A real-time scheduling method and computer system of hydrogen-electric hybrid energy storage power system

Abstract

The application relates to a real-time scheduling method and a computer system of a hydrogen-electric hybrid energy storage power system, and belongs to the technical field of power system scheduling. The method comprises the following steps: according to a model of the hydrogen-electric hybrid energy storage power system and constraint conditions, a mixed integer linear programming is used to solve a day-ahead scheduling target function and a real-time scheduling target function respectively to obtain a solving result, and an optimal solution for guiding real-time scheduling of the system is obtained according to the solving result. According to the model of the hydrogen-electric hybrid energy storage power system and the constraint conditions, the mixed integer linear programming is used to solve the day-ahead scheduling target function and the real-time scheduling target function respectively to obtain the solving result, the optimal solution for guiding the real-time scheduling of the system is obtained according to the solving result, the day-ahead scheduling operation cost of the hydrogen-electric hybrid energy storage power system is the lowest under the condition that new energy power generation and user load are both uncertain, and the new energy power generation of the hydrogen-electric hybrid energy storage power system can meet the user load demand in real time.

Description

TECHNICAL FIELD

[0001] The application relates to a real-time scheduling method and a computer system of a hydrogen-electric hybrid energy storage power system, and belongs to the technical field of power system scheduling. BACKGROUND

[0002] With the continuous growth of global energy demand, the consumption of traditional fossil energy has caused serious environmental impact, and the limited reserves of traditional fossil energy are difficult to support long-term energy demand. Therefore, human society is seeking cleaner and sustainable energy to meet energy demand. New energy power generation, especially solar power generation and wind power generation, has become a research and application hotspot due to its cleanliness and renewability.

[0003] However, new energy power generation has significant volatility and intermittency problems, which brings 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 has emerged. The integrated energy system realizes the complementary and optimal utilization of energy through the coupling of different energy forms such as heat, electricity and hydrogen, and improves the economy, flexibility and reliability of the system.

[0004] Energy storage technology is one of the key solutions of integrated energy system. 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, but the cycle life and environmental adaptability of lithium batteries limit their application in large-scale energy storage. Similarly, hydrogen energy as a clean energy carrier is concerned for its high energy density and flexible storage method. Fuel cell technology can efficiently convert hydrogen energy into electrical energy, while electrolysis cell technology can convert electrical energy into hydrogen energy, realizing the storage and conversion of electrical energy.

[0005] Currently, there have been studies combining fuel cells with lithium batteries to form a hybrid energy storage system in order to take advantage of each other 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 aspect of hybrid energy storage scheduling method, there is no research, and there is a lack of corresponding systematic scheduling method.

[0006] In order to fully utilize new energy, reduce grid connection difficulty, and improve the safety and economy of the system, more efficient and reliable energy storage technology needs to be developed, and at the same time, the scheduling strategy of integrated energy system needs to be further optimized to realize the optimal configuration and utilization of energy. SUMMARY

[0007] The application aims to provide a real-time scheduling method and computer system of 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 mode, and it is difficult to dynamically meet the user demand in real time under the condition of uncertain user load and power generation.

[0008] To achieve the above-mentioned purpose, the scheme of the application includes:

[0009] The real-time scheduling method of the hydrogen-electric hybrid energy storage power system includes: constructing a model of the hydrogen-electric hybrid energy storage power system and its constraint conditions, constructing a day-ahead scheduling objective function of the system with the lowest day-ahead scheduling operation cost, and constructing a real-time scheduling objective function of the system with the minimum power deviation under the condition that both new energy power generation and user load are uncertain.

[0010] The real-time scheduling objective function is based on a lithium battery priority energy storage strategy, and the real-time scheduling objective function is optimized step by step with a set optimization step.

[0011] According to the model and the constraint conditions, the day-ahead scheduling objective function and the real-time scheduling objective function are solved by using mixed integer linear programming to obtain a solution result, and the optimal solution for guiding the real-time scheduling of the system is obtained according to the solution result, so that the day-ahead scheduling operation cost of the system is the lowest and the new energy power generation of the system can meet the user load demand in real time under the condition that both new energy power generation and user load are uncertain.

[0012] Further, 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] Further, the day-ahead scheduling objective function is:

[0014]

[0015] In the formula, is the day-ahead scheduling objective function, is the cost of purchasing electricity from the power grid every hour, , is the operation cost of each device in the system that generates operation cost every hour, is the system demand response compensation cost, is the system wind and light abandonment penalty.

[0016] Further, the real-time scheduling objective function is:

[0017]

[0018] In the formula, is the real-time scheduling objective function, a system electric energy deviation for a current optimization step, a system thermal energy difference for a current optimization step, is a proportionality coefficient, the proportionality coefficient being a ratio of an average thermal energy cost obtained by the day-ahead scheduling to an average electric energy cost obtained by the day-ahead scheduling.

[0019] Further, the optimization step is set according to a step length of collecting power data.

[0020] Further, the model of the hydrogen-electric hybrid energy storage power system comprises an energy storage system model, a new energy power station model and a load end model; the energy storage system model comprises 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 comprises a photovoltaic power station model and / or a wind power station model.

[0021] Further, the constraint conditions of the hydrogen-electric hybrid energy storage power system comprise 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.

[0022] Further, the fuel cell model is:

[0023]

[0024]

[0025] In the formula, is the heat generated by the fuel cell operation, is the energy generated by the fuel cell operation, is the hydrogen energy consumed by the fuel cell operation, is the calorific value of hydrogen, is the efficiency of heat released in the fuel cell reaction process, is the efficiency of the fuel cell gas chemical energy conversion into electric energy;

[0026] The electrolytic cell model is:

[0027]

[0028]

[0029] In the formula, is the energy required by the electrolytic cell operation; is the heat absorbed by the electrolytic cell operation; is the efficiency of heat consumed in the electrolytic cell reaction process; is the efficiency of electric energy conversion into gas chemical energy in the electrolytic cell reaction process;

[0030] The hydrogen storage tank model is:

[0031]

[0032]

[0033] In the formula, is the hydrogen energy stored in the hydrogen storage tank at the current time, is the hydrogen storage tank capacity, is the maximum power of hydrogen storage tank charging / discharging, is the hydrogen energy stored in the hydrogen storage tank at the last time;

[0034] The lithium battery model is:

[0035]

[0036]

[0037]

[0038] In the formula, is the electric energy stored in the lithium battery; is the electric energy stored in the lithium battery at the last time; is the lithium battery charging capacity; is the lithium battery discharging capacity; is the lithium battery charging efficiency, is the lithium battery discharging efficiency; is the maximum power of lithium battery charging, is the maximum power of lithium battery discharging;

[0039] The photovoltaic power station model is:

[0040]

[0041] In the formula, is the electric quantity generated by the photovoltaic power station, is the system planning capacity of the photovoltaic power station, is the photovoltaic power generation capacity factor, is the photovoltaic power generation fluctuation coefficient at real-time scheduling;

[0042] The wind power station model is:

[0043]

[0044] In the formula, is the electric quantity generated by the wind power station, is the system planning capacity of the wind power station, is the wind power generation capacity factor, is the wind power generation fluctuation coefficient at real-time scheduling;

[0045] The load end model includes a heat pump model, and the heat pump model is:

[0046]

[0047] wherein, is the heat produced by the heat pump, is the electricity consumed by the heat pump, is the heat-electricity conversion efficiency of the heat pump.

[0048] Further, the hydrogen energy balance equation is:

[0049]

[0050] wherein, is the hydrogen energy produced by the electrolyzer, is the hydrogen energy stored in the hydrogen tank at the previous time, is the hydrogen energy consumed by the fuel cell, is the hydrogen energy stored in the hydrogen tank;

[0051] The electricity balance equation is:

[0052]

[0053] wherein, is the electricity produced by the photovoltaic power station; is the electricity produced by the wind power station, is the electricity produced by the fuel cell, is the electricity purchased by the system from the grid per hour, is the discharge amount of the lithium battery, is the electricity consumed by the electrolyzer, is the electrical load, is the charge amount of the lithium battery, is the electricity consumed by the heat pump;

[0054] The heat energy balance equation is:

[0055]

[0056] wherein, is the heat produced by the fuel cell, is the heat produced by the heat pump, is the heat consumed by the electrolyzer, is the thermal load of the system;

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

[0058]

[0059] wherein, is the electricity produced by the photovoltaic power station optimized by real-time scheduling; is the electricity generated by the wind power station for real-time scheduling optimization, is the energy generated by the fuel cell, is the electricity purchased by the system from the grid per hour for real-time scheduling optimization, is the lithium battery discharge amount for real-time scheduling optimization, is the energy consumed by the electrolytic cell, is the electrical load for real-time scheduling optimization, is the lithium battery charging amount for real-time scheduling optimization, is the energy consumed by the heat pump for real-time scheduling optimization;

[0060] The real-time optimized heat balance equation is:

[0061]

[0062] In the formula, is the heat generated by the fuel cell, is the heat generated by the heat pump for real-time scheduling optimization, is the heat consumed by the electrolytic cell, is the thermal load of the system for real-time scheduling optimization.

[0063] A computer system of the present application comprises a processor for executing 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.

[0064] The present application has the following beneficial effects:

[0065] The present application provides a real-time scheduling method and a 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 constraint conditions, a mixed integer linear programming is used to solve the day-ahead scheduling objective function and the real-time scheduling objective function respectively to obtain a solution result. According to the solution result, an optimal solution is obtained for guiding the real-time scheduling of the system. In the case where both new energy generation and user load are uncertain, the day-ahead scheduling operation cost of the hydrogen-electric hybrid energy storage power system is the lowest, and the new energy generation of the hydrogen-electric hybrid energy storage power system can meet the user load demand in real time. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 is a structural schematic diagram of the hydrogen-electric hybrid energy storage power system of the embodiment of the present application;

[0067] Figure 2 is a curve graph of the new energy power station capacity factor of the embodiment of the present application;

[0068] Figure 3 is a user load curve graph of the embodiment of the present application;

[0069] Figure 4is a balance column chart of an electricity system scheduled by an embodiment of the present application in day-ahead;

[0070] Figure 5 is a balance column chart of a thermal energy system scheduled by an embodiment of the present application in day-ahead;

[0071] Figure 6 is a balance column chart of an electricity system scheduled by an embodiment of the present application in real time;

[0072] Figure 7 is a balance column chart of a thermal energy system scheduled by an embodiment of the present application in real time;

[0073] Figure 8 is a real-time change curve chart of an electricity deviation scheduled by an embodiment of the present application in real time;

[0074] Figure 9 is a real-time change curve chart of a thermal energy deviation scheduled by an embodiment of the present application in real time. DETAILED DESCRIPTION

[0075] To solve the problems in the background art, the present application provides a real-time scheduling method and computer system for a hydrogen-electric hybrid energy storage power system, which specifically solves a day-ahead scheduling target function and a real-time scheduling target function by using mixed integer linear programming according to a model of the hydrogen-electric hybrid energy storage power system and constraint conditions thereof to obtain a solution, and obtains an optimal solution for guiding real-time scheduling of the system according to the solution, so that the hydrogen-electric hybrid energy storage power system has the lowest day-ahead scheduling operation cost in the case that new energy power generation and user load are both uncertain, and the new energy power generation of the hydrogen-electric hybrid energy storage power system meets the user load demand in real time.

[0076] The hydrogen-electric hybrid energy storage power system is composed of an energy storage system, a new energy power station, a power grid and a load. The energy storage system includes a fuel cell, an electrolytic cell, a lithium battery and a hydrogen storage tank, which together realize mutual conversion and storage of electric energy, hydrogen energy and thermal energy. When new energy power generation is excessive, the electrolytic cell converts excess electric energy into hydrogen and stores it in the hydrogen storage tank, which not only realizes large-scale diffusion of renewable energy, but also enhances the independence of the system and the power grid. When the power system needs additional electric energy, the fuel cell converts hydrogen in the hydrogen storage tank into electric energy, while releasing thermal energy, which provides a predictable, controllable and high-deterministic power generation device for the power system, effectively limiting power fluctuations. The waste heat generated by the fuel cell during power generation and the thermal energy converted by the heat pump system can provide the required heat for the electrolytic cell operation, and can also be sold to users as heating to meet the user's heat load demand, thereby improving the comprehensive utilization efficiency of energy.

[0077] The power system of the application gives compensation to part of adjustable working time electric load through demand response mechanism, optimizes the operation of the power system, and improves the self-consumption rate of energy. When the new energy power generation capacity is greater than the required energy of the load, the electrolytic cell starts to convert the excess electric energy into hydrogen gas for storage; when the new energy capacity is insufficient, the fuel cell starts to convert the stored chemical energy into electric energy to cope with the power peak and valley of the power grid. The application uses digital optimization technology to construct a target function of the power system day-ahead dispatching operation cost, considers the purchase cost, operation cost, demand response compensation cost and new energy wind and light punishment, and realizes the minimization of the cost. The application also constructs a real-time scheduling model and a real-time scheduling target function to make the system meet the user demand under the uncertainty of load and power generation, and improve the robustness of the system.

[0078] To make the purpose, technical solutions and advantages of the application more clear and explicit, the application will be further described in detail below with reference to the drawings and examples.

[0079] An embodiment of a real-time scheduling method of a hydrogen-electric hybrid energy storage power system:

[0080] A real-time scheduling method of a hydrogen-electric hybrid energy storage power system, comprising: constructing a model of the hydrogen-electric hybrid energy storage power system and constraint conditions of the hydrogen-electric hybrid energy storage power system, also constructing a day-ahead dispatching target function of the system (hydrogen-electric hybrid energy storage power system) with the lowest day-ahead dispatching operation cost, and also constructing a real-time dispatching target function of the system (hydrogen-electric hybrid energy storage power system) with the minimum power deviation under the uncertainty of new energy power generation and user load.

[0081] According to the model and the constraint conditions, the day-ahead dispatching target function and the real-time dispatching target function are solved by using mixed integer linear programming to obtain a solution result, and the optimal solution for guiding the real-time dispatching of the system is obtained according to the solution result, so that the day-ahead dispatching operation cost of the system is the lowest and the real-time new energy power generation of the system meets the user load demand under the uncertainty of new energy power generation and user load.

[0082] The real-time dispatching target function is based on a lithium battery priority energy storage strategy, and the real-time dispatching target function is optimized step by step with a set optimization step.

[0083] 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.

[0084] Specifically, the solution result includes a first result of solving the day-ahead dispatching target function and a second result of solving the real-time dispatching target function, and the intersection of the first result and the second result is taken as the optimal solution.

[0085] The day-ahead scheduling of the application is to determine the system operation scheme according to the previous day data, and the wind and light power generation and the load are fluctuant, in the specific operation process of the day, the specific operation scheme needs to be determined according to the specific wind and light power generation and the load fluctuation. Among them, the significance of day-ahead scheduling is to determine the operation scheme for the equipment with high start-stop cost and long time such as hydrogen energy storage, and plan the scheme with low overall cost, which can also reduce the operation amount of real-time scheduling; the significance of real-time scheduling is to meet the user demand as much as possible through the scheme such as electric energy storage under the uncertain situation of power generation and conformity.

[0086] Specifically, the day-ahead scheduling objective function is:

[0087]

[0088] In the formula, is the day-ahead scheduling objective function, is the cost of purchasing electricity from the power grid every hour, , is the operation cost of each device in the system that generates operation cost per hour, is the system demand response compensation cost, is the system new energy wind and light punishment.

[0089] Among them, the devices in the system that generate operation cost include wind and light power generation, electric and hydrogen energy storage devices and heat pumps.

[0090] Specifically, the real-time scheduling objective function is:

[0091]

[0092] In the formula, is the real-time scheduling objective function, is the system electric energy deviation of the current optimization step, is the system thermal energy difference of the current optimization step, is the proportional coefficient.

[0093] The setting of the proportional coefficient comprehensively considers the elastic setting of the user's thermal and electric load. For example, in the system, electric energy is the main one, the system electric energy has a greater impact on the cost, and the average thermal energy cost obtained by day-ahead scheduling divided by the average electric energy cost obtained by day-ahead scheduling is the proportional coefficient. That is, the proportional coefficient is selected as the ratio of the average thermal energy cost obtained by day-ahead scheduling to the average electric energy cost obtained by day-ahead scheduling.

[0094] Specifically, the optimization step length is set according to the step length of the collected power data. The optimization step length is related to the data step length, and the common scales of power data are 1h and 15min, and the existing related work is also referred to. The optimization step length of day-ahead scheduling is 1h, because the accuracy requirement is not high, and only a rough scheme is needed. The optimization step length of real-time scheduling is 15min, because the common step length of power data is 15min, and the related real-time scheduling work also takes this as the optimization step length, which can achieve better results in optimization time and results.

[0095] 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.

[0096] The fuel cell model is:

[0097]

[0098]

[0099] In the formula, is the heat generated by the operation of the fuel cell, is the energy generated by the operation of the fuel cell, is the hydrogen energy consumed by the operation of the fuel cell, is the calorific value of hydrogen, is the efficiency of heat released in the fuel cell reaction process, is the efficiency of converting gas chemical energy into electric energy in the fuel cell.

[0100] The electrolytic cell model is:

[0101]

[0102]

[0103] In the formula, is the energy required for the operation of the electrolytic cell; is the heat absorbed by the operation of the electrolytic cell; is the efficiency of heat consumption in the electrolytic cell reaction process; is the efficiency of converting electric energy into gas chemical energy in the electrolytic cell reaction process.

[0104] The hydrogen storage tank model is:

[0105]

[0106]

[0107] In the formula, is the hydrogen energy stored in the hydrogen tank at the current time, is the hydrogen tank capacity, is the maximum power of the hydrogen tank charging / discharging, Take 50 , is the hydrogen energy stored in the hydrogen tank at the last time.

[0108] The lithium battery model is:

[0109]

[0110]

[0111]

[0112] In the formula, is the electric energy stored in the lithium battery; is the electric energy stored in the lithium battery at the last time; is the charging capacity of the lithium battery; is the discharging capacity of the lithium battery; is the charging efficiency of the lithium battery, is the discharging efficiency of the lithium battery; is the maximum power of the lithium battery charging, is the maximum power of the lithium battery discharging.

[0113] Considering the output of new energy power stations, the photovoltaic power station (photovoltaic power station) model is:

[0114]

[0115] In the formula, is the electric quantity generated by the photovoltaic power station, is the system planning capacity of the photovoltaic power station, is the photovoltaic power generation capacity factor, is the photovoltaic power generation fluctuation coefficient at real-time scheduling, which is a Gaussian noise, used to simulate the uncertainty of photovoltaic power generation in actual situation.

[0116] The wind power station model is:

[0117]

[0118] In the formula, is the electric quantity generated by the wind power station, is the system planning capacity of the wind power station, is the wind power generation capacity factor, is the wind power generation fluctuation coefficient at real-time scheduling, which is a Gaussian noise, used to simulate the uncertainty of wind power generation (wind power generation) in actual situation.

[0119] The load end model comprises a heat pump model, and the heat pump model is:

[0120]

[0121] In the formula, is heat generated by the heat pump, is power consumption of the heat pump, is an electric-heat conversion efficiency of the heat pump.

[0122] Specifically, the constraint conditions 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.

[0123] The hydrogen energy balance equation is:

[0124]

[0125] In the formula, is hydrogen energy generated by electrolysis, is hydrogen energy stored in the hydrogen storage tank at the last time, is hydrogen energy consumed by the fuel cell, is hydrogen energy stored in the hydrogen storage tank.

[0126] The electric energy balance equation is:

[0127]

[0128] In the formula, is an amount of electricity generated by a photovoltaic power station; is an amount of electricity generated by a wind power station, is energy generated by the fuel cell, is an amount of electricity purchased by the system from the power grid per hour, is a discharge amount of the lithium battery, is energy consumed by the electrolysis, is an electric load, is a charging amount of the lithium battery, is energy consumed by the heat pump.

[0129] The thermal energy balance equation is:

[0130]

[0131] In the formula, is heat generated by the fuel cell, is heat generated by the heat pump, is heat consumed by the electrolysis, is a thermal load of the system.

[0132] The real-time optimized electric energy balance equation is:

[0133]

[0134] In the formula, is the real-time scheduling optimized photovoltaic power station generated electric quantity; is the real-time scheduling optimized wind power station generated electric quantity, is the fuel cell working generated energy, is the real-time scheduling optimized system hourly purchased electric quantity from the power grid, is the real-time scheduling optimized lithium battery discharge quantity, is the electrolytic cell working energy consumption, is the real-time scheduling optimized electric load, is the real-time scheduling optimized lithium battery charging quantity, is the real-time scheduling optimized electric heat pump working energy consumption;

[0135] The real-time optimized thermal energy balance equation is:

[0136]

[0137] In the formula, is the fuel cell working generated heat, is the real-time scheduling optimized electric heat pump produced heat, is the electrolytic cell working consumed heat, is the real-time scheduling optimized system thermal load.

[0138] As Figure 1 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.

[0139] The new energy power station is used to transmit electric energy to users and the power grid, and 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. The new energy power station includes a photovoltaic power station or a wind power station, or includes a photovoltaic power station and a wind power station to provide a main power source for the power system, and in addition, the system can purchase electricity from the power grid side when the system power is insufficient. In addition to the fuel cell waste heat, the user side thermal load can be heated by a heat pump to meet the load.

[0140] The energy storage system in the hydrogen-electric hybrid energy storage power system adopts a fuel cell-lithium battery hybrid energy storage system, which includes a fuel cell, an electrolytic cell, a lithium battery and a hydrogen storage tank. The fuel cell can convert hydrogen energy into electric energy to transmit to users and release heat; the electrolytic cell converts electric energy into hydrogen energy and stores it in the hydrogen storage tank, and absorbs heat; and the lithium battery can store and release electric energy.

[0141] The system comprehensively considers hydrogen-electric start-stop cost, the energy priority of system operation is lithium battery first, hydrogen electricity second, the remaining demand is met by purchasing electricity from the power grid, and new energy consumption is increased.

[0142] The system uses mixed integer linear programming to solve day-ahead scheduling, considers load and power generation fluctuation, and performs real-time scheduling with a set optimization step as a scale, so that the power system meets the load demand.

[0143] The set optimization step is set according to the step of collecting power data, effectively balancing the efficiency and accuracy of real-time scheduling.

[0144] The set optimization step here is related to the step of collecting power data. Common scales of power data collection include 1h (hour) and 15min (minute), and existing related work is also referred to. Among them, the commonly used optimization step of day-ahead scheduling is 1h, because it has low precision requirement and only needs to get a rough scheme. While the real-time scheduling optimization step selects a smaller 15min, because it is a common step of power data, and related real-time scheduling work also takes this as the optimization step, which can achieve good results in optimization time and results.

[0145] A real-time scheduling method of a hydrogen-electric hybrid energy storage power system, comprising the following steps:

[0146] Step 1: respectively construct a model of the energy storage system in the power system, a model of the new energy power station, and a constraint condition of the power system;

[0147] Step 2: according to the model of each part in step 1 and the constraint condition of the power system, construct a balance equation containing power, hydrogen energy and thermal energy constraints;

[0148] Step 3: construct a target function of the power system day-ahead scheduling with the lowest operation cost, while considering the system constraint condition and meeting the power, hydrogen energy and thermal energy constraints, solve the optimal solution of the target function;

[0149] Step 4: construct a real-time scheduling target function of the power system, consider real-time scheduling under new energy power generation and user load uncertainty, construct a target function, while meeting the above constraints, real-time adjust the scheduling strategy according to the load and power generation in the past period of time, realize dynamic meeting user demand. Among them, the system considers electric load, thermal load and demand response, and electric energy is transmitted from the power grid, new energy power station and energy storage system to the user side; thermal energy comes from fuel cell power generation waste heat and heat pump heat production.

[0150] The demand response mechanism is specifically: for part of the electric load that can change the working time, such as air conditioner, etc., the working time can be changed, and the user is given additional compensation (i.e. demand response compensation cost of the system), so that it reduces the power load in the peak period and runs in the valley period.

[0151] The network balance equation of the integrated energy system of power-hydrogen-thermal energy is constructed according to the energy storage system model and the new energy power station model, and the network balance equation includes the power balance equation (the electric energy balance equation), the thermal energy balance equation (the thermal energy system balance equation) and the hydrogen energy balance equation. For day-ahead scheduling, the objective function of the minimum operation cost of the power system is constructed:

[0152]

[0153] In the formula, is the objective function of the minimum operation cost of the power system, is the cost of purchasing power from the power grid every hour, and the total time length is one day, i.e. 24 hours, when the cost is considered, is the operation cost of each device, is the system demand response compensation cost, is the system wind and light curtailment penalty.

[0154] For real-time scheduling, the system optimization step is 15 minutes for step-by-step rolling optimization, the difference between load and energy supply is considered, and the lithium battery is preferentially stored. The fuel cell and the electrolytic cell do not need to be scheduled in this process, so the hydrogen energy balance does not need to be considered. The power system should meet the real-time electric and thermal energy balance equation:

[0155]

[0156]

[0157] In the formula, the meanings of the variables are the same as those in the foregoing, and the real superscript indicates that the real-time scheduling needs to be re-planned. It should be noted that the power load and the system state 15 minutes ago are used as constraints, so that the actual scheduling will produce an electric energy deviation.

[0158] For real-time scheduling, in order to make the power generation meet the load demand, the power deviation is minimized in each optimization step, and the system objective function is constructed as:

[0159]

[0160] In the formula, is the system electric energy deviation in the step, is the thermal energy difference of the system in the step, is a proportional coefficient, and the proportional 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.

[0161] Finally, through the above system, the day-ahead scheduling and real-time scheduling are solved respectively using mixed integer linear programming, which adapts to user demand under the uncertain situation of load and power generation in the hydrogen-electric hybrid power system.

[0162] Compared with the prior art, the present application has the beneficial effects that:

[0163] (1) By constructing a hybrid energy storage system, the effective coupling of electricity and hydrogen energy is realized, the conversion and utilization efficiency of energy is optimized, compared with a single energy system, the intermittency and instability of new energy generation can be more flexibly coped with, and the overall stability and energy utilization rate of the system are improved.

[0164] (2) The strategy of using lithium batteries first, then hydrogen electricity, and the insufficient part being supplemented by purchasing electricity from the power grid effectively increases the accommodation capacity of new energy. This not only reduces the phenomenon of curtailment of wind and light, but also improves the economic benefits of new energy power stations, and promotes the sustainable development of new energy.

[0165] (3) The objective function of the lowest operation cost of the power system is constructed, and mixed integer linear programming is adopted to realize the minimization of cost. Compared with the traditional energy storage system, while ensuring the reliability of the system, the total operation cost of the power system is significantly reduced by optimizing the purchase cost, operation cost and demand response compensation cost, and the economy is improved.

[0166] (4) The differences between day-ahead scheduling and real-time scheduling in actual problems are considered, a real-time scheduling model is constructed, a real-time scheduling objective function is established, the robustness of the system under the condition of load and power generation uncertainty is improved, and the user satisfaction is improved.

[0167] (5) The present technology uses hydrogen energy as the energy storage medium, and efficiently converts hydrogen energy into electricity through fuel cells, reducing the dependence on fossil energy and reducing 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 has good environmental friendliness.

[0168] In summary, the present application realizes the coupling of electricity and hydrogen by introducing fuel cells, electrolytic cells and lithium battery energy storage, effectively improves the utilization efficiency of energy, reduces the occurrence of curtailment of wind and light, and can also improve the accommodation capacity of new energy generation, optimize the operation efficiency of the power system, reduce the operation cost, and enhance the reliability and flexibility of the system, effectively improving the utilization efficiency of energy.

[0169] In the following case analysis, the wind power and photovoltaic power generation and load data are from the data of a certain region of the Elia website, and the data time label is GMT time. The case selects a day with large fluctuations of load and new energy output as a case to show the performance of the present application in energy scheduling and new energy accommodation.

[0170] The curve of the capacity factor of the new energy power station, as shown in Figure 2 , includes a photovoltaic capacity factor curve and a wind power capacity factor curve; the capacity factor of the new energy power station includes the photovoltaic capacity factor and the wind power capacity factor, and the capacity factor represents the proportion of the actual output of the device at the moment to the system planning capacity.

[0171] The user load curve of the system, as shown in Figure 3 , includes an electrical load curve and a thermal load curve; the user load of the system includes the electrical load and the thermal load, and the electrical load is larger in the morning and in the evening.

[0172] In this case, the system operation cost, in addition to the electricity price, is the equipment operation cost converted to the price per day. For example, the photovoltaic unit cost is 817 , and the annual operation cost converted from the investment cost is 3%; the wind power unit cost is 2513 , and the annual operation cost converted from the investment cost is 1%; considering that the operation and maintenance cost of the photovoltaic is higher, the conversion ratio is higher. The investment cost of the hydrogen storage tank is only 400 , and the operation cost is not calculated. The investment costs of the lithium battery, fuel cell, and electrolytic cell heat pump are shown in Table 1.

[0173] Table 1 Investment cost, operation efficiency, and device power consumption of various devices

[0174]

[0175] This case carries out day-ahead scheduling and real-time scheduling based on existing data, and plans the output of each device for the above system. The day-ahead scheduling realizes the lowest operation cost and the largest possible consumption of new energy power generation under the condition that the system meets the user load. The real-time scheduling meets the user demand under the uncertainty of load and power generation. The output of each device in the day-ahead scheduling is shown in Figure 4 and Figure 5 .

[0176] Figure 4 is the image of the electrical load and the electrical power of the equipment, and the stacked column chart represents the power generation or power consumption of different power generation equipment at the moment. The part above the coordinate axis represents the power output at the moment, and the part below the coordinate axis represents the power consumption at the moment. At each moment, the power consumption and the power output are balanced.

[0177] From Figure 4It can be found that in 0-8, 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, and the system power supply mainly comes from the lithium battery, wind power and grid electricity sales. In addition, a small part of the power demand is shifted through demand response. In 9-16, the power load decreases, and the photovoltaic output is high at this time, which can meet the user's power demand. At this time, the heat load increases, 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 shifted to this time. In 17-21, the system electric 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 fluctuation is mainly supplemented by lithium battery discharging. In 22-24, the electric load and wind power generation are relatively low, the lithium battery is in a charging state, the electrolytic cell works, and the fuel cell starts. At this time, it is in the valley time electricity price, and the power gap is supplemented by grid electricity sales. Through analysis, it can be found that the optimization result is logical, and therefore the mathematical model is reliable from the perspective of dispatching of power generation equipment.

[0178] Figure 5 is the image of the heat load and the heat power of the heat generating equipment at each time. The stacked column chart represents the heat power of the heat storage device, the electric heat pump, the fuel cell and the electrolytic cell, and Figure 4 The same, the part above the coordinate axis represents the heat energy output corresponding to this time, and the part below the coordinate axis represents the heat energy consumption corresponding to this time. At each time, the heat energy consumption and output reach a balance.

[0179] Figure 5 In the 1-5, the system's heat load is low, and only the fuel cell waste heat can meet the system's heat load, reducing the system's heating cost. In 6-16, the heat load is mainly provided by the heat released by the working heat pump; this is because in this period, the fuel cell is shut down, and the electrolytic cell is working, the system's heat load demand is high, and the heat pump can only meet the demand after it is turned on. In 17-24, the heat load is relatively low, and the fuel cell is not in working state, at this time, the heat is produced by the heat pump, thereby bringing more benefits. Since the heat output of the fuel cell is limited, the main source of heat for the system is still the heat produced by the heat pump. Through the dispatching analysis of the heat load and the heat generating equipment, it can be found that the optimization result is logical, and therefore the mathematical model is reliable from the perspective of dispatching of heat generating equipment.

[0180] The real-time dispatching of electric energy and heat output is shown in Figure 6 and Figure 7 It can be seen that the system electric and heat energy state is basically consistent with the day-ahead scheduling result trend. 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 electric energy and heat energy deviation. However, due to the real-time scheduling optimization control, the deviation is always controlled within a small range.

[0181] Under the condition of load and power generation uncertainty, considering whether there is real-time scheduling or not, the power and heat energy deviation is as shown in the following table: Figure 8 Figure 9 From the figure, if only considering day-ahead scheduling, the system in actual operation will often cause the phenomenon of power and heat supply surplus or deficiency, however, through the combination of day-ahead scheduling and real-time scheduling, the phenomenon of power and heat supply deficiency or surplus is greatly improved, which not only improves the stability of the system, but also improves the user satisfaction.

[0182] According to the implementation case, it can be seen that the comprehensive energy system taking the electricity-hydrogen hybrid energy storage as the core can not only effectively solve the problems faced by new energy grid connection, but also greatly improve the comprehensive utilization efficiency of energy. In the specific power system design, the lithium battery, fuel cell and electrolytic cell are used as the energy hub to realize the mutual conversion between electric energy, heat energy and hydrogen energy. In addition, through the use of hydrogen storage tank, the system can realize the function of peak clipping and valley filling and provide long-time energy storage. Through the combination of day-ahead scheduling and real-time scheduling in scheduling, the system has better performance under uncertain load and power generation scenarios, thereby significantly improving the comprehensive utilization efficiency of energy and bringing higher economic benefits to the micro-grid.

[0183] The power system of the present application is composed of an energy storage system, a new energy power station, a power grid and a load, and uses a scheduling method combining day-ahead scheduling and real-time scheduling to improve the flexibility and reliability of the system. The energy storage system includes a fuel cell, an electrolytic cell, a lithium battery and a hydrogen storage tank, realizing the mutual conversion and storage of electric energy, hydrogen energy and heat energy. By constructing the energy storage system model and the new energy power station model in the power system, forming the network balance equation of the electricity-hydrogen-heat three networks, day-ahead scheduling and constructing the objective function of the minimum operation cost of the power system, the preliminary scheduling scheme of the system is obtained, and the real-time scheduling scheme is obtained by combining the preliminary scheduling scheme with real-time load and power generation fluctuation. The present application is verified by actual operation data and simulation results, effectively improves the consumption rate of new energy, makes the resources more flexible, and improves the flexibility of the system and the user satisfaction.

[0184] An embodiment of a computer system:

[0185] A computer system includes a processor configured to execute a computer program to implement the steps of a real-time scheduling method of a hydrogen-electric hybrid energy storage power system. The real-time scheduling method of a hydrogen-electric hybrid energy storage power system has been described in detail in the embodiment of the real-time scheduling method of a hydrogen-electric hybrid energy storage power system, and will not be repeated here.​

Claims

1. A real-time scheduling method of a hydrogen-electric hybrid energy storage power system, characterized in that, The application relates to a hydrogen-electric hybrid energy storage power system real-time scheduling method. The model of the hydrogen-electric hybrid energy storage power system and constraint conditions thereof are constructed, a day-ahead scheduling objective function of the system with the lowest day-ahead scheduling operation cost is constructed, and a real-time scheduling objective function of the system with the minimum power deviation under the condition that new energy power generation and user load are both uncertain is constructed. The real-time scheduling objective function is based on a lithium battery priority energy storage strategy, and the real-time scheduling objective function is gradually optimized in a set optimization step. According to the model and the constraint conditions, mixed integer linear programming is used to solve the day-ahead scheduling objective function and the real-time scheduling objective function to obtain a solution result, and an optimal solution for guiding real-time scheduling of the system is obtained according to the solution result, so that the day-ahead scheduling operation cost of the system is the lowest and the new energy power generation of the system can meet the user load demand in real time under the condition that new energy power generation and user load are both uncertain. 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. The day-ahead scheduling objective function is as follows: ， In the formula, is the day-ahead dispatch objective function, is the cost of purchasing electricity from the grid per hour, , is the operating cost of each device in the system per hour, is the system demand response compensation cost, is the system new energy wind and light punishment; The real-time scheduling objective function is as follows: ， wherein, is the real-time dispatch target function, is the system electric energy deviation of the current optimization step, is the system thermal energy difference of the current optimization step, is a proportional coefficient, which 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.

2. The method of real-time scheduling of hydrogen-electric hybrid energy storage power system according to claim 1, wherein, The set optimization step is set according to a power data collection step.

3. The method of claim 1, wherein, 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.

4. The method of claim 1, wherein, The constraint conditions 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.

5. The method of real-time scheduling of hydrogen-electric hybrid energy storage power system as claimed in claim 3, wherein, The fuel cell model is as follows: ， ， wherein, is the heat generated by the fuel cell operation, is the energy generated by the fuel cell operation, is the hydrogen energy consumed by the fuel cell operation, is the heat value of hydrogen gas, is the efficiency of heat released during the fuel cell reaction, is the efficiency of the fuel cell in converting gas chemical energy into electrical energy; The electrolytic cell model is as follows: ， ， wherein is the energy required for the electrolysis cell to work; is the heat absorbed by the electrolysis cell to work; is the efficiency of the heat consumed during the electrolysis cell reaction process; is the efficiency of the electrical energy conversion into gas chemical energy during the electrolysis cell reaction process; The hydrogen storage tank model is as follows: ， ， wherein, is the hydrogen energy stored in the hydrogen storage tank at the current time, is the hydrogen storage tank capacity, is the maximum power of charging / discharging of the hydrogen storage tank, is the hydrogen energy stored in the hydrogen storage tank at the previous time; The lithium battery model is as follows: ， ， ， wherein, is the energy stored in the lithium battery; is the energy stored in the lithium battery at the previous time instant; is the charging capacity of the lithium battery; is the discharging capacity of the lithium battery; is the charging efficiency of the lithium battery, is the discharging efficiency of the lithium battery; is the maximum charging power of the lithium battery, is the maximum discharging power of the lithium battery; The photovoltaic power station model is as follows: , wherein, is the amount of electricity generated by the photovoltaic power plant, is the system planning capacity of the photovoltaic power plant, is the photovoltaic power generation capacity factor, is the photovoltaic power generation fluctuation coefficient at real-time scheduling; The wind power station model is as follows: ， wherein is the amount of electricity generated by the wind power plant, is the system planning capacity of the wind power plant, is the wind power capacity factor, is the wind power fluctuation coefficient at real-time dispatch. The load end model includes a heat pump model, and the heat pump model is as follows: ， wherein is the heat produced by the heat pump, is the electricity consumed by the heat pump, is the electrical-to-heat conversion efficiency of the heat pump.

6. The method of real-time scheduling of hydrogen-electric hybrid energy storage power system of claim 4, wherein, The hydrogen energy balance equation is as follows: ， wherein, is hydrogen energy generated by the operation of the electrolytic cell, is hydrogen energy stored in the hydrogen storage tank at the previous time, is hydrogen energy consumed by the operation of the fuel cell, is hydrogen energy stored in the hydrogen storage tank; The electric energy balance equation is as follows: ， wherein is the amount of electricity produced by the photovoltaic power plant; is the amount of electricity produced by the wind power plant, is the amount of energy produced by the fuel cell, is the amount of electricity purchased by the system from the grid per hour, is the amount of discharge of the lithium battery, is the amount of energy consumed by the electrolysis cell, is the electrical load, is the amount of charge of the lithium battery, is the amount of energy consumed by the electric heat pump; The thermal energy balance equation is as follows: ， wherein is the heat generated by the fuel cell operation, is the heat produced by the electrothermal pump, is the heat consumed by the electrolyzer operation, is the system thermal load; The real-time optimized electric energy balance equation is as follows: ， wherein, is the energy produced by the photovoltaic plant optimized for real-time dispatch, is the energy produced by the wind plant optimized for real-time dispatch, is the energy produced by the fuel cell operation, is the energy purchased from the grid by the system per hour optimized for real-time dispatch, is the energy discharged by the lithium battery optimized for real-time dispatch, is the energy consumed by the electrolyzer operation, is the electrical load optimized for real-time dispatch, is the energy charged by the lithium battery optimized for real-time dispatch, is the energy consumed by the heat pump operation optimized for real-time dispatch; The real-time optimized thermal energy balance equation is as follows: ， wherein, is the heat generated by the operation of the fuel cell, is the heat produced by the operation of the real-time dispatch optimized electric heat pump, is the heat consumed by the operation of the electrolyzer, is the system heat load of the real-time dispatch optimized system.

7. A computer system comprising a processor, characterised in that The processor is used to execute a computer program to realize the steps of the hydrogen-electric hybrid energy storage power system real-time scheduling method in any one of claims 1 to 6.

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