CN113783226B - Layered prediction energy management method of offshore wind power hydrogen production grid-connected power generation system - Google Patents

Layered prediction energy management method of offshore wind power hydrogen production grid-connected power generation system Download PDF

Info

Publication number
CN113783226B
CN113783226B CN202110995549.9A CN202110995549A CN113783226B CN 113783226 B CN113783226 B CN 113783226B CN 202110995549 A CN202110995549 A CN 202110995549A CN 113783226 B CN113783226 B CN 113783226B
Authority
CN
China
Prior art keywords
hydrogen
time scale
hydrogen production
grid
power
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
CN202110995549.9A
Other languages
Chinese (zh)
Other versions
CN113783226A (en
Inventor
马泽涛
舒杰
崔琼
吕杰
袁倩
陈雄
张洪岩
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.)
Guangzhou Institute of Energy Conversion of CAS
Shenzhen China Guangdong Nuclear Engineering Design Co Ltd
Original Assignee
Guangzhou Institute of Energy Conversion of CAS
Shenzhen China Guangdong Nuclear Engineering Design Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangzhou Institute of Energy Conversion of CAS, Shenzhen China Guangdong Nuclear Engineering Design Co Ltd filed Critical Guangzhou Institute of Energy Conversion of CAS
Priority to CN202110995549.9A priority Critical patent/CN113783226B/en
Publication of CN113783226A publication Critical patent/CN113783226A/en
Application granted granted Critical
Publication of CN113783226B publication Critical patent/CN113783226B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy 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
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • 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
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • 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
    • H02J2300/40—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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/30—Hydrogen technology
    • Y02E60/36—Hydrogen production from non-carbon containing sources, e.g. by water electrolysis
    • 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
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • Economics (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Control Of Eletrric Generators (AREA)

Abstract

The invention discloses a layered prediction energy management method of an offshore wind power hydrogen production grid-connected power generation system, and belongs to the field of new energy. The method specifically comprises the following steps: respectively establishing an optimization control model of the sea wind hydrogen production grid-connected power generation system under a long time scale and a short time scale, predicting the active power and grid-connected load of a fan under the long time scale and the short time scale, optimizing the switch sequence control and the energy storage and hydrogen storage state track of the electrolytic cell array under the long time scale by adopting a dynamic programming and convex optimization combination iteration method, preheating the electrolytic cell array in advance according to a switch optimization result, and taking the dual variable value of the energy storage and hydrogen storage state obtained by optimization under the long time scale as a known condition of the short time scale prediction control problem, thereby obtaining the output optimization value of the current fan, each electrolytic cell array and the energy storage.

Description

Layered prediction energy management method of offshore wind power hydrogen production grid-connected power generation system
Technical Field
The invention relates to the technical field of offshore wind power generation and wind curtailment, in particular to a layered prediction energy management method of an offshore wind power hydrogen production grid-connected power generation system.
Background
The offshore wind power resources of China are rich, and the construction of the offshore wind power plant can improve the renewable energy source ratio of the southeast coastal developed area and relieve the energy pressure. However, the offshore wind power has strong intermittence, randomness, volatility and uncontrollability, and a large amount of abandoned wind is easy to generate, so that energy waste is caused. The method of combining storage battery energy storage and seawater electrolysis hydrogen production is utilized, the abandoned wind is converted into electric energy and hydrogen energy to be stored, and the operation efficiency of the offshore wind farm can be effectively improved.
The energy management strategy based on predictive control can realize the efficient operation of the offshore wind power hydrogen production grid-connected power generation system. The offshore wind power hydrogen production grid-connected power generation system relates to equipment with different time responses, such as a fan, a hydrogen production electrolytic tank, a storage battery energy storage tank, a hydrogen storage tank and the like, and has the advantages of high predictive energy management solving calculation amount, complex solving process and unfavorable real-time operation of management strategies.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a layered prediction energy management method of an offshore wind power hydrogen production grid-connected power generation system, which realizes the prediction energy management of the offshore wind power hydrogen production grid-connected power generation system through layered optimization, and achieves the purposes of absorbing abandoned wind energy and maximizing the economic income of a wind farm.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
respectively establishing an optimal control model of the system under a long time scale and an optimal control model under a short time scale;
predicting the active power and grid-connected load of a fan under a long time scale, wherein the switching state of an electrolytic cell array of an optimization control model under the long time scale is solved by adopting a method of combining dynamic planning and convex optimization, and the dual variable value is obtained by continuously iterating the updated switching state;
preheating the electrolytic cell array in advance according to the optimized switch state;
and predicting the active power of the fan under a short time scale, wherein the dual variable value obtained by optimizing the optimization control model under the long time scale is used as a known condition of the optimization control model under the short time scale, and the optimization control model under the short time scale is solved by utilizing the Pontriya Jin Zuixiao principle, so that the power output optimization value of the fan, each electrolytic cell array and the storage battery at the current moment of the system is obtained.
A hierarchical prediction energy management method as described above, further, the long time scale and the short time scale dimensions include, but are not limited to, minutes, seconds.
The hierarchical prediction energy management method as described above, further, the optimization control model under the long time scale is expressed as:
s.t.
B b (k)＝η b |P b (k)| (1-2)
P bmin ≤P b (k)≤P bmax (1-4)
E bmin ≤E b (k)≤E bmax (1-5)
E tkmin ≤E tk (k)≤E tkmax (1-6)
…
…
|P g (k)-P g (k-1)|≤D (1-14)
wherein alpha is e And alpha h Grid-connected electricity price and hydrogen selling price respectively, alpha elz Is the start-stop cost of the hydrogen production electrolytic tank, P b And eta b For battery output power and loss factor, E b Is a storage batteryEnergy of B b For loss caused by internal resistance of accumulator E tk For the residual capacity of the hydrogen storage tank E tkmin And E is tkmin Is the upper and lower limit of the hydrogen storage capacity of the hydrogen storage tank, E bmin And E is bmax Is the upper and lower limit of the storable energy of the storage battery, P bmin And P bmax Is the upper and lower limits of the output power of the storage battery,the mass of hydrogen produced for each cell, < >>Is the input power of each electrolytic cell, +.>Is the minimum input power of the individual cells, < >>Is the maximum input power of each electrolytic cell, n elz For the number of electrolytic cells, < > for>Is the active power of each fan, n wt Is the number of fans, P g For grid-connected active power, D is grid-connected active power fluctuation constraint, < ->And->Fitting coefficient of hydrogen production quality of electrolytic tank, +.>Is the switch state of the y-th hydrogen production electrolytic tank.
The hierarchical prediction energy management method as described above, further, the process of obtaining the dual variable value specifically includes:
s31: the problem of convex optimization of the economic operation of the offshore wind power-hydrogen-storage coupling system is established, namely, a switching state sequence of the hydrogen production electrolytic cell array is preset, and convex relaxation is carried out on constraint conditions (1-15) and (1-16), as shown in a formula (2), a formula (3) and a formula (4):
in the formula (2), the amino acid sequence of the compound,the method is a preset electrolytic tank switch state;
s32: solving the convex optimization problem in S31 by adopting a convex optimization method to obtain the optimal power distribution P of the hydrogen production electrolytic tank, the storage battery and each fan gopt ，P bopt Anddual variable lambda of energy storage state and hydrogen storage state b And lambda (lambda) tk ；
Wherein P is gopt Is grid-connected optimal output, P bopt Is the optimal output force of the storage battery,is the optimal output of the hydrogen production electrolytic tank;
s33: according to the optimal output result in the step S32, constructing a dynamic programming problem of the switching state of the hydrogen production electrolytic cell array in the offshore wind power-hydrogen storage coupling system, namely, rewriting the target function formula (1) as shown in a formula (4);
wherein m is hopt Is corresponding to the hydrogen yield under the optimal output, E bopt And E is bopt Is the energy storage and hydrogen storage state under the optimal output;
s34: solving the optimal switching state of the hydrogen production electrolytic cell array in the step (5) by adopting dynamic programming, and taking the switching state as a preset switching stateRepeating S31 to S34 until the terminal value condition of the formula (6) is satisfied:
wherein,,and->Is the initial value and the final value of the energy storage state.
The hierarchical prediction energy management method as described above, further, the optimization control model at the short time scale is expressed as:
s.t.(1-2)～(1-17)(7-2)。
compared with the prior art, the invention has the beneficial effects that: according to the invention, the predicted energy management of the offshore wind power hydrogen production grid-connected power generation system is realized through layered optimization, so that the purposes of absorbing the abandoned wind energy and maximizing the economic income of a wind farm are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly explain the drawings needed in the embodiments, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of the general algorithm of the present invention.
FIG. 2 shows a marine wind power hydrogen production grid-connected power generation system applied by the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Examples:
it should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1-2, fig. 1 is a block diagram of the overall algorithm of the present invention. FIG. 2 shows a offshore wind power hydrogen generation grid-connected power generation system (wind/storage/hydrogen) applied by the invention.
The invention provides a layered prediction energy management method of an offshore wind power hydrogen production grid-connected power generation system, which realizes the prediction energy management of the offshore wind power hydrogen production grid-connected power generation system through layered optimization, and achieves the purposes of absorbing abandoned wind energy and maximizing the economic income of a wind farm.
The invention relates to a layered prediction energy management method of a marine wind power hydrogen production grid-connected power generation system, which comprises the following steps:
s1: predicting the active power of the fan under a long time scale, wherein the time scale comprises, but is not limited to, time and minutes;
the maximum active power of each fan at time k can be expressed asWherein n is wt The number of fans of the wind power plant; the grid-tie load may be expressed as P g (k)；
S2: establishing a steady-state model and an economic optimization problem of the offshore wind power hydrogen production grid-connected power generation system under a long time scale;
s3: solving the economic problem of the offshore wind power hydrogen production grid-connected power generation system in a prediction time domain under a long time scale by adopting a dynamic programming and convex optimization method, obtaining the value lambda (k) of a dual variable in the current time period and the switch control of the electrolytic tank in the next time period, and starting and preheating the electrolytic tank started in the next time period in advance according to the optimization result of the long time scale;
s4: predicting the active power of the fan at a short time scale including, but not limited to, minutes and seconds;
s5: establishing a steady-state model and an economic optimization problem of the offshore wind power hydrogen production grid-connected power generation system in a short time scale;
s6: lambda obtained on a long time scale b (k) As a known condition, solving an economic optimization problem of a predicted offshore wind power hydrogen production grid-connected power generation system in a time domain by utilizing a Pontrian sub Jin Zuixiao principle, and controlling the output of a fan, a storage battery and an electrolytic cell array by taking a control variable at the current moment obtained by solving as a reference instruction;
the steady-state model and the economic optimization problem of the offshore wind power hydrogen production grid-connected power generation system under the long time scale in the S2 can be expressed as follows:
s.t.
B b (k)＝η b |P b (k)| (1-2)
P bmin ≤P b (k)≤P bmax (1-4)
E bmin ≤E b (k)≤E bmax (1-5)
E tkmin ≤E tk (k)≤E tkmax (1-6)
…
…
|P g (k)-P g (k-1)|≤D (1-14)
wherein alpha is e And alpha h Grid-connected electricity price and hydrogen selling price respectively, alpha elz Is the start-stop cost of the hydrogen production electrolytic tank, P b And eta b For battery output power and loss factor, E b For the energy of the accumulator, B b For loss caused by internal resistance of accumulator E tk For the residual capacity of the hydrogen storage tank E tkmin And E is tkmin Is the upper and lower limit of the hydrogen storage capacity of the hydrogen storage tank, E bmin And E is bmax Is the upper and lower limit of the storable energy of the storage battery, P bmin And P bmax Is the upper and lower limits of the output power of the storage battery,the mass of hydrogen produced for each cell, < >>Is the input power of each electrolytic cell, +.>Is the minimum input power of the individual cells, < >>Is the maximum input power of each electrolytic cell, n elz For the number of electrolytic cells, < > for>Is the active power of each fan, n wt Is the number of fans, P g For grid-connected active power, D is grid-connected active power fluctuation constraint, < ->And->Fitting coefficient of hydrogen production quality of electrolytic tank, +.>Is the on-off state of the y-th hydrogen production electrolytic cell;
the step of solving the economic problem by combining the dynamic programming and convex optimization method in the step S3 is as follows:
s31: the problem of convex optimization of the economic operation of the offshore wind power-hydrogen-storage coupling system is established, namely, a switching state sequence of the hydrogen production electrolytic cell array is preset, and convex relaxation is carried out on constraint conditions (1-15) and (1-16), as shown in a formula (2), a formula (3) and a formula (4):
in the formula (2), the amino acid sequence of the compound,the method is a preset electrolytic tank switch state;
s32: solving the convex optimization problem in S31 by adopting a convex optimization method to obtain the optimal power distribution P of the hydrogen production electrolytic tank, the storage battery and each fan gopt ，P bopt Anddual variable lambda of energy storage state and hydrogen storage state b And lambda (lambda) tk ；
Wherein P is gopt Is grid-connected optimal output, P bopt Is the optimal output force of the storage battery,is the optimal output of the hydrogen production electrolytic tank;
s33: according to the optimal output result in the step S32, constructing a dynamic programming problem of the switching state of the hydrogen production electrolytic cell array in the offshore wind power-hydrogen storage coupling system, namely, rewriting the target function formula (1) as shown in a formula (4);
wherein m is hopt Is corresponding to the hydrogen yield under the optimal output, E bopt And E is bopt Is the energy storage and hydrogen storage state under the optimal output;
s34: solving the optimal switching state of the hydrogen production electrolytic cell array in the step (5) by adopting dynamic programming, and taking the switching state as a preset switching stateRepeating S31 to S34 until the terminal value condition of the formula (6) is satisfied:
wherein,,and->Is the initial value and the final value of the energy storage state;
the steady-state model and the economic optimization problem of the offshore wind power hydrogen production grid-connected power generation system in the short time scale in the S5 can be expressed as follows:
s.t.(1-2)～(1-17)(7-2)
the S6 is the lambda obtained in S3 b (k) As an economic optimization problem of substituting the known condition into the S5, calculating an ultra-short-term real-time optimal output value by utilizing the Pontrisis minimum value principle.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made in accordance with the essence of the present invention are intended to be included within the scope of the present invention.

Claims (4)

1. The layered prediction energy management method is used for an offshore wind power hydrogen production grid-connected power generation system, and is characterized by comprising the following steps of:
respectively establishing an optimal control model of the system under a long time scale and an optimal control model under a short time scale;
predicting the active power and grid-connected load of a fan under a long time scale, wherein the switching state of an electrolytic cell array of an optimization control model under the long time scale is solved by adopting a method of combining dynamic planning and convex optimization, and the dual variable value is obtained by continuously iterating the updated switching state;
preheating the electrolytic cell array in advance according to the optimized switch state;
predicting the active power of a fan in a short time scale, wherein the dual variable value obtained by optimizing an optimization control model in the long time scale is used as a known condition of the optimization control model in the short time scale, and the optimization control model in the short time scale is solved by utilizing the Pontriya Jin Zuixiao principle, so that the power output optimization value of the fan, each electrolytic cell array and the storage battery at the current moment of the system is obtained;
the process of obtaining the dual variable value specifically comprises the following steps:
s31: the problem of convex optimization of the economic operation of the offshore wind power-hydrogen-storage coupling system is established, namely, the on-off state sequence of the hydrogen production electrolytic cell array is preset, the constraint conditions (1-15) and (1-16) are subjected to convex relaxation,
as shown in the formula (2), the formula (3) and the formula (4):
in the formula (2), the amino acid sequence of the compound,the method is a preset electrolytic tank switch state;
s32: solving the convex optimization problem in S31 by adopting a convex optimization method to obtain the optimal power distribution of the hydrogen production electrolytic tank, the storage battery and each fanP bopt And->Dual variable lambda of energy storage state and hydrogen storage state b And lambda (lambda) tk ；
Wherein,,is grid-connected optimal output, P bopt Is the optimal output of the storage battery, ">Is the optimal output of the hydrogen production electrolytic tank;
s33: according to the optimal output result in S32, constructing a dynamic programming problem of the switch state of the hydrogen production electrolytic cell array in the offshore wind power-hydrogen-storage coupling system, namely a target function formula (1-1)
The rewriting is as shown in formula (4);
wherein,,is corresponding to the hydrogen yield under the optimal output, E bopt And E is tkopt Is the energy storage and hydrogen storage state under the optimal output;
s34: solving the optimal switching state of the hydrogen production electrolytic cell array in the step (5) by adopting dynamic programming, and taking the switching state as a preset switching stateRepeating S31 to S34 until the terminal value condition of the formula (6) is satisfied:
wherein,,and->Is the initial value and the final value of the energy storage state; alpha e And alpha h Grid-connected electricity price and hydrogen selling price respectively, alpha elz Is the start-stop cost of the hydrogen production electrolytic tank, P b And eta b For battery output power and loss factor, E b For the energy of the accumulator, B b For loss caused by internal resistance of accumulator E tk For the residual capacity of the hydrogen storage tank E tkmin And E is tkmin Is the upper and lower limit of the hydrogen storage capacity of the hydrogen storage tank, E bmin And E is bmax Is a storage batteryUpper and lower limits of stored energy, P bmin And P bmax Is the upper and lower limits of the output power of the storage battery,the mass of hydrogen produced for each cell, < >>Is the input power of each electrolytic cell, +.>Is the minimum input power of each electrolytic cell,is the maximum input power of each electrolytic cell, n elz For the number of cells to be electrolyzed,is the active power of each fan, n wt Is the number of fans, P g For grid-connected active power, D is grid-connected active power fluctuation constraint, < ->And->Fitting coefficient of hydrogen production quality of electrolytic tank, +.>Is the switch state of the y-th hydrogen production electrolytic tank.
2. The hierarchical prediction energy management method of claim 1, wherein the dimensions of the long time scale and the short time scale comprise minutes and seconds.
3. The hierarchical predictive energy management method of claim 2, wherein the optimal control model over the long time scale is expressed as:
s.t.
B b (k)＝η b |P b (k)| (1-2)
P bmin ≤P b (k)≤P bmax (1-4)
E bmin ≤E b (k)≤E bmax (1-5)
E tkmin ≤E tk (k)≤E tkmax ( 1-6)
…
…
|P g (k)-P g (k-1)|≤D (1-14)
wherein alpha is e And alpha h Grid-connected electricity price and hydrogen selling price respectively, alpha elz Is the start-stop cost of the hydrogen production electrolytic tank, P b And eta b For battery output power and loss factor, E b For the energy of the accumulator, B b For loss caused by internal resistance of accumulator E tk For the residual capacity of the hydrogen storage tank E tkmin And E is tkmin Is the upper and lower limit of the hydrogen storage capacity of the hydrogen storage tank, E bmin And E is bmax Is the upper and lower limit of the storable energy of the storage battery, P bmin And P bmax Is the upper and lower limits of the output power of the storage battery,the mass of hydrogen produced for each cell, < >>Is the input power of each electrolytic cell, +.>Is the minimum input power of the individual cells, < >>Is the maximum input power of each electrolytic cell, n elz For the number of electrolytic cells, < > for>Is the active power of each fan, n wt Is the number of fans, P g For grid-connected active power, D is grid-connected active power fluctuation constraint, < ->And->Fitting coefficient of hydrogen production quality of electrolytic tank, +.>Is the switch state of the y-th hydrogen production electrolytic tank.
4. A hierarchical predictive energy management method in accordance with claim 3, characterized in that said optimization control model at a short time scale is expressed as:
s.t.(1-2)～(1-17) (7-2)。
CN202110995549.9A 2021-08-27 2021-08-27 Layered prediction energy management method of offshore wind power hydrogen production grid-connected power generation system Active CN113783226B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110995549.9A CN113783226B (en) 2021-08-27 2021-08-27 Layered prediction energy management method of offshore wind power hydrogen production grid-connected power generation system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110995549.9A CN113783226B (en) 2021-08-27 2021-08-27 Layered prediction energy management method of offshore wind power hydrogen production grid-connected power generation system

Publications (2)

Publication Number Publication Date
CN113783226A CN113783226A (en) 2021-12-10
CN113783226B true CN113783226B (en) 2023-07-25

Family

ID=78839587

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110995549.9A Active CN113783226B (en) 2021-08-27 2021-08-27 Layered prediction energy management method of offshore wind power hydrogen production grid-connected power generation system

Country Status (1)

Country Link
CN (1) CN113783226B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115310373B (en) * 2022-10-11 2023-05-23 国网浙江省电力有限公司电力科学研究院 Hydrogen production electrolytic tank residual life prediction method
CN115360690B (en) * 2022-10-21 2023-01-24 湖南隆深氢能科技有限公司 New energy hydrogen production system and method
CN117526374B (en) * 2023-12-28 2024-05-10 国网浙江省电力有限公司电力科学研究院 Control method and device of new energy hydrogen production system matched with real-time output of fluctuation power supply

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106953359A (en) * 2017-04-21 2017-07-14 中国农业大学 A kind of active reactive coordinating and optimizing control method of power distribution network containing distributed photovoltaic
CN112865174A (en) * 2021-01-07 2021-05-28 福州大学 Micro-energy network multi-time scale optimization control method based on double-layer model prediction control

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106684915A (en) * 2017-01-12 2017-05-17 国家电网公司 Wind-hydrogen coupling power generation system optimization method and device thereof
CN110009152B (en) * 2019-04-03 2021-12-07 东南大学 Regional comprehensive energy system operation robustness optimization method considering electric-to-gas and uncertainty

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106953359A (en) * 2017-04-21 2017-07-14 中国农业大学 A kind of active reactive coordinating and optimizing control method of power distribution network containing distributed photovoltaic
CN112865174A (en) * 2021-01-07 2021-05-28 福州大学 Micro-energy network multi-time scale optimization control method based on double-layer model prediction control

Also Published As

Publication number Publication date
CN113783226A (en) 2021-12-10

Similar Documents

Publication Publication Date Title
CN113783226B (en) Layered prediction energy management method of offshore wind power hydrogen production grid-connected power generation system
Vivas et al. A review of energy management strategies for renewable hybrid energy systems with hydrogen backup
CN106849190B (en) A kind of microgrid real-time scheduling method of providing multiple forms of energy to complement each other based on Rollout algorithm
Adefarati et al. Reliability and economic evaluation of a microgrid power system
Kafetzis et al. Energy management strategies based on hybrid automata for islanded microgrids with renewable sources, batteries and hydrogen
Jahangir et al. Economic analysis and optimal design of hydrogen/diesel backup system to improve energy hubs providing the demands of sport complexes
Hakimi et al. Optimal sizing of reliable hybrid renewable energy system considered various load types
CN113241803B (en) Energy storage scheduling method based on new energy consumption and computer medium
CN107732958B (en) Independent micro-grid black start optimization method considering source-load cooperative recovery
CN111864750B (en) Operation method for maximizing benefits of wind power-hydrogen production system under market mechanism
Li et al. Modeling and multi-objective optimization of a stand-alone photovoltaic-wind turbine-hydrogen-battery hybrid energy system based on hysteresis band
Mohammed et al. Hybrid generation systems planning expansion forecast state of the art review: Optimal design vs technical and economical constraints
Abdelshafy et al. Optimal grid connected hybrid energy system for Egyptian residential area
Rathod et al. Renewable energy generation system connected to micro grid and analysis of energy management: a critical review
CN115491720A (en) Off-grid hydrogen production pem electrolytic cell array control method based on power efficiency characteristic
CN113689023B (en) Wind-abandoning energy-dissipating management method for wind/storage/hydrogen grid-connected power generation system
Gonzalez et al. Model predictive control for the energy management of a hybrid PV/battery/fuel cell power plant
Oussama et al. Intelligent Energy Management Strategy For Multi-Sources Isolated DC-Microgrid
Rosato et al. A smart grid in ponza island: Battery energy storage management by echo state neural network
CN114172175B (en) Hydrogen storage configuration and control collaborative optimization method for improving economic benefit of wind farm
Habib et al. Quasi-dynamic load and battery sizing and scheduling for stand-alone solar system using mixed-integer linear programming
CN112202210B (en) Information energy coupling micro-grid operation system and method based on robust optimization
Rouhani et al. A teaching learning based optimization for optimal design of a hybrid energy system
Di Carlo et al. Optimizing renewable power management in transmission congestion. an energy hub model using hydrogen storage
Heo et al. Energy resource capacity optimization for zero-energy housing complex

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