CN114709866A - Fractional order model prediction control method for power of electricity-hydrogen hybrid energy storage system - Google Patents

Fractional order model prediction control method for power of electricity-hydrogen hybrid energy storage system Download PDF

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CN114709866A
CN114709866A CN202210254740.2A CN202210254740A CN114709866A CN 114709866 A CN114709866 A CN 114709866A CN 202210254740 A CN202210254740 A CN 202210254740A CN 114709866 A CN114709866 A CN 114709866A
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voltage
fractional order
current
energy storage
power
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彭子舜
戴瑜兴
赵振兴
廖石波
朱勇
刘增
杨亚超
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SHENZHEN JINGQUANHUA ELECTRONICS CO LTD
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    • 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
    • C—CHEMISTRY; METALLURGY
    • C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
    • C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
    • C25B1/00—Electrolytic production of inorganic compounds or non-metals
    • C25B1/01—Products
    • C25B1/02—Hydrogen or oxygen
    • C25B1/04—Hydrogen or oxygen by electrolysis of water
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    • G06F17/13—Differential equations
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/36—Arrangements for transfer of electric power between ac networks via a high-tension dc link
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M1/00—Details of apparatus for conversion
    • H02M1/08—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M3/00—Conversion of dc power input into dc power output
    • H02M3/02—Conversion of dc power input into dc power output without intermediate conversion into ac
    • H02M3/04—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters
    • H02M3/10—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
    • H02M3/145—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
    • H02M3/155—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only
    • H02M3/156—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of output voltage or current, e.g. switching regulators
    • H02M3/158—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of output voltage or current, e.g. switching regulators including plural semiconductor devices as final control devices for a single load
    • H02M3/1582—Buck-boost converters
    • 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]
    • 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
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/30—The power source being a fuel cell
    • 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

Abstract

The invention discloses a fractional order model prediction control method for power of an electric-hydrogen hybrid energy storage system, which comprises 8 steps of respectively providing equivalent circuit diagrams of a BOOST BOOST circuit and a BUCK BUCK circuit when a switch tube is switched on and switched off in two working modes, monitoring the variation process of inductance current and the variation process of capacitance voltage and the like, adopting a fractional order calculus algorithm and model prediction control to be combined for power control of an electric-hydrogen combined hybrid energy storage system and a device to obtain a fractional order type voltage and current prediction model, adopting double-loop control, selecting the switching state of an energy storage converter through prediction output to accurately control the output current of the converter relative to a bus, more finely inhibiting distributed power fluctuation, overcoming the slow response speed of the current commonly used double-loop PI (proportional integral) control, and possibly causing deviation during prediction control of the existing integer order model, Low current control precision and the like.

Description

Fractional order model prediction control method for power of electricity-hydrogen hybrid energy storage system
Technical Field
The invention relates to the technical field of control of an electricity-hydrogen combined hybrid energy storage system, in particular to a fractional order model prediction control method for power of an electricity-hydrogen combined hybrid energy storage system.
Background
With the proposal of double carbon targets of carbon peak reaching and carbon neutralization and the urgent need of energy transformation faced by China, the construction of a low-carbon and safe energy system and the development of a green and efficient energy storage system are imperative. China has numerous ocean islands, and important equipment on the islands urgently needs power supply with high reliability and high power quality, so that an energy storage system plays a central role in stabilizing power fluctuation in island micro-grids. With the steep increase of installed capacities of wind power, photovoltaic and the like in China, the phenomena of wind abandoning and light abandoning appear in recent years. The redundant electric energy is used for hydrogen production by water electrolysis, and the development of hydrogen storage technology becomes an important direction for the innovation of a mixed energy storage system form in the field of new energy power generation. The carbon dioxide (CO2) in the nature enters a special fuel cell device to generate electricity, so that the power generation becomes an advanced distributed power generation mode, meets the target requirement of 'double carbon' in China, conforms to the trend of energy strategic development in China, and is expected to increase the proportion of the power generation in a more advanced hybrid energy storage system of an intelligent micro-grid in the future.
Referring to fig. 1, no matter on an island or in a land microgrid, various distributed new energy power generation units such as ocean current energy, wave energy, traditional wind energy, photovoltaic power generation units and the like have the characteristics of high output randomness and high power fluctuation. The power generation and hydrogen generation combined hybrid energy storage system can overcome the ubiquitous high-frequency and low-frequency fluctuation inherent defects of new energy power generation, wherein electricity in the power generation refers to a traditional electrochemical hybrid energy storage mode, and is formed by combining a high-capacity energy storage battery or a lithium battery and a high-power density super capacitor, so that a low-frequency slow fluctuation part and a high-frequency short-time fluctuation part in distributed generation power can be respectively stabilized, the electric energy quality indexes such as the bus voltage and the frequency of a micro-grid are maintained at a good level, the running stability of the micro-grid is improved, and stable power supply to important loads is realized; the hydrogen is used for storing hydrogen energy, and comprises a series of links such as hydrogen production by electricity, hydrogen storage, hydrogen transportation and hydrogen fuel power generation. The introduction of hydrogen energy storage enriches the energy storage form of the hybrid energy storage system, improves the flexibility of the hybrid energy storage system, and reduces carbon emission, thereby assisting the dual-carbon target.
As disclosed in prior patent documents CN201811539383.4 or CN201811048743.0, each part of the energy storage devices in the hybrid energy storage system is generally connected in parallel to the microgrid bus through a DC/DC bidirectional converter, the conventional converter control method adopts voltage/current dual closed loop control, utilizes system droop characteristics and PI regulation, and adopts PWM technology to generate pulse signals to control the switching state of the switching tubes on the converter, so that the current on the inductor tracks the current reference value generated by the voltage outer loop. Although the double-closed-loop control can effectively adjust the charging and discharging power of the energy storage system and meet the requirement of the steady-state control performance of the system, the micro-grid is weak in anti-interference capability, and a plurality of PI links exist in the double-loop control, so that the double-closed-loop control is a hysteresis control system and cannot quickly respond when the micro-grid suffers from large disturbance. Therefore, the Model Predictive Control (MPC) is widely applied to the control of the hybrid energy storage system, carries out state space form predictive model modeling on a control object, predicts the output state in advance through forward Euler differentiation, selects the optimal control quantity through a mathematical programming method, can continuously carry out rolling optimization, and greatly improves the power control effect of the energy storage device. Particularly, when power distribution of the electricity-hydrogen combined hybrid energy storage system is responded, hydrogen production, hydrogen storage, SOC constraint of a battery and a capacitor can be considered, current variation of battery charging and discharging is limited, safety of hydrogen energy storage is improved, and service life of the battery is prolonged. However, model predictive control still has a deficiency in control accuracy.
The fractional order prediction control introduces the idea of fractional order calculus into the prediction control. The order of the integral calculus is rewritten into the fractional order, and a new calculus operation method is promoted. After expanding the integer order calculus to a continuous full real range, the fractional order calculus exhibits strong real power. The characteristics exhibited in some real physical or chemical processes cannot be accurately described by integer-order differential equations, and just because the order of the fractional order can be flexibly and continuously selected, the fractional calculus can better describe the process characteristics of the system when facing such dynamic systems, and can capture more physical essence of which the integer order is possibly ignored for a specific object. For the controller, only one order adjustable parameter is added, and the control effect of the prediction model controller is further optimized by debugging the order.
Disclosure of Invention
The invention aims to provide a fractional order model prediction control method for power control of an electricity-hydrogen combined hybrid energy storage system and device, and aims to solve the problems that the conventional double-loop PI control is low in response speed and low in control accuracy, and power fluctuation of distributed power generation is smoothly suppressed. In order to achieve the purpose, the invention provides the following technical scheme: a fractional order model prediction control method for power of an electricity-hydrogen hybrid energy storage system comprises the following steps:
s1, respectively providing equivalent circuit diagrams when the switch T is switched on and off in two working modes of the BOOST voltage boosting circuit and the BUCK voltage reducing circuit, and monitoring the change process of the inductance current and the change process of the capacitance voltage;
s2, writing a dynamic KVL equation for a loop column containing an inductor in an equivalent circuit at the conduction time of the switch T, and writing a dynamic KCL equation for a loop column containing a capacitor, wherein the KVL comprises an energy storage end voltage, an inductor voltage and a line loss voltage drop, and the KCL equation comprises a capacitor voltage and a bus voltage; the fractional calculus idea is adopted, the inductance voltage in the KVL equation is expressed as the current, the fractional derivative is obtained, and then the product is multiplied by the inductance value, and the capacitance voltage in the KCL equation is expressed as the current, the fractional derivative is obtained, and then the product is multiplied by the capacitance value;
s3, calculating to obtain a fractional order differential expression of the inductance current and a fractional order differential expression of the capacitance voltage;
s4, decomposing fractional order differential terms of the inductive current and the capacitive voltage into an integral order differential term and a fractional order differential term superposition action by applying the properties of fractional order calculus linear superposition and satisfying the exchange law;
s5, writing an integer order differential term into a difference form by using a forward Eulerian method, calculating a fractional order differential term by using an Oustaloup filtering algorithm, obtaining a fractional order predicted value of the inductor current and the capacitor voltage at the next moment, and establishing a fractional order prediction model of the inductor current and the capacitor voltage;
s6, respectively predicting a current-voltage fractional order predicted value at the next moment obtained by turning on the switch T at the moment and a current-voltage fractional order predicted value at the next moment obtained by turning off the switch T at the moment according to the switching of the switch states 1 and 0 of the switch T, and further obtaining the output predicted power of the converter;
s7, providing cost functions for evaluation, comparing two predicted power results with the voltage shortage power of the direct-current microgrid bus respectively, selecting the minimum value through the cost functions to select the optimal switching state at the next moment, and obtaining an online optimized control rate by utilizing the rolling optimization capability of a prediction model to obtain PWM signals acting on the DC-DC converter switch;
and S8, analyzing and calculating the predicted power when the switching tube of the BUCK voltage reduction circuit is switched on and off.
In a further improvement, the process of changing the inductive current in step S1 includes:
at the conduction time of a switching tube T of the BOOST booster circuit in a switching period, the side of an energy storage battery charges an inductor, the inductor current gradually rises, and the rising process of the inductor current is described by a fractional order method, namely the variation of the inductor current is the fractional order integral of the voltage at two ends of the inductor;
at the closing time of a switching tube T of the BOOST circuit in a switching period, energy stored in an inductor and an energy storage battery supply power to the direct current bus side of an output end together, the current value of the inductor is gradually reduced by fractional order integration, the inductor plays a role in pump-up, and the BOOST process is completed;
the stored energy in the inductor is equal to the released energy during a switching cycle.
In a further improvement, the process of changing the capacitor voltage in step S1 includes:
at the conducting time of a switching tube T of the BOOST circuit in a switching period, the capacitor plays a role in maintaining the voltage of the output end, and the discharging voltage also gradually decreases by fractional order integral of current;
at the closing time of the BOOST circuit switch tube T in a switching period, the energy storage battery and the inductor charge the capacitor while supplying power to the bus, and the voltage of the capacitor gradually rises.
In a further improvement, the method for writing KVL and KCL to the column of equivalent circuits in step S2 includes:
s21, according to the collected energy storage side battery terminal voltage UbAnd IbCollected bus voltage UdcThe inductance loop KVL equation comprises the battery terminal voltage U at the T conduction time in the BOOST circuitbLine loss voltage drop UrFractional order differential form inductance voltage expression ULThe capacitance current in the capacitance loop KCL equation is equal to the load current;
s22, T turn-off time, inductance loop KVL equation contains battery terminal voltage UbLine loss voltage drop UrFractional order differential form inductance voltage expression ULBus voltage UdcDiode conduction voltage drop UdAnd the capacitance current in the capacitance loop KCL equation is equal to the difference between the output current of the battery end and the load current.
In a further improvement, the step S3 specifically includes:
obtaining a fractional differential expression of current and voltage by using a Capotu fractional calculus definition form:
Figure BDA0003548302410000051
Figure BDA0003548302410000052
wherein the content of the first and second substances,
Figure BDA0003548302410000053
represents a differential action from 0 → t, wherein a is a fraction between 0 and 1; i.e. ib(1)、Uc(1)Inb、UcThe method is a differential object and represents the inductive current and the capacitor voltage, and (1) represents the prediction condition of the conduction state of the switch T at the next moment; rrRepresents a line resistance value ibIndicating the value of the current flowing through the inductor, UbRepresents the battery voltage;
Figure BDA0003548302410000054
Figure BDA0003548302410000055
similarly, (0) represents the prediction condition of the turn-off state of the switch T at the next moment; u shapecRepresenting the value of the capacitor voltage, UdRepresenting the diode drop.
In a further improvement, the step S4 specifically includes:
s41, decomposing the fractional order differential terms into integer order differential terms and fractional order differential terms, wherein the superposition principle is as follows:
it is assumed that,
Figure BDA0003548302410000056
s42, writing the integral order differential term into a forward Euler differential form, and after simplification and item shifting, obtaining a prediction model of the target value at the k +1 moment in the fractional order integral form as follows:
Figure BDA0003548302410000061
in the formula (I), the compound is shown in the specification,
Figure BDA0003548302410000062
in (c) ptRepresents the integral within 0 → T, 1-a is also a fraction between 0 and 1, TsFor the sampling interval, f (t)kIs the value of the function at the previous moment, f (t)k+1Is the next time value.
In a further improvement, the specific method for establishing the fractional order prediction model of the inductor current and the capacitor voltage in step S5 includes:
s51, the inductor current prediction model is as follows,
Figure BDA0003548302410000063
Figure BDA0003548302410000064
s52, the capacitance-voltage prediction model is as follows,
Figure BDA0003548302410000065
Figure BDA0003548302410000066
s53, approximately calculating the fractional order integral term by utilizing the improved Oustaloup filtering algorithm, selecting a reasonable fitting frequency band to obtain an approximate transfer function model of the fractional order differential operator
Figure BDA0003548302410000067
Wherein a is order, d and b are derived from experience, H1(s) is a transfer function obtained by calculation, and N can be 2-4; therefore, the approximate transfer function of the fractional order differential operator can be obtained.
S54, processing and calculating fractional order processes in the prediction model through the fractional order approximate transfer function model, so as to obtain a current fractional order prediction model and a voltage fractional order prediction model at the moment of k +1 as follows,
Figure BDA0003548302410000068
Figure BDA0003548302410000069
Figure BDA0003548302410000071
Figure BDA0003548302410000072
in a further improvement, the step S6 specifically includes:
s61, collecting the voltage of a direct current bus of the microgrid, subtracting the voltage from a bus reference voltage to obtain a voltage error, calculating a bus current reference value output by the DC converter according to the droop characteristic of the DC-DC converter of the energy storage system, and multiplying the voltage and the current to obtain a converter output power reference value;
s62, converter fractional order prediction output power P when switch T is conducted(1)k+1＝ib(1)k+1×Uc(1)k+1Predicted output power P at turn-off of T(0)k+1＝ib(0)k+1×Uc(0)k+1。
In a further improvement, the cost function in step S7 is set as:
J＝||Pk+1-P*||2
the optimal switching state at the moment k +1 can be selected by evaluating the cost function, online rolling optimization can be carried out at each moment through a fractional order prediction model, a control law changing in real time is obtained, and a PWM signal for controlling the conduction of a device is obtained.
In a further improvement, the step S8 specifically includes:
aiming at the charging process of the energy storage system, namely the circuit works in a BUCK state, the BUCK circuit is suitable for the DC-DC voltage reduction process of the electrolytic cell, the U-I equation of the electrolytic cell is shown as follows,
Figure BDA0003548302410000073
the parameters are selected as follows: a is 0.01, r1＝3.54e-4,r2＝-3.02e-6,s＝0.224,t1＝5.13,t2＝-2.40e2,t3＝3.41e3；
The hydrogen production process of the electrolytic cell has the following relation with the introduced current, and the fractional order type hydrogen production rate can be described as,
Figure BDA0003548302410000074
f1＝2.5T+50,f2＝1-T*6.25e-6,z＝2,F＝96485.3C/mol；
the amount of hydrogen storage is described as,
Figure BDA0003548302410000081
the hydrogen storage pressure is described as being,
Figure BDA0003548302410000082
after fractional order calculation, the hydrogen storage pressure is expressed as a function of the input current I, namely P (k +1) ═ f (I); according to the BUCK circuit, the input current of the electrolytic cell is predicted by a fractional order method, then a predicted value P (k +1) of hydrogen storage pressure is obtained, and the predicted value P is activated within the upper and lower limits of the hydrogen storage pressure through hydrogen storage restriction.
Compared with the existing control method of the hybrid energy storage system, the technical scheme provided by the invention can achieve the following technical effects:
(1) the fractional order model prediction method is used for power control of an electricity-hydrogen combined hybrid energy storage system, and comprises the processes of compensation power supply of the hybrid energy storage system to a direct-current bus and charging of the hybrid energy storage system by the bus;
when the hybrid energy storage system is charged by the direct-current bus power, the redundant power is absorbed by the lithium battery and the electrolytic cell, and when the lithium battery reaches the SOC upper limit, the full-power electrolytic water is converted to prepare hydrogen gas for storing energy;
(2) when the electricity-hydrogen combined hybrid energy storage system supplies power for the DC bus in a compensating mode, the lithium battery and the fuel cell supply power to the bus, the hydrogen fuel cell is preferentially adopted for power generation, and when the SOH (state of hydrogen) reaches the lower limit, the energy type battery supplies power to stabilize the power fluctuation of the bus; the high-frequency fluctuation power on the direct current bus is supplemented or absorbed by the super capacitor for a short time;
(3) the method comprises the steps of carrying out fractional order model prediction control on energy storage converters connected with lithium batteries, super capacitors, electrolytic tanks and fuel batteries, dividing the fractional order model prediction control into double-layer control, calculating reference current for an inner ring by voltage deviation obtained by an outer ring through a droop coefficient, and then calculating reference power. And respectively obtaining an inductive current fractional order prediction model and a capacitance voltage fractional order prediction model according to a fractional order differential dynamic equation established for the converter, thereby obtaining a converter output power fractional order prediction model, finding out a control state with the predicted power being closest to the reference power at the next moment, and obtaining switching tube PWM signals of the BOOST circuit and the BUCK circuit.
Compared with the traditional PI double-loop control, the fractional order model prediction control method predicts the next moment in a fractional order level mode based on the circuit state of each moment, grasps more essence of the operation process of a conversion circuit, ensures the dynamic response capability of control, enables the system state quantity to better follow a reference value compared with the integer order prediction control, and improves the current control precision of the output end of the converter;
the fractional order model predictive control provides an adjustable parameter a for the system, different control performances can be tested by debugging the order a, and when the order a is 1, the fractional order predictive control is changed into common integer order model predictive control.
Drawings
FIG. 1 is a topological structure diagram of an island direct current micro-grid based on ocean energy and wind power generation;
FIG. 2 is a flow chart of stored energy in the BOOST boosting process according to the present invention;
FIG. 3 is a flow chart of energy stored in the BUCK voltage reduction process according to the present invention;
FIG. 4 is a fractional order model predictive control block diagram of the hydrogen combined hybrid energy storage system of the present invention;
FIG. 5 is a U-I characteristic diagram of the alkaline electrolytic cell of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The invention adopts a fractional order calculus algorithm and model prediction control to be combined for power control of an electricity-hydrogen combined hybrid energy storage system and device, obtains a fractional order form voltage and current prediction model, adopts double-loop control, selects the on-off state of an energy storage converter through prediction output, accurately controls the output current of the converter relative to a bus, more finely restrains the power fluctuation of distributed generation, and overcomes the defects that the current commonly used double-loop PI control has low response speed, the current integer order model prediction control has deviation possibly during prediction, and the current control precision is low.
Aiming at the problem that prediction deviation possibly occurs due to insufficient control precision of the current hybrid energy storage system model prediction control scheme, the invention aims to provide a fractional order model prediction control method for power control of an electricity-hydrogen combined hybrid energy storage system and a device, and smoothly suppress power fluctuation of distributed power generation.
Referring to fig. 1-4, the present invention provides a technical solution: a fractional order model predictive control method for power of an electric-hydrogen hybrid energy storage system comprises the following steps:
s1, respectively providing equivalent circuit diagrams of the BOOST voltage boosting circuit and the BUCK voltage reducing circuit when the switch tube is switched on and switched off in two working modes, and monitoring the change process of the inductance current and the change process of the capacitance voltage;
s2, writing a dynamic KVL equation for a circuit column containing an inductance ground in an equivalent circuit at the conduction time of a switch T, and writing a dynamic KCL equation for a circuit column containing a capacitance, wherein the KVL comprises an energy storage end voltage, an inductance voltage and a line loss voltage drop, and the KCL equation comprises a capacitance voltage and a bus voltage; by adopting the thought of fractional calculus, the inductance voltage in the KVL equation is multiplied by the inductance value after the fractional derivative is solved by the current, and the capacitance voltage in the KCL equation is multiplied by the capacitance value after the fractional derivative is solved by the current;
s3, calculating to obtain a fractional order differential expression of the inductance current and a fractional order differential expression of the capacitance voltage;
s4, decomposing fractional differential terms of the inductive current and the capacitive voltage into superposition of adjacent integer orders and fractional orders by applying the fractional calculus linear superposition and the property of meeting the commutative law;
s5, writing an integer order differential term into a difference form by using a forward Eulerian method, calculating a fractional order differential term by using an Oustaloup filtering algorithm, obtaining a fractional order predicted value of the inductor current and the capacitor voltage at the next moment, and establishing a fractional order prediction model of the inductor current and the capacitor voltage;
s6, according to the switching of the switching tube switch states 1 and 0, respectively predicting the current and voltage fractional order predicted value at the next moment when the current T is switched on and the current and voltage fractional order predicted value at the next moment when the current T is switched off, and further obtaining the output predicted power of the converter;
s7, providing cost functions for evaluation, comparing two predicted power results with the voltage shortage power of the direct-current microgrid bus respectively, designing a fractional order type cost function, selecting the minimum value through the cost function to evaluate the optimal switching state at the next moment, obtaining the online optimized control rate by utilizing the rolling optimization capability of the prediction model, and obtaining PWM signals acting on the DC-DC converter switch;
and S8, analyzing and calculating the predicted power when the switching tube of the BUCK voltage reduction circuit is switched on and off. As shown in fig. 3, energy flow diagrams of turn-off and turn-on of the switch T of the BUCK circuit are respectively given. When T is turned off, writing a KVL equation into a loop column comprising an inductor and a diode, wherein the KVL equation comprises a battery end voltage, an inductor voltage, a circuit voltage loss and a diode drop, and writing a KCL equation into a loop column comprising a capacitor, wherein the KCL equation comprises a capacitor voltage and a DC bus voltage; and when the T is conducted, writing a KVL equation into a loop column containing the inductor, and writing a KCL equation into a branch column where the capacitor is located, wherein the KCL equation contains capacitor voltage, current flowing to the inductor and DC bus terminal voltage. And deducing and calculating to obtain an inductance current predicted value and a capacitance voltage predicted value through column write fractional order form KVL and KCL equations, and further multiplying to obtain predicted power.
As a preferred embodiment of the present invention, the process of changing the inductive current in step S1 includes:
at the conduction time of a switching tube T of the BOOST booster circuit in a switching period, the side of an energy storage battery charges an inductor, the inductor current gradually rises, and the rising process of the inductor current is described by a fractional order method, namely the variation of the inductor current is the fractional order integral of the voltage at two ends of the inductor;
at the closing time of a switching tube T of the BOOST circuit in a switching period, energy stored in an inductor and an energy storage battery supply power to the direct current bus side of an output end together, the current value of the inductor is gradually reduced by fractional order integration, the inductor plays a role in pump-up, and the BOOST process is completed;
the stored energy in the inductor is equal to the released energy during a switching cycle.
As a preferred embodiment of the present invention, the process of changing the capacitor voltage in step S1 includes:
at the conducting time of a switching tube T of the BOOST circuit in a switching period, a capacitor plays a role in maintaining the voltage of an output end, and the discharging voltage also gradually decreases by fractional order integral of current;
at the closing time of a switching tube T of the BOOST circuit in a switching period, an energy storage battery and an inductor charge a capacitor while supplying power to a bus, and the voltage of the capacitor gradually rises;
assuming that the capacitance value is sufficiently large, the voltage on the capacitor can be considered approximately constant.
As a preferred embodiment of the present invention, the method for ranking KVL and KCL of the equivalent circuit in step S2 includes:
s21, according to the collected battery end voltage Ub and Ib of the energy storage side, the collected bus voltage Udc and the T conduction time in the BOOST circuit, an inductance loop KVL equation comprises the battery end voltage Ub, the line loss voltage drop Ur and a fractional order differential form inductance voltage expression UL, and the capacitance current in a capacitance loop KCL equation is equal to the load current;
at the turn-off time of S22 and T, an inductance loop KVL equation comprises a battery end voltage Ub, a line loss voltage drop Ur, a fractional order differential form inductance voltage expression UL, a bus voltage Udc and a diode conduction voltage drop Ud, and capacitance current in a capacitance loop KCL equation is equal to the difference between the output current of the battery end and load current.
As a preferred embodiment of the present invention, the step S3 specifically includes:
and (3) obtaining a fractional differential expression of the current and the voltage by utilizing a Capitu fractional calculus definition form:
Figure BDA0003548302410000121
Figure BDA0003548302410000122
wherein the content of the first and second substances,
Figure BDA0003548302410000123
represents a differential action of a times from 0 → t, wherein a is a fraction between 0 and 1; i.e. ib(1)、Uc(1)Inb、UcThe method is a differentiation object and represents the inductive current and the capacitor voltage, and (1) represents the prediction condition of the conduction state of the switch T at the next moment; rrRepresents a line resistance value ibIndicating the value of the current flowing through the inductor, UbRepresents the battery voltage;
Figure BDA0003548302410000124
Figure BDA0003548302410000125
similarly, (0) represents the prediction condition of the turn-off state of the switch T at the next moment; u shapecRepresenting the value of the capacitor voltage, UdRepresenting the diode drop.
As a preferred embodiment of the present invention, the step S4 specifically includes:
s41, decomposing the fractional order differential terms into integer order differential terms and fractional order differential terms, wherein the superposition principle is as follows:
it is assumed that,
Figure BDA0003548302410000131
s42, writing the integral order differential term into a forward Euler differential form, and after simplification and item shifting, obtaining a prediction model of the target value at the k +1 moment in the fractional order integral form as follows:
Figure BDA0003548302410000132
in the formula (I), the compound is shown in the specification,
Figure BDA0003548302410000133
in (c) ptRepresents the integral within 0 → T, 1-a is a fraction between 0 and 1, and TsFor the sampling interval, f (t)kAs a value of the previous time of the function, f (t)k+1Is the next time value.
As a preferred embodiment of the present invention, the specific method for establishing the fractional order prediction model of the inductor current and the capacitor voltage in step S5 includes:
s51, the inductor current prediction model is as follows,
Figure BDA0003548302410000134
Figure BDA0003548302410000135
s52, the capacitance-voltage prediction model is as follows,
Figure BDA0003548302410000136
Figure BDA0003548302410000137
s53, approximately calculating the fractional order integral term by utilizing the improved Oustaloup filtering algorithm, selecting a reasonable fitting frequency band to obtain an approximate transfer function model of the fractional order differential operator
Figure BDA0003548302410000138
Wherein a is order, d and b are derived from experience, H1(s) is a transfer function obtained by calculation, and N can be 2-4; therefore, the approximate transfer function of the fractional order differential operator can be obtained.
As a preferred embodiment of the present invention, the step S6 specifically includes:
s61, firstly, collecting the DC bus voltage U of the microgriddcThe bus reference voltage is subtracted from the bus reference voltage U to obtain Uerro, the current value I from the DC converter to the bus output is calculated according to the droop characteristic of the DC-DC converter of the energy storage system, and the I is multiplied by the U to obtain a converter output power reference value P;
s62, converter fractional order prediction output power P when switch T is conducted(1)k+1＝ib(1)k+1×Uc(1)k+1Predicted output power P at turn-off of T(0)k+1＝ib(0)k+1×Uc(0)k+1。
As a preferred embodiment of the present invention, the cost function in step S7 is set as:
J＝||Pk+1-P*||2
the optimal switching state at the moment k +1 can be selected by evaluating the cost function, online rolling optimization can be carried out at each moment through a fractional order prediction model, a control law changing in real time is obtained, and a PWM signal for controlling the conduction of a device is obtained.
As a preferred embodiment of the present invention, the step S8 specifically includes:
aiming at the charging process of the energy storage system, namely the circuit works in a BUCK state, the BUCK circuit is suitable for the DC-DC voltage reduction process of the electrolytic cell, the U-I equation of the electrolytic cell is shown as follows,
Figure BDA0003548302410000141
the parameters are selected as follows: a is 0.01,r1＝3.54e-4,r2＝-3.02e-6,s＝0.224,t1＝5.13,t2＝-2.40e2,t3＝3.41e3；
The external characteristics of the electrolytic cell are distributed in a cluster curve according to the temperature change as shown in figure 5, the alkaline electrolytic cell only absorbs the power on a bus when in work, the hydrogen production process of the electrolytic cell has the following relation with the current,
the fractional order form of hydrogen production rate can be described as,
Figure BDA0003548302410000142
f1＝2.5T+50,f2＝1-T*6.25e-6,z＝2,F＝96485.3C/mol；
the amount of hydrogen storage is described as,
Figure BDA0003548302410000151
the hydrogen storage pressure is described as being,
Figure BDA0003548302410000152
the hydrogen storage pressure is expressed as a function of the input current I through fractional order calculation, namely P (k +1) f (I); according to the BUCK circuit, the input current of the electrolytic cell is predicted by a fractional order method, then a predicted value P (k +1) of hydrogen storage pressure is obtained, and the predicted value P is activated within the upper and lower limits of the hydrogen storage pressure through hydrogen storage restriction.

Claims (10)

1. A fractional order model predictive control method for power of an electric-hydrogen hybrid energy storage system is characterized by comprising the following steps:
s1, respectively providing equivalent circuit diagrams when the switch T is switched on and off in two working modes of the BOOST voltage boosting circuit and the BUCK voltage reducing circuit, and monitoring the change process of the inductance current and the change process of the capacitance voltage;
s2, writing a dynamic KVL equation for a loop column containing an inductor in an equivalent circuit at the conduction time of the switch T, and writing a dynamic KCL equation for a loop column containing a capacitor, wherein the KVL comprises an energy storage end voltage, an inductor voltage and a line loss voltage drop, and the KCL equation comprises a capacitor voltage and a bus voltage; by adopting the thought of fractional calculus, the inductance voltage in the KVL equation is multiplied by the inductance value after the fractional derivative is solved by the current, and the capacitance voltage in the KCL equation is multiplied by the capacitance value after the fractional derivative is solved by the current;
s3, calculating to obtain a fractional order differential expression of the inductance current and a fractional order differential expression of the capacitance voltage;
s4, decomposing fractional order differential terms of the inductive current and the capacitive voltage into an integral order differential term and a fractional order differential term superposition action by applying the properties of fractional order calculus linear superposition and satisfying the exchange law;
s5, writing an integer order differential term into a difference form by using a forward Eulerian method, calculating a fractional order differential term by using an Oustaloup filtering algorithm, obtaining a fractional order predicted value of the inductor current and the capacitor voltage at the next moment, and establishing a fractional order prediction model of the inductor current and the capacitor voltage;
s6, respectively predicting a current-voltage fractional order predicted value at the next moment obtained by turning on the switch T at the moment and a current-voltage fractional order predicted value at the next moment obtained by turning off the switch T at the moment according to the switching of the switch states 1 and 0 of the switch T, and further obtaining the output predicted power of the converter;
s7, cost functions for evaluation are given, two predicted power results are compared with the voltage shortage power of the direct-current microgrid bus respectively, the minimum value is selected through the cost functions, the optimal switching state at the next moment is selected, the online optimized control rate is obtained by utilizing the rolling optimization capability of the prediction model, and PWM signals acting on the DC-DC converter switch are obtained;
and S8, analyzing and calculating the predicted power when the switching tube of the BUCK voltage reduction circuit is switched on and off.
2. The fractional order model predictive control method for power of an electric-hydrogen hybrid energy storage system according to claim 1, wherein the inductance current variation process in the step S1 includes:
at the conduction time of a switching tube T of the BOOST booster circuit in a switching period, the side of an energy storage battery charges an inductor, the inductor current gradually rises, and the rising process of the inductor current is described by a fractional order method, namely the variation of the inductor current is the fractional order integral of the voltage at two ends of the inductor;
at the closing time of a switching tube T of the BOOST circuit in a switching period, energy stored in an inductor and an energy storage battery supply power to the direct current bus side of an output end together, the current value of the inductor is gradually reduced by fractional order integration, the inductor plays a role in pump-up, and the BOOST process is completed;
the stored energy in the inductor is equal to the released energy during a switching cycle.
3. The fractional order model predictive control method for power of an electro-hydrogen hybrid energy storage system according to claim 1, wherein the variation process of the capacitor voltage in the step S1 comprises:
at the conducting time of a switching tube T of the BOOST circuit in a switching period, the capacitor plays a role in maintaining the voltage of the output end, and the discharging voltage also gradually decreases by fractional order integral of current;
at the closing time of the BOOST circuit switch tube T in a switching period, the energy storage battery and the inductor charge the capacitor while supplying power to the bus, and the voltage of the capacitor gradually rises.
4. The fractional order model predictive control method for power of an electro-hydrogen hybrid energy storage system of claim 1, wherein the method of writing KVL and KCL to the equivalent circuit column in step S2 comprises:
s21, according to the collected battery terminal voltage U of the energy storage sidebAnd IbCollected bus voltage UdcThe inductance loop KVL equation comprises the battery terminal voltage U at the T conduction time in the BOOST circuitbLine loss voltage drop UrFractional order differential form inductance voltage expression ULThe capacitance current in the capacitance loop KCL equation is equal to the load current;
s22, T offAt the moment of interruption, the inductive loop KVL equation contains the battery terminal voltage UbLine loss voltage drop UrFractional order differential form inductance voltage expression ULBus voltage UdcDiode conduction voltage drop UdAnd the capacitance current in the capacitance loop KCL equation is equal to the difference between the output current of the battery end and the load current.
5. The fractional order model predictive control method for power of an electric-hydrogen hybrid energy storage system according to claim 1, wherein the step S3 specifically includes:
obtaining a fractional differential expression of current and voltage by using a Capotu fractional calculus definition form:
Figure FDA0003548302400000031
Figure FDA0003548302400000032
wherein the content of the first and second substances,
Figure FDA0003548302400000037
represents a differential action of a times from 0 → t, wherein a is a fraction between 0 and 1; i.e. ib(1)、Uc(1)Inb、UcThe method is a differentiation object and represents the inductive current and the capacitor voltage, and (1) represents the prediction condition of the conduction state of the switch T at the next moment; rrRepresents a line resistance value ibIndicating the value of the current flowing through the inductor, UbRepresents the battery voltage;
Figure FDA0003548302400000033
Figure FDA0003548302400000034
similarly, (0) represents the prediction condition of the turn-off state of the switch T at the next moment; u shapecRepresenting the value of the capacitor voltage, UdRepresenting the diode drop.
6. The fractional order model predictive control method for power of an electricity-hydrogen hybrid energy storage system according to claim 1, characterized in that the step S4 specifically comprises:
s41, decomposing the fractional order differential terms into integer order differential terms and fractional order differential terms, wherein the superposition principle is as follows:
Figure FDA0003548302400000035
s42, writing the integral order differential terms into a forward Euler differential form, and after simplifying and shifting the terms, obtaining a prediction model of the target value at the k +1 moment in a fractional order integral form as follows:
Figure FDA0003548302400000036
in the formula (I), the compound is shown in the specification,
Figure FDA00035483024000000410
in (c) ptRepresents the integral within 0 → T, 1-a is also a fraction between 0 and 1, TsFor the sampling interval, f (t)kIs the value of the function at the previous moment, f (t)k+1Is the next time value.
7. The fractional order model predictive control method for power of the electricity-hydrogen hybrid energy storage system according to claim 1, wherein the specific method for establishing the fractional order model of the inductor current and the capacitor voltage in step S5 includes:
s51, the inductor current prediction model is as follows,
Figure FDA0003548302400000041
Figure FDA0003548302400000042
s52, the capacitance-voltage prediction model is as follows,
Figure FDA0003548302400000043
Figure FDA0003548302400000044
s53, approximately calculating the fractional order integral term by utilizing the improved Oustaloup filtering algorithm, selecting a reasonable fitting frequency band to obtain an approximate transfer function model of the fractional order differential operator
Figure FDA0003548302400000045
Wherein a is order, d and b are derived from experience, H1(s) is a transfer function obtained by calculation, and N can be 2-4;
s54, processing and calculating fractional order processes in the prediction model through the fractional order approximate transfer function model, so as to obtain a current fractional order prediction model and a voltage fractional order prediction model at the moment of k +1 as follows,
Figure FDA0003548302400000046
Figure FDA0003548302400000047
Figure FDA0003548302400000048
Figure FDA0003548302400000049
8. the fractional order model predictive control method for power of an electricity-hydrogen hybrid energy storage system according to claim 1, characterized in that the step S6 specifically comprises:
s61, collecting the voltage of a direct current bus of the microgrid, subtracting the voltage from a bus reference voltage to obtain a voltage error, calculating a bus current reference value output by the DC converter according to the droop characteristic of the DC-DC converter of the energy storage system, and multiplying the voltage and the current to obtain a converter output power reference value;
s62, converter fractional order prediction output power P when switch T is conducted(1)k+1＝ib(1)k+1×Uc(1)k+1Predicted output power P at turn-off of T(0)k+1＝ib(0)k+1×Uc(0)k+1。
9. The fractional order model predictive control method for power of an electro-hydrogen hybrid energy storage system according to claim 1, wherein the cost function in step S7 is set as:
J＝||Pk+1-P*||2
the optimal switching state at the moment k +1 can be evaluated and selected by evaluating the cost function, online rolling optimization can be performed at each moment through a fractional order prediction model, a real-time changing control law is obtained, and a PWM signal for controlling the conduction of a device is obtained.
10. The fractional order model predictive control method for power of an electric-hydrogen hybrid energy storage system according to claim 1, wherein the step S8 specifically includes:
aiming at the charging process of the energy storage system, namely the circuit works in a BUCK state, the BUCK circuit is suitable for the DC-DC voltage reduction process of the electrolytic cell, the U-I equation of the electrolytic cell is shown as follows,
Figure FDA0003548302400000051
the parameters are selected as follows: a is 0.01, r1＝3.54e-4,r2＝-3.02e-6,s＝0.224,t1＝5.13,t2＝-2.40e2,t3＝3.41e3；
The hydrogen production process of the electrolytic cell has the following relation with the introduced current, and the fractional order type hydrogen production rate can be described as,
Figure FDA0003548302400000052
f1＝2.5T+50,f2＝1-T*6.25e-6,z＝2,F＝96485.3C/mol；
the amount of hydrogen storage is described as,
Figure FDA0003548302400000061
the hydrogen storage pressure is described as being,
Figure FDA0003548302400000062
after fractional order calculation, the hydrogen storage pressure is expressed as a function of the input current I, namely P (k +1) ═ f (I); according to the BUCK circuit, the input current of the electrolytic cell is predicted by a fractional order method, then a predicted value P (k +1) of hydrogen storage pressure is obtained, and the predicted value P is activated within the upper and lower limits of the hydrogen storage pressure through hydrogen storage restriction.
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