CN113629775B - Fuzzy logic-based hydrogen energy storage system cluster output decision method - Google Patents

Fuzzy logic-based hydrogen energy storage system cluster output decision method Download PDF

Info

Publication number
CN113629775B
CN113629775B CN202110842900.0A CN202110842900A CN113629775B CN 113629775 B CN113629775 B CN 113629775B CN 202110842900 A CN202110842900 A CN 202110842900A CN 113629775 B CN113629775 B CN 113629775B
Authority
CN
China
Prior art keywords
energy storage
storage system
hydrogen
hydrogen energy
fuzzy
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
CN202110842900.0A
Other languages
Chinese (zh)
Other versions
CN113629775A (en
Inventor
李建林
李光辉
宋洁
梁忠豪
梁丹曦
马速良
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.)
Beijing Lianzhi Huineng Technology Co ltd
North China University of Technology
Global Energy Interconnection Research Institute
Original Assignee
Beijing Lianzhi Huineng Technology Co ltd
North China University of Technology
Global Energy Interconnection Research Institute
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 Beijing Lianzhi Huineng Technology Co ltd, North China University of Technology, Global Energy Interconnection Research Institute filed Critical Beijing Lianzhi Huineng Technology Co ltd
Priority to CN202110842900.0A priority Critical patent/CN113629775B/en
Publication of CN113629775A publication Critical patent/CN113629775A/en
Application granted granted Critical
Publication of CN113629775B publication Critical patent/CN113629775B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/17—Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • 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
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Computational Mathematics (AREA)
  • Algebra (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to a fuzzy logic-based hydrogen energy storage system cluster output decision method. The method comprises the following steps: under the constraints of power balance, SOHR value of the HESS system and the like, determining a charging and discharging state according to the power grid requirement, if the power grid has surplus electric quantity, using the HESS as a load to produce hydrogen through electrolysis to consume surplus electric power, adopting an outer layer multi-target fuzzy comprehensive evaluation method to obtain the optimal HESS as a standby, and carrying out fuzzy decision on the SOHR value of the hydrogen storage system and the hydrogen production and power generation efficiency on the inner layer fuzzy, so as to distribute the output of the remaining n-1 HESS, and if the power grid requirement cannot be met after distribution is finished, the remaining required power is born by the standby HESS selected previously. When the power grid is insufficient, the HESS generates power through the hydrogen fuel cell to supply power to the power grid, and the control process is equivalent. The invention fully considers the characteristic of the parameter difference of each subsystem, and can effectively improve the running economy and stability.

Description

Fuzzy logic-based hydrogen energy storage system cluster output decision method
Technical field:
the invention belongs to the field of micro-grid management, and particularly relates to a fuzzy logic-based hydrogen energy storage system cluster output decision method.
The background technology is as follows:
the hydrogen energy is used as a green energy source with rich reserves, high heat value, high energy density and various sources, is praised as a final energy source in the 21 st century, has better energy storage capacity and larger scale compared with the electric energy, is a better storage and transportation medium for the electric energy converted from unstable and rich renewable energy sources, and is used as an energy storage technology with zero carbon emission to perfectly fit with fourteen clean energy planning in China, so that the hydrogen is produced by electrolysis to store energy, the wind photovoltaic Internet surfing fluctuation power is stabilized, the wind photovoltaic Internet surfing electric energy quality is guaranteed, and the method is one of important development directions for the global energy utilization in the future. Along with the guidance of the policy in China and the implementation of the landing of a large number of hydrogen energy projects, the hydrogen energy technology is broken through continuously, the industrial system is gradually perfected, and the development of the hydrogen energy field in China is accelerated to enter the industrialization stage. Through industrial accumulation for many years, china is the largest hydrogen production country in the world, and the market potential of hydrogen energy is huge.
The hydrogen energy storage system, english Hydrogen Energy Storage System, abbreviated as HESS, consists of an electrolytic cell hydrogen production unit, a hydrogen storage tank unit and a hydrogen fuel cell power generation unit, wherein the three units are decoupled in power and flexible in charging and discharging, and is an excellent green energy storage system.
Considering that the number of the hydrogen energy storage clusters is numerous, and the power load requirement has stronger uncertainty, the power distribution and control strategy of each subsystem in the hydrogen energy storage clusters and the key thereof under the power grid dispatching instruction directly influence the economy and the stability of the operation of the micro-grid and the service life of the hydrogen storage system.
The complexity of the hydrogen energy storage cluster is high, technical indexes such as SOHR and charge-discharge efficiency in the system have the characteristics of time variation and nonlinearity, real-time accurate control of each subsystem in the hydrogen energy storage cluster is difficult, and the problem of high-precision control of the time variation and nonlinearity system can be solved by fuzzy control of traditional scheduling. The basic idea of fuzzy control is to convert a fuzzy control strategy of a person into a control algorithm acceptable by a computer by utilizing a fuzzy set theory, so as to implement a theory and a technology of control, and the fuzzy control algorithm can simulate a thinking mode of the person, so that a plurality of systems which cannot construct a mathematical model can be effectively described and controlled, and the uncertainty of different types of continuously-changed variables such as SOHR, hydrogen production/power generation efficiency and the like and a power distribution method in a hydrogen storage system can be flexibly processed.
The invention comprises the following steps:
aiming at the problems of insufficient regulation and control technology of the existing hydrogen energy storage system and real-time balance of electric quantity and voltage stability in the running process of a power grid, the invention provides a fuzzy logic-based hydrogen energy storage system cluster output decision method based on the basis of better serving a micro-grid system and improving the running economy and stability of the micro-grid system.
According to the method, parameters such as SOHR state, hydrogen production efficiency and power generation efficiency of a hydrogen energy storage system are considered according to power requirements of a power grid, an optimal individual hydrogen energy storage in a hydrogen energy storage cluster in the current power shortage state of the power grid is dynamically screened by adopting a multi-objective fuzzy decision method, and charging and discharging power of each hydrogen energy storage unit is dynamically adjusted according to the states of various indexes of the hydrogen energy storage unit, so that the states of each hydrogen energy storage can be balanced to the greatest extent, the accelerated equipment damage and the reduced hydrogen production/power generation efficiency caused by excessive cyclic charging and discharging of a certain hydrogen energy storage are avoided, the consistency of all subsystems in the hydrogen energy storage cluster is effectively ensured, the problem of cluster power distribution of the hydrogen energy storage system in a micro-grid is solved, and the operation economy of the hydrogen energy storage can be effectively improved, and the cyclic charging and discharging life of the hydrogen energy storage can be effectively prolonged. The specific technical scheme is as follows:
a hydrogen energy storage system cluster output decision method based on fuzzy logic comprises the following steps:
step 1: parameters including SOHR, hydrogen production efficiency, power generation efficiency and storage tank capacity of the n hydrogen energy storage systems are obtained;
step 2: the power grid issues a scheduling instruction according to the requirements, and the charging and discharging states of the hydrogen energy storage system are judged; screening an optimal hydrogen energy storage system under the current power grid instruction and the state condition of the optimal hydrogen energy storage system for standby;
when the electric power of the power grid is surplus, namely delta P is more than or equal to 0, the power grid gives instructions to n hydrogen energy storage systems so that the n hydrogen energy storage systems are ready for charging and hydrogen production, and the step 3 is performed; when the power grid is in short supply, namely delta P is less than or equal to 0, the power grid gives a power supply instruction, and the step 4 is shifted; wherein Δp=p G -P load I.e. the difference between the power supply of the power grid and the power required by the load;
step 3: taking one optimal hydrogen energy storage system under the current power grid instruction and the state condition of the current power grid instruction as a standby, and obtaining the output of each hydrogen energy storage system by adopting a hydrogen energy storage system output decision method based on a fuzzy control algorithm by the remaining n-1 systems; if the remaining n-1 hydrogen energy storage systems cannot meet the power grid demand after the power distribution condition is decided, the remaining power grid demand is met by the optimal hydrogen energy storage selected dynamically; the SOHR value of each hydrogen energy storage system in the charging process can not exceed the maximum hydrogen storage quantity S_max, when n hydrogen energy storage power consumption meets the power grid command, energy distribution is finished, the hydrogen production of the hydrogen energy storage system is stopped, and the SOHR state of each hydrogen energy storage system, the electrolyzer and the HFC power generation efficiency are estimated again; returning to the step 1;
step 4: taking one optimal hydrogen energy storage system under the current power grid instruction and the state condition of the current power grid instruction as a standby, adopting a hydrogen energy storage system output decision method based on a fuzzy control algorithm for the remaining n-1 systems to obtain the output of each hydrogen energy storage system, and then generating power through HFC; each hydrogen energy storage and hydrogen storage amount is proportional to the storage amount, so the storage amount range is S min ～S max When the hydrogen storage amount of the hydrogen energy storage system is low to S low Stopping generating electricity when the power generation is stopped; returning to the step 1;
wherein S is max An SOHR upper limit value representing hydrogen storage; s is S min And S is max Respectively representing the minimum hydrogen storage amount and the maximum hydrogen storage amount set by the hydrogen energy storage system; s is S low And S is high Respectively represent a lower limit and an upper limit of the hydrogen storage amount when the hydrogen storage system is optimally operated.
In a first preferred aspect, the discriminating of the optimal hydrogen energy storage system in the step 2 includes the following steps:
first determining SOHR and charge of a hydrogen storage systemEfficiency eta elec Discharge efficiency eta fc Number of charge and discharge times
Figure GDA0004192754360000023
The five indexes of the working temperature T are taken as factors, namely, the factor theory domain U of the hydrogen energy storage cluster is determined, and the +.>
Figure GDA0004192754360000021
Determining a comment level universe V, V= (A, B, C, D, E), namely dividing the level into five levels A-B, wherein A is the highest level, E is the lowest level, and different levels are used for comprehensively evaluating the performance of a certain hydrogen energy storage system in all indexes;
step 2.1: and (3) performing single-factor judgment on each factor, and establishing a fuzzy relation matrix R:
Figure GDA0004192754360000022
wherein r is ij For each factor in U, membership to a class in V, where n=m=5;
step 2.2: determining a judgment factor weight vector A= (a) 1 ，a 2 ，…，a 5 ) A is the membership of each index in U to the hydrogen energy storage system, and weight is distributed according to the importance of each index in judgment; each weight in the hydrogen storage system is given by historical experience data, and each index of the hydrogen storage system is given; when delta P is more than or equal to 0, the state of charge SOHR of each subsystem in the hydrogen energy storage system cluster from high to low is considered i Charging efficiency
Figure GDA0004192754360000031
Cycle charge and discharge times->
Figure GDA0004192754360000032
Operating temperature T i Wherein i=1, 2, … n-1; the charging efficiency is the electrolytic hydrogen production efficiency; when delta P is less than or equal to 0, considering that the charge states of all index weight levels of all subsystems in the hydrogen energy storage system cluster are respectively from high to lowSOHR i Discharge efficiency->
Figure GDA0004192754360000033
Cycle charge and discharge times->
Figure GDA0004192754360000034
Operating temperature T i Wherein i=1, 2, … n-1; the discharging efficiency is HFC power generation efficiency;
step 2.3: selecting a synthesis operator of the fuzzy decision evaluation to obtain an evaluation result B, wherein B is obtained by synthesizing A and R, namely
Figure GDA0004192754360000035
Step 2.4: and (3) quantizing the fuzzy comments, calculating a priority comment quantizing set of each object to be S, and obtaining the priorities of n hydrogen energy storage through the following formula, so that the hydrogen energy storage system which is most suitable for the current power grid instruction can be screened out:
Figure GDA0004192754360000036
wherein m is the index number, k is the number of single hydrogen energy storage systems in the hydrogen energy storage cluster, and n hydrogen energy storage systems are arranged to form the hydrogen energy storage cluster, and k=n;
and screening out the hydrogen energy storage system in the optimal state under the power grid dispatching instruction at a certain moment by the steps to serve as a standby.
In the second preferred scheme, in the step 3, power distribution is carried out on the remaining n-1 hydrogen energy storage systems by adopting a second-layer fuzzy control algorithm, and the power distribution of the hydrogen energy storage clusters under each power grid dispatching instruction is specifically completed through the following processes:
step 3.1: for each hydrogen storage system, its state of charge SOHR is determined i Charging efficiency
Figure GDA0004192754360000037
Discharge efficiency
Figure GDA0004192754360000038
Wherein i=1, 2, … n-1 is normalized;
step 3.2: determining membership functions of each index in factor theory domain, and respectively defining the membership functions as variables
Figure GDA0004192754360000039
Step 3.3: will SOHR i ，
Figure GDA00041927543600000310
As an input of the fuzzy controller, the output of the fuzzy controller is set to be the power value +.>
Figure GDA00041927543600000311
I.e., the power value that the current hydrogen storage system should dissipate; the fuzzy sets of fuzzy input and output are set to five stages { NB, NS, ZO, PS, PB }, which respectively represent the state of charge SOHR of each hydrogen storage system i Charging efficiency->
Figure GDA00041927543600000312
Discharge efficiency->
Figure GDA00041927543600000313
Output value of hydrogen energy storage system>
Figure GDA00041927543600000314
Is of a size of (2); NB represents negative big, NS represents negative small, ZO represents zero, PS represents positive small, PB represents positive big;
the input and output of the fuzzy control are explained specifically as follows:
input 1: SOHR i The fuzzy universe is [ -1,1]The SOHR is-1, which indicates that the energy storage system reaches the maximum discharge value and can not discharge any more; when the SOHR is 1, the energy storage system has reached a charge maximum value, no further charge can be performed, the fuzzy subset is set to five levels { NB, NS, ZO, PS, PB }, representing the state of charge SOHR { very much } of a single hydrogen energy storage system, respectivelyLow, moderate, high, very high }; NB represents negative big, NS represents negative small, ZO represents zero, PS represents positive small, PB represents positive big;
input 2:
Figure GDA0004192754360000041
the fuzzy universe is [ -1,1]The fuzzy subset is set to be three-level { NB, ZO, PB }, which respectively represent the hydrogen production efficiency { high, moderate, low };
and (3) outputting: adjusting power value for hydrogen energy storage system
Figure GDA0004192754360000042
The fuzzy universe is [ -5,5]The fuzzy subset is set to five stages { NB, NS, ZO, PS, PB }, and the fuzzy subset respectively represents the power which needs to be consumed by a single hydrogen energy storage system according to the power grid instruction requirement;
the input/output membership function of fuzzy control selects common triangle and question membership function;
making a fuzzy rule:
the hydrogen energy storage system obtains electric energy from the power grid, and consumes surplus electric power through hydrogen production by electrolysis,
Figure GDA0004192754360000043
as an input of the fuzzy control, the fuzzy rule is specifically expressed as follows:
when the charge state of the hydrogen energy storage system and the electrolytic hydrogen production efficiency are both intermediate values, the fuzzy control output is also the intermediate value; when the state of charge of the hydrogen energy storage system gradually approaches the upper limit and the electrolytic hydrogen production efficiency gradually approaches the lower limit, the charging power of the hydrogen energy storage system is reduced, namely the hydrogen production amount of the hydrogen energy storage system is reduced; when the state of charge of the hydrogen energy storage system gradually approaches the lower limit and the electrolytic hydrogen production efficiency gradually approaches the upper limit, the charging power of the hydrogen energy storage system is increased, namely the hydrogen production amount of the hydrogen energy storage system is increased;
step 3.4: defuzzification of fuzzy controller output: deblurring the output of n-1 hydrogen energy storage systems by adopting a weighted average method to obtain theoretical power distribution values of single hydrogen energy storage systems, wherein the specific formula is as follows:
Figure GDA0004192754360000044
in the method, in the process of the invention,
Figure GDA0004192754360000045
respectively represent the input quantity SOHR i ，/>
Figure GDA0004192754360000046
Membership value, Z i And the abscissa value corresponding to the sharp point of the fuzzy membership function of the output power value.
In the third preferred scheme, in the step 4, power distribution is performed on the remaining n-1 hydrogen energy storage systems by adopting a second-layer fuzzy control algorithm, and particularly, the power distribution of the hydrogen energy storage clusters under each power grid dispatching instruction is completed through the following processes:
step 4.1: normalizing the indexes in the factor theory domain, and carrying out SOHR on the state of charge of each hydrogen energy storage system i Charging efficiency
Figure GDA0004192754360000047
Discharge efficiency->
Figure GDA0004192754360000048
Wherein i=1, 2, … n-1 is normalized;
step 4.2: determining membership functions of each index in factor theory domain, and respectively defining the membership functions as variables
Figure GDA0004192754360000049
Step 4.3: will SOHR i ，
Figure GDA00041927543600000410
As an input of the fuzzy controller, the output of the fuzzy controller is set to be the power value +.>
Figure GDA00041927543600000411
I.e. currentlyA power value that the hydrogen energy storage system should deliver; the fuzzy sets of fuzzy input and output are set to five stages { NB, NS, ZO, PS, PB }, which respectively represent the state of charge SOHR of each hydrogen storage system i Charging efficiency->
Figure GDA00041927543600000412
Discharge efficiency->
Figure GDA0004192754360000051
Output value of hydrogen energy storage system>
Figure GDA0004192754360000052
Is of a size of (2); the input and output of the fuzzy control are explained specifically as follows:
input 1: SOHR i The fuzzy universe is [ -1,1]The SOHR is-1, which indicates that the energy storage system reaches the maximum discharge value and can not discharge any more; when the SOHR is 1, the energy storage system reaches the maximum charging value and cannot be charged any more, and the fuzzy subset is set to be five stages { NB, NS, ZO, PS, PB }, which respectively represent the states of charge SOHR { very low, moderate, high and very high } of the single hydrogen energy storage system;
input 2:
Figure GDA0004192754360000053
the fuzzy universe is [ -1,1]The fuzzy subset is set to be three-level { NB, ZO, PB }, which respectively represent the power generation efficiency { high, moderate and low };
and (3) outputting: adjusting power value for hydrogen energy storage system
Figure GDA0004192754360000054
The fuzzy universe is [ -5,5]The fuzzy subset is set to five stages { NB, NS, ZO, PS, PB }, which respectively represent the power required to be sent by a single hydrogen energy storage system according to the power grid instruction requirement;
the input/output membership function of fuzzy control selects common triangle and question membership function; making a fuzzy rule:
the hydrogen energy storage system provides electric energy for the power grid through HFC provides the electric power to the electric power,
Figure GDA0004192754360000055
as an input of the fuzzy control, the fuzzy rule is specifically expressed as follows:
when the charge state of the hydrogen energy storage system and the HFC power generation efficiency are both intermediate values, the fuzzy control output is also the intermediate value; when the state of charge of the hydrogen energy storage system gradually approaches the upper limit, the HFC power generation efficiency gradually approaches the upper limit, and the power generation power of the hydrogen energy storage system is increased, namely the hydrogenation amount of the hydrogen fuel cell is increased; when the state of charge of the hydrogen energy storage system gradually approaches the lower limit, the HFC power generation efficiency gradually approaches the lower limit, and the charging power of the hydrogen energy storage system is reduced, namely the HFC hydrogen supply is reduced;
step 4.4: defuzzifying the output quantity of the fuzzy controller; deblurring the output of n-1 hydrogen energy storage systems by adopting a weighted average method to obtain theoretical power distribution values of single hydrogen energy storage systems, wherein the specific formula is as follows:
Figure GDA0004192754360000056
in the method, in the process of the invention,
Figure GDA0004192754360000057
respectively represent the input quantity SOHR i ，/>
Figure GDA0004192754360000058
Membership value, Z i And the abscissa value corresponding to the sharp point of the fuzzy membership function of the output power value.
The invention has the beneficial effects that aiming at the problem of cluster power distribution of a hydrogen storage system in a micro-grid, a micro-grid distributed hydrogen storage cluster power distribution method based on a double-layer fuzzy decision technology is provided, wherein the factors such as the charge state and the charge and discharge efficiency of an energy storage unit are considered. The double-layer fuzzy decision-making technology provided by the invention fully considers the inconsistency among all main parameters of all subsystems in the hydrogen energy storage cluster, and can effectively improve the running economy of the hydrogen energy storage and the cyclic charge and discharge life of the hydrogen energy storage.
Description of the drawings:
FIG. 1 is a general flow chart of the present invention; in the figure, hes represents a hydrogen storage system.
FIG. 2 is a membership function diagram of the input quantity SOHR in the second-tier fuzzy control decision; the abscissa represents the SOHR value and the ordinate represents the membership value.
FIG. 3 is a graph of input in a second level fuzzy control decision
Figure GDA0004192754360000061
Is a membership function graph of (1); the abscissa represents
Figure GDA0004192754360000062
The value, the ordinate, represents the membership value.
FIG. 4 is a graph showing hydrogen storage system output values in a second level fuzzy control strategy
Figure GDA0004192754360000063
Is a membership function graph of (1); the abscissa represents the output value of the hydrogen storage system>
Figure GDA0004192754360000064
The value, the ordinate, represents the membership value.
FIG. 5 is an indication of different regions of the hydrogen storage system SOHR.
The specific embodiment is as follows:
the details, as well as other features and advantages of the present invention, are set forth in the accompanying drawings and the description below.
The micro-grid comprises renewable energy power generation equipment such as wind and light and energy storage equipment, the energy storage equipment mainly refers to a hydrogen energy storage system cluster, each hydrogen energy storage system consists of an electrolytic hydrogen production unit, a hydrogen storage tank unit and a hydrogen fuel cell power generation unit, and the screening and decision of the energy distribution process of each subsystem in the hydrogen energy storage cluster are mainly carried out.
Grid power deficiency Δp=p in the present invention G -P load The difference value between the power supply quantity of the power grid and the load demand quantity is referred to. When the power supply of the power grid is insufficient to meet the load demand, namely delta P is less than or equal to 0, the power grid issues instructions to the hydrogen energy storage systems, and the energy management system of the hydrogen energy storage systems distributes energy to each hydrogen energy storage system through the double-layer fuzzy control decision technology, so that the hydrogen energy storage systems generate power through the hydrogen fuel cells to supply power to the power grid; when the power of the power grid is surplus, namely delta P is more than or equal to 0, the power grid gives instructions to n hydrogen energy storage systems, so that the n hydrogen energy storage systems are ready to absorb redundant power through electrolytic hydrogen production, and each hydrogen energy storage system absorbs power values and power distribution is carried out by the double-layer fuzzy decision technology provided by the invention.
The SOHR value of each hydrogen storage during charging cannot exceed S max When the power consumed by the n hydrogen storage systems meets the power grid command, ending energy distribution, stopping hydrogen production by the hydrogen storage systems, and re-estimating SOHR states of each hydrogen storage system and power generation efficiency of the electrolytic tank and the hydrogen fuel cell; the SOHR value, i.e. the hydrogen storage capacity, of each hydrogen storage during discharge is not lower than S min And ending the energy distribution when the n hydrogen energy storage generating capacity meets the power grid command.
A hydrogen energy storage system cluster output decision method based on fuzzy logic comprises the following steps:
step 1: parameters including SOHR, hydrogen production efficiency, power generation efficiency and storage tank capacity of the n hydrogen energy storage systems are obtained;
step 2: the power grid issues a scheduling instruction according to the requirements, and the charging and discharging states of the hydrogen energy storage system are judged; screening an optimal hydrogen energy storage system under the current power grid instruction and the state condition of the optimal hydrogen energy storage system for standby; when the electric power of the power grid is surplus, namely delta P is more than or equal to 0, the power grid gives instructions to n hydrogen energy storage systems so that the n hydrogen energy storage systems are ready for charging and hydrogen production, and the step 3 is performed; when the power grid is in short supply, namely delta P is less than or equal to 0, the power grid gives a power supply instruction, and the step 4 is shifted; wherein Δp=p G -P load I.e. the difference between the power supply of the power grid and the power required by the load;
the discrimination of the optimal hydrogen energy storage system comprises the following steps:
first determining SOHR, charging efficiency eta of a hydrogen storage system elec Discharge efficiency eta fc Number of charge and discharge times
Figure GDA0004192754360000071
The five indexes of the working temperature T are taken as factors, namely, the factor theory domain U of the hydrogen energy storage cluster is determined, and the +.>
Figure GDA0004192754360000072
Determining a comment level universe V, V= (A, B, C, D, E), namely dividing the level into five levels A-B, wherein a is the highest level, E is the lowest level, and different levels are used for comprehensively evaluating the performance of a certain hydrogen energy storage system in all indexes;
step 2.1: and (3) performing single-factor judgment on each factor, and establishing a fuzzy relation matrix R:
Figure GDA0004192754360000073
wherein r is ij For each factor in U, membership to a class in V, where n=m=5;
step 2.2: determining a judgment factor weight vector A= (a) 1 ，a 2 ，…，a 5 ) A is the membership of each index in U to the hydrogen energy storage system, and weight is distributed according to the importance of each index in judgment; each weight in the hydrogen storage system is given by historical experience data, and each index of the hydrogen storage system is given; when delta P is more than or equal to 0, the state of charge SOHR of each subsystem in the hydrogen energy storage system cluster from high to low is considered i Charging efficiency
Figure GDA0004192754360000074
Cycle charge and discharge times->
Figure GDA0004192754360000075
Operating temperature T i Wherein i=1, 2, … n-1; the charging efficiency is the electrolytic hydrogen production efficiency; when delta P is less than or equal to 0, each subsystem in the hydrogen energy storage system cluster is consideredThe index weight levels are respectively the charge states SOHR from high to low i Discharge efficiency->
Figure GDA0004192754360000076
Cycle charge and discharge times->
Figure GDA0004192754360000077
Operating temperature T i Wherein i=1, 2, … n-1; the discharging efficiency is HFC power generation efficiency;
step 2.3: selecting a synthesis operator of the fuzzy decision evaluation to obtain an evaluation result B, wherein B is obtained by synthesizing A and R, namely
Figure GDA0004192754360000078
Step 2.4: and (3) quantizing the fuzzy comments, calculating a priority comment quantizing set of each object to be S, and obtaining the priorities of n hydrogen energy storage through the following formula, so that the hydrogen energy storage system which is most suitable for the current power grid instruction can be screened out:
Figure GDA0004192754360000079
wherein m is the index number, k is the number of single hydrogen energy storage systems in the hydrogen energy storage cluster, and n hydrogen energy storage systems are arranged to form the hydrogen energy storage cluster, and k=n;
the hydrogen energy storage system in the optimal state under the power grid dispatching instruction at a certain moment is screened out for standby through the steps;
step 3: taking one optimal hydrogen energy storage system under the current power grid instruction and the state condition of the current power grid instruction as a standby, and obtaining the output of each hydrogen energy storage system by adopting a hydrogen energy storage system output decision method based on a fuzzy control algorithm by the remaining n-1 systems; if the remaining n-1 hydrogen energy storage systems cannot meet the power grid demand after the power distribution condition is decided, the remaining power grid demand is met by the optimal hydrogen energy storage selected dynamically; the SOHR value of each hydrogen energy storage system in the charging process can not exceed the maximum hydrogen storage quantity S_max, when n hydrogen energy storage power consumption meets the power grid command, energy distribution is finished, the hydrogen production of the hydrogen energy storage system is stopped, and the SOHR state of each hydrogen energy storage system, the electrolyzer and the HFC power generation efficiency are estimated again; returning to the step 1;
the power distribution of the remaining n-1 hydrogen energy storage systems is carried out by adopting a second-layer fuzzy control algorithm, and the power distribution of the hydrogen energy storage clusters under each power grid dispatching instruction is specifically completed through the following processes:
step 3.1: for each hydrogen storage system, its state of charge SOHR is determined i Charging efficiency
Figure GDA0004192754360000081
Discharge efficiency
Figure GDA0004192754360000082
Wherein i=1, 2, … n-1 is normalized;
step 3.2: determining membership functions of each index in factor theory domain, and respectively defining the membership functions as variables
Figure GDA0004192754360000083
Step 3.3: will SOHR i ，
Figure GDA0004192754360000084
As an input of the fuzzy controller, the output of the fuzzy controller is set to be the power value +.>
Figure GDA0004192754360000085
I.e., the power value that the current hydrogen storage system should dissipate; the fuzzy sets of fuzzy input and output are set to five stages { NB, NS, ZO, PS, PB }, which respectively represent the state of charge SOHR of each hydrogen storage system i Charging efficiency->
Figure GDA0004192754360000086
Discharge efficiency->
Figure GDA0004192754360000087
Output value of hydrogen energy storage system>
Figure GDA0004192754360000088
Is of a size of (2); NB represents negative big, NS represents negative small, ZO represents zero, PS represents positive small, PB represents positive big;
the input and output of the fuzzy control are explained specifically as follows:
input 1: SOHR i The fuzzy universe is [ -1,1]The SOHR is-1, which indicates that the energy storage system reaches the maximum discharge value and can not discharge any more; when the SOHR is 1, the energy storage system reaches the maximum charging value and cannot be charged any more, and the fuzzy subset is set to be five stages { NB, NS, ZO, PS, PB }, which respectively represent the states of charge SOHR { very low, moderate, high and very high } of the single hydrogen energy storage system; NB represents negative big, NS represents negative small, ZO represents zero, PS represents positive small, PB represents positive big;
input 2:
Figure GDA0004192754360000089
the fuzzy universe is [ -1,1]The fuzzy subset is set to be three-level { NB, ZO, PB }, which respectively represent the hydrogen production efficiency { high, moderate, low };
and (3) outputting: adjusting power value for hydrogen energy storage system
Figure GDA00041927543600000811
The fuzzy universe is [ -5,5]The fuzzy subset is set to five stages { NB, NS, ZO, PS, PB }, and the fuzzy subset respectively represents the power which needs to be consumed by a single hydrogen energy storage system according to the power grid instruction requirement;
the input and output membership functions of fuzzy control are common triangle and T-shaped membership functions;
making a fuzzy rule:
the hydrogen energy storage system obtains electric energy from the power grid, and consumes surplus electric power through hydrogen production by electrolysis,
Figure GDA00041927543600000810
as an input of the fuzzy control, the fuzzy rule is shown in Table 1, and the specific expression isThe following is described:
when the charge state of the hydrogen energy storage system and the electrolytic hydrogen production efficiency are both intermediate values, the fuzzy control output is also the intermediate value; when the state of charge of the hydrogen energy storage system gradually approaches the upper limit and the electrolytic hydrogen production efficiency gradually approaches the lower limit, the charging power of the hydrogen energy storage system is reduced, namely the hydrogen production amount of the hydrogen energy storage system is reduced; when the state of charge of the hydrogen energy storage system gradually approaches the lower limit and the electrolytic hydrogen production efficiency gradually approaches the upper limit, the charging power of the hydrogen energy storage system is increased, namely the hydrogen production amount of the hydrogen energy storage system is increased;
TABLE 1
Figure GDA0004192754360000091
Step 3.4: defuzzification of fuzzy controller output: deblurring the output of n-1 hydrogen energy storage systems by adopting a weighted average method to obtain theoretical power distribution values of single hydrogen energy storage systems, wherein the specific formula is as follows:
Figure GDA0004192754360000092
in the method, in the process of the invention,
Figure GDA0004192754360000093
respectively represent the input quantity SOHR i ，/>
Figure GDA0004192754360000094
Membership value, Z i The abscissa value corresponding to the sharp point of the fuzzy membership function of the output power value;
step 4: taking one optimal hydrogen energy storage system under the current power grid instruction and the state condition of the current power grid instruction as a standby, adopting a hydrogen energy storage system output decision method based on a fuzzy control algorithm for the remaining n-1 systems to obtain the output of each hydrogen energy storage system, and then generating power through HFC; each hydrogen energy storage and hydrogen storage amount is proportional to the storage amount, so the storage amount range is S min ～S max When the hydrogen energy storage system stores hydrogenThe amount is as low as S low Stopping generating electricity when the power generation is stopped; returning to the step 1;
wherein S is max An SOHR upper limit value representing hydrogen storage; s is S min And S is max Respectively representing the minimum hydrogen storage amount and the maximum hydrogen storage amount set by the hydrogen energy storage system; s is S low And S is high Respectively representing a lower limit and an upper limit of the hydrogen storage amount when the hydrogen energy storage system is in optimal operation;
the power distribution of the remaining n-1 hydrogen energy storage systems is carried out by adopting a second-layer fuzzy control algorithm, and the power distribution of the hydrogen energy storage clusters under each power grid dispatching instruction is specifically completed through the following processes:
step 4.1: normalizing the indexes in the factor theory domain, and carrying out SOHR on the state of charge of each hydrogen energy storage system i Charging efficiency
Figure GDA0004192754360000095
Discharge efficiency->
Figure GDA0004192754360000096
Wherein i=1, 2, … n-1 is normalized;
step 4.2: determining membership functions of each index in factor theory domain, and respectively defining the membership functions as variables
Figure GDA0004192754360000097
Step 4.3: will SOHR i ，
Figure GDA0004192754360000098
As an input of the fuzzy controller, the output of the fuzzy controller is set to be the power value +.>
Figure GDA0004192754360000099
I.e., the current power value that the hydrogen storage system should deliver; the fuzzy sets of fuzzy input and output are set to five stages { NB, NS, ZO, PS, PB }, which respectively represent the state of charge SOHR of each hydrogen storage system i Charging efficiency->
Figure GDA00041927543600000910
Discharge efficiency->
Figure GDA00041927543600000911
Output value of hydrogen energy storage system>
Figure GDA00041927543600000912
Is of a size of (2); the input and output of the fuzzy control are explained specifically as follows:
input 1: SOHR i The fuzzy universe is [ -1,1]The SOHR is-1, which indicates that the energy storage system reaches the maximum discharge value and can not discharge any more; when the SOHR is 1, the energy storage system reaches the maximum charging value and cannot be charged any more, and the fuzzy subset is set to be five stages { NB, NS, ZO, PS, PB }, which respectively represent the states of charge SOHR { very low, moderate, high and very high } of the single hydrogen energy storage system;
input 2:
Figure GDA0004192754360000101
the fuzzy universe is [ -1,1]The fuzzy subset is set to be three-level { NB, ZO, PB }, which respectively represent the power generation efficiency { high, moderate and low };
and (3) outputting: adjusting power value for hydrogen energy storage system
Figure GDA0004192754360000102
The fuzzy universe is [ -5,5]The fuzzy subset is set to five stages { NB, NS, ZO, PS, PB }, which respectively represent the power required to be sent by a single hydrogen energy storage system according to the power grid instruction requirement;
the input and output membership functions of fuzzy control are common triangle and T-shaped membership functions; making a fuzzy rule:
the hydrogen energy storage system provides electric energy for the power grid, provides electric power through HFC,
Figure GDA0004192754360000103
as an input of the fuzzy control, the fuzzy rule is shown in table 2, and the specific expression is as follows:
when the charge state of the hydrogen energy storage system and the HFC power generation efficiency are both intermediate values, the fuzzy control output is also the intermediate value; when the state of charge of the hydrogen energy storage system gradually approaches the upper limit, the HFC power generation efficiency gradually approaches the upper limit, and the power generation power of the hydrogen energy storage system is increased, namely the hydrogenation amount of the hydrogen fuel cell is increased; when the state of charge of the hydrogen energy storage system gradually approaches the lower limit, the HFC power generation efficiency gradually approaches the lower limit, and the charging power of the hydrogen energy storage system is reduced, namely the HFC hydrogen supply is reduced;
TABLE 2
Figure GDA0004192754360000104
Step 4.4: defuzzifying the output quantity of the fuzzy controller; deblurring the output of n-1 hydrogen energy storage systems by adopting a weighted average method to obtain theoretical power distribution values of single hydrogen energy storage systems, wherein the specific formula is as follows:
Figure GDA0004192754360000105
in the method, in the process of the invention,
Figure GDA0004192754360000106
respectively represent the input quantity SOHR i ，/>
Figure GDA0004192754360000107
Membership value, Z i And the abscissa value corresponding to the sharp point of the fuzzy membership function of the output power value.
In the control process, because the power grid requirements of different periods are different, after a new instruction is issued by the power grid, the control process is repeated, each system in the HESS cluster is dynamically screened again, and HESS cluster energy is distributed again, so that the dynamic screening of the HESS cluster and the flexible distribution of the energy are realized;
supplementary explanation: the invention is not limited to the number of indexes and the number of grades set in the specific embodiment, and based on the technical scheme disclosed by the invention, a person skilled in the art can make some substitutions and modifications to some technical features without creative labor according to the technical content disclosed by the invention, and the substitutions and modifications are all within the protection scope of the invention.

Claims (4)

1. The hydrogen energy storage system cluster output decision method based on fuzzy logic is characterized by comprising the following steps of:
step 1: parameters including SOHR, hydrogen production efficiency, power generation efficiency and storage tank capacity of the n hydrogen energy storage systems are obtained;
step 2: the power grid issues a scheduling instruction according to the requirements, and the charging and discharging states of the hydrogen energy storage system are judged; screening an optimal hydrogen energy storage system under the current power grid instruction and the state condition of the optimal hydrogen energy storage system for standby;
when the electric power of the power grid is surplus, namely delta P is more than or equal to 0, the power grid gives instructions to n hydrogen energy storage systems so that the n hydrogen energy storage systems are ready for charging and hydrogen production, and the step 3 is performed; when the power grid is in short supply, namely delta P is less than or equal to 0, the power grid gives a power supply instruction, and the step 4 is shifted; wherein Δp=p G -P load I.e. the difference between the power supply of the power grid and the power required by the load;
step 3: taking one optimal hydrogen energy storage system under the current power grid instruction and the state condition of the current power grid instruction as a standby, and obtaining the output of each hydrogen energy storage system by adopting a hydrogen energy storage system output decision method based on a fuzzy control algorithm by the remaining n-1 systems; if the remaining n-1 hydrogen energy storage systems cannot meet the power grid demand after the power distribution condition is decided, the remaining power grid demand is met by the optimal hydrogen energy storage selected dynamically; the SOHR value of each hydrogen storage during charging cannot exceed the self-set maximum hydrogen storage quantity S max When the power consumed by the n hydrogen storage systems meets the power grid command, ending energy distribution, stopping hydrogen production by the hydrogen storage systems, and re-estimating SOHR states of each hydrogen storage system and power generation efficiency of the electrolytic cell and HFC; returning to the step 1;
step 4: an optimal hydrogen energy storage system under the current power grid instruction and the state condition of the system is used as a standby, and the rest n-1 systems adopt hydrogen energy storage based on a fuzzy control algorithmThe system output decision method obtains the output of each hydrogen energy storage system, and then generates electricity through HFC; each hydrogen energy storage and hydrogen storage amount is proportional to the storage amount, so the storage amount range is S min ～S max When the hydrogen storage amount of the hydrogen energy storage system is low to S low Stopping generating electricity when the power generation is stopped; returning to the step 1;
wherein S is min And S is max Respectively representing the minimum hydrogen storage amount and the maximum hydrogen storage amount set by the hydrogen energy storage system; s is S low And S is high Respectively represent a lower limit and an upper limit of the hydrogen storage amount when the hydrogen storage system is optimally operated.
2. The fuzzy logic based hydrogen storage system cluster power decision method of claim 1, wherein the discriminating of the optimal hydrogen storage system in step 2 comprises the steps of:
first determining SOHR, charging efficiency eta of a hydrogen storage system elec Discharge efficiency eta fc Number of charge and discharge times
Figure FDA0004192754350000011
The five indexes of the working temperature T are taken as factors, namely, the factor theory domain U of the hydrogen energy storage cluster is determined, and the +.>
Figure FDA0004192754350000012
Determining a comment level universe V, V= (A, B, C, D, E), namely dividing the level into five levels A-E, wherein A is the highest level, E is the lowest level, and different levels are used for comprehensively evaluating the performance of a certain hydrogen energy storage system in all indexes;
step 2.1: and (3) performing single-factor judgment on each factor, and establishing a fuzzy relation matrix R:
Figure FDA0004192754350000013
wherein r is ij For each factor in U, the membership of the factor to the class in V, where w=m=5;
step 2.2: determining a judgment factor weight vector A= (a) 1 ，a 2 ，…，a 5 ) A is the membership of each index in U to the hydrogen energy storage system, and weight is distributed according to the importance of each index in judgment; each weight in the hydrogen storage system is given by historical experience data, and each index of the hydrogen storage system is given; when delta P is more than or equal to 0, the state of charge SOHR of each subsystem in the hydrogen energy storage system cluster from high to low is considered i Charging efficiency
Figure FDA0004192754350000021
Cycle charge and discharge times->
Figure FDA0004192754350000022
Operating temperature T i Wherein i=1, 2, … n-1; the charging efficiency is the electrolytic hydrogen production efficiency; when delta P is less than or equal to 0, the state of charge SOHR of each subsystem in the hydrogen energy storage system cluster from high to low is considered i Discharge efficiency->
Figure FDA0004192754350000023
Number of charge and discharge cycles
Figure FDA0004192754350000024
Operating temperature T i Wherein i=1, 2, … n-1; the discharging efficiency is HFC power generation efficiency;
step 2.3: selecting a synthesis operator of the fuzzy decision evaluation to obtain an evaluation result B, wherein B is obtained by synthesizing A and R, namely
Figure FDA0004192754350000025
Step 2.4: and (3) quantizing the fuzzy comments, calculating a priority comment quantizing set of each object to be S, and obtaining the priorities of n hydrogen energy storage through the following formula, so that the hydrogen energy storage system which is most suitable for the current power grid instruction can be screened out:
Figure FDA0004192754350000026
wherein m is the index number, k represents the kth hydrogen energy storage system in the hydrogen energy storage cluster, and n hydrogen energy storage systems form the hydrogen energy storage cluster, so that k=1, 2, … … and n;
and screening out the hydrogen energy storage system in the optimal state under the power grid dispatching instruction at a certain moment by the steps to serve as a standby.
3. The fuzzy logic-based hydrogen energy storage system cluster output decision method is characterized in that in the step 3, power distribution is carried out on the remaining n-1 hydrogen energy storage systems by adopting a second-layer fuzzy control algorithm, and particularly the power distribution of the hydrogen energy storage clusters under each power grid dispatching instruction is completed through the following processes:
step 3.1: for each hydrogen storage system, its state of charge SOHR is determined i Charging efficiency
Figure FDA0004192754350000027
Discharge efficiency->
Figure FDA0004192754350000028
Wherein i=1, 2, … n-1 is normalized;
step 3.2: determining membership functions of each index in factor theory domain, and respectively defining the membership functions as variables
Figure FDA0004192754350000029
Input quantity SOHR i And output->
Figure FDA00041927543500000210
Set to five stages { NB, NS, ZO, PS, PB }, input amount +.>
Figure FDA00041927543500000211
Setting as three-level { NB, ZO, PB };
step 3.3: will SOHR i ，
Figure FDA00041927543500000212
As an input to the fuzzy controller, the output of the fuzzy controller is set to be the power value
Figure FDA00041927543500000213
I.e., the power value that the current hydrogen storage system should dissipate; the fuzzy sets of fuzzy input and output are set to five stages { NB, NS, ZO, PS, PB }, which respectively represent the state of charge SOHR of each hydrogen storage system i Charging efficiency->
Figure FDA00041927543500000214
Discharge efficiency
Figure FDA00041927543500000215
Output value of hydrogen energy storage system>
Figure FDA00041927543500000216
Is of a size of (2); NB represents negative big, NS represents negative small, ZO represents zero, PS represents positive small, PB represents positive big;
the input and output of the fuzzy control are explained specifically as follows:
input 1: SOHR i The fuzzy universe is [ -1,1]The SOHR is-1, which indicates that the energy storage system reaches the maximum discharge value and can not discharge any more; when the SOHR is 1, the energy storage system reaches the maximum charging value and cannot be charged any more, and the fuzzy subset is set to be five stages { NB, NS, ZO, PS, PB }, which respectively represent the states of charge SOHR { very low, moderate, high and very high } of the single hydrogen energy storage system; NB represents negative big, NS represents negative small, ZO represents zero, PS represents positive small, PB represents positive big;
input 2:
Figure FDA0004192754350000031
the fuzzy universe is [ -1,1]The fuzzy subset is set to be three-level { NB, ZO, PB }, which respectively represent the hydrogen production of the hydrogen energy storage systemEfficiency { high, moderate, low };
and (3) outputting: adjusting power value for hydrogen energy storage system
Figure FDA0004192754350000032
The fuzzy universe is [ -5,5]The fuzzy subset is set to five stages { NB, NS, ZO, PS, PB }, and the fuzzy subset respectively represents the power which needs to be consumed by a single hydrogen energy storage system according to the power grid instruction requirement;
the input and output membership functions of fuzzy control are common triangle and T-shaped membership functions;
making a fuzzy rule:
the hydrogen energy storage system obtains electric energy from the power grid, and consumes surplus electric power through hydrogen production by electrolysis,
Figure FDA0004192754350000033
as an input of the fuzzy control, the fuzzy rule is specifically expressed as follows:
when the charge state of the hydrogen energy storage system and the electrolytic hydrogen production efficiency are both intermediate values, the fuzzy control output is also the intermediate value; when the state of charge of the hydrogen energy storage system gradually approaches the upper limit and the electrolytic hydrogen production efficiency gradually approaches the lower limit, the charging power of the hydrogen energy storage system is reduced, namely the hydrogen production amount of the hydrogen energy storage system is reduced; when the state of charge of the hydrogen energy storage system gradually approaches the lower limit and the electrolytic hydrogen production efficiency gradually approaches the upper limit, the charging power of the hydrogen energy storage system is increased, namely the hydrogen production amount of the hydrogen energy storage system is increased;
step 3.4: defuzzification of fuzzy controller output: deblurring the output of n-1 hydrogen energy storage systems by adopting a weighted average method to obtain theoretical power distribution values of single hydrogen energy storage systems, wherein the specific formula is as follows:
Figure FDA0004192754350000034
in the method, in the process of the invention,
Figure FDA0004192754350000035
respectively represent input quantitiesSOHR i ，/>
Figure FDA0004192754350000036
Membership value, Z i And the abscissa value corresponding to the sharp point of the fuzzy membership function of the output power value.
4. The fuzzy logic-based hydrogen energy storage system cluster output decision method is characterized in that in the step 4, power distribution is carried out on the remaining n-1 hydrogen energy storage systems by adopting a second-layer fuzzy control algorithm, and particularly the power distribution of the hydrogen energy storage clusters under each power grid dispatching instruction is completed through the following processes:
step 4.1: normalizing the indexes in the factor theory domain, and carrying out SOHR on the state of charge of each hydrogen energy storage system i Charging efficiency
Figure FDA0004192754350000037
Discharge efficiency->
Figure FDA0004192754350000038
Wherein i=1, 2, … n-1 is normalized;
step 4.2: determining membership functions of each index in factor theory domain, and respectively defining the membership functions as variables
Figure FDA0004192754350000039
Input quantity SOHR i And output->
Figure FDA00041927543500000310
Set to five stages { NB, NS, ZO, PS, PB }, input amount +.>
Figure FDA00041927543500000311
Setting as three-level { NB, ZO, PB };
step 4.3: will SOHR i ，
Figure FDA0004192754350000041
As an input to the fuzzy controller, the output of the fuzzy controller is set to be the power value
Figure FDA0004192754350000042
I.e., the current power value that the hydrogen storage system should deliver; the fuzzy sets of fuzzy input and output are set to five stages { NB, NS, ZO, PS, PB }, which respectively represent the state of charge SOHR of each hydrogen storage system i Charging efficiency->
Figure FDA0004192754350000043
Discharge efficiency
Figure FDA0004192754350000044
Output value of hydrogen energy storage system>
Figure FDA0004192754350000045
Is of a size of (2); the input and output of the fuzzy control are explained specifically as follows:
input 1: SOHR i The fuzzy universe is [ -1,1]The SOHR is-1, which indicates that the energy storage system reaches the maximum discharge value and can not discharge any more; when the SOHR is 1, the energy storage system reaches the maximum charging value and cannot be charged any more, and the fuzzy subset is set to be five stages { NB, NS, ZO, PS, PB }, which respectively represent the states of charge SOHR { very low, moderate, high and very high } of the single hydrogen energy storage system;
input 2:
Figure FDA0004192754350000046
the fuzzy universe is [ -1,1]The fuzzy subset is set to be three-level { NB, ZO, PB }, which respectively represent the power generation efficiency { high, moderate and low };
and (3) outputting: adjusting power value for hydrogen energy storage system
Figure FDA0004192754350000047
The fuzzy universe is [ -5,5]The fuzzy subset is set to five stages { NB, NS, ZO, PSPB, which respectively represent the power required to be sent by a single hydrogen energy storage system according to the power grid instruction;
the input and output membership functions of fuzzy control are common triangle and T-shaped membership functions; making a fuzzy rule:
the hydrogen energy storage system provides electric energy for the power grid, provides electric power through HFC,
Figure FDA0004192754350000048
as an input of the fuzzy control, the fuzzy rule is specifically expressed as follows:
when the charge state of the hydrogen energy storage system and the HFC power generation efficiency are both intermediate values, the fuzzy control output is also the intermediate value; when the state of charge of the hydrogen energy storage system gradually approaches the upper limit, the HFC power generation efficiency gradually approaches the upper limit, and the power generation power of the hydrogen energy storage system is increased, namely the hydrogenation amount of the hydrogen fuel cell is increased; when the state of charge of the hydrogen energy storage system gradually approaches the lower limit, the HFC power generation efficiency gradually approaches the lower limit, and the charging power of the hydrogen energy storage system is reduced, namely the HFC hydrogen supply is reduced;
step 4.4: defuzzifying the output quantity of the fuzzy controller; deblurring the output of n-1 hydrogen energy storage systems by adopting a weighted average method to obtain theoretical power distribution values of single hydrogen energy storage systems, wherein the specific formula is as follows:
Figure FDA0004192754350000049
in the method, in the process of the invention,
Figure FDA00041927543500000410
respectively represent the input quantity SOHR i ，/>
Figure FDA00041927543500000411
Membership value, Z i And the abscissa value corresponding to the sharp point of the fuzzy membership function of the output power value. />
CN202110842900.0A 2021-07-26 2021-07-26 Fuzzy logic-based hydrogen energy storage system cluster output decision method Active CN113629775B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110842900.0A CN113629775B (en) 2021-07-26 2021-07-26 Fuzzy logic-based hydrogen energy storage system cluster output decision method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110842900.0A CN113629775B (en) 2021-07-26 2021-07-26 Fuzzy logic-based hydrogen energy storage system cluster output decision method

Publications (2)

Publication Number Publication Date
CN113629775A CN113629775A (en) 2021-11-09
CN113629775B true CN113629775B (en) 2023-05-26

Family

ID=78380989

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110842900.0A Active CN113629775B (en) 2021-07-26 2021-07-26 Fuzzy logic-based hydrogen energy storage system cluster output decision method

Country Status (1)

Country Link
CN (1) CN113629775B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115566706B (en) * 2022-11-10 2023-03-28 西南交通大学 Fuzzy control method for alkaline electrolysis hydrogen production system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104135025A (en) * 2014-05-30 2014-11-05 国家电网公司 Microgrid economic operation optimization method based on fuzzy particle swarm algorithm and energy saving system
CN104301985A (en) * 2014-09-19 2015-01-21 华北电力大学（保定） Energy distribution method between power grid and cognition base station in mobile communication
CN106125552A (en) * 2016-08-08 2016-11-16 国家电网公司 Pump-storage generator governing system fuzzy score rank PID control method
CN109494777A (en) * 2018-12-07 2019-03-19 重庆大学 A kind of mixed energy storage system energy compatibility distribution control method
CN109755965A (en) * 2019-03-20 2019-05-14 河北科技大学 Wind light generation and hydrogen-preparing hydrogen-storing system and its progress control method
CN111245105A (en) * 2018-11-28 2020-06-05 国网新疆电力有限公司经济技术研究院 Capacity configuration method for pre-installed energy storage power station

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104135025A (en) * 2014-05-30 2014-11-05 国家电网公司 Microgrid economic operation optimization method based on fuzzy particle swarm algorithm and energy saving system
CN104301985A (en) * 2014-09-19 2015-01-21 华北电力大学（保定） Energy distribution method between power grid and cognition base station in mobile communication
CN106125552A (en) * 2016-08-08 2016-11-16 国家电网公司 Pump-storage generator governing system fuzzy score rank PID control method
CN111245105A (en) * 2018-11-28 2020-06-05 国网新疆电力有限公司经济技术研究院 Capacity configuration method for pre-installed energy storage power station
CN109494777A (en) * 2018-12-07 2019-03-19 重庆大学 A kind of mixed energy storage system energy compatibility distribution control method
CN109755965A (en) * 2019-03-20 2019-05-14 河北科技大学 Wind light generation and hydrogen-preparing hydrogen-storing system and its progress control method

Also Published As

Publication number Publication date
CN113629775A (en) 2021-11-09

Similar Documents

Publication Publication Date Title
CN109325608B (en) Distributed power supply optimal configuration method considering energy storage and considering photovoltaic randomness
CN109492815B (en) Energy storage power station site selection and volume fixing optimization method for power grid under market mechanism
CN109361237B (en) Micro-grid capacity optimization configuration method based on improved hybrid particle swarm algorithm
CN114336702B (en) Wind-solar storage station group power distribution collaborative optimization method based on double-layer random programming
WO2023060815A1 (en) Energy storage capacity optimization configuration method for improving reliability of power distribution network
CN110956324B (en) Day-ahead high-dimensional target optimization scheduling method for active power distribution network based on improved MOEA/D
CN106159944B (en) Multi-stage transmission expansion planning method under low-carbon environment based on bilevel programming model
CN113783224A (en) Power distribution network double-layer optimization planning method considering operation of various distributed energy sources
CN115622101A (en) Energy storage optimal configuration double-layer planning method for promoting renewable energy consumption
Huangfu et al. An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogen hybrid energy system
CN113629775B (en) Fuzzy logic-based hydrogen energy storage system cluster output decision method
CN116316694A (en) Energy storage power station frequency modulation optimal parameter selection method based on two-stage robust optimization
CN111682536A (en) Random-robust optimization operation method for virtual power plant participating in day-ahead double market
CN115036914A (en) Power grid energy storage double-layer optimization method and system considering flexibility and new energy consumption
Linlin et al. Research on Multi-Objective Reactive Power Optimization of Power Grid With High Proportion of New Energy
CN114462854A (en) Hierarchical scheduling method and system containing new energy and electric vehicle grid connection
CN113644674A (en) Hydrogen hybrid energy storage capacity configuration system and method based on quantum particle swarm and application
CN113364043A (en) Micro-grid group optimization method based on condition risk value
CN115936265B (en) Robust planning method for electric hydrogen energy system by considering electric hydrogen coupling
CN113673141B (en) Energy router modeling and optimization control method based on data driving
CN114725961A (en) Hydrogen production system capacity layering optimization configuration method for stabilizing wind power fluctuation
CN115940284A (en) Operation control strategy of new energy hydrogen production system considering time-of-use electricity price
CN106953346A (en) A kind of off-network type microgrid energy management method containing sodium-sulphur battery
CN113488990A (en) Micro-grid optimization scheduling method based on improved bat algorithm
Haiyun et al. Optimal Capacity Allocation Method of Multi-types of Energy Storage for Wind Power Plant

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