Micro energy network fuzzy optimization scheduling method and system considering electricity-to-ammonia flexibility

Abstract

The invention provides a micro-energy grid fuzzy optimization scheduling method considering electricity-to-ammonia flexibility, and relates to the field of micro-grid energy scheduling, and the method comprises the steps: constructing an electricity-gas-heat coupled micro-energy grid frame; constructing a P2A energy storage-based micro-energy network model on the basis of a micro-energy network framework; the P2A energy storage-based micro energy network model comprises a flexible load-based electricity-to-ammonia conversion model, a new energy automobile energy charging model and a micro energy network model; on the basis of a Markowitz mean-variance model, performing fuzzy optimization on the micro energy network model based on P2A energy storage; and fuzzy optimization scheduling of the micro-energy network is realized through the micro-energy network model based on P2A energy storage after fuzzy optimization. Through the technical scheme of the invention, the P2A is modeled as a flexible load, and the supply-demand relationship between the P2A and renewable energy sources is coordinated. And a membership function is constructed and solved, so that the flexibility of the system is improved and the operation cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of microgrid energy dispatching, and in particular to a fuzzy optimization dispatching method and system for a micro energy network that takes into account the flexibility of electricity-to-ammonia conversion. Background Art

[0002] In the context of global energy transformation, the popularization of new energy vehicles and the construction of micro-energy networks complement each other, providing new opportunities for the efficient use of renewable energy sources (RESs). At the same time, power-to-ammonia (P2A) technology has also begun to receive more and more attention. As an important carrier of hydrogen energy, the clean and efficient production of ammonia is of great significance to the realization of a sustainable energy system.

[0003] Currently, most studies still consider the P2A modeling process as a constant load, which results in it not being able to match the volatility of RESs well. Summary of the invention

[0004] The purpose of the present invention is to provide a fuzzy optimization scheduling method and system for a micro-energy network that takes into account the flexibility of electricity-to-ammonia in order to solve the problem that the prior art considers the electricity-to-ammonia modeling process with a constant load, which makes it unable to match the volatility of renewable energy well.

[0005] The above-mentioned purpose of the present application is achieved through the following technical solutions: S1: Constructing the framework of electric-gas-heat coupled micro energy grid; S2: Based on the micro energy network framework, a micro energy network model based on P2A energy storage is constructed; the micro energy network model based on P2A energy storage includes: an electricity-to-ammonia model based on flexible load, a new energy vehicle charging model and a micro energy network model; S3: Based on the Markowitz mean-variance model, fuzzy optimization of the micro-energy grid model based on P2A energy storage is performed; S4: Through the fuzzy optimized micro energy grid model based on P2A energy storage, the fuzzy optimization scheduling of the micro energy grid is realized.

[0006] Optionally, step S2 includes: The specific steps of constructing the power-to-ammonia model based on flexible load include: The electrolytic cell model is established as follows: Calculate the thermal resistance of the outside air Thermal resistance of the wall Thermal resistance of the electrolyte reaction zone ,as follows:

[0007] in, and is the height of the electrolyte reaction zone and the wall, and For the The Nusselt coefficient of the electrolyte reaction zone and the Nusselt coefficient of the wall in the electrolytic cell, , and For the The thermal conductivity of the electrolyte reaction zone of each electrolytic cell, the thermal conductivity of the wall and the thermal conductivity of the external air; For the The width of the electrolytic cell; For the The length of the electrolytic cell; based on , establish the thermoelectrochemical dynamic model of the electrolyzer as follows:

[0008]

[0009]

[0010]

[0011] in, It is Moment Joule heating of renewable energy from electrolyzers; It is Moment The electrolytic cell provides heat energy for the heating reaction; It is The electrical energy provided to the heating reaction at all times; is the conversion efficiency of electric boiler; No. The equivalent capacitance of the electrolyte reaction zone of an electrolytic cell; and Respectively Moment The temperature of the electrolyte reaction zone and the wall temperature of each electrolytic cell; For the The outside air temperature at all times; For the Moment Ammonia production per electrolyzer, and are enthalpy flux; For the The equivalent capacitance of the walls of the electrolyzer; The electrical power consumed by the electrolytic cell is:

[0012] in, It is The electrolysis efficiency at each moment; Establish a P2A gas production model; Assume that the electrolyzer module consists of Series units and It consists of parallel units. The yield is:

[0013]

[0014] in, For the The Faraday efficiency at that moment, For the Moment The molar volume of an electrolytic cell, For the Time passes The current of the electrolytic cell, is the Faraday constant; According to the preset reference values ​​in different temperature ranges , solve for different temperature ranges and ,Right now:

[0015] in, , , Minimum values ​​set for different temperature intervals; , , The maximum value set for different temperature intervals; Indicates preset values ​​for different temperature ranges; , , It is expressed as the slope of the efficiency curve in different temperature ranges; According to the fixed specifications of the electrolytic cell, set The yield is as follows:

[0016] in, Is the unit time The maximum output value allowed; According to the buffer time of the electrolytic cell, set The increase in yield is as follows:

[0017] in, Is the unit time the maximum increment of output allowed; The mechanical wear of the electrolyzer membrane increases during startup and shutdown. The degradation cost of the electrolyzer is expressed as:

[0018] in, For the Moment The total depreciation cost of an electrolyzer is It is The start-up depreciation cost of each electrolyzer, It is The depreciation cost of closing an electrolyser, It is The investment cost of an electrolyzer is It is The life of an electrolyzer; The flexibility modeling of the power-to-ammonia model is as follows: Will Considered as quasi-steady-state condition, ,in ;for The values ​​of are:

[0019] in, is the number of quasi-steady-state conditions, is the total scheduling duration, is the time step of system scheduling, The time step after which the electrolyser flexibility is modeled; For quasi-steady-state conditions, , represents the maximum value of the quasi-steady-state condition, represents the value of quasi-steady-state conditions; The first-order transition process is used to simplify the description of the dynamic behavior of the system. As the time sampling step decreases, the feedback mechanism of ammonia production becomes more sensitive. The specific model is as follows:

[0020]

[0021] in for By time step After sampling The output at the moment, For the A quasi-steady-state condition, is the time constant; set up The increment range per unit time is as follows:

[0022] in, Is the unit time The maximum increment allowed.

[0023] Optionally, the new energy vehicle charging station model includes: a new energy vehicle charging model and a dynamic urgency assessment and charging strategy model; Construct a new energy vehicle charging model as follows: Set the charging power and discharging power of new energy vehicles, and the constraints are as follows:

[0024]

[0025] in, It is Moment Vehicle charging power, It is Moment The vehicle discharge power, is the maximum charging power allowed per unit time, is the maximum discharge power allowed per unit time; The charging power and discharging power of all electric vehicles at time t are as follows:

[0026]

[0027]

[0028] in, It is The charging power of all vehicles at the moment, M is the total number of vehicles, is the conversion efficiency of hydrogen to electricity, is the conversion efficiency of ammonia to hydrogen, It is Moment The volume of ammonia consumed by the vehicle, No. Discharge power of all vehicles at all times; represents the sum of the charging power and discharging power of all electric vehicles at time t; Construct a dynamic urgency assessment and charging strategy model as follows: Calculate the electricity consumption of each vehicle:

[0029] in, It is Moment The SOC value consumed by the vehicle, The fuel consumption per 100 kilometers. It is the day The mileage of the vehicle, is the total capacity of the car; The charging time for each vehicle is obtained as:

[0030] in, For the Moment Charging time of the vehicle; By introducing the urgency assessment index , the dynamic urgency assessment and charging strategy model is obtained as follows:

[0031]

[0032] in, For the Moment The vehicle’s dwell time in the network.

[0033] Optionally, the micro energy grid model includes: battery and ammonia tank energy storage model, battery degradation cost model, electric boiler electricity-heat conversion model, gas boiler gas-heat conversion model, cogeneration device gas-electricity-heat conversion model, load reduction model and micro energy grid hub model.

[0034] Optionally, the steps of constructing a micro energy grid model include: Building a battery and ammonia tank energy storage model Set the charge and discharge power, SOC value and increment per unit time of the battery energy storage device as follows:

[0035]

[0036]

[0037]

[0038] in, and Respectively The charging and discharging power of the battery at all times, and The maximum power allowed for charging and discharging of the battery, For the The SOC value of the battery at the moment, is the total capacity of the battery, and is the charge and discharge efficiency of the battery; Indicates The minimum SOC value and maximum SOC value of the battery at all times; Set the charging and discharging power, SOC value and increment per unit time of the ammonia tank energy storage device as follows:

[0039]

[0040]

[0041] in, For the The output power of the gas tank at any moment, and is the power limit value of the gas tank, For the The SOC value of the gas tank at the moment, is the total capacity of the gas tank; Modeling battery degradation costs:

[0042] in, For the The degradation cost of the battery at all times, is the investment cost of the battery, is the rated cycle life of the battery; is the life coefficient, is the reference SOC value of the battery; For the The outside air temperature at all times.

[0043] Optionally, the steps of constructing the micro energy grid model also include: The electric-heat conversion model and power constraints of the electric boiler are constructed as follows:

[0044]

[0045] in, For the The heating power of the electric boiler at that moment, For the The power consumption of electric boiler at every moment, is the conversion efficiency of the electric boiler, is the maximum power limit of the electric boiler; The gas-heat conversion model and power constraints of the gas boiler are constructed as follows:

[0046]

[0047] in, For the The heating power of the electric boiler at that moment, For the Ammonia consumption of gas boiler at each moment, is the gas boiler conversion efficiency, The maximum power limit for gas boilers; is the reaction enthalpy change; The gas-electricity-heat conversion model and power constraints of the cogeneration device are constructed as follows:

[0048]

[0049]

[0050] in, For the The power generated by the combined heat and power generation at any time, is the CHP power conversion efficiency, For the CHP heat generation power at the moment, is the CHP heat conversion efficiency, is the maximum power limit of CHP; For the Ammonia consumption of CHP at the moment.

[0051] Optionally, the steps of constructing the micro energy grid model also include: Building load shedding models; When energy supply fails to meet load demand, the reduction of electricity and heat loads increases, and the reduction constraint is:

[0052]

[0053] in, For the The amount of electricity load reduction at any given moment, is the maximum reduction in electrical load, For the The amount of heat load reduction at a time, is the maximum reduction in heat load; Based on the load reduction model, the load reduction cost model is constructed as follows:

[0054] in, is the load shedding cost; Construct a micro energy network hub model; The electrical energy, thermal energy and gas energy required by the micro energy grid should be consistent with the corresponding supply and meet the following constraints:

[0055]

[0056]

[0057] in It is Total power of electrical load at any moment, It is The power generated by the wind turbine at the moment, It is The power generation capacity of the solar thermal power cogeneration device at the moment, For the The total amount of electricity purchased at any time, It is Total electricity sold at any given moment; It is The discharge power of the electrolyzer at the moment; It is The electrical energy provided to the heating reaction at all times; It is Total heat load power at any moment, It is The heat production power of the solar thermal cogeneration device at any moment; , express It is Moment The electrolytic cell provides heat energy for the heating reaction; It is The output power of the gas tank at any moment, It is Ammonia production at each moment, It is Ammonia consumption of CHP at the moment, It is Ammonia consumption of gas boiler at each moment, It is Ammonia consumption when the car is inflated at any given moment.

[0058] Optionally, step S3 includes: Based on the micro energy grid model of P2A energy storage, the total cost model of the micro energy grid is obtained as follows:

[0059] in, Indicates Total cost of micro energy grid at all times; For the The cost of buying electricity from the micro energy grid at all times; For the The profit of selling electricity by the Micro Energy Network; For the The cost of purchasing electricity for the car at any given moment; The Markowitz mean-variance model is as follows:

[0060]

[0061] in, For the The expected cost of the plan, For the The probability value of the solution, For the The standard deviation of the scheme; right Right now For the sake of differentiation, we linearize Left Recorded as , on the right Recorded as , as follows:

[0062]

[0063]

[0064]

[0065] in and for The minimum and maximum values ​​of ; and for The minimum and maximum values ​​of ; is the discrete number of times; is a binary variable; The Markowitz mean-variance model Discretization, converting the product between continuous variables into the product of binary variables and continuous variables; Weighted sum of expectation and variance:

[0066] in, For the The weighted sum of the expected and variance values ​​of the solutions, and are the weights of expectation and variance; The membership function corresponding to the fuzzy target is expressed as follows:

[0067] in, For the The membership function value of the scheme, i.e., satisfaction; By maximizing the objective function of minimum satisfaction Perform fuzzy optimization; When the objective function When the value of meets the convergence condition, the iterative process is terminated and the fuzzy optimization of the micro energy grid model based on P2A energy storage is completed.

[0068] A micro-energy grid fuzzy optimization scheduling system considering the flexibility of electricity-to-ammonia conversion, the system includes: battery and ammonia tank energy storage modules, electric boiler electricity-heat conversion modules, gas boiler gas-heat conversion modules, combined heat and power device CHP gas-electricity-heat conversion modules, load modules, charging piles and data processing modules; The battery and ammonia tank energy storage module includes: a battery, a wind energy unit, a solar energy unit, and an ammonia unit; The battery and ammonia tank energy storage module, the electric-heat conversion module of the electric boiler, the gas-heat conversion module of the gas boiler, the gas-heat conversion module of the cogeneration device, the load module and the charging pile are all connected to the data processing module; The data processing module is used to construct an electricity-gas-heat coupled micro energy network framework; The data processing module is also used to construct a micro-energy network model based on P2A energy storage based on the micro-energy network framework; the micro-energy network model based on P2A energy storage includes: an electricity-to-ammonia model based on flexible load, a new energy vehicle charging model and a micro-energy network model; The data processing module is also used to perform fuzzy optimization on the micro energy grid model based on P2A energy storage based on the Markowitz mean-variance model; The data processing module is also used to realize fuzzy optimization scheduling of the micro energy network through the micro energy network model based on P2A energy storage after fuzzy optimization.

[0069] The beneficial effects of the technical solution provided by this application are: 1. A flexibility model for ammonia production in electrolyzers was proposed. By introducing time constants and quasi-steady-state conditions, the micro-energy grid system was efficiently transitioned from one steady state to another, achieving a smooth transition between steady states and ensuring stable operation of the micro-energy grid under fluctuating conditions.

[0070] 2. A micro-energy grid model based on Markowitz mean-variance is proposed. This method takes into account both total cost and risk (variance), optimizes the balance between the expected value and volatility of the target, and improves the economy and stability of the micro-energy grid under uncertain conditions.

[0071] 3. A fuzzy optimization strategy for the micro energy network is proposed. By constructing a membership function and balancing the weights between different objective functions, the maximum minimum satisfaction is achieved, ensuring the best operation effect of the micro energy network under multiple objectives. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The present application will be further described below with reference to the accompanying drawings and embodiments, in which: Figure 1 It is a framework diagram of the micro energy network system in the embodiment of the present application; Figure 2 is a physical model diagram of an electrolytic cell in an embodiment of the present application; Figure 3 is a circuit model diagram of an electrolytic cell in an embodiment of the present application; Figure 4 It is a membership function diagram in the embodiment of the present application. DETAILED DESCRIPTION

[0073] In order to have a clearer understanding of the technical features, purposes and effects of the present application, the specific implementation methods of the present application are now described in detail with reference to the accompanying drawings.

[0074] An embodiment of the present application provides a fuzzy optimization scheduling method for a micro energy grid that takes into account the flexibility of electricity-to-ammonia conversion.

[0075] Please refer to Figure 1 , Figure 1 It is a micro-energy network system framework diagram of a micro-energy network fuzzy optimization scheduling method considering the flexibility of electricity-to-ammonia conversion in an embodiment of the present application, including: S1: Constructing the framework of electric-gas-heat coupled micro energy grid; Specifically, Figure 1 As shown in the figure, a micro energy grid framework based on electricity-gas-heat coupling technology is constructed. The framework includes multiple components: wind power generation system, solar cogeneration system, electricity-to-ammonia system, ammonia storage tank, gas boiler, electric boiler, cogeneration equipment, electric heat load and charging station. S2: Based on the micro energy network framework, a micro energy network model based on P2A energy storage is constructed; the micro energy network model based on P2A energy storage includes: an electricity-to-ammonia model based on flexible load, a new energy vehicle charging model and a micro energy network model; S3: Based on the Markowitz mean-variance model, fuzzy optimization of the micro-energy grid model based on P2A energy storage is performed; S4: Through the fuzzy optimized micro energy grid model based on P2A energy storage, the fuzzy optimization scheduling of the micro energy grid is realized.

[0076] Step S2 includes: The specific steps of constructing the power-to-ammonia model based on flexible load include: The electrolytic cell model is established as follows: Calculate the thermal resistance of the outside air Thermal resistance of the wall Thermal resistance of the electrolyte reaction zone ,as follows:

[0077] in, and is the height of the electrolyte reaction zone and the wall, and For the The Nusselt coefficient of the electrolyte reaction zone and the Nusselt coefficient of the wall in the electrolytic cell, , and For the The thermal conductivity of the electrolyte reaction zone of each electrolytic cell, the thermal conductivity of the wall and the thermal conductivity of the external air; For the The width of the electrolytic cell; For the The length of the electrolytic cell; based on , establish the thermoelectrochemical dynamic model of the electrolyzer as follows:

[0078]

[0079]

[0080]

[0081] in, It is Moment Joule heating of renewable energy from electrolyzers; It is Moment The electrolytic cell provides heat energy for the heating reaction; It is The electrical energy provided to the heating reaction at all times; is the conversion efficiency of electric boiler; No. The equivalent capacitance of the electrolyte reaction zone of an electrolytic cell; and Respectively Moment The temperature of the electrolyte reaction zone and the wall temperature of each electrolytic cell; For the The outside air temperature at all times; For the Moment Ammonia production per electrolyzer, and are enthalpy flux; For the The equivalent capacitance of the walls of the electrolyzer; The electrical power consumed by the electrolytic cell is:

[0082] in, It is The electrolysis efficiency at each moment; Specifically, the operating characteristics of the electrolytic cell array are modeled and analyzed. The electrolytic cell is usually composed of an anode, a cathode, a diaphragm, and an external DC power supply. When the voltage between the electrodes reaches a certain level, the electrolysis reaction will occur on the electrodes, such as Figure 2 Considering that the overall thermal environment of the electrolytic cell is affected by various heat changes, such as the heat generated by the externally injected RESs and the heat changes during the reaction, a resistor-capacitor thermal circuit model was established to simulate the temperature dynamic changes of the P2A physical electrolysis environment, as shown in Figure 3 As shown. Figure 3 It can be seen that when the electrolysis model reaches a stable state, that is, the structural characteristics of the electrolytic cell in all directions remain constant and unchanged, the thermal resistance of the external air, wall and electrolyte reaction zone can be calculated by the Nusselt number, characteristic length and thermal conductivity. A thermo-electrochemical dynamic model of the electrolytic cell is established to quantitatively analyze the heat exchange between the electrolyte reaction zone and the wall and the reuse of RESs heat.

[0083] Establish a P2A gas production model; Assume that the electrolyzer module consists of Series units and It consists of parallel units. The yield is:

[0084]

[0085] in, For the The Faraday efficiency at that moment, For the Moment The molar volume of an electrolytic cell, For the Time passes The current of the electrolytic cell, is the Faraday constant; According to the preset reference values ​​in different temperature ranges , solve for different temperature ranges and ,Right now:

[0086] in, , , Minimum values ​​set for different temperature intervals; , , The maximum value set for different temperature intervals; Indicates preset values ​​for different temperature ranges; , , It is expressed as the slope of the efficiency curve in different temperature ranges; According to the fixed specifications of the electrolytic cell, set The yield is as follows:

[0087] in, Is the unit time The maximum output value allowed; According to the buffer time of the electrolytic cell, set The increase in yield is as follows:

[0088] in, Is the unit time the maximum increment of output allowed; The mechanical wear of the electrolyzer membrane increases during startup and shutdown. The degradation cost of the electrolyzer is expressed as:

[0089] in, For the Moment The total depreciation cost of an electrolyzer is It is The start-up depreciation cost of each electrolyzer, It is The depreciation cost of closing an electrolyser, It is The investment cost of an electrolyzer is It is The life of an electrolyzer; Specifically, the permeability of the electrolyzer allows hydrogen and oxygen to diffuse during the mass transfer process, especially at lower current densities, where gas crossover is more pronounced, leading to a decrease in Faradaic efficiency. The yield is positively correlated, so The calculation of the yield needs to be combined with the Faraday efficiency. In order to reduce the calculation scale, three electrolyzer modules are selected in series and one in parallel.

[0090] The flexibility modeling of the power-to-ammonia model is as follows: Will Considered as quasi-steady-state condition, ,in ;for The values ​​of are:

[0091] in, is the number of quasi-steady-state conditions, is the total scheduling duration, is the time step of system scheduling, The time step after which the electrolyser flexibility is modeled; Specifically, the time step selected in this paper is 15 minutes, that is, For 15 minutes.

[0092] For quasi-steady-state conditions, , represents the maximum value of the quasi-steady-state condition, represents the value of quasi-steady-state conditions; The first-order transition process is used to simplify the description of the dynamic behavior of the system. As the time sampling step decreases, the feedback mechanism of ammonia production becomes more sensitive. The specific model is as follows:

[0093]

[0094] in for By time step After sampling The output at the moment, For the A quasi-steady-state condition, is the time constant; set up The increment range per unit time is as follows:

[0095] in, Is the unit time The maximum increment allowed.

[0096] Specifically, traditional models usually assume that the system can quickly reach a steady state, while flexibility modeling is achieved by introducing a time constant ( ) can describe the transition process from one steady state to another, achieve a smooth transition from the initial state to the new steady state, and reflect the response of the real system.

[0097] The new energy vehicle charging station model includes: a new energy vehicle charging model and a dynamic urgency assessment and charging strategy model; Construct a new energy vehicle charging model as follows: Set the charging power and discharging power of new energy vehicles, and the constraints are as follows:

[0098]

[0099] in, It is Moment Vehicle charging power, It is Moment The vehicle discharge power, is the maximum charging power allowed per unit time, is the maximum discharge power allowed per unit time; The charging power and discharging power of all electric vehicles at time t are as follows:

[0100]

[0101]

[0102] in, It is The charging power of all vehicles at the moment, M is the total number of vehicles, is the conversion efficiency of hydrogen to electricity, is the conversion efficiency of ammonia to hydrogen, It is Moment The volume of ammonia consumed by the vehicle, No. Discharge power of all vehicles at all times; represents the sum of the charging power and discharging power of all electric vehicles at time t; Construct a dynamic urgency assessment and charging strategy model as follows: Specifically, in the dynamic urgency assessment and charging strategy model, dynamically assessing the charging urgency of each vehicle and formulating corresponding charging strategies can improve the efficiency and stability of the overall energy system.

[0103] Calculate the electricity consumption of each vehicle:

[0104] in, It is Moment The SOC value consumed by the vehicle, The fuel consumption per 100 kilometers. It is the day The mileage of the vehicle, is the total capacity of the car; The charging time for each vehicle is obtained as:

[0105] in, For the Moment Charging time of the vehicle; By introducing the urgency assessment index , the dynamic urgency assessment and charging strategy model is obtained as follows:

[0106]

[0107] in, For the Moment The vehicle’s dwell time in the network.

[0108] Specifically, when the stay time The shorter the The larger the value, the greater the urgency of charging, and the faster charging mode will be preferred. SOC (state of charge, SOC) value.

[0109] The micro energy network model includes: battery and ammonia tank energy storage model, battery degradation cost model, electric boiler electricity-heat conversion model, gas boiler gas-heat conversion model, cogeneration device gas-electricity-heat conversion model, load reduction model and micro energy network hub model.

[0110] The steps to build a micro energy grid model include: Building a battery and ammonia tank energy storage model Set the charge and discharge power, SOC value and increment per unit time of the battery energy storage device as follows:

[0111]

[0112]

[0113]

[0114] in, and Respectively The charging and discharging power of the battery at all times, and The maximum power allowed for charging and discharging of the battery, For the The SOC value of the battery at the moment, is the total capacity of the battery, and is the charge and discharge efficiency of the battery; Indicates The minimum SOC value and maximum SOC value of the battery at all times; Set the charging and discharging power, SOC value and increment per unit time of the ammonia tank energy storage device as follows:

[0115]

[0116]

[0117] in, For the The output power of the gas tank at any moment, and is the power limit value of the gas tank, For the The SOC value of the gas tank at the moment, is the total capacity of the gas tank; Modeling battery degradation costs:

[0118] in, For the The degradation cost of the battery at all times, is the investment cost of the battery, is the rated cycle life of the battery; is the life coefficient, is the reference SOC value of the battery; For the The outside air temperature at all times.

[0119] Specifically, the degradation cost of the gas tank has a negligible impact on the overall economic benefits and can usually be regarded as a negligible item.

[0120] The steps of building a micro energy grid model also include: The electric-heat conversion model and power constraints of the electric boiler are constructed as follows:

[0121]

[0122] in, For the The heating power of the electric boiler at that moment, For the The power consumption of electric boiler at every moment, is the conversion efficiency of the electric boiler, is the maximum power limit of the electric boiler; The gas-heat conversion model and power constraints of the gas boiler are constructed as follows:

[0123]

[0124] in, For the The heating power of the electric boiler at that moment, For the Ammonia consumption of gas boiler at each moment, is the gas boiler conversion efficiency, The maximum power limit for gas boilers; is the reaction enthalpy change; The gas-electricity-heat conversion model and power constraints of the cogeneration device are constructed as follows:

[0125]

[0126]

[0127] in, For the The power generated by the combined heat and power generation at any time, is the CHP power conversion efficiency, For the CHP heat generation power at the moment, is the CHP heat conversion efficiency, is the maximum power limit of CHP; For the Ammonia consumption of CHP at the moment.

[0128] The steps of building a micro energy grid model also include: Building load shedding models; When energy supply fails to meet load demand, the reduction of electricity and heat loads increases, and the reduction constraint is:

[0129]

[0130] in, For the The amount of electricity load reduction at any given moment, is the maximum reduction in electrical load, For the The amount of heat load reduction at a time, is the maximum reduction in heat load; Based on the load reduction model, the load reduction cost model is constructed as follows:

[0131] in, is the load shedding cost; Construct a micro energy network hub model; The electrical energy, thermal energy and gas energy required by the micro energy grid should be consistent with the corresponding supply and meet the following constraints:

[0132]

[0133]

[0134] in It is Total power of electrical load at any moment, It is The power generated by the wind turbine at the moment, It is The power generation capacity of the solar thermal power cogeneration device at the moment, For the The total amount of electricity purchased at any time, It is Total electricity sold at any given moment; It is The discharge power of the electrolyzer at the moment; It is The electrical energy provided to the heating reaction at all times; It is Total heat load power at any moment, It is The heat production power of the solar thermal cogeneration device at any moment; , express It is Moment The electrolytic cell provides heat energy for the heating reaction; It is The output power of the gas tank at any moment, It is Ammonia production at each moment, It is Ammonia consumption of CHP at the moment, It is Ammonia consumption of gas boiler at each moment, It is Ammonia consumption when the car is inflated at any given moment.

[0135] Step S3 includes: Based on the micro energy grid model of P2A energy storage, the total cost model of the micro energy grid is obtained as follows:

[0136] in, Indicates Total cost of micro energy grid at all times; For the The cost of buying electricity from the micro energy grid at all times; For the The profit of selling electricity by the Micro Energy Network; For the The cost of purchasing electricity for the car at any given moment; Specifically, when directly optimizing the total cost, the system will only focus on reducing the cost, while ignoring the uncertainty of cost changes. The core of the mean-variance theory is to consider both the expected value and risk (variance) of the return.

[0137] The Markowitz mean-variance model is as follows:

[0138]

[0139] in, For the The expected cost of the plan, For the The probability value of the solution, For the The standard deviation of the scheme; right Right now For the sake of differentiation, we linearize Left Recorded as , on the right Recorded as , as follows:

[0140]

[0141]

[0142]

[0143] in and for The minimum and maximum values ​​of ; and for The minimum and maximum values ​​of ; is the discrete number of times; is a binary variable; The Markowitz mean-variance model Discretization, converting the product between continuous variables into the product of binary variables and continuous variables; Specifically, the above expectation and standard deviation are set to minimize the total cost and risk. In order to balance the two goals, an acceptable range value needs to be set. Therefore, the satisfaction value is introduced into this optimization problem. To balance the needs between different objective functions, the membership function corresponding to the fuzzy target is established, such as Figure 4 shown.

[0144] Weighted sum of expectation and variance:

[0145] in, For the The weighted sum of the expected and variance values ​​of the solutions, and are the weights of expectation and variance; The membership function corresponding to the fuzzy target is expressed as follows:

[0146] in, For the The membership function value of the scheme, i.e., satisfaction; Specifically, when the total cost or risk value is close to its lower limit, the satisfaction reaches 1, and when the total cost or risk value reaches its upper limit, Close to 0.

[0147] By maximizing the objective function of minimum satisfaction Perform fuzzy optimization; When the objective function When the value of meets the convergence condition, the iterative process is terminated and the fuzzy optimization of the micro energy grid model based on P2A energy storage is completed.

[0148] A micro-energy grid fuzzy optimization scheduling system considering the flexibility of electricity-to-ammonia conversion, the system includes: battery and ammonia tank energy storage modules, electric boiler electricity-heat conversion modules, gas boiler gas-heat conversion modules, combined heat and power device CHP gas-electricity-heat conversion modules, load modules, charging piles and data processing modules; The battery and ammonia tank energy storage module includes: a battery, a wind energy unit, a solar energy unit, and an ammonia unit; The battery and ammonia tank energy storage module, the electric-heat conversion module of the electric boiler, the gas-heat conversion module of the gas boiler, the gas-heat conversion module of the cogeneration device, the load module and the charging pile are all connected to the data processing module; The data processing module is used to construct an electricity-gas-heat coupled micro energy network framework; The data processing module is also used to construct a micro-energy network model based on P2A energy storage based on the micro-energy network framework; the micro-energy network model based on P2A energy storage includes: an electricity-to-ammonia model based on flexible load, a new energy vehicle charging model and a micro-energy network model; The data processing module is also used to perform fuzzy optimization on the micro energy grid model based on P2A energy storage based on the Markowitz mean-variance model; The data processing module is also used to realize fuzzy optimization scheduling of the micro energy network through the micro energy network model based on P2A energy storage after fuzzy optimization.

[0149] This application verifies the differences in system optimization by comparing three schemes: Scheme 1: fuzzy optimization strategy of micro energy network, in which expectation is given a larger weight and variance is given a smaller weight; Scheme 2: fuzzy optimization strategy of micro energy network, in which expectation and variance are given the same weight; Scheme 3: joint optimization strategy of micro energy network, in which expectation variance is directly summed. The specific cost data is shown in Table 1: Table 1 Planning optimization results of Schemes 1-3

[0150] As shown in Table 1, due to the small weight given to the variance in Scheme 1, a certain load reduction cost is generated, and the electricity purchase cost of the electric vehicle is negative, indicating that the car has made a profit. At the same time, the profit from selling electricity reaches 34.407, and the electricity purchase cost remains at 33.822, which ultimately makes the total cost close to zero. In contrast, Scheme 2 considers the weights of expectation and variance equally, so that the load reduction cost reaches 0, but the profit from selling electricity drops to 0.040, so the total cost is still as high as 37.5. Without the application of Fuzzy solution, Scheme 3 presents the initial cost structure, in which the load reduction cost is as high as 48.132. Although the profit from selling electricity reaches 83.526, the high electricity purchase cost leads to a total cost of 99.994, lacking a reasonable configuration strategy for electricity purchase demand. In summary, compared with Schemes 2 and 3, Scheme 1 gives the most weight to expectation, so the total cost is smaller, but it also leads to a certain increase in the load reduction cost.

[0151] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0152] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A fuzzy optimization scheduling method for micro energy grid considering the flexibility of electricity-to-ammonia conversion, characterized in that: The method comprises the following steps: S1: Constructing the framework of electric-gas-heat coupled micro energy grid; S2: Based on the micro energy network framework, a micro energy network model based on P2A energy storage is constructed; the micro energy network model based on P2A energy storage includes: an electricity-to-ammonia model based on flexible load, a new energy vehicle charging model and a micro energy network model; S3: Based on the Markowitz mean-variance model, fuzzy optimization of the micro-energy grid model based on P2A energy storage is performed; S4: Through the fuzzy optimized micro energy grid model based on P2A energy storage, the fuzzy optimization scheduling of the micro energy grid is realized.

2. A fuzzy optimization scheduling method for micro-energy grid considering the flexibility of electricity-to-ammonia conversion as claimed in claim 1, characterized in that: Step S2 includes: The specific steps of constructing the power-to-ammonia model based on flexible load include: The electrolytic cell model is established as follows: Calculate the thermal resistance of the outside air Thermal resistance of the wall Thermal resistance of the electrolyte reaction zone ,as follows: in, and is the height of the electrolyte reaction zone and the wall, and For the The Nusselt coefficient of the electrolyte reaction zone and the Nusselt coefficient of the wall in the electrolytic cell, , and For the The thermal conductivity of the electrolyte reaction zone of each electrolytic cell, the thermal conductivity of the wall and the thermal conductivity of the external air; For the The width of the electrolytic cell; For the The length of the electrolytic cell; based on , establish the thermoelectrochemical dynamic model of the electrolyzer as follows: in, It is Moment Joule heating of renewable energy from electrolyzers; It is Moment The electrolytic cell provides heat energy for the heating reaction; It is The electrical energy provided to the heating reaction at all times; is the conversion efficiency of electric boiler; No. The equivalent capacitance of the electrolyte reaction zone of an electrolytic cell; and Respectively Moment The temperature of the electrolyte reaction zone and the wall temperature of each electrolytic cell; For the The outside air temperature at all times; For the Moment Ammonia production per electrolyzer, and are enthalpy flux; For the The equivalent capacitance of the walls of the electrolyzer; The electrical power consumed by the electrolytic cell is: in, It is The electrolysis efficiency at each moment; Establish a P2A gas production model; Assume that the electrolyzer module consists of Series units and It consists of parallel units. The yield is: in, For the The Faraday efficiency at time, For the Moment The molar volume of an electrolytic cell, For the Time passes The current of the electrolytic cell, is the Faraday constant; According to the preset reference values ​​in different temperature ranges , solve for different temperature ranges and ,Right now: in, , , Minimum values ​​set for different temperature intervals; , , The maximum value set for different temperature intervals; Indicates preset values ​​for different temperature ranges; , , It is expressed as the slope of the efficiency curve in different temperature ranges; According to the fixed specifications of the electrolytic cell, set The yield is as follows: in, In unit time The maximum output value allowed; According to the buffer time of the electrolytic cell, set The increase in yield is as follows: in, In unit time the maximum increment of output allowed; The mechanical wear of the electrolyzer membrane increases during startup and shutdown. The degradation cost of the electrolyzer is expressed as: in, For the Moment The total depreciation cost of an electrolyzer is It is The start-up depreciation cost of each electrolyzer, It is The depreciation cost of closing an electrolyser, It is The investment cost of an electrolyzer is It is The life of an electrolyzer; The flexibility modeling of the power-to-ammonia model is as follows: Will Considered as quasi-steady-state condition, ,in ;for The values ​​of are: in, is the number of quasi-steady-state conditions, is the total scheduling duration, is the time step of system scheduling, The time step after which the electrolyser flexibility is modeled; For quasi-steady-state conditions, , represents the maximum value of the quasi-steady-state condition, represents the value of quasi-steady-state conditions; The first-order transition process is used to simplify the description of the dynamic behavior of the system. As the time sampling step decreases, the feedback mechanism of ammonia production becomes more sensitive. The specific model is as follows: in for By time step After sampling The output at the moment, For the A quasi-steady-state condition, is the time constant; set up The increment range per unit time is as follows: in, In unit time The maximum increment allowed.

3. A fuzzy optimization scheduling method for a micro-energy grid considering the flexibility of electricity-to-ammonia conversion as claimed in claim 1, characterized in that: The new energy vehicle charging station model includes: a new energy vehicle charging model and a dynamic urgency assessment and charging strategy model; Construct a new energy vehicle charging model as follows: Set the charging power and discharging power of new energy vehicles, and the constraints are as follows: in, It is Moment Vehicle charging power, It is Moment The vehicle discharge power, is the maximum charging power allowed per unit time, is the maximum discharge power allowed per unit time; The charging power and discharging power of all electric vehicles at time t are as follows: in, It is The charging power of all vehicles at the moment, M is the total number of vehicles, is the conversion efficiency of hydrogen to electricity, is the conversion efficiency of ammonia to hydrogen, It is Moment The volume of ammonia consumed by the vehicle, No. Discharge power of all vehicles at all times; represents the sum of the charging power and discharging power of all electric vehicles at time t; Construct a dynamic urgency assessment and charging strategy model as follows: Calculate the electricity consumption of each vehicle: in, It is Moment The SOC value consumed by the vehicle, The fuel consumption per 100 kilometers. It is the day The mileage of the vehicle, is the total capacity of the car; The charging time for each vehicle is: in, For the Moment Charging time of the vehicle; By introducing the urgency assessment index , the dynamic urgency assessment and charging strategy model is obtained as follows: in, For the Moment The vehicle’s dwell time in the network.

4. A fuzzy optimization scheduling method for a micro-energy grid considering the flexibility of electricity-to-ammonia conversion as claimed in claim 2, characterized in that: The micro energy network model includes: battery and ammonia tank energy storage model, battery degradation cost model, electric boiler electricity-heat conversion model, gas boiler gas-heat conversion model, cogeneration device gas-electricity-heat conversion model, load reduction model and micro energy network hub model.

5. A fuzzy optimization scheduling method for a micro-energy grid considering the flexibility of electricity-to-ammonia conversion as claimed in claim 4, characterized in that: The steps to build a micro energy grid model include: Building a battery and ammonia tank energy storage model Set the charge and discharge power, SOC value and increment per unit time of the battery energy storage device as follows: in, and Respectively The charging and discharging power of the battery at all times, and The maximum power allowed for charging and discharging of the battery, For the The SOC value of the battery at the moment, is the total capacity of the battery, and is the charge and discharge efficiency of the battery; Indicates The minimum SOC value and maximum SOC value of the battery at all times; Set the charging and discharging power, SOC value and increment per unit time of the ammonia tank energy storage device as follows: in, For the The output power of the gas tank at any moment, and is the power limit value of the gas tank, For the The SOC value of the gas tank at the moment, is the total capacity of the gas tank; Modeling battery degradation costs: in, For the The degradation cost of the battery at all times, is the investment cost of the battery, is the rated cycle life of the battery; is the life coefficient, is the reference SOC value of the battery; For the The outside air temperature at all times.

6. A fuzzy optimization scheduling method for a micro-energy grid considering the flexibility of electricity-to-ammonia conversion as claimed in claim 5, characterized in that: The steps of building a micro energy grid model also include: The electric-heat conversion model and power constraints of the electric boiler are constructed as follows: in, For the The heating power of the electric boiler at any time, For the The power consumption of electric boiler at every moment, is the conversion efficiency of the electric boiler, is the maximum power limit of the electric boiler; The gas-heat conversion model and power constraints of the gas boiler are constructed as follows: in, For the The heating power of the electric boiler at any time, For the Ammonia consumption of gas boiler at each moment, is the gas boiler conversion efficiency, The maximum power limit for gas boilers; is the reaction enthalpy change; The gas-electricity-heat conversion model and power constraints of the cogeneration device are constructed as follows: in, For the The power generated by the combined heat and power generation at any time, is the CHP power conversion efficiency, For the CHP heat generation power at the moment, is the CHP heat conversion efficiency, is the maximum power limit of CHP; For the Ammonia consumption of CHP at the moment.

7. A fuzzy optimization scheduling method for a micro-energy grid considering the flexibility of electricity-to-ammonia conversion as claimed in claim 6, characterized in that: The steps of building a micro energy grid model also include: Building load shedding models; When energy supply fails to meet load demand, the reduction of electricity and heat loads increases, and the reduction constraint is: in, For the The amount of electricity load reduction at any given moment, is the maximum reduction in electrical load, For the The amount of heat load reduction at a time, is the maximum reduction in heat load; Based on the load reduction model, the load reduction cost model is constructed as follows: in, is the load shedding cost; Construct a micro energy network hub model; The electrical energy, thermal energy and gas energy required by the micro energy grid should be consistent with the corresponding supply and meet the following constraints: in It is Total power of electrical load at any moment, It is The power generated by the wind turbine at the moment, It is The power generation capacity of the solar thermal power cogeneration device at the moment, For the The total amount of electricity purchased at any time, It is Total electricity sold at any given moment; It is The discharge power of the electrolyzer at the moment; It is The electrical energy provided to the heating reaction at all times; It is Total heat load power at any moment, It is The heat production power of the solar thermal cogeneration device at any moment; , express It is Moment The electrolytic cell provides heat energy for the heating reaction; It is The output power of the gas tank at any moment, It is Ammonia production at each moment, It is Ammonia consumption of CHP at the moment, It is Ammonia consumption of gas boiler at each moment, It is Ammonia consumption when the car is inflated at any given moment.

8. A fuzzy optimization scheduling method for micro energy grid considering the flexibility of electricity-to-ammonia conversion as claimed in claim 7, characterized in that: Step S3 includes: Based on the micro energy grid model of P2A energy storage, the total cost model of the micro energy grid is obtained as follows: in, Indicates Total cost of micro energy grid at all times; For the The cost of buying electricity from the micro energy grid at all times; For the The profit of selling electricity by the Micro Energy Network; For the The cost of purchasing electricity for the car at any given moment; The Markowitz mean-variance model is as follows: in, For the The expected cost of the scheme, For the The probability value of the solution, For the The standard deviation of the scheme; right Right now For the sake of differentiation, we linearize Left Recorded as , on the right Recorded as , as follows: in and for The minimum and maximum values ​​of ; and for The minimum and maximum values ​​of ; is the discrete number; is a binary variable; The Markowitz mean-variance model Discretization, converting the product between continuous variables into the product of binary variables and continuous variables; Weighted sum of expectation and variance: in, For the The weighted sum of the expected and variance values ​​of the solutions, and are the weights of expectation and variance; The membership function corresponding to the fuzzy target is expressed as follows: in, For the The membership function value of the scheme, i.e., satisfaction; By maximizing the objective function of minimum satisfaction Perform fuzzy optimization; When the objective function When the value of meets the convergence condition, the iterative process is terminated and the fuzzy optimization of the micro energy grid model based on P2A energy storage is completed.

9. A fuzzy optimization scheduling system for a micro-energy network considering the flexibility of electricity-to-ammonia conversion, used to implement a fuzzy optimization scheduling method for a micro-energy network considering the flexibility of electricity-to-ammonia conversion as described in any one of claims 1-8, characterized in that: The system includes: Battery and ammonia tank energy storage modules, electric boiler electric-heat conversion modules, gas boiler gas-heat conversion modules, combined heat and power CHP gas-electric-heat conversion modules, load modules, charging piles and data processing modules; The battery and ammonia tank energy storage module includes: a battery, a wind energy unit, a solar energy unit, and an ammonia unit; The battery and ammonia tank energy storage module, the electric-heat conversion module of the electric boiler, the gas-heat conversion module of the gas boiler, the gas-heat conversion module of the cogeneration device, the load module and the charging pile are all connected to the data processing module; The data processing module is used to construct an electricity-gas-heat coupled micro energy network framework; The data processing module is also used to construct a micro-energy network model based on P2A energy storage based on the micro-energy network framework; the micro-energy network model based on P2A energy storage includes: an electricity-to-ammonia model based on flexible load, a new energy vehicle charging model and a micro-energy network model; The data processing module is also used to perform fuzzy optimization on the micro energy grid model based on P2A energy storage based on the Markowitz mean-variance model; The data processing module is also used to realize fuzzy optimization scheduling of the micro energy network through the micro energy network model based on P2A energy storage after fuzzy optimization.

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