CN116131291B - Hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power - Google Patents

Hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power Download PDF

Info

Publication number
CN116131291B
CN116131291B CN202310398831.8A CN202310398831A CN116131291B CN 116131291 B CN116131291 B CN 116131291B CN 202310398831 A CN202310398831 A CN 202310398831A CN 116131291 B CN116131291 B CN 116131291B
Authority
CN
China
Prior art keywords
power
hydrogen
coupling system
electric coupling
super capacitor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202310398831.8A
Other languages
Chinese (zh)
Other versions
CN116131291A (en
Inventor
卢昕宇
谢长君
杜帮华
邓坚
朱世昊
彭颜玉
杨扬
朱文超
王喆伟
王瀚
赵波
章雷其
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan University of Technology WUT
State Grid Zhejiang Electric Power Co Ltd
Original Assignee
Wuhan University of Technology WUT
State Grid Zhejiang Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuhan University of Technology WUT, State Grid Zhejiang Electric Power Co Ltd filed Critical Wuhan University of Technology WUT
Priority to CN202310398831.8A priority Critical patent/CN116131291B/en
Publication of CN116131291A publication Critical patent/CN116131291A/en
Application granted granted Critical
Publication of CN116131291B publication Critical patent/CN116131291B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J15/00—Systems for storing electric energy
    • H02J15/008—Systems for storing electric energy using hydrogen as energy vector
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/06—Power analysis or power optimisation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Power Engineering (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biophysics (AREA)
  • Computer Hardware Design (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Biomedical Technology (AREA)
  • Geometry (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

According to the hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power, a hydrogen electric coupling system is established, a rolling optimization objective function is set by taking the minimum charge and discharge power of the hydrogen electric coupling system as a target, and an ideal grid-connected power predicted value and a total power predicted value of the hydrogen electric coupling system are solved through setting constraint conditions; performing Hilbert-Huang transform on a curve formed by the total power predicted value of the hydrogen electric coupling system to obtain instantaneous frequency, and selecting demarcation frequency based on the range of the instantaneous frequency; setting an objective function; and (3) setting constraint conditions of charge-discharge balance, power fluctuation and energy storage capacity based on a fluctuation stabilizing effect maximization objective function by adopting a self-adaptive multi-objective particle swarm optimization algorithm, and performing parameter optimization to obtain an optimal capacity configuration scheme for guaranteeing the fluctuation power stabilizing effect and guarantee the fluctuation power stabilizing effect.

Description

Hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power
Technical Field
The invention relates to the technical field of hydrogen electric coupling, in particular to a hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power.
Background
In recent years, with the increasing exhaustion of fossil energy and global warming problems, renewable energy has been rapidly developed. Renewable energy sources such as wind power, photovoltaic and the like generate electricity with the influence of natural conditions such as wind speed and the like greatly, so that the fluctuation and intermittence of output power are obvious, the power cannot be completely connected to the grid for consumption, and great waste is caused. In some areas where the electricity load center is far away from the renewable energy rich area, large-scale centralized power generation is generally adopted, and electric energy is conveyed remotely. In such cases, it is often desirable to configure the hybrid energy storage system HESS (Hybrid energy storage system) for large scale wind farms or photovoltaic arrays to compensate for fluctuations in output power with a high degree of accuracy.
At present, a typical HESS topological structure is a storage battery and a super capacitor, the storage battery is used for compensating long-term low-frequency power, the super capacitor is used for compensating short-term high-frequency power, hydrogen energy is used as a clean energy source, and the HESS has the characteristics of high energy density, long service life, convenience in storage and transportation and the like, and the application proportion in HESS is improved in recent years. The HESS with the structure has the advantages of high energy density, large capacity, low cost, high degradation rate, short service life, poor flexibility and the like, and because the fluctuation power and frequency are different, the capacity configuration of the system is greatly affected by factors such as an operation mode, equipment states and the like, so that the HESS capacity configuration under different targets is considered to be an important research direction.
Disclosure of Invention
The invention provides a hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power, which aims to solve the technical problem of HESS capacity configuration.
In order to solve the technical problems, the invention provides a hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power, which comprises the following steps:
step S1: establishing a hydrogen electric coupling system, wherein the hydrogen electric coupling system comprises a hydrogen energy storage unit, a super capacitor array and a DC/DC converter which are connected through a direct current bus; the hydrogen energy storage unit comprises an electrolytic tank, a fuel cell and a hydrogen storage tank;
step S2: setting a rolling optimization objective function by taking the minimum charge and discharge power of the hydrogen electric coupling system as a target, restraining by a grid-connected power fluctuation rate limit value and an upper limit of the charge and discharge power of the hydrogen electric coupling system, establishing a state space model by the relation among the original power, the ideal grid-connected power and the total power of the hydrogen electric coupling system, and solving by adopting a quadratic programming method to obtain an ideal grid-connected power predicted value and a total power predicted value of the hydrogen electric coupling system;
step S3: performing Hilbert-Huang transform on a curve formed by the total power predicted value of the hydrogen electric coupling system to obtain instantaneous frequency, and selecting demarcation frequency based on the range of the instantaneous frequency;
step S4: setting an objective function, wherein the objective function comprises a set fluctuation stabilizing effect maximization objective function which aims at a fluctuation non-suppression rate based on a total power predicted value of the hydrogen electric coupling system;
the fluctuation non-suppression ratio represents the sum of overflow power and deficiency power when the hydrogen electric coupling system is distributed for consumption;
step S5: and adopting a self-adaptive multi-target particle swarm optimization algorithm, setting constraint conditions of charge-discharge balance, power fluctuation and energy storage capacity based on a fluctuation stabilizing effect maximization objective function, and optimizing five parameters of maximum charge-discharge power of a super capacitor array, rated capacity of the super capacitor array, rated power of an electrolytic tank, rated power of a fuel cell and rated capacity of a hydrogen storage tank to obtain an optimal capacity configuration scheme for guaranteeing the fluctuation stabilizing effect.
Preferably, the rolling optimization objective function J in step S2 g The expression of (2) is:
；
wherein M represents the predicted time length, P g,ideal Represents ideal grid-connected power, T s The sampling period is denoted, and t is denoted time.
Preferably, in step S2, the expression for restraining the upper limit of the charging and discharging power of the grid-connected power fluctuation ratio limit value and the hydrogen electric coupling system is as follows:
；
；
wherein delta is the grid-connected power fluctuation rate limit value, P hecs,max Represents the maximum charge-discharge power limit value, P of the hydrogen electric coupling system hecs And (t+1) represents the predicted total power value of the hydrogen electric coupling system at the time t+1.
Preferably, the method for solving by using the quadratic programming method in the step S2 includes the following steps:
step S21: the state space model is established through the relation among the original power, the ideal grid-connected power and the total power of the hydrogen electric coupling system, and the expression is as follows:
；
in the formula, u (t) =p hecs (t) represents a control variable, x (t) =p g,ideal (t) represents a state variable, r (t) =p w (t) represents the disturbance input, P w (t) represents the original power, y (t) =p g,ideal (t) represents output, a=b 1 =C 1 = 0，B =–1，C = A 1 = 1；
Step S22: converting the rolling optimization objective function into a quadratic programming standard type, wherein the expression is as follows:
；
wherein, R=2,；
step S23: converting an expression for restraining the grid-connected power fluctuation rate limit value and the upper limit of the charging and discharging power of the hydrogen electric coupling system into a quadratic programming standard type expression, wherein the expression is as follows:
；
；
in the method, in the process of the invention,
；
；
；
；
step S24: and (4) obtaining a matrix z through the quadratic programming standard converted in the steps S22 and S23 to obtain an ideal grid-connected power predicted value and a total power predicted value of the hydrogen electric coupling system.
Preferably, the state space model is modified by using a modification parameter m, and r (t) =pw (t) -m, so as to offset the electric quantity which is lost by internal heat generated during the operation of the hydrogen electric coupling system.
Preferably, the surge suppression effect in step S4 maximizes the objective function J cc,2 The expression of (2) is:
；
wherein R is FU Represents the non-inhibition rate of fluctuation, R S Indicating overflow rate, R V Rate of deficiency, R max Indicating maximum overflow and deficit rates;
；
wherein T represents the total duration of operation, T s Representing sampling time intervals, E w,day Represents the total energy of daily renewable energy power generation, P S,sc (t) and P S,hu (t) respectively representing the distributed power parts which cannot be absorbed by the super capacitor array and the hydrogen energy storage unit at the moment t even if the super capacitor array and the hydrogen energy storage unit are charged with the maximum power; p (P) V,sc (t) and P V,hu And (t) represents the part of the distributed power which cannot be met by the super capacitor array and the hydrogen energy storage unit at the moment t even if the super capacitor array and the hydrogen energy storage unit are discharged at the maximum power.
Preferably, in step S5, the constraint conditions of charge-discharge balance, power fluctuation and energy storage capacity are expressed as follows:
1) Charge-discharge balance constraint:
；
wherein P is sc (t)、P el (t) and P fc (t) the working powers of the super capacitor array, the electrolytic tank and the fuel cell at the moment t, eta DC/DC Representing converter efficiency, P sc,ref (t) represents the power above the boundary frequency portion, P hu,ref (t) represents a power of not higher than the boundary frequency portion;
2) Power fluctuation:
；
3) Energy storage capacity:
；
wherein E is sc (t) and E hst (t) representing the residual energy and SOC of the super capacitor array and the hydrogen storage tank at the time t respectively sc,max And SOC (System on chip) sc,min SOC upper limit and SOC lower limit of the super capacitor array respectively, LOH hst,max And LOH (Low-Density parity) hst,min Respectively represent the upper limit and the lower limit of LOH of the hydrogen storage tank and SOC sc (t) represents the SOC, LOH of the super capacitor array at time t hst (t) represents LOH of the hydrogen tank at time t.
Preferably, in step S5, the formula for performing the speed update of the adaptive multi-objective particle swarm optimization algorithm is as follows:
；
where k represents the number of iterations, i represents the ith particle in the population, d represents the dimension, V represents the speed, P and P g Respectively representing the current optimal positions of the particles and the population, X represents the current position of the particles, r 1 And r 2 Represents a random value between 0 and 1, ω represents an inertial factor, c 1 And c 2 Representing the acceleration factor.
PreferablyTo improve the convergence performance of the adaptive multi-objective particle swarm optimization algorithm, the inertia factor omega and the acceleration factor c are calculated 1 ，c 2 The expression for performing the adaptation improvement is:
；
wherein omega is max Represents the maximum value of inertia factor omega min Minimum value, k, representing the maximum value of the inertia factor max Representing the maximum iteration number, k representing the current iteration number;
；
wherein, c 1s And c 2s Respectively represent c 1 And c 2 Initial value of c 1e And c 2e Respectively represent c 1 And c 2 Final value of (2).
Preferably, in step S5, the method for optimizing the five parameters of the maximum charge and discharge power of the supercapacitor array, the rated capacity of the supercapacitor array, the rated power of the electrolytic cell, the rated power of the fuel cell and the rated capacity of the hydrogen storage tank includes the following steps:
step S51: based on the demarcation frequency, a power P higher than the demarcation frequency is determined sc,ref (t) a power P distributed to the supercapacitor array to be not greater than the demarcation frequency portion hu,ref (t) being distributed to the hydrogen storage unit; calculation of spillover capacitive power P by control strategy of hydrogen electric coupling system s,sc (t) deficit capacitor Power P v,sc (t) Hydrogen production Power P by overflow S,hv (t) and deficit discharge Power P V,hv (t)；
1) When said P is sc,ref (t) when greater than 0, indicating that the supercapacitor array absorbs electrical energy; calculating capacitor charging power and deficit charging power based on the maximum power of the super capacitor array; calculating the SOC of the current super capacitor array through the capacitor charging power; integrating the SOC of the current super capacitor array with the super powerComparing the maximum SOC of the capacitor array to obtain an deficit charging SOC; calculating the spill-over capacitor power P which cannot be absorbed when the super capacitor array is charged with the maximum power through the shortage charging power and the shortage charging SOC s,sc (t)；
2) When said P is sc,ref (t) when not greater than 0, indicating that the supercapacitor array releases electrical energy; calculating capacitor discharge power and deficiency discharge power based on the maximum power of the super capacitor array; calculating the SOC of the current super capacitor array through the capacitor discharge power; comparing the SOC of the current super capacitor array with the minimum SOC of the super capacitor array to obtain a shortage discharge SOC; calculating the insufficient capacitor power P which cannot be met when the super capacitor array is charged with the maximum power through the insufficient discharge power and the insufficient discharge SOC v,sc (t)；
3) When said P is hu,ref (t) when greater than 0, indicating that the hydrogen storage unit absorbs electrical energy to produce hydrogen; calculating hydrogen production power and hydrogen shortage power based on the maximum power of the electrolytic cell; calculating LOH of the current hydrogen storage tank according to the hydrogen production power; comparing the LOH of the current hydrogen storage tank with the maximum LOH of the hydrogen storage tank to obtain an deficiency LOH; calculating overflow hydrogen production power P which cannot be absorbed when the hydrogen energy storage unit produces hydrogen with maximum power through the deficiency hydrogen production power and the deficiency LOH S,hv (t)；
4) When said P is hu,ref (t) when not greater than 0, indicating that the hydrogen storage unit consumes hydrogen to discharge; calculating a battery discharge power and an absence battery discharge power based on the maximum power of the fuel cell; calculating LOH of the current hydrogen storage tank through the battery discharge power; comparing the LOH of the current hydrogen storage tank with the minimum LOH of the hydrogen storage tank to obtain an deficiency LOH; calculating the shortage discharging power P which cannot be met when the hydrogen energy storage unit discharges at the maximum power by the shortage battery discharging power and the shortage LOH V,hv (t)；
Step S52: by the objective function, to the P s,sc (t)、P v,sc (t)、P S,hv (t) and P V,hv (t) performing analysis and iteration to obtain the maximum charge and discharge power of the super capacitor arrayOptimal capacity allocation scheme for five parameters of super capacitor array rated capacity, electrolytic tank rated power, fuel cell rated power and hydrogen storage tank rated capacity
The beneficial effects of the invention at least comprise: the hydrogen energy storage unit consisting of the electrolytic tank, the fuel cell and the hydrogen storage tank is used for absorbing low-frequency fluctuation power, so that the capacity configuration flexibility is improved, and the pollution to the environment is reduced; carrying out power prediction, system power allocation and capacity configuration optimization through capacity configuration planning strategies of three levels, wherein each level is independently operated by using different algorithms and is mutually coupled through data and energy transmission; and carrying out capacity configuration optimizing by adopting a self-adaptive multi-target particle swarm optimization algorithm, and ensuring the stabilizing effect of the fluctuation power.
As an additional technical feature, a correction parameter m is designed to realize that the hydrogen electric coupling system only maintains running with fluctuating power, and the internal running loss of the hydrogen electric coupling system is counteracted by a small part of original power, so that the prediction is corrected, and an accurate capacity configuration scheme can be obtained.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a topology of a hydrogen electrical coupling system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an optimization flow of an adaptive multi-objective particle swarm optimization algorithm according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of the operation control parameters of the hydrogen electric coupling system according to the embodiment of the invention;
FIG. 5 is a schematic diagram of the primary power and the ideal grid-tied power in an embodiment of the present invention;
FIG. 6 is a graph of predicted total power for a hydrogen-electric coupling system according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of empirical mode decomposition of a predicted total power value of a hydrogen electrical coupling system according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of an embodiment of a super capacitor array operating domain P sc,ref (t) schematic and Hydrogen storage Unit operating Domain P hu,ref (t) schematic;
FIG. 9 is a schematic diagram of the storage levels of the supercapacitor array and the hydrogen storage unit of the present invention;
FIG. 10 is a schematic diagram of the change in energy storage level of the system under no power capacity constraints of the SOCsc (t) and LOHhst, and the actual run domain, the unexecuted run domain, and the SOCsc (t) and LOHhst of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is evident that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present invention, based on the embodiments of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for configuring a capacity of a hydrogen electric coupling system for stabilizing fluctuating power, including the steps of:
step S1: establishing a hydrogen electric coupling system, wherein the hydrogen electric coupling system comprises a hydrogen energy storage unit, a super capacitor array and a DC/DC converter which are connected through a direct current bus; the hydrogen energy storage unit comprises an electrolytic tank, a fuel cell and a hydrogen storage tank.
Specifically, the topology of the hydrogen electric coupling system is shown in fig. 2, the super capacitor array SC absorbs and emits high-frequency power, and the hydrogen energy storage unit HU absorbs and emits low-frequency power. In the hydrogen storage unit HU, the electrolytic tank EL absorbs excessive power for hydrogen production by electrolysis of water, hydrogen is stored in the hydrogen storage tank HST, and the fuel cell FC consumes hydrogen discharge in the hydrogen storage tank HST to compensate for the shortage power
Step S2: and setting a rolling optimization objective function by taking the minimum charge and discharge power of the hydrogen electric coupling system as a target, restraining by using a grid-connected power fluctuation rate limit value and the upper limit of the charge and discharge power of the hydrogen electric coupling system, establishing a state space model by using the relation among the original power, the ideal grid-connected power and the total power of the hydrogen electric coupling system, and solving by adopting a quadratic programming method to obtain an ideal grid-connected power predicted value and a total power predicted value of the hydrogen electric coupling system.
Specifically, the embodiment of the invention is setRolling optimization objective function J g The expression of (2) is:
；
wherein M represents the predicted time length, P g,ideal Represents ideal grid-connected power, T s The sampling period is denoted, and t is denoted time.
The expression for restraining the grid-connected power fluctuation rate limit value and the upper limit of the charging and discharging power of the hydrogen electric coupling system is as follows:
；
；
wherein delta is the grid-connected power fluctuation rate limit value, P hecs,max Represents the maximum charge-discharge power limit value, P of the hydrogen electric coupling system hecs And (t+1) represents the predicted total power value of the hydrogen electric coupling system at the time t+1.
The method for solving by adopting the quadratic programming method comprises the following steps:
step S21: the state space model is established through the relation among the original power, the ideal grid-connected power and the total power of the hydrogen electric coupling system, and the expression is as follows:
；
in the formula, u (t) =p hecs (t) represents a control variable, x (t) =p g,ideal (t) represents a state variable, r (t) =p w (t) represents the disturbance input, P w (t) represents the original power, y (t) =p g,ideal (t) represents output, a=b 1 =C 1 = 0，B =–1，C = A 1 =1; wherein P is hecs More than 0 time represents charging of hydrogen electric coupling system, P hecs Less than 0 time represents discharge.
Step S22: converting the rolling optimization objective function into a quadratic programming standard type, wherein the expression is as follows:
；
wherein, R=2,；
step S23: converting an expression for restraining the grid-connected power fluctuation rate limit value and the upper limit of the charging and discharging power of the hydrogen electric coupling system into a quadratic programming standard type expression, wherein the expression is as follows:
；
；
in the method, in the process of the invention,
；
；
；
；
step S24: and (4) obtaining a matrix z through the quadratic programming standard converted in the steps S22 and S23 to obtain an ideal grid-connected power predicted value and a total power predicted value of the hydrogen electric coupling system.
In the embodiment of the invention, in order to realize that the hydrogen electric coupling system only maintains operation with fluctuating power, a part of original power needs to be additionally taken to offset the internal operation loss of the hydrogen electric coupling system. And the correction parameter m is designed in the feedback correction link, the state space model is corrected by adopting the correction parameter m, and r (t) =pw (t) -m is taken to offset the electric quantity which is generated and lost by the internal heat during the working of the hydrogen electric coupling system.
Step S3: and performing Hilbert-Huang transform on a curve formed by the total power predicted value of the hydrogen electric coupling system to obtain instantaneous frequency, and selecting demarcation frequency based on the range of the instantaneous frequency.
Hilbert-Huang transform is a signal processing method, which requires first performing empirical mode decomposition on the original signal to obtain eigenmode components c in different frequency ranges i And residual component r n I is the number of preset intrinsic mode components IMFs, and in the embodiment of the present invention, the original signal takes the total power predicted value of the hydrogen electric coupling system obtained in step S2.
Specifically, in the embodiment of the invention, the total power predicted value P of the hydrogen electric coupling system hecs (t) after empirical mode decomposition is expressed as follows:
；
to obtain each c i To determine the demarcation frequency f of the hydrogen-electric coupling system power d C, respectively i Convolving with 1/pi t, which is effectively an all-pass phase-shifting complex process that lags the phase by 90 deg., this process is the Hilbert-Huang transform, expressed as follows:
；
where τ represents the concept of time.
C (t) and H [ c (t) ] are combined into an analytical signal z (t), defined as follows:
；
wherein a (t),For the instantaneous amplitude and instantaneous phase of the signal, the instantaneous frequency can be calculated:
；
obtaining a frequency range by the obtained instantaneous frequency, and selecting a frequency value from the frequency range as a decomposition frequency f d Will have a frequency higher than the demarcation frequency f d Part P of (2) sc,ref (t) assigning a frequency to the supercapacitor array SC that is lower than the demarcation frequency f d Part P of (2) hu,ref (t) is assigned to the hydrogen storage unit HU,。
step S4: setting an objective function, wherein the objective function comprises a set fluctuation stabilizing effect maximization objective function which aims at a fluctuation non-suppression rate based on a total power predicted value of the hydrogen electric coupling system;
the ripple non-suppression ratio represents a sum of overflow power and deficiency power allocated to the hydrogen electric coupling system for consumption.
Specifically, in evaluating the effect of the hydrogen electric coupling system to stabilize the fluctuating power, the fluctuating non-suppression rate R is generally defined FU To express the power portion of the system assigned to the hydrogen-electrically coupled system but not to be executed, R FU Smaller proves that the higher the execution rate of the distributed power is, the better the stabilizing effect is, R FU From the overflow rate R S And deficiency rate R V The composition is as follows:
；
；
wherein R is FU Represents the non-inhibition rate of fluctuation, R S Indicating overflow rate, R V Rate of absence, T represents total duration of operation, T s Representing the sampling time intervalPartition E w,day Represents the total energy of daily renewable energy power generation, P S,sc (t) and P S,hu (t) respectively representing the distributed power parts which cannot be absorbed by the super capacitor array and the hydrogen energy storage unit at the moment t even if the super capacitor array and the hydrogen energy storage unit are charged with the maximum power; p (P) V,sc (t) and P V,hu And (t) represents the part of the distributed power which cannot be met by the super capacitor array and the hydrogen energy storage unit at the moment t even if the super capacitor array and the hydrogen energy storage unit are discharged at the maximum power. R is R S = R V =0 means that the hydrogen electrical coupling system operates entirely in accordance with the assigned operating domain.
Setting the objective function J for maximizing the wave stabilizing effect cc,2 The expression of (2) is:
；
wherein R is max Indicating maximum overflow and deficit rates.
Step S5: and adopting a self-adaptive multi-target particle swarm optimization algorithm, setting constraint conditions of charge-discharge balance, power fluctuation and energy storage capacity based on a fluctuation stabilizing effect maximization objective function, and optimizing five parameters of maximum charge-discharge power of a super capacitor array, rated capacity of the super capacitor array, rated power of an electrolytic tank, rated power of a fuel cell and rated capacity of a hydrogen storage tank to obtain an optimal capacity configuration scheme for guaranteeing the fluctuation stabilizing effect.
Specifically, the expression of the constraint conditions of charge-discharge balance, power fluctuation and energy storage capacity set in the embodiment of the invention is:
1) Charge-discharge balance constraint:
；
wherein P is sc (t)、P el (t) and P fc (t) the working powers of the super capacitor array, the electrolytic tank and the fuel cell at the moment t, eta DC/DC Representing converter efficiency, P sc,ref (t) represents the power above the boundary frequency portion, P hu,ref (t) represents not higher than the scorePower of the boundary frequency portion;
2) Power fluctuation:
；
3) Energy storage capacity:
；
wherein E is sc (t) and E hst (t) representing the residual energy and SOC of the super capacitor array and the hydrogen storage tank at the time t respectively sc,max And SOC (System on chip) sc,min SOC upper limit and SOC lower limit of the super capacitor array respectively, LOH hst,max And LOH (Low-Density parity) hst,min Respectively represent the upper limit and the lower limit of LOH of the hydrogen storage tank and SOC sc (t) represents the SOC, LOH of the super capacitor array at time t hst (t) represents LOH of the hydrogen tank at time t.
As shown in fig. 3, which is a schematic flow chart of an adaptive multi-objective particle swarm optimization algorithm, the overall flow is the prior art, only the part related to the present invention is described, and the set speed update formula is as follows:
；
where k represents the number of iterations, i represents the ith particle in the population, d represents the dimension, V represents the speed, P and P g Respectively representing the current optimal positions of the particles and the population, X represents the current position of the particles, r 1 And r 2 Represents a random value between 0 and 1, ω represents an inertial factor, c 1 And c 2 Representing the acceleration factor.
The embodiment of the invention improves the convergence performance of the self-adaptive multi-target particle swarm optimization algorithm, and aims at the inertia factor omega and the acceleration factor c 1 ，c 2 The expression for performing the adaptation improvement is:
；/>
wherein omega is max Represents the maximum value of inertia factor omega min Minimum value, k, representing the maximum value of the inertia factor max Representing the maximum iteration number, k representing the current iteration number;
；
wherein, c 1s And c 2s Respectively represent c 1 And c 2 Initial value of c 1e And c 2e Respectively represent c 1 And c 2 Final value of (2).
The method for optimizing the five parameters of the maximum charge and discharge power of the super capacitor array, the rated capacity of the super capacitor array, the rated power of the electrolytic tank, the rated power of the fuel cell and the rated capacity of the hydrogen storage tank by solving an objective function through an operation control strategy of the hydrogen electric coupling system comprises the following steps:
step S51: based on the demarcation frequency, a power P higher than the demarcation frequency is determined sc,ref (t) a power P distributed to the supercapacitor array to be not greater than the demarcation frequency portion hu,ref (t) being distributed to the hydrogen storage unit;
1) When said P is sc,ref (t) when greater than 0, indicating that the supercapacitor array absorbs electrical energy; calculating capacitor charging power and deficit charging power based on the maximum power of the super capacitor array; calculating the SOC of the current super capacitor array through the capacitor charging power; comparing the SOC of the current super capacitor array with the maximum SOC of the super capacitor array to obtain a shortage charging SOC; calculating the distribution power part P which is still not absorbed by the super capacitor array and is charged with the maximum power through the shortage charging power and the shortage charging SOC s,sc (t)；
2) When said P is sc,ref (t) when not greater than 0, indicating that the supercapacitor array releases electrical energy; calculating capacitor discharge power and deficiency discharge power based on the maximum power of the super capacitor array; calculating the current super capacitor through the capacitor discharge powerSOC of the array; comparing the SOC of the current super capacitor array with the minimum SOC of the super capacitor array to obtain a shortage discharge SOC; calculating the distribution power part P which is still unsatisfied by charging the super capacitor array with the maximum power through the shortage discharge power and the shortage discharge SOC v,sc (t)；
3) When said P is hu,ref (t) when greater than 0, indicating that the hydrogen storage unit absorbs electrical energy to produce hydrogen; calculating hydrogen production power and hydrogen shortage power based on the maximum power of the electrolytic cell; calculating LOH of the current hydrogen storage tank according to the hydrogen production power; comparing the LOH of the current hydrogen storage tank with the maximum LOH of the hydrogen storage tank to obtain an deficiency LOH; calculating the distribution power part P which is still not absorbed and is produced by the hydrogen energy storage unit with the maximum power through the hydrogen production power shortage and the LOH shortage S,hv (t)；
4) When said P is hu,ref (t) when not greater than 0, indicating that the hydrogen storage unit consumes hydrogen to discharge; calculating a battery discharge power and an absence battery discharge power based on the maximum power of the fuel cell; calculating LOH of the current hydrogen storage tank through the battery discharge power; comparing the LOH of the current hydrogen storage tank with the minimum LOH of the hydrogen storage tank to obtain an deficiency LOH; calculating the distribution power part P which can not be met yet when the hydrogen energy storage unit discharges with the maximum power through the discharge power of the deficiency battery and the deficiency LOH V,hv (t)；
Step S52: by the objective function, to the P s,sc(t) 、P v,sc (t)、P S,hv (t) and P V,hv And (t) analyzing and iterating to obtain an optimal capacity configuration scheme comprising five parameters of maximum charge and discharge power of the super capacitor array, rated capacity of the super capacitor array, rated power of an electrolytic cell, rated power of a fuel cell and rated capacity of a hydrogen storage tank.
Specifically, as shown in fig. 4, a to L in fig. 4 represent the operation states of the system at corresponding moments, as shown in table 1:
the calculation method of each variable in fig. 4 is shown in table 2:
in the table, eta sc ，η el And eta fc The efficiency of the supercapacitor array SC, the electrolytic cell EL and the fuel cell FC are shown, respectively.
The following further describes the implementation of the present invention in connection with a specific application scenario.
The measured data of a certain 2MW wind power plant for a certain month 43200 min is taken as the original power input, and the simulation parameters of the improved model predictive control MMPC are set as shown in Table 3.
Original power P w (t) the ideal grid-connected power P is obtained through prediction g,ideal (t) as shown in FIG. 5, where (b) in FIG. 5 is an enlarged view of the dotted line box portion in (a) of FIG. 5, it can be seen that, by prediction, the short-term fluctuation of the original power is improved, the power fluctuation is relatively smooth, the fluctuation amplitude becomes small, the peak position is particularly remarkable, and P g,ideal The following property of (t) is good, and excessive hysteresis P does not occur w (t) phenomenon.
HECS distributed power P of hydrogen electric coupling system hecs (t) as shown in FIG. 6, the total power absorbed/compensated by HECS in FIG. 6 is subjected to empirical mode decomposition, and in the embodiment of the invention, the maximum number of screening iterations is set to 100, and the number of intrinsic mode components IMF is set to 5, so as to obtain intrinsic mode components IMF1-5 and residual component r n 。
As shown in FIG. 7, IMF1 and IMF2 were obtained as principal components with a 95.1% duty cycle in the frequency range of 9.47×10-5 Hz to 2.17×10-3 Hz.
Therefore, in the embodiment of the invention, hilbert-Huang transformation HHT is only performed on IMF1 and IMF2 to obtain Hilbert spectrum, and boundary frequency f in the range is selected d . The frequencies of IMF1 and IMF2 can be seenMainly concentrated in the range of 2.2X10-4 Hz to 2.1X10-3 Hz, i.e. the hydrogen electric coupling system distributes power P hecs Every positive and negative alternation of (t) is concentrated in the order of hours and minutes, let f d =2.8x10-4 Hz, the frequency assumed by the hydrogen storage unit HU is lower than f d Is subjected to an hourly power fluctuation, the hydrogen storage unit SC assumes a high frequency f d Is a minute-scale power fluctuation. According to the demarcation frequency f d SC operation domain P obtained by power distribution sc,ref (t) and HU run Domain P hu,ref (t) As shown in FIG. 9, P sc,ref (t) positive value represents SC charge, negative value represents discharge, P hu,ref Positive values of (t) represent EL hydrogen consumption and negative values represent FC hydrogen consumption and discharge. It can be seen that in the partial enlarged view, the frequency of power fluctuation allocated to SC is significantly large.
Because the adaptive multi-objective particle swarm optimization algorithm can be adopted to carry out optimizing solution on multiple objectives, the objective function with capacity allocation economy as the objective is set for explanation, and the capacity allocation economy objective function J is set in the embodiment of the invention cc,1 Expressed as:
；
；
wherein s.t. represents a constraint, C ACS Representing the total investment cost, C ACS,max Represents the maximum investment cost when the surge non-suppression rate is zero, C cap Represents initial capital investment, CRF (r) 0 ,Y sys ) Representing the ratio of the current values of the year, r 0 Express interest rate, Y sys Representing service life of system, C om Represents maintenance cost, n represents the number of devices, C rep，i Representing the reset cost of the ith device, SFF (r 0 ,Y dev,i ) Representing the coefficient of the fund to be paid, Y dev,i Indicating the service life of the i-th device.
Initial capital C cap The expression of (2) is:
；
wherein C is cap,main Representing the initial investment cost of main equipment of the system, C cap,aux Representing the initial investment cost of the auxiliary equipment, gamma sc,P And gamma sc,E Initial investment cost coefficients, gamma, representing rated power and capacity, respectively, of a supercapacitor array el 、γ fc And gamma hst Representing initial investment cost coefficients, P, of the electrolyzer, the fuel cell and the hydrogen storage tank, respectively sc,max Represents the maximum charge and discharge power of the super capacitor array, E sc,r Representing the rated capacity of the super capacitor array, P el,r Indicating rated power of electrolytic cell, P fc,r Indicating rated power of fuel cell, E hst,r Indicating the hydrogen storage tank capacity rating.
Year present ratio CRF (r) 0 ,Y sys ) The expression of (2) is:
；
the maintenance cost C om The expression of (2) is:
；
beta in sc,P 、β el,r 、β fc,r And beta hst Representing operation and maintenance cost coefficients of the super capacitor array, the electrolytic cell, the fuel cell and the hydrogen storage tank respectively;
reset cost C of ith device rep，i The expression of (2) is:
；
wherein C is rep,sc 、C rep,el 、C rep,fc And C rep,hst Respectively representing replacement cost, χ of the super capacitor array, the electrolytic tank, the fuel cell and the hydrogen storage tank sc,P And χ (x) sc,E Representing reset cost coefficients, χ, for rated power and capacity, respectively, of a supercapacitor array el 、χ fc And χ (x) hst Representing the reset cost coefficients of the electrolyzer, the fuel cell and the hydrogen storage tank, respectively;
the repayment fund coefficient SFF (r 0 ,Y dev,i ) The expression of (2) is:
；
by J cc,1 And J cc,2 The expression for constructing the dual objective function is:
；
wherein C is ACS,max R represents FU Maximum annual investment cost of system when=0, R max Indicating the maximum overflow or deficit rate allowed.
For the above-described configuration economy objective function, the efficiency and cost parameters of each device in the embodiment of the present invention are shown in table 4:
the initial energy of the hydrogen electric coupling system is set to be 0 under the condition of no power capacity constraint. SC storage level E sc (t) and HST energy storage level E hst (t) variation in the allocation domain of fig. 8 as shown in fig. 9, the correction parameter m=0 is set in fig. 9 (a), and the correction parameter m=7.8 is set in fig. 9 (b). When m=0, the loss of the hydrogen electric coupling system, which is mainly generated by heat, wastes a part of electric quantity, wherein FC is the most serious, so that the actual charging quantity cannot meet the requirement of the system for discharging electric quantity. When m=7.8, a small part of wind power which is needed to be connected with the grid counteracts various losses in the hydrogen electric coupling system, and charge-discharge balance is achieved.
In the capacity configuration, simulation parameters of the hydrogen electric coupling system are set as shown in Table 5, wherein C ACS,max To completely stabilize wavesDynamic power, i.e., rfu=0, requires annual investment costs for the system and the like. Embedding HECS operation control strategy into algorithm to obtain optimal capacity configuration under multiple objective functions as follows: super capacitor array maximum charge and discharge power P sc,max =446.2 kW, super capacitor array rated capacity E sc,r = 244.9 kWh, cell rated power P el,r =51.2 kW, fuel cell rated power P fc,r =48.8 kW and hydrogen storage tank rated capacity E hst,r =11.2 kg, at this time, R of the system S = 2.276%，R V = 1.801%，C ACS = 150 212 $。
The capacity configuration scheme was verified, and both the initial SOC of the SC and the initial LOH of the HST were set to the minimum value of 10%. In this capacity allocation scheme, the actual operating powers of SC and HU are as shown in (a) of fig. 10, and the actual operating ranges of SC and HU are limited to the rated range due to the restrictions of the rated powers and capacities of the respective devices, so that the allocated powers not performed by SC and HU are as shown in (b) of fig. 10, and the allocated powers exceeding the rated values in this part are very small. SOC of SC sc (t) and LOH of HST hst (t) as shown in fig. 10 (c), the capacity of both energy storage units does not appear to be full or too low during operation. In addition, under the action of the correction parameter m, the axes in the actual operation domain of the SC and the HU are both above 0.
Annual investment cost C of system under the capacity allocation ACS 150 212 $ compared with C ACS,max The reduction is 41.1%, and the fluctuation non-inhibition rate is only 4.077%.
The hydrogen electric coupling system capacity configuration method for stabilizing the fluctuation power effectively stabilizes the fluctuation power, carries out multi-objective solution through the economic objective function configured by the embodiment, and greatly reduces the system cost while guaranteeing the stabilizing effect.
The foregoing embodiments may be combined in any way, and all possible combinations of the features of the foregoing embodiments are not described for brevity, but only the preferred embodiments of the invention are described in detail, which should not be construed as limiting the scope of the invention. The scope of the present specification should be considered as long as there is no contradiction between the combinations of these technical features.
It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.

Claims (9)

1. A hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power is characterized in that: the method comprises the following steps:
step S1: establishing a hydrogen electric coupling system, wherein the hydrogen electric coupling system comprises a hydrogen energy storage unit, a super capacitor array and a DC/DC converter which are connected through a direct current bus; the hydrogen energy storage unit comprises an electrolytic tank, a fuel cell and a hydrogen storage tank;
step S2: setting a rolling optimization objective function by taking the minimum charge and discharge power of the hydrogen electric coupling system as a target, restraining by a grid-connected power fluctuation rate limit value and an upper limit of the charge and discharge power of the hydrogen electric coupling system, establishing a state space model by the relation among the original power, the ideal grid-connected power and the total power of the hydrogen electric coupling system, and solving by adopting a quadratic programming method to obtain an ideal grid-connected power predicted value and a total power predicted value of the hydrogen electric coupling system;
step S3: performing Hilbert-Huang transform on a curve formed by the total power predicted value of the hydrogen electric coupling system to obtain instantaneous frequency, and selecting demarcation frequency based on the range of the instantaneous frequency;
step S4: setting an objective function, wherein the objective function comprises a set fluctuation stabilizing effect maximization objective function which aims at a fluctuation non-suppression rate based on a total power predicted value of the hydrogen electric coupling system;
the fluctuation non-suppression ratio represents the sum of overflow power and deficiency power when the hydrogen electric coupling system is distributed for consumption;
the wave-stabilizing effect maximizes the objective function J cc,2 The expression of (2) is:
J CC,2 ＝minR FU ,s.t.R S ≤R max ,R V ≤R max
wherein R is FU Represents the non-inhibition rate of fluctuation, R S Indicating overflow rate, R V Rate of deficiency, R max Indicating maximum overflow and deficit rates;
R FU ＝R S +R V
wherein T represents the total duration of operation, T s Representing sampling time intervals, E w,day Represents the total energy of daily renewable energy power generation, P S,sc (t) and P S,hu (t) respectively representing the distributed power parts which cannot be absorbed by the super capacitor array and the hydrogen energy storage unit at the moment t even if the super capacitor array and the hydrogen energy storage unit are charged with the maximum power; p (P) V,sc (t) and P V,hu (t) represents the part of the distributed power which cannot be met by the super capacitor array and the hydrogen energy storage unit at the moment t even if the super capacitor array and the hydrogen energy storage unit are discharged at the maximum power;
step S5: and adopting a self-adaptive multi-target particle swarm optimization algorithm, setting constraint conditions of charge-discharge balance, power fluctuation and energy storage capacity based on a fluctuation stabilizing effect maximization objective function, and optimizing five parameters of maximum charge-discharge power of a super capacitor array, rated capacity of the super capacitor array, rated power of an electrolytic tank, rated power of a fuel cell and rated capacity of a hydrogen storage tank to obtain an optimal capacity configuration scheme for guaranteeing the fluctuation stabilizing effect.
2. According to claim 1The hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power is characterized by comprising the following steps of: the scroll optimization objective function J in step S2 g The expression of (2) is:
wherein M represents the predicted time length, P g,ideal Represents ideal grid-connected power, T s The sampling period is denoted, and t is denoted time.
3. The hydrogen electric coupling system capacity configuration method for stabilizing fluctuating power according to claim 2, characterized in that: in the step S2, the expression for restraining the grid-connected power fluctuation rate limit value and the upper limit of the charging and discharging power of the hydrogen electric coupling system is as follows:
-P hecs,max ≤P hecs (t+i)≤P hecs,max ,i＝0,1,…,(M-1)
wherein delta is the grid-connected power fluctuation rate limit value, P hecs,max Represents the maximum charge-discharge power limit value, P of the hydrogen electric coupling system hecs And (t+1) represents the predicted total power value of the hydrogen electric coupling system at the time t+1.
4. A hydrogen electric coupling system capacity allocation method for stabilizing fluctuating power according to claim 3, characterized in that: the method for solving by adopting the quadratic programming method in the step S2 comprises the following steps:
step S21: the state space model is established through the relation among the original power, the ideal grid-connected power and the total power of the hydrogen electric coupling system, and the expression is as follows:
P g,ideal (t+1)＝P w (t)-P hecs (t)
in the formula, u (t) =p hecs (t) represents a control variable, x (t) =p g,ideal (t) represents a state variable, r (t) =p w (t) represents the disturbance input, P w (t) represents the original power, y (t) =p g,ideal (t) represents output, a=b 1 ＝C 1 ＝0，B＝–1，C＝A 1 ＝1；
Step S22: converting the rolling optimization objective function into a quadratic programming standard type, wherein the expression is as follows:
wherein r=2, z= [ u (t), x (t+1), u (t+1), …, u (t+m-1), x (t+m)] T ；
Step S23: converting an expression for restraining the grid-connected power fluctuation rate limit value and the upper limit of the charging and discharging power of the hydrogen electric coupling system into a quadratic programming standard type expression, wherein the expression is as follows:
in the method, in the process of the invention,
step S24: and (4) obtaining a matrix z through the quadratic programming standard converted in the steps S22 and S23 to obtain an ideal grid-connected power predicted value and a total power predicted value of the hydrogen electric coupling system.
5. The hydrogen electric coupling system capacity configuration method for stabilizing fluctuating power according to claim 4, characterized in that: and correcting the state space model by adopting a correction parameter m, wherein r (t) =pw (t) -m so as to offset the electric quantity which is lost by internal heat generated during the working of the hydrogen electric coupling system.
6. The hydrogen electric coupling system capacity configuration method for stabilizing fluctuating power according to claim 1, characterized in that: in step S5, the expression of the constraint conditions of charge-discharge balance, power fluctuation and energy storage capacity is:
1) Charge-discharge balance constraint:
wherein P is sc (t)、P el (t) and P fc (t) the working powers of the super capacitor array, the electrolytic tank and the fuel cell at the moment t, eta DC/DC Representing converter efficiency, P sc,ref (t) represents the power above the boundary frequency portion, P hu,ref (t) represents a power of not higher than the boundary frequency portion;
2) Power fluctuation:
3) Energy storage capacity:
wherein E is sc (t) and E hst (t) representing the residual energy and SOC of the super capacitor array and the hydrogen storage tank at the time t respectively sc,max And SOC (System on chip) sc,min SOC upper limit and SOC lower limit of the super capacitor array respectively, LOH hst,max And LOH (Low-Density parity) hst,min Respectively represent the upper limit and the lower limit of LOH of the hydrogen storage tank and SOC sc (t) represents the SOC, LOH of the super capacitor array at time t hst (t) represents LOH of the hydrogen tank at time t.
7. The hydrogen electric coupling system capacity configuration method for stabilizing fluctuating power according to claim 1, characterized in that: in step S5, the formula for performing the speed update of the adaptive multi-target particle swarm optimization algorithm is as follows:
where k represents the number of iterations, i represents the ith particle in the population, d represents the dimension, V represents the speed, P and P g Respectively representing the current optimal positions of the particles and the population, X represents the current position of the particles, r 1 And r 2 Represents a random value between 0 and 1, ω represents an inertial factor, c 1 And c 2 Representing the acceleration factor.
8. The hydrogen electric coupling system capacity configuration method for stabilizing fluctuating power according to claim 7, characterized in that: to improve the convergence performance of the adaptive multi-target particle swarm optimization algorithm, the inertia factor omega and the acceleration factor c are calculated 1 ，c 2 The expression for performing the adaptation improvement is:
wherein omega is max Represents the maximum value of inertia factor omega min Minimum value, k, representing the maximum value of the inertia factor max Representing the maximum iteration number, k representing the current iteration number;
wherein, c 1s And c 2s Respectively represent c 1 And c 2 Initial value of c 1e And c 2e Respectively represent c 1 And c 2 Final value of (2).
9. The hydrogen electric coupling system capacity configuration method for stabilizing fluctuating power according to claim 1, characterized in that: in step S5, the method for optimizing the five parameters of the maximum charge and discharge power of the supercapacitor array, the rated capacity of the supercapacitor array, the rated power of the electrolytic tank, the rated power of the fuel cell and the rated capacity of the hydrogen storage tank includes the following steps:
step S51: based on the demarcation frequency, a power P higher than the demarcation frequency is determined sc,ref (t) a power P distributed to the supercapacitor array to be not greater than the demarcation frequency portion hu,ref (t) calculating the spillover capacitive power P by means of a control strategy of the hydrogen electrical coupling system, distributed to the hydrogen storage unit s,sc (t) deficit capacitor Power P v,sc (t) Hydrogen production Power P by overflow S,hv (t) and deficit discharge Power P V,hv (t)；
1) When said P is sc,ref (t) when greater than 0, indicating that the supercapacitor array absorbs electrical energy; calculating capacitor charging power and deficit charging power based on the maximum power of the super capacitor array; calculating the SOC of the current super capacitor array through the capacitor charging power; s of the current super capacitor arrayThe OC is compared with the maximum SOC of the super capacitor array to obtain an deficit charging SOC; calculating the spill-over capacitor power P which cannot be absorbed when the super capacitor array is charged with the maximum power through the shortage charging power and the shortage charging SOC s,sc (t)；
2) When said P is sc,ref (t) when not greater than 0, indicating that the supercapacitor array releases electrical energy; calculating capacitor discharge power and deficiency discharge power based on the maximum power of the super capacitor array; calculating the SOC of the current super capacitor array through the capacitor discharge power; comparing the SOC of the current super capacitor array with the minimum SOC of the super capacitor array to obtain a shortage discharge SOC; calculating the insufficient capacitor power P which cannot be met when the super capacitor array is charged with the maximum power through the insufficient discharge power and the insufficient discharge SOC v,sc (t)；
3) When said P is hu,ref (t) when greater than 0, indicating that the hydrogen storage unit absorbs electrical energy to produce hydrogen; calculating hydrogen production power and hydrogen shortage power based on the maximum power of the electrolytic cell; calculating LOH of the current hydrogen storage tank according to the hydrogen production power; comparing the LOH of the current hydrogen storage tank with the maximum LOH of the hydrogen storage tank to obtain an deficiency LOH; calculating overflow hydrogen production power P which cannot be absorbed when the hydrogen energy storage unit produces hydrogen with maximum power through the deficiency hydrogen production power and the deficiency LOH S,hv (t)；
4) When said P is hu,ref (t) when not greater than 0, indicating that the hydrogen storage unit consumes hydrogen to discharge; calculating a battery discharge power and an absence battery discharge power based on the maximum power of the fuel cell; calculating LOH of the current hydrogen storage tank through the battery discharge power; comparing the LOH of the current hydrogen storage tank with the minimum LOH of the hydrogen storage tank to obtain an deficiency LOH; calculating the shortage discharging power P which cannot be met when the hydrogen energy storage unit discharges at the maximum power by the shortage battery discharging power and the shortage LOH V,hv (t)；
Step S52: by the objective function, to the P s,sc(t) 、P v,sc (t)、P S,hv (t) and P V,hv (t) performing analysis and iteration to obtain the maximum charge and discharge of the super capacitor arrayThe optimal capacity allocation scheme of five parameters of power, super capacitor array rated capacity, electrolytic tank rated power, fuel cell rated power and hydrogen storage tank rated capacity.
CN202310398831.8A 2023-04-14 2023-04-14 Hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power Active CN116131291B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310398831.8A CN116131291B (en) 2023-04-14 2023-04-14 Hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310398831.8A CN116131291B (en) 2023-04-14 2023-04-14 Hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power

Publications (2)

Publication Number Publication Date
CN116131291A CN116131291A (en) 2023-05-16
CN116131291B true CN116131291B (en) 2023-08-29

Family

ID=86304889

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310398831.8A Active CN116131291B (en) 2023-04-14 2023-04-14 Hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power

Country Status (1)

Country Link
CN (1) CN116131291B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116914784B (en) * 2023-09-13 2023-11-17 四川大学 Multi-hydrogen production unit coordination control method based on energy storage mutual assistance
CN117477615B (en) * 2023-12-28 2024-03-26 国网浙江省电力有限公司电力科学研究院 Optimal configuration method and equipment for electric-hydrogen composite energy storage system

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103580041A (en) * 2013-11-08 2014-02-12 国家电网公司 Capacity configuration method of hybrid energy storage system for stabilizing wind power fluctuation
CN109103941A (en) * 2018-10-26 2018-12-28 贵州电网有限责任公司 It is a kind of that method is stabilized based on the fluctuation of the wind-powered electricity generation of HHT and filtering algorithm
CN112636367A (en) * 2020-12-09 2021-04-09 南京工程学院 Hybrid energy storage capacity optimal configuration method for stabilizing wind power fluctuation
CN113422375A (en) * 2021-06-25 2021-09-21 南京工程学院 Double-layer coordination control method for stabilizing wind power fluctuation of hybrid energy storage system
CN114725961A (en) * 2022-03-16 2022-07-08 华北电力大学 Hydrogen production system capacity layering optimization configuration method for stabilizing wind power fluctuation

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103580041A (en) * 2013-11-08 2014-02-12 国家电网公司 Capacity configuration method of hybrid energy storage system for stabilizing wind power fluctuation
CN109103941A (en) * 2018-10-26 2018-12-28 贵州电网有限责任公司 It is a kind of that method is stabilized based on the fluctuation of the wind-powered electricity generation of HHT and filtering algorithm
CN112636367A (en) * 2020-12-09 2021-04-09 南京工程学院 Hybrid energy storage capacity optimal configuration method for stabilizing wind power fluctuation
CN113422375A (en) * 2021-06-25 2021-09-21 南京工程学院 Double-layer coordination control method for stabilizing wind power fluctuation of hybrid energy storage system
CN114725961A (en) * 2022-03-16 2022-07-08 华北电力大学 Hydrogen production system capacity layering optimization configuration method for stabilizing wind power fluctuation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于变分模态分解下利用混合储能平抑风电出力波动的控制策略;焦东东;电测与仪表;第58卷(第5期);全文 *

Also Published As

Publication number Publication date
CN116131291A (en) 2023-05-16

Similar Documents

Publication Publication Date Title
CN116131291B (en) Hydrogen electric coupling system capacity configuration method for stabilizing fluctuation power
CN105846461B (en) Control method and system for large-scale energy storage power station self-adaptive dynamic planning
CN107069814B (en) Fuzzy opportunity constraint planning method and system for distribution network distributed power capacity distribution
AU2011213745A1 (en) System and method for distribution of inverter VAR support
CN112636367A (en) Hybrid energy storage capacity optimal configuration method for stabilizing wind power fluctuation
CN110956324B (en) Day-ahead high-dimensional target optimization scheduling method for active power distribution network based on improved MOEA/D
Chen et al. Smoothing photovoltaic power fluctuations for cascade hydro-PV-pumped storage generation system based on a fuzzy CEEMDAN
Guo et al. Two‐stage optimal MPC for hybrid energy storage operation to enable smooth wind power integration
CN110323979A (en) A kind of generator excitation difference coefficient optimization setting method considering voltage stabilization
Li et al. Fuzzy logic-based coordinated control method for multi-type battery energy storage systems
Raza et al. Robust nonlinear control of regenerative fuel cell, supercapacitor, battery and wind based direct current microgrid
CN113644698A (en) Source load storage capacity optimal configuration method and system for island micro-grid
CN109274133A (en) A kind of energy accumulation capacity configuration suitable for distributing wind power plant
CN113746120A (en) Energy storage system optimal configuration method based on GA
CN115940284B (en) Operation control strategy of new energy hydrogen production system considering time-of-use electricity price
CN114914942B (en) Polymorphic distributed energy storage primary frequency modulation control method based on consistency algorithm
Atif et al. Battery integrated optimal power smoothing of solar PV output using meta-heuristic optimization
CN115619144A (en) Method and system for planning cloud energy storage operation at user side and electronic equipment
US11398730B2 (en) Method for optimizing the supply of energy from an electrical energy source in an electrical installation and device for implementing such a method
CN110601239A (en) Energy scheduling method and system for composite energy storage capacity of quantum particle swarm optimization
Lu et al. A bi‐level planning strategy of a hydrogen‐supercapacitor hybrid energy storage system based on APA‐MOHHO
Tadie et al. Optimal planning and sizing of BESS for wind energy ramp rate control through DES initiated large data clustering and PSO technique
Liao et al. Optimal Configuration of Capacity for Hybrid Energy Storage Based on ISDPC–K-means Clustering and Cluster Division
CN113394805B (en) Method for optimizing intra-day rolling of flexible interconnection power distribution network based on self-adaptive dynamic update
CN117638944B (en) Power grid capacity margin dynamic configuration method and system based on source load storage interaction

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant