CN-122000960-A - Park micro-grid regulation and control method based on improved C-type equivalent circuit

Abstract

The invention discloses a garden micro-grid regulation and control method based on an improved C-type equivalent circuit, and belongs to a garden micro-grid regulation and control technology. The method comprises the steps of constructing an energy storage optimizing framework of electric-hydrogen-heat multi-energy flow coupling, building a multi-energy flow coupling model based on an improved C-type equivalent circuit, adopting an improved quantum evolution algorithm to carry out optimal solution, searching an objective function optimal solution meeting constraint conditions, outputting optimal equivalent circuit parameters and a scheduling strategy, sending operation control instructions by all units, coordinating the operation states of all devices, and realizing collaborative optimization regulation and control of electric-hydrogen-heat multi-energy flows in a micro-grid of a park. According to the invention, through improving the C-shaped equivalent circuit model, three energy flows of electricity, hydrogen and heat are innovatively brought into a unified modeling framework, and the hydrogen energy storage equivalent resistance and the heat recovery equivalent capacitance are newly added, so that the complete mathematical description of the coupling characteristics of the multi-energy flows is realized, and the multi-energy cooperative regulation and control efficiency and the comprehensive energy utilization degree of the micro-grid in the park are effectively improved.

Inventors

• Gu Xinfa
• ZHUANG XUZHOU
• XIANG QI
• SHI YUANYUAN
• Yue Zhuangwei
• LI HAOYU
• ZHANG YUXIANG
• JI JIE
• HUANG HUI
• WANG LE

Assignees

• 淮阴工学院

Dates

Publication Date

20260508

Application Date

20260127

Claims (6)

1. 1. A garden micro-grid regulation and control method based on an improved C-type equivalent circuit is characterized by comprising the following steps: Step 1, an energy storage optimizing framework of electric-hydrogen-heat multi-energy flow coupling is constructed, an electric automobile energy storage unit, an electrolyzed water hydrogen production unit, a hydrogen energy storage unit, a fuel cell power generation unit and an absorption refrigerator are integrated, the waste heat output end of the fuel cell power generation unit is connected with the heat source input end of the absorption refrigerator, and the absorption refrigerator is driven to operate by the waste heat of the fuel cell, so that the triple heat supply of cold and heat is realized; step 2, a multi-energy flow coupling model is established 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 framework constructed in the step 1, mathematical modeling is carried out on charging and discharging behaviors of the electric automobile, the hydrogen production rhythm of the electrolytic water and the supply and demand balance of the cold and hot electric loads, an objective function is set to maximize comprehensive energy efficiency and minimize carbon emission intensity, and constraint conditions of the objective function comprise charging and discharging power constraint of the electric automobile, hydrogen production energy consumption constraint of the electrolytic water, output constraint of a fuel cell, balance constraint of the cold and hot electric supply and demand and dynamic constraint of hydrogen energy storage; Step 3, adopting an improved quantum evolution algorithm to carry out optimal solution on the multi-energy flow coupling model established in the step 2, searching an objective function optimal solution meeting constraint conditions, and outputting optimal equivalent circuit parameters and a scheduling strategy; And step 4, sending operation control instructions to an electric automobile energy storage unit, an electrolyzed water hydrogen production unit, a hydrogen energy storage unit, a fuel cell power generation unit and an absorption refrigerator according to the control parameters output in the step 3, coordinating the operation states of all the equipment, and realizing the cooperative optimization and regulation of electric-hydrogen-heat multi-energy flows in the micro-grid of the park.
2. 2. The improved C-type equivalent circuit based campus microgrid control method of claim 1, wherein the multi-energy flow coupling model in step 2 includes a system of dynamic equations describing the transmission, conversion and storage of the electric-hydrogen-thermal energy flow, including an electric energy storage dynamic equation, a hydrogen energy storage dynamic equation, and a heat recovery dynamic equation, wherein the expression of the electric energy storage dynamic equation is: , the expression of the hydrogen storage dynamic equation is: , the expression of the heat recovery dynamic equation is: , Wherein, the In order to store the energy of the current, In order to store the energy in the capacitor, For the purpose of electric energy storage equivalent resistance, For the energy-storage voltage to be stored, Load current for the microgrid; in order to produce hydrogen and electrical power input, Is the equivalent voltage of the hydrogen energy, Is the equivalent resistance of hydrogen energy storage, Outputting electric power for the fuel cell; In order to recover the equivalent capacitance of the heat, For the rate of generation of the thermal energy, Is the equivalent resistance of heat energy.
3. 3. The improved C-type equivalent circuit based campus microgrid control method of claim 2, wherein the expression of the objective function in step 2 is: , Wherein, the For the energy efficiency priority weighting factor, Is a carbon emission priority weighting coefficient, and The energy efficiency and emission reduction target are balanced; In order to integrate the energy efficiency of the energy-saving device, For carbon emission intensity, the expressions are respectively: , , Wherein, the In order to store energy effectively in the form of electricity, In order to store energy effectively and effectively, In order to recover the effective energy of the heat, Inputting total energy for the outside of the micro-grid; is the total carbon emission of the system.
4. 4. The improved C-type equivalent circuit based campus microgrid control method of claim 3, wherein step 3 comprises: step 31, coupling parameters in the model with the multi-energy flow 、 、 Charging and discharging power sequence of electric automobile with micro-grid scheduling variable Hydrogen production power sequence by water electrolysis For a coded object, constructing an initial population represented by a quantum bit string, wherein each quantum individual corresponds to a group of combination of model parameters to be optimized and scheduling variables; Each model parameter/schedule variable is composed of Encoding of the quantum bits, the states of the quantum bits satisfying In which, in the process, For the purpose of the qubit index, In order to take the probability of a low value, To take high value probability, initially, the probability of all the quanta bits is distributed uniformly, i.e Ensuring physical constraint of initial population coverage parameters; setting the quantum individual quantity as ; Step 32, decoding the quantum units into specific parameter values, expressed as: , Wherein, the Indicating the maximum allowable charging power, Indicating the maximum allowable discharge power of the battery, Representation coding All relevant qubits of the decision variable, and the probability breadth square sum or equivalent probability of the combined quantum state collapsing to represent the high value direction during measurement, are scalar quantities between 0 and 1; probability amplitude of qubits The values mapped to model parameters/schedule variables are expressed as: , Wherein, the Representing the minimum and maximum values of the equivalent resistance of the electrical energy storage unit respectively, Representation coding The quantum bit of the model parameter, the quantum state of which collapses to the square of the probability amplitude or the equivalent probability representing the high value direction; step 33, taking the objective function as a fitness function, and solving the fitness value of the individual through the decoding parameters; Step 34, based on the fitness evaluation result, driving the quantum population to evolve towards a better fitness value through quantum selection, quantum crossing and quantum mutation operation; Step 35, continuously optimizing the quantum population by utilizing the evaluation result of the improved C model through multi-generation iteration, outputting the optimal combination of model parameters and scheduling variables, replacing parent with offspring to form a new generation quantum population, and forming a new generation quantum population according to the adaptability variance Dynamically adjusting the crossover probability and the variation probability; Step 36, determining whether the current iteration number reaches the maximum iteration number or not If the current quantum parameter is smaller than the convergence threshold value, outputting the model parameter corresponding to the current optimal quantum unit 、 、 And schedule variables 、 Otherwise, returning to the step 32, and continuing the optimization iteration.
5. 5. The improved C-type equivalent circuit based campus microgrid control method of claim 4, wherein step 34 comprises: Selecting operations based on fitness values Calculating individual selection probabilities Wherein Represent the first The fitness value of each quantum unit, namely a group of candidate solutions, is selected by a roulette method to select a high-adaptability parent, and the fitness is kept Directly entering offspring; crossover operation, namely, crossing probability is carried out on selected parent quantum chromosomes Performing quantum revolving door operation on the first First of all parameters A quantum bit for adjusting rotation angle according to parent adaptability difference In which, in the process, The probability amplitude is updated: , Wherein, the And Respectively the first In individual subjects of Probability breadth of individual qubits, which are complex, satisfy ; Variation operation according to variation probability Performing flipping on child qubits, i.e ↔ And breaking local optimum.
6. 6. The improved C-type equivalent circuit based campus microgrid control method of claim 5, wherein the constraints of the objective function include: the electric automobile charge and discharge power constraint is expressed as: , Wherein, the Indicating time of day The charging and discharging power of the electric automobile is positive in discharging and negative in charging; 、 respectively the minimum and maximum power values allowed by the power values; The constraint of hydrogen production energy consumption by water electrolysis is expressed as: , Wherein, the Indicating time of day The electric power is input into the electrolytic cell, 、 Respectively minimum and maximum allowable input power; the fuel cell output constraints include a power output constraint and a thermal power output constraint, the expression of the electrical power output constraint being: , Wherein, the Indicating time of day Electric power output by the fuel cell; 、 Respectively, its allowable minimum and maximum output electric power; The expression of the thermal power output constraint is: , Wherein, the Indicating time of day The thermal power output from the fuel cell is, 、 Respectively the allowable minimum and maximum thermal powers; is the thermoelectric conversion efficiency coefficient; The cold-hot electricity supply and demand balance constraint comprises an electric power balance constraint, a hot power balance constraint and a cold power balance constraint, wherein the expression of the electric power balance constraint is as follows: , Wherein, the The power is exchanged between the micro-grid and the upper-level power grid, electricity purchasing is positive, and electricity selling is negative; And The discharging power and the charging power of the electric automobile are respectively non-negative; Is the power consumption of the electric refrigerator; the expression of the thermal power balance constraint is: , Wherein, the The heat release is positive and the heat storage is negative for the heat power of the heat storage device; Is a thermal load; And The coefficient of performance of absorption and electric refrigerators respectively, Is a cold load; the expression of the cold power balance constraint is: , Wherein, the For the heat power consumed by the absorption refrigerator, satisfy ; The expression of the dynamic constraint of hydrogen energy storage is: , Wherein, the Indicating time of day Is used for the hydrogen storage amount of the (a), 、 A lower limit and an upper limit of the hydrogen storage capacity, respectively; For the efficiency of the electrolytic hydrogen production, Is the power generation efficiency of the fuel cell.

Description

Park micro-grid regulation and control method based on improved C-type equivalent circuit Technical Field The invention relates to a garden micro-grid regulation technology, in particular to a garden micro-grid regulation method based on an improved C-type equivalent circuit. Background At present, a conventional C-shaped equivalent circuit model is mostly adopted for a micro-grid in a park, and the model only considers basic parameters such as resistance, inductance and the like of an electric energy storage link and cannot represent the multi-energy flow coupling characteristics such as hydrogen energy storage, heat recovery and the like. The method has the essential defects that (1) the model coverage is incomplete, the traditional model only describes electric energy storage, dynamic association of electric-hydrogen-heat multi-energy flow conversion is ignored, and (2) the optimization target is single, the existing method mostly takes energy efficiency or economy as a single target, and the quantitative constraint on the carbon emission intensity is lacked. (3) The optimization algorithm and the multi-energy flow scene are not suitable, the traditional optimization methods such as a genetic algorithm, a particle swarm algorithm and the like are commonly adopted in the conventional micro-grid optimization, but the optimization method has the advantages that premature convergence is easy to fall into local optimum, global optimum solution is difficult to search under a complex multi-energy flow coupling scene, parameter sensitivity is high, algorithm parameters (such as crossing rate and variation rate) depend on experience setting, and self-adaptive adjustment cannot be carried out to match wind-light load fluctuation characteristics. (4) The control strategy is disjointed from actual operation, namely the current system lacks a closed loop mechanism of modeling, optimizing, controlling and feedback, so that model accuracy is attenuated, model parameters are solidified and cannot be dynamically corrected according to actual operation data, strategy robustness is poor, a scheduling strategy ignores real-time changes such as equipment loss, environmental factors and the like, and the deviation between actual energy efficiency and theoretical value is remarkable. Disclosure of Invention Aiming at the problems, the invention aims to provide a garden microgrid control method based on an improved C-type equivalent circuit. The garden micro-grid regulation method based on the improved C-type equivalent circuit comprises the following steps: Step 1, an energy storage optimizing framework of electric-hydrogen-heat multi-energy flow coupling is constructed, an electric automobile energy storage unit, an electrolyzed water hydrogen production unit, a hydrogen energy storage unit, a fuel cell power generation unit and an absorption refrigerator are integrated, the waste heat output end of the fuel cell power generation unit is connected with the heat source input end of the absorption refrigerator, and the absorption refrigerator is driven to operate by the waste heat of the fuel cell, so that the triple heat supply of cold and heat is realized; step 2, a multi-energy flow coupling model is established 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 framework constructed in the step 1, mathematical modeling is carried out on charging and discharging behaviors of the electric automobile, the hydrogen production rhythm of the electrolytic water and the supply and demand balance of the cold and hot electric loads, an objective function is set to maximize comprehensive energy efficiency and minimize carbon emission intensity, and constraint conditions of the objective function comprise charging and discharging power constraint of the electric automobile, hydrogen production energy consumption constraint of the electrolytic water, output constraint of a fuel cell, balance constraint of the cold and hot electric supply and demand and dynamic constraint of hydrogen energy storage; Step 3, adopting an improved quantum evolution algorithm to carry out optimal solution on the multi-energy flow coupling model established in the step 2, searching an objective function optimal solution meeting constraint conditions, and outputting optimal equivalent circuit parameters and a scheduling strategy; And step 4, sending operation control instructions to an electric automobile energy storage unit, an electrolyzed water hydrogen production unit, a hydrogen energy storage unit, a fuel cell power generation unit and an absorption refrigerator according to the control parameters output in the step 3, coordinating the operation states of all the equipment, and realizing the cooperative optimization and regulation of electric-hydrogen-heat multi-energy flows in the micro-grid of the park. Further, the multi-energy flow coupling model i