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 PDFInfo
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- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 87
- 239000001257 hydrogen Substances 0.000 title claims abstract description 87
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 81
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 50
- 238000010248 power generation Methods 0.000 title claims abstract description 23
- 238000007726 management method Methods 0.000 title claims abstract description 21
- 238000003860 storage Methods 0.000 claims abstract description 48
- 238000005457 optimization Methods 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 20
- 238000004146 energy storage Methods 0.000 claims abstract description 14
- 230000009977 dual effect Effects 0.000 claims abstract description 9
- 230000008878 coupling Effects 0.000 claims description 6
- 238000010168 coupling process Methods 0.000 claims description 6
- 238000005859 coupling reaction Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 230000005611 electricity Effects 0.000 claims description 4
- 150000002431 hydrogen Chemical class 0.000 claims description 4
- 125000003275 alpha amino acid group Chemical group 0.000 claims description 3
- 150000001875 compounds Chemical class 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005868 electrolysis reaction Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 239000013535 sea water Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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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
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)。
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