen-electric hybrid energy storage power system as described above.

[0061] Beneficial effects of the present invention:

[0062] The present invention is a pioneering invention, which provides a real-time scheduling method and computer system for a hydrogen-electric hybrid energy storage power system. Specifically, according to the model of the hydrogen-electric hybrid energy storage power system and its constraints, a mixed integer linear programming is used to solve the day-ahead scheduling objective function and the real-time scheduling objective function to obtain solution results. According to the solution results, an optimal solution for guiding the real-time scheduling of the system is obtained. When both the new energy power generation and the user load are uncertain, the day-ahead scheduling operation cost of the hydrogen-electric hybrid energy storage power system can be minimized, and the new energy power generation of the hydrogen-electric hybrid energy storage power system can meet the user load demand in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a structural schematic diagram of a hydrogen-electric hybrid energy storage power system according to an embodiment of the present invention;

[0064] Figure 2 is a curve diagram of the capacity factor of a new energy power station according to an embodiment of the present invention;

[0065] Figure 3 is a user load curve diagram of an embodiment of the present invention;

[0066] Figure 4 is a bar graph of the electric energy system balance scheduled a day ago according to an embodiment of the present invention;

[0067] Figure 5 is a balance bar graph of a thermal energy system scheduled a day ago according to an embodiment of the present invention;

[0068] Figure 6 is a bar graph of the balance of the electric energy system in real-time scheduling according to an embodiment of the present invention;

[0069] Figure 7 is a balance bar graph of a thermal energy system scheduled in real time according to an embodiment of the present invention;

[0070] Figure 8 is a real-time variation curve diagram of electric energy deviation in real-time scheduling according to an embodiment of the present invention;

[0071] Fig. 9 It is a real-time variation curve diagram of the thermal energy deviation in real-time scheduling according to an embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to solve the problems in the background technology, the present invention provides a real-time scheduling method and computer system for a hydrogen-electric hybrid energy storage power system. Specifically, according to the model of the hydrogen-electric hybrid energy storage power system and its constraints, mixed integer linear programming is used to solve the day-ahead scheduling objective function and the real-time scheduling objective function respectively to obtain solution results, and an optimal solution for guiding the real-time scheduling of the system is obtained according to the solution results. When both new energy power generation and user load are uncertain, the day-ahead scheduling operation cost of the hydrogen-electric hybrid energy storage power system can be minimized, and the new energy power generation of the hydrogen-electric hybrid energy storage power system can meet the user load demand in real time.

[0073] Among them, the hydrogen-electric hybrid energy storage power system consists of an energy storage system, a new energy power station, a power grid and a load. The energy storage system includes fuel cells, electrolytic cells, lithium batteries and hydrogen storage tanks, which together realize the mutual conversion and storage of electric energy, hydrogen energy and thermal energy. When there is excess power generation from new energy, the electrolytic cell converts the excess electric energy into hydrogen and stores it in the hydrogen storage tank. This process not only realizes the large-scale diffusion of renewable energy energy, but also enhances the independence of the system and the power grid. When the power system needs additional electric energy, the hydrogen in the hydrogen storage tank is converted into electric energy through the fuel cell, while releasing thermal energy. This process provides the power system with a predictable, controllable and highly deterministic power generation equipment, effectively limiting power fluctuations. The waste heat generated by the fuel cell during the power generation process and the heat energy converted by the heat pump system can provide the required heat for the electrolytic cell to work, and can also be sold to users as heating, which can meet the user's thermal load requirements and improve the comprehensive utilization efficiency of energy.

[0074] The power system of the present invention compensates for some electric loads with adjustable working hours through a demand response mechanism, optimizes the operation of the power system, and improves the self-consumption rate of energy. When the power generation capacity of new energy is greater than the energy required by the load, the electrolytic cell of the present invention starts to convert the excess electric energy into hydrogen for storage; when the power generation capacity of new energy is insufficient, the fuel cell starts to convert the stored chemical energy into electric energy to cope with the peak and valley of power in the power grid. The present invention uses digital optimization technology to construct an objective function for the lowest cost of day-ahead scheduling of the power system, taking into account the cost of purchasing electricity, operating cost, demand response compensation cost, and penalty for abandoning wind and solar power of new energy, to minimize the cost. The present invention also constructs a real-time scheduling model and a real-time scheduling objective function, so that the system can meet user needs under the uncertainty of load and power generation, and improve the robustness of the system.

[0075] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0076] An embodiment of a real-time dispatching method for a hydrogen-electric hybrid energy storage power system:

[0077] A real-time dispatching method for a hydrogen-electric hybrid energy storage power system comprises: constructing a model of the hydrogen-electric hybrid energy storage power system and constraints of the hydrogen-electric hybrid energy storage power system, constructing a day-ahead dispatching objective function for the system (hydrogen-electric hybrid energy storage power system) with the lowest day-ahead dispatching operation cost, and constructing a real-time dispatching objective function for the system (hydrogen-electric hybrid energy storage power system) with the minimum power deviation under the condition that both new energy generation and user load are uncertain.

[0078] According to the model and constraints, mixed integer linear programming is used to solve the day-ahead scheduling objective function and the real-time scheduling objective function respectively to obtain the solution results. According to the solution results, the optimal solution for guiding the real-time scheduling of the system is obtained, so that when both renewable energy power generation and user load are uncertain, the day-ahead scheduling operation cost of the system is the lowest and the renewable energy power generation of the system can meet the user load demand in real time.

[0079] Among them, the real-time scheduling objective function is based on the energy storage strategy that prioritizes lithium batteries, and the real-time scheduling objective function is gradually optimized by setting the optimization step size.

[0080] Among them, the hydrogen-electric hybrid energy storage power system includes an energy storage system and a new energy power station, and the model of the hydrogen-electric hybrid energy storage power system includes an energy storage system model and a new energy power station model.

[0081] Specifically, the solution result includes a first result of solving the day-ahead scheduling objective function and a second result of solving the real-time scheduling objective function, and the intersection of the first result and the second result is taken as the optimal solution.

[0082] The day-ahead scheduling of the present invention is to roughly determine the system operation plan based on the data of the previous day. Wind and solar power generation and load are fluctuating. During the specific operation of the day, it is necessary to determine the specific operation plan based on the specific wind and solar power generation and load fluctuations. Among them, the significance of day-ahead scheduling is to determine the operation plan for equipment with high start-up and shutdown costs and long time, such as hydrogen energy storage, and plan a plan with a lower overall cost, which can also reduce the amount of real-time scheduling calculations; the significance of real-time scheduling is to meet user needs as much as possible through solutions such as electric energy storage under uncertain power generation and compliance.

[0083] Specifically, the day-ahead scheduling objective function is:

[0084]

[0085] In the formula, min(f cost ) is the day-ahead scheduling objective function, C buy (t) is the cost of purchasing electricity from the power grid per hour, t∈[0,24], is the hourly operating cost of each device in the system that generates operating costs, C DR Compensation cost for system demand response, Cpun Punishment for abandoning wind and solar power in the system's renewable energy.

[0086] Among them, the devices that generate operating costs in the system include wind and solar power generation, electricity, hydrogen energy storage equipment and heat pumps.

[0087] Specifically, the real-time scheduling objective function is:

[0088] min(f Δ (t))=min(ΔP real (t)+α·ΔH real (t))

[0089] In the formula, min(f Δ (t)) is the real-time scheduling objective function, ΔP real (t) is the system power deviation of the current optimization step, ΔH real (t) is the system thermal energy difference at the current optimization step, and α is the proportionality coefficient.

[0090] The setting of the proportionality coefficient comprehensively considers the elasticity of the user's thermal and electric load. For example, in this system, electric energy is the main energy, and the system electric energy has a greater impact on the cost. The proportionality coefficient is the average thermal energy cost obtained by the day-ahead dispatch divided by the electric energy cost. That is, the proportionality coefficient uses the ratio of the average thermal energy cost obtained by the day-ahead dispatch to the average electric energy cost obtained by the day-ahead dispatch.

[0091] Specifically, the optimization step size is set according to the step size of collecting power data. The optimization step size here is related to the data step size. The common scales of power data are 1h and 15min, respectively, and the existing related work is also referred to. The commonly used optimization step size for day-ahead scheduling is 1h. This is because it does not require high accuracy and only needs to get a rough plan. The optimization step size for real-time scheduling is 15min. This is because the common step size of power data, related real-time scheduling work also uses this as the optimization step size, which can achieve better results in optimization time and results.

[0092] Specifically, the model of the hydrogen-electric hybrid energy storage power system includes an energy storage system model, a new energy power station model and a load end model; the energy storage system model includes a fuel cell model, an electrolytic cell model, a hydrogen storage tank model and a lithium battery model, and the new energy power station model includes a photovoltaic power station model and / or a wind power station model.

[0093] The fuel cell model is:

[0094]

[0095] In the formula, H SOFC (t) is the heat generated by the fuel cell, P SOFC (t) is the energy generated by the fuel cell, V SOFC(t) is the hydrogen energy consumed by the fuel cell, ΔH is the calorific value of hydrogen, is the efficiency of releasing heat during the fuel cell reaction. It is the efficiency of converting the chemical energy of fuel cell gas into electrical energy.

[0096] The electrolytic cell model is:

[0097]

[0098] Where P ec (t) is the energy required for the electrolytic cell to work; H ec (t) is the heat absorbed by the electrolytic cell during operation; It is the efficiency of heat consumption during the electrolytic cell reaction; It is the efficiency of converting electrical energy into gas chemical energy during the electrolytic cell reaction.

[0099] The hydrogen storage tank model is:

[0100]

[0101] -V SSmax ≤V SS (t)-V SS (t-1)≤V SSmax

[0102] Where V SS (t) is the hydrogen energy stored in the hydrogen storage tank at the current moment, is the hydrogen tank capacity, V SSmax Maximum power for charging / discharging the hydrogen tank, V SSmax Take 50m 3 / h,V SS (t-1) is the hydrogen energy stored in the hydrogen storage tank at the previous moment.

[0103] The lithium battery model is:

[0104]

[0105] Where P LB (t) is the electrical energy stored in the lithium battery; P LB (t-1) is the electrical energy stored in the lithium battery at the previous moment; is the charge level of the lithium battery; is the discharge capacity of the lithium battery; For lithium battery charging efficiency, is the discharge efficiency of lithium battery; Maximum power for charging lithium batteries, The maximum discharge power of the lithium battery.

[0106] Considering the output of new energy power stations, the photovoltaic power station (photovoltaic power station) model is:

[0107] P PV (t) = PV·α PV (t) rand PV

[0108] Where P PV (t) is the amount of electricity generated by the photovoltaic power station, PV is the planned capacity of the photovoltaic power station system, α PV (t) is the photovoltaic power generation capacity factor, rand PV is the fluctuation coefficient of photovoltaic power generation during real-time scheduling. The fluctuation coefficient is a Gaussian noise and is used to simulate the uncertainty of photovoltaic power generation in actual situations.

[0109] The wind power station model is:

[0110] P Wind (t) = Wind·α Wind (t) rand Wind

[0111] Where P Wind (t) is the amount of electricity generated by the wind power station, Wind is the planned capacity of the wind power station system, α Wind (t) is the wind power capacity factor, rand Wind It is the wind power generation fluctuation coefficient in real-time scheduling. The fluctuation coefficient is a Gaussian noise, which is used to simulate the uncertainty of wind power generation (wind power generation) in actual situations.

[0112] The load end model includes a heat pump model, which is:

[0113] H HP (t) = P HP (t)·η HP

[0114] In the formula, H HP (t) is the heat generated by the heat pump, P HP (t) is the power consumption of the heat pump, η HP is the heat pump electrical heat conversion efficiency.

[0115] Specifically, the constraints of the hydrogen-electric hybrid energy storage power system include a hydrogen energy balance equation, an electric energy balance equation, a thermal energy balance equation, a real-time optimized electric energy balance equation, and a real-time optimized thermal energy balance equation.

[0116] The hydrogen energy balance equation is:

[0117] V SOEC (t)+V SS (t-1) = V SOFC (t)+VSS (t)

[0118] Where V SOEC (t) is the hydrogen energy produced by the electrolytic cell, V SS (t-1) is the hydrogen energy stored in the hydrogen storage tank at the previous moment, V SOFC (t) is the hydrogen energy consumed by the fuel cell, V SS (t) is the hydrogen energy stored in the hydrogen storage tank.

[0119] The electric energy balance equation is:

[0120]

[0121] Where P PV (t) is the amount of electricity generated by the photovoltaic power station; P Wind (t) is the amount of electricity generated by the wind power station, P SOFC (t) is the energy generated by the fuel cell, P grid (t) is the amount of electricity purchased by the system from the grid per hour, is the discharge capacity of the lithium battery, P SOEC (t) is the energy consumption of the electrolytic cell, P load (t) is the electrical load, is the charge capacity of the lithium battery, P HP (t) is the energy consumption of the electric heat pump.

[0122] The heat balance equation is:

[0123] H SOFC (t)+H HP (t) = H SOEC (t)+H load (t)

[0124] In the formula, H SOFC (t) is the heat generated by the fuel cell, H HP (t) is the heat produced by the electric heat pump, H SOEC (t) is the heat consumed by the electrolytic cell, H load (t) is the system heat load.

[0125] The real-time optimized power balance equation is:

[0126]

[0127] In the formula, The amount of electricity generated by photovoltaic power plants optimized for real-time dispatch; The amount of electricity generated by wind power plants optimized for real-time dispatch, P SOFC (t) is the energy generated by the fuel cell operation, It is the amount of electricity purchased from the power grid every hour by the real-time dispatch optimization system. is the lithium battery discharge capacity optimized by real-time scheduling, P SOEC (t) is the working energy consumption of the electrolytic cell, It is the electric load optimized by real-time dispatch. Optimized lithium battery charging capacity for real-time scheduling, It is the real-time dispatch optimization of the working energy consumption of the electric heat pump;

[0128] The real-time optimized thermal energy balance equation is:

[0129]

[0130] In the formula, H SOFC (t) is the heat generated by the operation of the fuel cell, is the heat produced by the electric heat pump with real-time scheduling optimization, H SOEC (t) is the heat consumed by the electrolytic cell, It is the system heat load optimized by real-time scheduling.

[0131] like Figure 1 As shown, the hydrogen-electric hybrid energy storage power system includes an energy storage system, a new energy power station, a power grid and a load.

[0132] Among them, the new energy power station is used to transmit electric energy to users and power grids. The excess electric energy of the new energy power station can be transmitted to the energy storage system, so as to realize the consumption of new energy power generation, and realize the rapid response to the load through day-ahead scheduling and real-time scheduling. Among them, the new energy power station includes photovoltaic power station or wind power station, or photovoltaic power station and wind power station to provide the main source of electricity for the power system. In addition, when the system power is insufficient, it can purchase electricity from the power grid. In addition to the waste heat of fuel cells, the heat load on the user side can be met by heat pump heating.

[0133] The energy storage system in the hydrogen-electric hybrid energy storage power system adopts a fuel cell-lithium battery hybrid energy storage system. The fuel cell-lithium battery hybrid energy storage system includes a fuel cell, an electrolytic cell, a lithium battery and a hydrogen storage tank. The fuel cell can convert hydrogen energy into electrical energy and transmit it to users, and release heat; the electrolytic cell converts electrical energy into hydrogen energy and stores it in the hydrogen storage tank, and absorbs heat. The lithium battery can store and release electrical energy.

[0134] The system comprehensively considers the start-up and shutdown costs of hydrogen and electricity. The system operation energy priority is lithium batteries first, followed by hydrogen and electricity. The remaining demand is met by purchasing electricity from the power grid, while increasing the consumption of new energy.

[0135] The system uses mixed integer linear programming to solve the day-ahead dispatch, taking into account load and power generation fluctuations, and performs real-time dispatch based on the set optimization step size, so that the power system can meet the load demand.

[0136] The optimization step size is set according to the step size of collected power data, effectively balancing the efficiency and accuracy of real-time scheduling.

[0137] The optimization step size here is related to the step size of collecting power data. Common scales for collecting power data include 1h (hour) and 15min (minute), which also refers to existing related work. Among them, the commonly used optimization step size for day-ahead dispatch is 1h, because it does not require high accuracy and only needs to get a rough solution. The optimization step size of real-time dispatch is a smaller 15min, because the step size is common in power data, and related real-time dispatch work also uses this as the optimization step size, which can achieve better results in optimization time and results.

[0138] A method for real-time dispatching of a power system with hydrogen-electric hybrid energy storage comprises the following steps:

[0139] Step 1: Construct the energy storage system model, new energy power station model and power system constraints in the power system respectively;

[0140] Step 2: Based on the models of each part in step 1 and the constraints of the power system, construct the balance equations including power, hydrogen energy and thermal energy constraints;

[0141] Step 3: Construct the objective function of the lowest day-ahead dispatching operation cost of the power system, and find the optimal solution of the objective function while considering the system constraints and satisfying the constraints of electricity, hydrogen energy and thermal energy;

[0142] Step 4: Construct the real-time dispatch objective function of the power system, consider the real-time dispatch under the uncertainty of renewable energy power generation and user load, construct the objective function, meet the above constraints, and adjust the dispatch strategy in real time according to the load and power generation in the past period of time to dynamically meet user needs.

[0143] Among them, the system takes into account the electrical load, thermal load and their demand response. Electric energy is transmitted from the power grid, new energy power stations and energy storage systems to the user side; thermal energy comes from the waste heat of fuel cell power generation and heat generated by heat pumps.

[0144] The demand response mechanism is specifically as follows: for some electrical loads whose working hours can be changed, such as air conditioners, their working hours can be changed and users can be given additional compensation (i.e. the system's demand response compensation cost) to reduce their electricity load during peak periods and operate during off-peak periods.

[0145] The network balance equation of the electricity-hydrogen-heat energy integrated energy system is constructed based on the energy storage system model and the new energy power station model. The network balance equation includes the electricity balance equation (electricity balance equation), the heat energy balance equation (thermal energy system balance equation) and the hydrogen energy balance equation.

[0146] Among them, for day-ahead dispatch, the objective function with the lowest power system operation cost is constructed:

[0147]

[0148] In the formula, min(f cost ) is the objective function for minimizing the power system operation cost, C buy (t) is the cost of purchasing electricity from the power grid per hour. When considering the cost, the total time is calculated as one day, that is, 24 hours. is the operating cost of each device, C DR is the system demand response compensation cost, C pun It is the system’s punishment for abandoning new energy sources such as wind and solar power.

[0149] For real-time scheduling, the system optimization step length is set to 15 minutes for step-by-step rolling optimization, taking into account the difference between load and energy supply, as well as the lithium battery-first energy storage strategy. Fuel cells and electrolyzers do not need to be scheduled during this process, so there is no need to consider hydrogen energy balance. The power system should satisfy the real-time electrical and thermal energy balance equations:

[0150]

[0151] In the formula, the meaning of each variable is the same as before. The superscript real indicates that re-planning is required in real-time scheduling. It should be noted that the power load and system status 15 minutes ago are used as constraints here, so power deviation will occur in actual scheduling.

[0152] For real-time scheduling, in order to make the power generation meet the load demand, the power deviation is minimized in each step of optimization, and the system objective function is constructed as:

[0153] min(f Δ (t))=min(ΔP real (t)+α·ΔH real (t))

[0154] In the formula, ΔP real (t) is the system power deviation in this step, ΔH real (t) is the thermal energy difference of the system in this step, α is the proportional coefficient, and the proportional coefficient is the ratio of the average thermal energy cost obtained by the day-ahead dispatch to the average electric energy cost obtained by the day-ahead dispatch.

[0155] Finally, through the above system, mixed integer linear programming is used to calculate the day-ahead scheduling and real-time scheduling respectively, so as to adapt to user needs under uncertain load and power generation scenarios in the hydrogen-electric hybrid power system.

[0156] Compared with the prior art, the beneficial effects of the present invention are:

[0157] (1) By building a hybrid energy storage system, the effective coupling of electricity and hydrogen energy is achieved, and the energy conversion and utilization efficiency is optimized. Compared with a single energy system, it can more flexibly cope with the intermittent and instability of renewable energy generation, and improve the overall stability and energy utilization of the system.

[0158] (2) The energy storage system uses lithium batteries first, followed by hydrogen electricity, and the shortfall is supplemented by purchasing electricity from the power grid, which effectively increases the absorption capacity of new energy. This not only reduces the phenomenon of wind and solar power abandonment in new energy power generation, but also improves the economic benefits of new energy power stations and promotes the sustainable development of new energy.

[0159] (3) The objective function of minimizing the operating cost of the power system is constructed, and mixed integer linear programming is used to achieve the minimum cost. Compared with traditional energy storage systems, this technology significantly reduces the total operating cost of the power system and improves economic efficiency by optimizing the power purchase cost, operating cost and demand response compensation cost while ensuring system reliability.

[0160] (4) The differences between day-ahead scheduling and real-time scheduling in practical problems are taken into consideration, a real-time scheduling model is constructed, and a real-time scheduling objective function is established to improve the robustness of the system under uncertainties in load and power generation, thereby improving user satisfaction.

[0161] (5) This technology uses hydrogen energy as an energy storage medium and efficiently converts hydrogen energy into electrical energy through fuel cells, reducing dependence on fossil energy and greenhouse gas emissions. At the same time, the system fully utilizes the waste heat of the fuel cell during operation, improves the comprehensive utilization efficiency of energy, and is very environmentally friendly.

[0162] In summary, the present invention realizes electricity-hydrogen coupling by introducing fuel cells, electrolytic cells and lithium battery energy storage, effectively improves the utilization efficiency of energy and reduces the occurrence of wind and solar power abandonment; it can also improve the absorption capacity of new energy power generation, optimize the operating efficiency of the power system, reduce operating costs, and enhance the reliability and flexibility of the system, effectively improving the utilization efficiency of energy.

[0163] In the following case analysis, the wind power and photovoltaic power generation and load data are from a certain region of the Elia website, and the data time label used is GMT time. This case selects a day with large fluctuations in load and renewable energy output as a case to demonstrate the performance of the present invention in energy scheduling and renewable energy consumption.

[0164] The curve diagram of the capacity factor of new energy power stations is as follows: Figure 2 As shown, it includes photovoltaic capacity factor curve and wind power capacity factor curve; the capacity factor of new energy power station includes photovoltaic capacity factor and wind power capacity factor, and the capacity factor represents the proportion of the actual output of the device at that moment to the planned capacity of the system.

[0165] The user load curve of the system, such as Figure 3 As shown, it includes the electric load curve and the thermal load curve; the user load of the system includes electric load and thermal load, among which the electric load is larger in the morning and evening.

[0166] In this case, in addition to the electricity price, the system operating cost is the equipment operating cost converted to the daily price. For example, the cost of the photovoltaic unit is 817 yuan / kWh, which is converted to an annual operating cost of 3% of the investment cost. The cost of the wind turbine unit is 2513 yuan / kWh, which is converted to an annual operating cost of 1% of the investment cost. Considering the high cost of photovoltaic operation and maintenance, the conversion ratio is higher. The hydrogen storage tank only considers the investment cost of 400 yuan / m 3 The operating costs are not included. The investment costs of lithium batteries, fuel cells, electrolytic cell heat pumps, etc. are shown in Table 1.

[0167] Table 1 Investment cost, operation efficiency and power consumption of various devices

[0168]

[0169] This case conducts day-ahead scheduling and real-time scheduling based on the existing data, and plans the output of each device in the above system. Day-ahead scheduling achieves the lowest operating cost and as much renewable energy generation as possible under the condition that the system meets the user load, and real-time scheduling meets user needs under the uncertainty of load and power generation. Figure 4 and Figure 5 shown.

[0170] Figure 4 It is an image of electrical load and equipment power. The stacked bar chart represents the power generation or power consumption of different power generation equipment at the current moment. The part above the coordinate axis represents the corresponding power output at that moment, and the part below the coordinate axis represents the corresponding power consumption at that moment. At each moment, the power consumption and output are balanced.

[0171] from Figure 4It can be found that at 0-8 o'clock, the system power load is large, the photovoltaic output is low, and the wind power output fluctuates in a small range. Since the electricity price is low at this time, the system purchases a large amount of electricity to meet the load demand. At this time, the lithium battery is in a discharging state. The system power supply mainly comes from lithium batteries, wind power and power grid sales. In addition, a small part of the power demand is transferred through demand response; at 9-16 o'clock, the power load is reduced. At this time, the photovoltaic output is high and can meet the power demand of users, while the thermal load increases. At this time, the lithium battery starts to charge and the electrolytic cell starts to work. In addition, the power demand response in the previous time period is transferred to this moment; at 17-21 o'clock, the system power load is high, but the wind power generation is high at this time, which can meet most of the power demand. At this time, the power gap caused by wind power fluctuations is mainly supplemented by lithium battery discharge; at 22-24 o'clock, the power load and wind power generation are both relatively low. At this time, the lithium battery is in a charging state, the electrolytic cell is working, and the fuel cell is turned on. At this time, it is at the valley electricity price, and the power gap is supplemented by power grid sales. Through analysis, it can be found that the optimization results are logical, so from the perspective of power generation equipment scheduling, the mathematical model is reliable.

[0172] Figure 5 is a graph of the heat load and the thermal power of the heat generating equipment at each moment. The stacked bar graph represents the thermal power of the heat storage device, electric heat pump, fuel cell and electrolyzer. Figure 4 Similarly, the part above the coordinate axis represents the heat energy output corresponding to that moment, and the part below the coordinate axis represents the heat energy consumption corresponding to that moment. At each moment, the heat energy consumption and output are balanced.

[0173] Figure 5 In the figure, at 1-5, the heat load of the system is low, and the waste heat of the fuel cell alone can meet the heat load of the system, reducing the heating cost of the system. At 6-16, the heat load is mainly provided by the heat energy released by the heat pump; this is because during this period, the fuel cell is shut down and the electrolytic cell is working, and the system heat load demand is high, which can only be met after the heat pump is turned on. At 17-24, the heat load is relatively low, and the fuel cell is not in working state. At this time, the heat pump generates heat, which brings more benefits. Since the heat generation of fuel cells is limited, the main source of heat for the system is still heat generated by the heat pump. The scheduling analysis of heat load and heat generating equipment shows that the optimization results are logical, so from the scheduling level of heat generating equipment, the mathematical model is reliable.

[0174] Real-time dispatch of electrical energy and thermal energy output Figure 6 and Figure 7 As shown in the figure, it can be seen that the power and heat energy status of the system is basically consistent with the trend of the day-ahead dispatch results. Due to the uncertainty of load and power generation, the upper and lower parts of the image are not strictly symmetrical, and there is a certain amount of power and heat energy deviation. However, due to real-time dispatch optimization control, the deviation is always controlled within a small range.

[0175] Taking into account the uncertainty of load and power generation, the deviation of electricity and heat energy is as follows: Figure 8 and Fig. 9 As shown in the figure, if only the day-ahead scheduling is considered, the system will often cause excess or insufficient power and heat supply in actual operation. However, by combining day-ahead scheduling with real-time scheduling, the system's insufficient or excess power and heat supply has been greatly improved, which not only improves the stability of the system, but also improves user satisfaction.

[0176] It can be seen from the implementation cases that the present invention can not only effectively solve the problems faced by the grid connection of new energy sources, but also greatly improve the comprehensive utilization efficiency of energy through a comprehensive energy system with electric-hydrogen hybrid energy storage as the core. In the specific power system design, the present invention uses lithium batteries, fuel cells and electrolytic cells as energy hubs to realize the mutual conversion between electrical energy, thermal energy and hydrogen energy. In addition, by combining the use of hydrogen storage tanks, the system can achieve the function of peak shaving and valley filling, and provide long-term energy storage. During scheduling, the system performs better under uncertain load and power generation scenarios by combining day-ahead scheduling with real-time scheduling, thereby significantly improving the comprehensive utilization efficiency of energy and bringing higher economic benefits to the microgrid.

[0177] The power system of the present invention is composed of an energy storage system, a new energy power station, a power grid and a load, and uses a scheduling method that combines day-ahead scheduling with real-time scheduling to improve system flexibility and reliability. The energy storage system includes a fuel cell, an electrolytic cell, a lithium battery and a hydrogen storage tank to achieve the mutual conversion and storage of electric energy, hydrogen energy and thermal energy. By constructing an energy storage system model and a new energy power station model in the power system, and forming network balance equations of the three networks of electricity, hydrogen and heat, day-ahead scheduling and constructing an objective function with the lowest operating cost of the power system, a preliminary scheduling plan for the system is obtained, and the preliminary scheduling plan is combined with the real-time load and power generation fluctuations to perform real-time scheduling to obtain a real-time scheduling plan. The present invention has been verified by actual operation data and simulation results, effectively improving the absorption rate of new energy, enabling resources to be more flexibly configured, and improving system flexibility and user satisfaction.

[0178] An embodiment of a computer system:

[0179] A computer system includes a processor, and the processor is used to execute a computer program to implement the steps of a real-time dispatching method for a hydrogen-electric hybrid energy storage power system. Among them, a real-time dispatching method for a hydrogen-electric hybrid energy storage power system has been described in detail in an embodiment of a real-time dispatching method for a hydrogen-electric hybrid energy storage power system, and will not be repeated here.

Claims

1. A real-time dispatching method for a hydrogen-electric hybrid energy storage power system, characterized in that: include: Construct the model of the hydrogen-electric hybrid energy storage power system and its constraints, construct the day-ahead dispatch objective function with the lowest day-ahead dispatch operation cost, and construct the real-time dispatch objective function with the minimum power deviation under the condition of uncertainty of both renewable energy generation and user load; The real-time scheduling objective function is based on the energy storage strategy with lithium battery priority, and the real-time scheduling objective function is gradually optimized by setting the optimization step size; According to the model and constraints, mixed integer linear programming is used to solve the day-ahead scheduling objective function and the real-time scheduling objective function respectively to obtain solution results, and the optimal solution for guiding the real-time scheduling of the system is obtained according to the solution results, so that when both the new energy power generation and the user load are uncertain, the day-ahead scheduling operation cost of the system is the lowest and the new energy power generation of the system meets the user load demand in real time.

2. The real-time dispatching method of the hydrogen-electric hybrid energy storage power system according to claim 1 is characterized in that: The solution result includes a first result of solving the day-ahead scheduling objective function and a second result of solving the real-time scheduling objective function, and the intersection of the first result and the second result is taken as the optimal solution.

3. The real-time dispatching method of the hydrogen-electric hybrid energy storage power system according to claim 1 is characterized in that: The day-ahead scheduling objective function is: In the formula, min(f cost ) is the day-ahead scheduling objective function, C buy (t) is the cost of purchasing electricity from the power grid per hour, t∈[0,24], is the hourly operating cost of each device in the system that generates operating costs, C DR Compensate for system demand response costs, C pun Punish the system for abandoning wind and solar power.

4. The real-time dispatching method of the hydrogen-electric hybrid energy storage power system according to claim 1 is characterized in that: The real-time scheduling objective function is: min(f Δ (t))=min(ΔP real (t)+α·ΔH real (t)) In the formula, min(f Δ (t)) is the real-time scheduling objective function, ΔP real (t) is the system power deviation of the current optimization step, ΔH real (t) is the system thermal energy difference of the current optimization step, α is the proportionality coefficient, and the proportionality coefficient is the ratio of the average thermal energy cost obtained by the day-ahead scheduling to the average electric energy cost obtained by the day-ahead scheduling.

5. The real-time dispatching method of the hydrogen-electric hybrid energy storage power system according to claim 1 is characterized in that: The optimization step size is set according to the step size of collecting power data.

6. The real-time dispatching method of the hydrogen-electric hybrid energy storage power system according to claim 1 is characterized in that: The model of the hydrogen-electric hybrid energy storage power system includes an energy storage system model, a new energy power station model and a load end model; the energy storage system model includes a fuel cell model, an electrolytic cell model, a hydrogen storage tank model and a lithium battery model, and the new energy power station model includes a photovoltaic power station model and / or a wind power station model.

7. The real-time dispatching method of the hydrogen-electric hybrid energy storage power system according to claim 1 is characterized in that: The constraints of the hydrogen-electric hybrid energy storage power system include a hydrogen energy balance equation, an electric energy balance equation, a thermal energy balance equation, a real-time optimized electric energy balance equation and a real-time optimized thermal energy balance equation.

8. The real-time dispatching method of the hydrogen-electric hybrid energy storage power system according to claim 6 is characterized in that: The fuel cell model is: In the formula, H SOFC (t) is the heat generated by the fuel cell, P SOFC (t) is the energy generated by the fuel cell, V SOFC (t) is the hydrogen energy consumed by the fuel cell, ΔH is the calorific value of hydrogen, is the efficiency of releasing heat during the fuel cell reaction. It is the efficiency of converting the chemical energy of fuel cell gas into electrical energy; The electrolytic cell model is: Where P ec (t) is the energy required for the electrolytic cell to work; H ec (t) is the heat absorbed by the electrolytic cell during operation; It is the efficiency of heat consumption during the electrolytic cell reaction; It is the efficiency of converting electrical energy into gas chemical energy during the electrolytic cell reaction; The hydrogen storage tank model is: -V SSmax ≤V SS (t)-V SS (t-1)≤V SSmax Where V SS (t) is the hydrogen energy stored in the hydrogen storage tank at the current moment, is the hydrogen tank capacity, V SSmax Maximum power for charging / discharging the hydrogen tank, V SS (t-1) is the hydrogen energy stored in the hydrogen storage tank at the previous moment; The lithium battery model is: Where P LB (t) is the electrical energy stored in the lithium battery; P LB (t-1) is the electrical energy stored in the lithium battery at the previous moment; is the charge level of the lithium battery; is the discharge capacity of the lithium battery; For lithium battery charging efficiency, is the discharge efficiency of lithium battery; Maximum power for charging lithium batteries, The maximum discharge power of the lithium battery; The photovoltaic power station model is: P PV (t)=PV·α PV (t)·rand PV Where P PV (t) is the amount of electricity generated by the photovoltaic power station, PV is the planned capacity of the photovoltaic power station system, α PV (t) is the photovoltaic power generation capacity factor, rand PV is the fluctuation coefficient of photovoltaic power generation during real-time scheduling; The wind power station model is: P Wind (t)=Wind·α Wind (t)·rand Wind Where P Wind (t) is the amount of electricity generated by the wind power station, Wind is the planned capacity of the wind power station system, α Wind (t) is the wind power capacity factor, rand Wind is the wind power generation fluctuation coefficient during real-time scheduling; The load end model includes a heat pump model, and the heat pump model is: H HP (t)=P HP (t)·η HP In the formula, H HP (t) is the heat generated by the heat pump, P HP (t) is the power consumption of the heat pump, η HP is the heat pump electrical heat conversion efficiency.

9. The real-time dispatching method of the hydrogen-electric hybrid energy storage power system according to claim 7 is characterized in that: The hydrogen energy balance equation is: V SOEC (t)+V SS (t-1)=V SOFC (t)+V SS (t) Where V SOEC (t) is the hydrogen energy produced by the electrolytic cell, V SS (t-1) is the hydrogen energy stored in the hydrogen storage tank at the previous moment, V SOFC (t) is the hydrogen energy consumed by the fuel cell, V SS (t) is the hydrogen energy stored in the hydrogen storage tank; The electric energy balance equation is: Where P PV (t) is the amount of electricity generated by the photovoltaic power station; P Wind (t) is the amount of electricity generated by the wind power station, P SOFC (t) is the energy generated by the fuel cell, P grid (t) is the amount of electricity purchased by the system from the grid per hour, is the discharge capacity of the lithium battery, P SOEC (t) is the energy consumption of the electrolytic cell, P load (t) is the electrical load, is the charge capacity of the lithium battery, P HP (t) is the energy consumption of the electric heat pump; The thermal energy balance equation is: H SOFC (t)+H HP (t)=H SOEC (t)+H load (t) In the formula, H SOFC (t) is the heat generated by the fuel cell, H HP (t) is the heat produced by the electric heat pump, H SOEC (t) is the heat consumed by the electrolytic cell, H load (t) is the system heat load; The real-time optimized electric energy balance equation is: In the formula, The amount of electricity generated by photovoltaic power plants optimized for real-time dispatch; The amount of electricity generated by wind power plants optimized for real-time dispatch, P SOFC (t) is the energy generated by the fuel cell operation, It is the amount of electricity purchased from the power grid every hour by the real-time dispatch optimization system. is the lithium battery discharge capacity optimized by real-time scheduling, P SOEC (t) is the working energy consumption of the electrolytic cell, It is the electric load optimized by real-time dispatch. Optimized lithium battery charging capacity for real-time scheduling, It is the real-time dispatch optimization of the working energy consumption of the electric heat pump; The real-time optimized thermal energy balance equation is: In the formula, H SOFC (t) is the heat generated by the operation of the fuel cell, is the heat produced by the electric heat pump with real-time scheduling optimization, H SOEC (t) is the heat consumed by the electrolytic cell, It is the system heat load optimized by real-time scheduling.

10. A computer system comprising a processor, characterized in that: The processor is used to execute a computer program to implement the steps of the real-time scheduling method of the hydrogen-electric hybrid energy storage power system according to any one of claims 1 to 9.

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