Park microgrid regulation and control method based on improved C-type equivalent circuit

Abstract

The invention discloses a park micro-grid regulation and control method based on an improved C-type equivalent circuit, and belongs to the park micro-grid regulation and control technology. The method comprises the following steps: constructing an energy storage optimization framework of electricity-hydrogen-heat multi-energy flow coupling; establishing a multi-energy flow coupling model based on the improved C-type equivalent circuit; an improved quantum evolutionary algorithm is adopted to solve an optimal solution, an objective function optimal solution meeting constraint conditions is searched, and an optimal equivalent circuit parameter and a scheduling strategy are output; and each unit sends an operation control instruction to coordinate the operation state of each device so as to realize collaborative optimization regulation and control of electricity-hydrogen-heat multi-energy flow in the park microgrid. According to the method, the C-type equivalent circuit model is improved, three energy flows of electricity, hydrogen and heat are innovatively incorporated into a unified modeling framework, a hydrogen energy storage equivalent resistor and a heat recovery equivalent capacitor are newly added, complete mathematical description of the multi-energy flow coupling characteristic is achieved, and the multi-energy cooperative regulation efficiency and the energy comprehensive utilization degree of the park microgrid are effectively improved.

Description

Technical Field

[0001] This invention relates to microgrid control technology for industrial parks, and particularly to a method for controlling industrial park microgrids based on an improved C-type equivalent circuit. Background Technology

[0002] Currently, most microgrids in the park adopt the traditional C-type equivalent circuit model. This model only considers the basic parameters such as resistance and inductance of the electric energy storage link and cannot characterize the coupling characteristics of multiple energy flows such as hydrogen energy storage and heat recovery. Its essential defects are: (1) Incomplete model coverage: The traditional model only describes electric energy storage and ignores the dynamic correlation of electric-hydrogen-heat multi-energy flow conversion; (2) Single optimization objective: Existing methods mostly take energy efficiency or economy as a single objective and lack quantitative constraints on carbon emission intensity; (3) Insufficient adaptation of optimization algorithm to multi-energy flow scenario: Existing microgrid optimization generally adopts traditional optimization methods such as genetic algorithm and particle swarm algorithm, but it has the following problems: premature convergence: it is easy to get trapped in local optima and it is difficult to search for the global optimal solution in complex multi-energy flow coupling scenarios; strong parameter sensitivity: the algorithm parameters (such as crossover rate and mutation rate) depend on experience setting and cannot be adaptively adjusted to match the fluctuation characteristics of wind and solar load. (4) Disconnect between regulation strategy and actual operation: The current system lacks a closed-loop mechanism of modeling-optimization-regulation-feedback, which leads to the decay of model accuracy: Model parameters are fixed and cannot be dynamically corrected according to actual operating data; Poor strategy robustness: The scheduling strategy ignores real-time changes such as equipment loss and environmental factors, and the actual energy efficiency deviates significantly from the theoretical value. Summary of the Invention

[0003] Purpose of the invention: To address the above problems, the purpose of this invention is to provide a campus microgrid control method based on an improved C-type equivalent circuit.

[0004] Technical solution: The present invention provides a campus microgrid control method based on an improved C-type equivalent circuit, comprising the following steps:

[0005] Step 1: Construct an energy storage optimization architecture that couples multiple energy flows of electricity, hydrogen, and heat, integrating an electric vehicle energy storage unit, an electrolytic hydrogen production unit, a hydrogen energy storage unit, a fuel cell power generation unit, and an absorption chiller. Connect the waste heat output end of the fuel cell power generation unit to the heat source input end of the absorption chiller, and drive the absorption chiller to operate through the waste heat of the fuel cell, thereby realizing combined cooling, heating, and power (CCHP).

[0006] Step 2: Establish a multi-energy flow coupling model based on the improved C-type equivalent circuit. The multi-energy flow coupling model is based on the physical topology of the energy storage optimization architecture constructed in Step 1. It mathematically models the charging and discharging behavior of electric vehicles, the rhythm of hydrogen production by water electrolysis, and the supply and demand balance of cooling, heating, and electricity loads. The objective function is set as maximizing comprehensive energy efficiency and minimizing carbon emission intensity. The constraints of the objective function include electric vehicle charging and discharging power constraints, hydrogen production energy consumption constraints by water electrolysis, fuel cell output constraints, cooling, heating, and electricity supply and demand balance constraints, and hydrogen energy storage dynamic constraints.

[0007] Step 3: The improved quantum evolution algorithm is used to solve the optimal solution of the multi-energy flow coupling model established in Step 2, search for the optimal solution of the objective function that satisfies the constraints, and output the optimal equivalent circuit parameters and scheduling strategy.

[0008] Step 4: Based on the control parameters output in Step 3, send operation control commands to the electric vehicle energy storage unit, water electrolysis hydrogen production unit, hydrogen energy storage unit, fuel cell power generation unit, and absorption chiller to coordinate the operating status of each device and achieve coordinated optimization and control of multiple energy flows of electricity, hydrogen, and heat within the park's microgrid.

[0009] Furthermore, the multi-energy flow coupling model in step 2 includes a set of dynamic equations describing the transfer, conversion, and storage of electrical-hydrogen-thermal energy flows, including dynamic equations for electrical energy storage, dynamic equations for hydrogen energy storage, and dynamic equations for heat recovery. The expression for the dynamic equation for electrical energy storage is as follows:

[0010] ,

[0011] The expression for the dynamic equation of hydrogen energy storage is:

[0012] ,

[0013] The expression for the dynamic equation of heat recovery is:

[0014] ,

[0015] in, For energy storage current, For energy storage capacitors, The equivalent resistance for electrical energy storage. For energy storage voltage, This refers to the microgrid load current. For hydrogen production electrical power input, The equivalent voltage for hydrogen energy. The equivalent resistance for hydrogen energy storage. To output electrical power for fuel cells; For heat recovery equivalent capacitance, For the rate of heat generation, It is the thermal equivalent resistance.

[0016] Furthermore, the expression for the objective function in step 2 is:

[0017] ,

[0018] in, This is the energy efficiency priority weighting coefficient. For carbon emission priority weighting coefficients, and This is used to balance energy efficiency and emission reduction targets; For comprehensive energy efficiency, For carbon emission intensity, the expressions are as follows:

[0019] ,

[0020] ,

[0021] in, For electrical energy storage, For storing effective energy, For effective energy recovery from heat, The total energy input to the microgrid from the outside; This represents the total carbon emissions of the system.

[0022] Furthermore, step 3 includes:

[0023] Step 31, using the parameters in the multi-energy flow coupling model , , Electric vehicle charging and discharging power sequence with microgrid scheduling variables Hydrogen production power sequence via water electrolysis For the encoding object, an initial population is constructed to represent the qubit string, and each quantum individual corresponds to a combination of model parameters and scheduling variables to be optimized;

[0024] Each model parameter / scheduling variable is determined by Each qubit is encoded, and the qubit state satisfies... In the formula, For qubit indexing, To determine the probability of taking the lower value, To determine the probability of taking a higher value; initially, the probability amplitudes of all qubits are uniformly distributed, i.e. Ensure that the initial population coverage parameters are physically constrained;

[0025] Set the number of quantum individuals as ;

[0026] Step 32: Decode the quantum individual into specific parameter values, represented as:

[0027] ,

[0028] in, Indicates the maximum allowable charging power, Indicates the maximum permissible discharge power. Representation encoding All the relevant qubits of this decision variable, when their joint quantum state collapses at measurement to the sum of squared probability amplitudes or equivalent probabilities representing the direction of higher values, is a scalar between 0 and 1.

[0029] The probability amplitude of the quantum bit The mapping to the values ​​of model parameters / scheduling variables is expressed as follows:

[0030] ,

[0031] in, These represent the minimum and maximum values ​​of the equivalent resistance of the energy storage unit, respectively. Representation encoding The quantum state of the qubits of this model parameter collapses to the square of the probability amplitude or the equivalent probability representing the direction of higher values;

[0032] Step 33: Use the objective function as the fitness function and solve for the fitness value of the individual by decoding the parameters;

[0033] Step 34: Based on the fitness assessment results, drive the quantum population to evolve toward a better fitness value through quantum selection, quantum crossover, and quantum mutation operations;

[0034] Step 35: Through multiple generations of iteration, continuously optimize the quantum population using the evaluation results of the improved C-type model, outputting the optimal combination of model parameters and scheduling variables; replace the parent generation with the offspring to form a new generation of quantum population; and calculate the fitness variance... Dynamically adjust crossover and mutation probabilities;

[0035] Step 36: Determine whether the current iteration count has reached the maximum iteration count or If the value is less than the convergence threshold, output the model parameters corresponding to the current optimal quantum individual. , , With scheduling variables , Otherwise, return to step 32 and continue the optimization iteration.

[0036] Furthermore, step 34 includes:

[0037] Selection operation: Based on fitness value Calculate the individual choice probability ,in Indicates the first Each quantum individual represents a set of candidate solutions, and a high-fitness parent is selected using a roulette wheel algorithm; meanwhile, the previous fitness values ​​are retained. Elite individuals directly enter their offspring;

[0038] Crossover operation: For the selected parent quantum chromosome, crossover is performed according to the crossover probability. Performing quantum rotating gate operations: for the first The parameter of the first Each qubit adjusts the rotation angle based on the parent's fitness difference. In the formula, Base angle; Update probability amplitude:

[0039] ,

[0040] in, and They are the first Among the individuals, the first The probability amplitudes of qubits are complex numbers that satisfy... ;

[0041] Mutation operation: based on mutation probability Perform a flip on the child qubit, i.e. ↔ This breaks the local optimum.

[0042] Furthermore, the constraints on the objective function include:

[0043] The charging and discharging power constraints of electric vehicles are expressed as follows:

[0044] ,

[0045] in, Indicates time The charging and discharging power of electric vehicles is positive when discharging and negative when charging. , These are its minimum and maximum allowable power values, respectively;

[0046] The energy consumption constraint for hydrogen production via water electrolysis is expressed as follows:

[0047] ,

[0048] in, Indicates time Input the electrical power of the electrolytic cell. , These are the minimum and maximum allowable input power, respectively.

[0049] The output constraints of a fuel cell include power output constraints and thermal power output constraints. The expression for the electrical power output constraint is as follows:

[0050] ,

[0051] in, Indicates time The electrical power output of a fuel cell; , These are its minimum and maximum permissible output power, respectively;

[0052] The expression for the thermal power output constraint is:

[0053] ,

[0054] in, Indicates time The thermal power output of the fuel cell , These are the minimum and maximum permissible thermal power, respectively; The thermoelectric conversion efficiency coefficient;

[0055] The supply and demand balance constraints for heating, cooling, and electricity include electrical power balance constraints, thermal power balance constraints, and cooling power balance constraints. The expression for the electrical power balance constraint is:

[0056] ,

[0057] in, This represents the exchange power between the microgrid and the upper-level power grid; purchasing electricity is positive, and selling electricity is negative. and These represent the discharge power and charging power of the electric vehicle, respectively, both of which are non-negative. This refers to the power consumption of the electric chiller;

[0058] The expression for the thermal power balance constraint is:

[0059] ,

[0060] in, The thermal power of the thermal storage device is positive for heat release and negative for heat storage. For heat load; and The performance coefficients of absorption chillers and electric chillers are respectively. This is for cooling load;

[0061] The expression for the cold power balance constraint is:

[0062] ,

[0063] in, The heat power consumed by the absorption chiller meets the requirements. ;

[0064] The expression for the dynamic constraints of hydrogen energy storage is:

[0065] ,

[0066] in, Indicates time Hydrogen storage capacity, , These represent the lower and upper limits of hydrogen storage capacity, respectively. For the efficiency of hydrogen production by electrolysis. This refers to the power generation efficiency of fuel cells.

[0067] Beneficial effects: Compared with the prior art, the significant advantages of this invention are:

[0068] 1. Breakthrough advantages in multi-energy flow coupling modeling: This invention innovatively incorporates three energy flows—electricity, hydrogen, and heat—into a unified modeling framework by improving the C-type equivalent circuit model. It adds equivalent resistance for hydrogen energy storage and equivalent capacitance for heat recovery, achieving a complete mathematical description of the multi-energy flow coupling characteristics. Specifically, in the electric energy storage stage, the charging and discharging losses are accurately quantified through the equivalent resistance of electric energy storage; in the hydrogen energy storage stage, the electricity-hydrogen-electricity conversion efficiency is characterized through the equivalent resistance of hydrogen energy storage; and in the heat recovery stage, the thermal energy storage characteristics are dynamically described through the equivalent capacitance of heat recovery. This multi-dimensional modeling method effectively solves the shortcomings of existing technologies in representing multi-energy flow coupling.

[0069] 2. This invention leverages the advantages of real-time tracking of dynamic characteristics. By establishing the dynamic relationship of a system of differential equations, the improved model achieves real-time tracking of the system's operating state, accurately describes the transient response characteristics of electrical energy storage, and provides a precise model basis for optimized control.

[0070] 3. High efficiency of quantum parallel search: This invention adopts a quantum evolutionary algorithm, which utilizes the superposition property of qubits to achieve parallel search of the solution space; through quantum encoding, a single individual can simultaneously represent the probability distribution of multiple solutions. Compared with the traditional genetic algorithm, the search efficiency of this invention is significantly improved, effectively avoiding premature convergence and ensuring that the global optimal solution is obtained. Attached Figure Description

[0071] Figure 1 This is an overall flowchart of the present invention;

[0072] Figure 2 This is an architecture diagram of a hydrogen energy system. Detailed Implementation

[0073] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and not intended to limit the scope of the invention. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the embodiments of the present invention, and not all structures.

[0074] In the following description, specific details such as target system architecture and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0075] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0076] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0077] Furthermore, in the description of this application and the appended claims, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0078] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include the target features, structures, or characteristics described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0079] The flowchart of the campus microgrid control method based on the improved C-type equivalent circuit described in this embodiment is as follows: Figure 1 As shown, the method includes the following steps:

[0080] Step 1: Construct an energy storage optimization architecture that couples multiple energy flows of electricity, hydrogen, and heat, integrating an electric vehicle energy storage unit, a water electrolysis hydrogen production unit, a hydrogen energy storage unit, a fuel cell power generation unit, and an absorption chiller. Connect the waste heat output end of the fuel cell power generation unit to the heat source input end of the absorption chiller, and drive the absorption chiller to operate through the waste heat of the fuel cell, thereby realizing combined cooling, heating, and power (CCHP).

[0081] Electric vehicle energy storage units are used for load regulation, charging during off-peak hours and discharging during peak hours to achieve peak shaving and valley filling; for absorbing renewable energy, storing excess energy from intermittent renewable energy sources such as photovoltaics and wind power to improve the utilization rate of clean energy; and for frequency support, responding to microgrid frequency fluctuations through rapid charging and discharging to enhance system stability. The electric vehicle energy storage unit connects to the microgrid's DC or AC bus via a charging and discharging control interface, and its power direction is controlled by the scheduling commands of the control execution module. Its operation, in conjunction with units such as hydrogen electrolysis and fuel cells, forms a power balance node in the electric-hydrogen coupling process.

[0082] The water electrolysis hydrogen production unit is used to convert electrical energy into hydrogen energy and is a key hub in the electricity-hydrogen coupling process. It can be used for hydrogen production and storage, consuming electricity to electrolyze water to produce hydrogen during periods of renewable energy surplus or low electricity prices; it also has a peak-shaving function, balancing microgrid power supply and demand by adjusting hydrogen production power; and it can be used for carbon reduction, replacing fossil fuel-based hydrogen production and reducing carbon emissions throughout its entire lifecycle. The power input of the water electrolysis hydrogen production unit is connected to the microgrid's AC / DC bus to receive electrical energy; the hydrogen output is connected to the hydrogen storage unit via pipeline, transporting the produced hydrogen to the storage device.

[0083] The hydrogen energy storage unit is used for long-term energy storage. Hydrogen can be stored for extended periods and has high energy density, making it suitable for scenarios with seasonal fluctuations in wind and solar resources. It can buffer energy, smoothing power fluctuations between the electrolyzer and fuel cell, and ensuring stable system operation. As a multi-energy flow coupling unit, it provides hydrogen fuel for combined heat and power (CHP). The upstream of the hydrogen energy storage unit receives hydrogen from the water electrolysis hydrogen production unit, and the downstream supplies hydrogen to the fuel cell power generation unit through a pressure reducing valve and a flow controller.

[0084] The fuel cell power generation unit is used to achieve the dual conversion of hydrogen energy into electrical and thermal energy. It has a power generation function, converting hydrogen energy into electricity to supply power during peak loads or when renewable energy is insufficient. The fuel cell power generation unit can also be used for combined heat and power (CHP), where waste heat generated during power generation drives an absorption chiller to achieve combined cooling, heating, and power (CCHP). Furthermore, the fuel cell power generation unit can serve as black start support, providing rapid backup power during grid failures. The hydrogen input of the fuel cell power generation unit is connected to a hydrogen storage unit to obtain fuel; its electrical output is connected to the microgrid bus to feed electrical energy; and its thermal output is coupled to the heat source input of the absorption chiller, forming a thermal cycle.

[0085] Step 2: Establish a multi-energy flow coupling model based on the improved C-type equivalent circuit. The multi-energy flow coupling model is based on the physical topology of the energy storage optimization architecture constructed in Step 1. It mathematically models the charging and discharging behavior of electric vehicles, the rhythm of hydrogen production by water electrolysis, and the supply and demand balance of cooling, heating, and electricity loads. The objective function is set as maximizing comprehensive energy efficiency and minimizing carbon emission intensity. The constraints of the objective function include electric vehicle charging and discharging power constraints, hydrogen production energy consumption constraints by water electrolysis, fuel cell output constraints, cooling, heating, and electricity supply and demand balance constraints, and hydrogen energy storage dynamic constraints.

[0086] Figure 2 The diagram illustrates the architecture and energy flow transfer logic of a hydrogen energy system. Hydrogen fuel enters the hydrogen fuel cell system via a hydrogen fuel refueling interface. The output of the hydrogen fuel cell system is delivered to a multi-energy flow distribution unit, which outputs high-voltage DC. The vehicle controller interacts with the hydrogen fuel cell system, the multi-energy flow distribution unit, the hydrogen energy management system, and the battery via control or signal exchange. The hydrogen energy management system connects the vehicle controller and the battery, and also links to the hydrogen-electric co-controller. After receiving signals, the hydrogen-electric co-controller transmits power and signals to the drive motor via three-phase power lines and signal lines. The hydrogen-electric co-controller is also connected to a waste heat recovery circulation pump and an absorption chiller to achieve functions such as waste heat utilization.

[0087] Furthermore, the constraints on the objective function include:

[0088] The charging and discharging power constraints of electric vehicles are expressed as follows:

[0089] ,

[0090] Among them, P ev (t) represents time. The charging and discharging power of electric vehicles is positive when discharging and negative when charging. , These are its minimum and maximum allowable power values, respectively;

[0091] The energy consumption constraint for hydrogen production via water electrolysis is expressed as follows:

[0092] ,

[0093] in, Indicates time Input the electrical power of the electrolytic cell. , These are the minimum and maximum allowable input power, respectively.

[0094] The output constraints of a fuel cell include power output constraints and thermal power output constraints. The expression for the electrical power output constraint is as follows:

[0095] ,

[0096] in, Indicates time The electrical power output of a fuel cell; , These are its minimum and maximum permissible output power, respectively;

[0097] The expression for the thermal power output constraint is:

[0098] ,

[0099] in, Indicates time The thermal power output of the fuel cell , These are the minimum and maximum permissible thermal power, respectively; The thermoelectric conversion efficiency coefficient;

[0100] The supply and demand balance constraints for heating, cooling, and electricity include electrical power balance constraints, thermal power balance constraints, and cooling power balance constraints. The expression for the electrical power balance constraint is:

[0101] ,

[0102] in, This represents the exchange power between the microgrid and the upper-level power grid; purchasing electricity is positive, and selling electricity is negative. and These represent the discharge power and charging power of the electric vehicle, respectively, both of which are non-negative. This refers to the power consumption of the electric chiller;

[0103] The expression for the thermal power balance constraint is:

[0104] ,

[0105] in, The thermal power of the thermal storage device is positive for heat release and negative for heat storage. For heat load; and The performance coefficients of absorption chillers and electric chillers are respectively. This is for cooling load;

[0106] The expression for the cold power balance constraint is:

[0107] ,

[0108] in, The heat power consumed by the absorption chiller meets the requirements. ;

[0109] The expression for the dynamic constraints of hydrogen energy storage is:

[0110] ,

[0111] in, Indicates time Hydrogen storage capacity, , These represent the lower and upper limits of hydrogen storage capacity, respectively. For the efficiency of hydrogen production by electrolysis. This refers to the power generation efficiency of fuel cells.

[0112] Optionally, the constraints also include mutual exclusion constraints for charge and discharge states, achieved by introducing binary variables. , Representing the charging and discharging states respectively, satisfying the following expressions:

[0113] .

[0114] Furthermore, the multi-energy flow coupling model in step 2 includes a set of dynamic equations describing the transfer, conversion, and storage of electrical-hydrogen-thermal energy flows, including dynamic equations for electrical energy storage, dynamic equations for hydrogen energy storage, and dynamic equations for heat recovery. The expression for the dynamic equation for electrical energy storage is as follows:

[0115] ,

[0116] The expression for the dynamic equation of hydrogen energy storage is:

[0117] ,

[0118] The expression for the dynamic equation of heat recovery is:

[0119] ,

[0120] in, For energy storage current, For energy storage capacitors, The equivalent resistance for electrical energy storage. For energy storage voltage, This refers to the microgrid load current. For hydrogen production electrical power input, The equivalent voltage for hydrogen energy. The equivalent resistance for hydrogen energy storage. To output electrical power for fuel cells; For heat recovery equivalent capacitance, For the rate of heat generation, It is the thermal equivalent resistance.

[0121] Furthermore, the expression for the objective function in step 2 is:

[0122] ,

[0123] in, This is the energy efficiency priority weighting coefficient. For carbon emission priority weighting coefficients, and This is used to balance energy efficiency and emission reduction targets; For comprehensive energy efficiency, For carbon emission intensity, the expressions are as follows:

[0124] ,

[0125] ,

[0126] in, For electrical energy storage, For storing effective energy, For effective energy recovery from heat, The total energy input to the microgrid from the outside; This represents the total carbon emissions of the system.

[0127] Step 3: Use the quantum evolution algorithm to find the optimal solution of the multi-energy flow coupling model established in Step 2, search for the optimal solution of the objective function that satisfies the constraints, and output the optimal equivalent circuit parameters and scheduling strategy.

[0128] Furthermore, step 3 includes:

[0129] Step 31, using the parameters in the multi-energy flow coupling model , , Electric vehicle charging and discharging power sequence with microgrid scheduling variables Hydrogen production power sequence via water electrolysis For the encoding object, an initial population is constructed to represent the qubit string, and each quantum individual corresponds to a combination of model parameters and scheduling variables to be optimized;

[0130] Each model parameter / scheduling variable is determined by Each qubit is encoded, and the qubit state satisfies... In the formula, For qubit indexing, To determine the probability of taking the lower value, To determine the probability of taking a higher value; initially, the probability amplitudes of all qubits are uniformly distributed, i.e. Ensure that the initial population coverage parameters are physically constrained;

[0131] Set the number of quantum individuals as ;

[0132] Step 32: Decode the quantum individual into specific parameter values, represented as:

[0133] ,

[0134] in, Indicates the maximum allowable charging power, Indicates the maximum permissible discharge power. Representation encoding All the relevant qubits of this decision variable, when their joint quantum state collapses at measurement to the sum of squared probability amplitudes or equivalent probabilities representing the direction of higher values, is a scalar between 0 and 1.

[0135] The probability amplitude of the quantum bit The mapping to the values ​​of model parameters / scheduling variables is expressed as follows:

[0136] ,

[0137] in, These represent the minimum and maximum values ​​of the equivalent resistance of the energy storage unit, respectively. Representation encoding The quantum state of the qubits of this model parameter collapses to the square of the probability amplitude or the equivalent probability representing the direction of higher values;

[0138] Step 33: Use the objective function as the fitness function and solve for the fitness value of the individual by decoding the parameters;

[0139] Step 34: Based on the fitness assessment results, drive the quantum population to evolve toward a better fitness value through quantum selection, quantum crossover, and quantum mutation operations;

[0140] Step 35: Through multiple generations of iteration, continuously optimize the quantum population using the evaluation results of the improved C-type model, outputting the optimal combination of model parameters and scheduling variables; replace the parent generation with the offspring to form a new generation of quantum population; and calculate the fitness variance... Dynamically adjust crossover and mutation probabilities. The population size is the number of quantum individuals. For the first The fitness value of each individual The average fitness of the population;

[0141] Step 36: Determine whether the current iteration count has reached the maximum iteration count or If the value is less than the convergence threshold, output the model parameters corresponding to the current optimal quantum individual. , , With scheduling variables , Otherwise, return to step 32 and continue the optimization iteration.

[0142] In quantum evolutionary algorithms, continuous variables in traditional optimization problems (such as power and resistance) need to be represented using the probability amplitudes of qubits. Step 31 of this example describes a common encoding mapping method: each qubit can be in either the |0> state or the |1> state, with probability amplitudes of respectively... and , This represents the probability that the qubit collapses to the |0> state during measurement. This represents the probability that the qubit will collapse to the |1> state during measurement. |αᵢ|² represents the probability of taking a lower value, that is, when the optimization variable encoded by this qubit (such as a resistance value) needs to be decoded into a specific value, if the qubit collapses to the |0> state, then the variable will take the minimum value (lower bound) within its allowed range. To ensure a high probability of taking a value, if the variable collapses to a |1> state, it will take the maximum value (upper bound) within the allowed range. Essentially, this is a binary extremum mapping, assuming that each qubit only represents whether the parameter takes its lower or upper bound. However, this is an extreme simplification of a single qubit. In practical applications, a parameter is encoded by multiple qubits, and their joint state can represent any intermediate value of the parameter within its range, rather than simply the two extremes.

[0143] Furthermore, step 34 includes:

[0144] Selection operation: Based on fitness value Calculate the individual choice probability ,in Indicates the first Each quantum individual represents a set of candidate solutions, and a high-fitness parent is selected using a roulette wheel algorithm; meanwhile, the previous fitness values ​​are retained. Elite individuals directly enter their offspring;

[0145] Crossover operation: For the selected parent quantum chromosome, crossover is performed according to the crossover probability. Performing quantum rotating gate operations: for the first The parameter of the first Each qubit adjusts the rotation angle based on the parent's fitness difference. In the formula, Base angle; Update probability amplitude:

[0146] ,

[0147] in, and They are the first Among the individuals, the first The probability amplitudes of qubits are complex numbers that satisfy... ;

[0148] Mutation operation: based on mutation probability Perform a flip on the child qubit, i.e. ↔ This breaks the local optimum.

[0149] In step 35, the crossover probability and mutation probability can be dynamically adjusted based on the fitness variance. High variance ( A relatively high variance indicates rich population diversity, significant differences in individual fitness, and that the algorithm is in the global exploration phase; a low variance indicates... When the fitness level is relatively low, it indicates that the population is becoming homogeneous, with individuals having similar fitness levels, and the algorithm may get stuck in a local optimum. Specific dynamic adjustment strategies include:

[0150] (1) Probability of variation The adjustment rules are as follows:

[0151] when At that time, among them The precocious puberty threshold is significantly increased. For example, increasing from the base value of 0.01 to By adding mutation operations, the homogeneity of the population can be broken, thus escaping the trap of local optima. hour, To achieve a sufficient diversity threshold, appropriately lower If the base value is maintained To avoid excessive mutations that could damage the optimal gene structure.

[0152] (2) Crossover probability The coordinated adjustment has the following adjustment rules:

[0153] when When the size is small, reduce appropriately. To reduce ineffective crossovers among similar individuals, while significantly improving... The focus is on enhancing population diversity; when When the population is large (with population diversity), maintain or increase Promotes the recombination of superior genes, accelerates convergence, and maintains normal function. Maintain basic diversity.

[0154] In one example, fitness variance This reflects population diversity. A smaller value is typically defined as the premature convergence risk range, indicating a severe lack of population diversity. Assuming fitness values ​​are normalized to the [0,1] interval, a threshold is set. :like This indicates that over 95% of individuals exhibit very small differences in fitness (e.g., maximum fitness). , minimum ,variance ).when When the fitness difference between the best and worst individuals in the population is less than 5%, it is considered small and requires immediate intervention.

[0155] Optimize the equivalent resistance of energy storage At that time, all individuals in the population The values ​​are all concentrated in (Permitted range) ), fitness Concentrated The calculation process is as follows:

[0156] Average fitness ,variance .

[0157] because The value is determined to be too small, triggering dynamic adjustment. The adjustment action is: reduce... To reduce invalid crossovers and significantly improve To inject diversity.

[0158] Mutation probability in quantum evolutionary algorithms Amplitude standard is usually set as Significantly, this means increasing the magnitude to 5 to 10 times the base value to quickly break the local optimum. For example, when the base... ,when When < 0, Increase it to 0.1 (10 times the base value) to ensure that 10% of individuals mutate in each generation. If the problem is complex, such as multi-peak optimization, it can be further increased to 0.15-0.2. After the mutation probability increases from 1% to 10%, it is expected to increase the population diversity index (such as Shannon entropy) by more than 50% within 10 generations, thus avoiding algorithm stagnation.

[0159] Large fitness variance This refers to a population in a healthy exploratory state, where individual fitness is highly dispersed. A threshold example is when... (After fitness normalization). If , This means that the individual fitness distribution is in Between these points, the population covers more than 50% of the solution space. At this point, no additional diversity injection is needed, and convergence should be accelerated as a priority. For example, in the early stages of optimization... The values ​​are scattered in fitness Distributed in Calculate the average fitness. , If the fitness variance is large, then maintain or increase the crossover probability. This promotes the recombination of superior genes.

[0160] Crossover probability The base value is usually set to 0.6–0.8, and the increase can be moderately increased by 10%–20%, for example, from 0.7 to 0.8–0.85. Adaptive rule: If And the best fitness in modern times If the improvement is more than 5% compared to the previous generation, then the crossover probability is... Increase it to 0.85 to accelerate convergence. If Stagnation (e.g., change <1%), then Maintain a baseline value of 0.7 to avoid excessive crossover. For example... , It increased from 0.8 to 0.84, an increase of 5%. The value increased from 0.7 to 0.85, promoting gene exchange among high-yielding individuals.

[0161] (3) Regarding the rotation angle Dynamic updates are performed, and the update formula is:

[0162] ,

[0163] In the formula, This represents the current iteration number. This represents the maximum number of iterations.

[0164] This mechanism, combined with probability adjustment, creates a dual adaptive effect, especially in the early stages of iteration ( (smaller) Larger scale, more exploratory cross-operations, and requires appropriate coordination To achieve global search; in the later stages ( near ), Reduce, enhance local development, and combine at this time Adjustments should be made to avoid precocious puberty.

[0165] Step 4: Based on the control parameters output in Step 3, send operation control commands to the electric vehicle energy storage unit, water electrolysis hydrogen production unit, hydrogen energy storage unit, fuel cell power generation unit, and absorption chiller to coordinate the operating status of each device and achieve coordinated optimization and control of multiple energy flows of electricity, hydrogen, and heat within the park's microgrid.

[0166] The microgrid control method for industrial parks based on an improved C-type equivalent circuit described in this invention constructs an energy storage optimization architecture that couples electricity, hydrogen, and heat, and utilizes waste heat from fuel cells to drive an absorption chiller to achieve combined cooling, heating, and power (CCHP). Based on this multi-energy flow coupling architecture, the charging and discharging rhythms of electric vehicles and the hydrogen production rhythm via water electrolysis are modeled, and the solution is obtained using a quantum evolutionary algorithm with energy efficiency optimization and carbon emission reduction as objective functions. The operation of each device is coordinated based on the solution results. Compared with existing technologies, the method described in this invention can effectively improve the multi-energy collaborative control efficiency and comprehensive energy utilization of industrial park microgrids.

Claims

1. A method for controlling a campus microgrid based on an improved C-type equivalent circuit, characterized in that, Includes the following steps: Step 1: Construct an energy storage optimization architecture that couples multiple energy flows of electricity, hydrogen, and heat, integrating an electric vehicle energy storage unit, an electrolytic hydrogen production unit, a hydrogen energy storage unit, a fuel cell power generation unit, and an absorption chiller. Connect the waste heat output end of the fuel cell power generation unit to the heat source input end of the absorption chiller, and drive the absorption chiller to operate through the waste heat of the fuel cell, thereby realizing combined cooling, heating, and power (CCHP). Step 2: Establish a multi-energy flow coupling model based on the improved C-type equivalent circuit. The multi-energy flow coupling model is based on the physical topology of the energy storage optimization architecture constructed in Step 1. It mathematically models the charging and discharging behavior of electric vehicles, the rhythm of hydrogen production by water electrolysis, and the supply and demand balance of cooling, heating, and electricity loads. The objective function is set as maximizing comprehensive energy efficiency and minimizing carbon emission intensity. The constraints of the objective function include electric vehicle charging and discharging power constraints, hydrogen production energy consumption constraints by water electrolysis, fuel cell output constraints, cooling, heating, and electricity supply and demand balance constraints, and hydrogen energy storage dynamic constraints. Step 3: The improved quantum evolution algorithm is used to solve the optimal solution of the multi-energy flow coupling model established in Step 2, search for the optimal solution of the objective function that satisfies the constraints, and output the optimal equivalent circuit parameters and scheduling strategy. Step 4: Based on the control parameters output in Step 3, send operation control commands to the electric vehicle energy storage unit, water electrolysis hydrogen production unit, hydrogen energy storage unit, fuel cell power generation unit, and absorption chiller to coordinate the operating status of each device and achieve coordinated optimization and control of multiple energy flows of electricity, hydrogen, and heat within the park's microgrid.

2. The campus microgrid control method based on improved C-type equivalent circuit according to claim 1, characterized in that, Step 2's multi-energy flow coupling model includes a set of dynamic equations describing the transfer, conversion, and storage of electrical-hydrogen-thermal energy flows. These equations include dynamic equations for electrical energy storage, hydrogen energy storage, and heat recovery. The expression for the electrical energy storage dynamic equation is as follows: ， The expression for the dynamic equation of hydrogen energy storage is: ， The expression for the dynamic equation of heat recovery is: ， in, For energy storage current, For energy storage capacitors, The equivalent resistance for electrical energy storage. For energy storage voltage, This refers to the microgrid load current. For hydrogen production electrical power input, The equivalent voltage for hydrogen energy. The equivalent resistance for hydrogen energy storage. To output electrical power for fuel cells; For heat recovery equivalent capacitance, For the rate of heat generation, It is the thermal equivalent resistance.

3. The campus microgrid control method based on the improved C-type equivalent circuit according to claim 2, characterized in that, The expression for the objective function in step 2 is: ， in, This is the energy efficiency priority weighting coefficient. For carbon emission priority weighting coefficients, and This is used to balance energy efficiency and emission reduction targets; For comprehensive energy efficiency, For carbon emission intensity, the expressions are as follows: ， ， in, For electrical energy storage, For storing effective energy, For effective energy recovery from heat, The total energy input to the microgrid from the outside; This represents the total carbon emissions of the system.

4. The campus microgrid control method based on the improved C-type equivalent circuit according to claim 3, characterized in that, Step 3 includes: Step 31, using the parameters in the multi-energy flow coupling model , , Electric vehicle charging and discharging power sequence with microgrid scheduling variables Hydrogen production power sequence via water electrolysis For the encoding object, an initial population is constructed to represent the qubit string, and each quantum individual corresponds to a combination of model parameters and scheduling variables to be optimized; Each model parameter / scheduling variable is determined by Each qubit is encoded, and the qubit state satisfies... In the formula, For qubit indexing, To determine the probability of taking the lower value, To determine the probability of taking a higher value; initially, the probability amplitudes of all qubits are uniformly distributed, i.e. Ensure that the initial population coverage parameters are physically constrained; The number of quantum individuals is set to ; Step 32: Decode the quantum individual into specific parameter values, represented as: ， in, Indicates the maximum allowable charging power, Indicates the maximum permissible discharge power. Representation encoding All the relevant qubits of this decision variable, when their joint quantum state collapses at measurement to the sum of squared probability amplitudes or equivalent probabilities representing the direction of higher values, is a scalar between 0 and 1. The probability amplitude of the quantum bit The mapping to the values ​​of model parameters / scheduling variables is expressed as follows: ， in, These represent the minimum and maximum values ​​of the equivalent resistance of the energy storage unit, respectively. Representation encoding The quantum state of the qubits of this model parameter collapses to the square of the probability amplitude or the equivalent probability representing the direction of higher values; Step 33: Use the objective function as the fitness function and solve for the fitness value of the individual by decoding the parameters; Step 34: Based on the fitness assessment results, drive the quantum population to evolve toward a better fitness value through quantum selection, quantum crossover, and quantum mutation operations; Step 35: Through multiple generations of iteration, continuously optimize the quantum population using the evaluation results of the improved C-type model, outputting the optimal combination of model parameters and scheduling variables; replace the parent generation with the offspring to form a new generation of quantum population; and calculate the fitness variance... Dynamically adjust crossover and mutation probabilities; Step 36: Determine whether the current iteration count has reached the maximum iteration count or If the value is less than the convergence threshold, output the model parameters corresponding to the current optimal quantum individual. , , With scheduling variables , Otherwise, return to step 32 and continue the optimization iteration.

5. The campus microgrid control method based on the improved C-type equivalent circuit according to claim 4, characterized in that, Step 34 includes: Selection operation: Based on fitness value Calculate the individual choice probability ,in Indicates the first Each quantum individual represents a set of candidate solutions, and a high-fitness parent is selected using a roulette wheel algorithm; meanwhile, the previous fitness values ​​are retained. Elite individuals directly enter their offspring; Crossover operation: For the selected parent quantum chromosome, crossover is performed according to the crossover probability. Performing quantum rotating gate operations: for the first The parameter of the first Each qubit adjusts the rotation angle based on the parent's fitness difference. In the formula, Base angle; Update probability amplitude: ， in, and They are the first Among the individuals, the first The probability amplitudes of qubits are complex numbers that satisfy... ; Mutation operation: based on mutation probability Perform a flip on the child qubit, i.e. ↔ This breaks the local optimum.

6. The campus microgrid control method based on the improved C-type equivalent circuit according to claim 5, characterized in that, The constraints of the objective function include: The charging and discharging power constraints of electric vehicles are expressed as follows: ， in, Indicates time The charging and discharging power of electric vehicles is positive when discharging and negative when charging. , These are its minimum and maximum allowable power values, respectively; The energy consumption constraint for hydrogen production via water electrolysis is expressed as follows: ， in, Indicates time Input the electrical power of the electrolytic cell. , These are the minimum and maximum allowable input power, respectively. The output constraints of a fuel cell include power output constraints and thermal power output constraints. The expression for the electrical power output constraint is as follows: ， in, Indicates time The electrical power output of a fuel cell; , These are its minimum and maximum permissible output power, respectively; The expression for the thermal power output constraint is: ， in, Indicates time The thermal power output of the fuel cell , These are the minimum and maximum permissible thermal power, respectively; The thermoelectric conversion efficiency coefficient; The supply and demand balance constraints for heating, cooling, and electricity include electrical power balance constraints, thermal power balance constraints, and cooling power balance constraints. The expression for the electrical power balance constraint is: ， in, This represents the exchange power between the microgrid and the upper-level power grid; purchasing electricity is positive, and selling electricity is negative. and These represent the discharge power and charging power of the electric vehicle, respectively, both of which are non-negative. This refers to the power consumption of the electric chiller; The expression for the thermal power balance constraint is: ， in, The thermal power of the thermal storage device is positive for heat release and negative for heat storage. For heat load; and The performance coefficients of absorption chillers and electric chillers are respectively. For cooling load; The expression for the cold power balance constraint is: ， in, The heat power consumed by the absorption chiller meets the requirements. ; The expression for the dynamic constraints of hydrogen energy storage is: ， in, Indicates time Hydrogen storage capacity, , These represent the lower and upper limits of hydrogen storage capacity, respectively. For the efficiency of hydrogen production by electrolysis. This refers to the power generation efficiency of fuel cells.

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