CN107732967B - P2H optimal capacity determination method for stabilizing wind power fluctuation - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 17
- 230000000087 stabilizing effect Effects 0.000 title claims abstract description 16
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 123
- 238000005868 electrolysis reaction Methods 0.000 claims abstract description 97
- 239000001257 hydrogen Substances 0.000 claims abstract description 20
- 229910052739 hydrogen Inorganic materials 0.000 claims abstract description 20
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims abstract description 19
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 16
- 239000001301 oxygen Substances 0.000 claims abstract description 16
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 16
- 238000006243 chemical reaction Methods 0.000 claims abstract description 10
- 238000013178 mathematical model Methods 0.000 claims description 30
- 238000012423 maintenance Methods 0.000 claims description 16
- 238000009434 installation Methods 0.000 claims description 10
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- 230000033228 biological regulation Effects 0.000 claims description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 abstract description 16
- 239000003345 natural gas Substances 0.000 abstract description 8
- 230000008859 change Effects 0.000 abstract description 3
- 230000008901 benefit Effects 0.000 abstract description 2
- 239000007789 gas Substances 0.000 description 4
- 238000009499 grossing Methods 0.000 description 4
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- 238000009776 industrial production Methods 0.000 description 2
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- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
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- C25B1/00—Electrolytic production of inorganic compounds or non-metals
- C25B1/01—Products
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- C25B1/04—Hydrogen or oxygen by electrolysis of water
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention discloses a method for determining P2H optimal capacity for stabilizing wind power fluctuation, which is characterized in that according to the technical specification of the power grid accessed by a national power grid wind farm, the method comprises the requirements of the wind power maximum power change rate of 1 minute and the maximum power of 10 minutes, wind power output which needs to be eliminated by a water electrolysis device for meeting the requirements is utilized for water electrolysis, wind energy which is abandoned without utilization is converted into a natural gas pipeline which can be used for industry or injected into the natural gas pipeline, the investment cost and the operation cost of water electrolysis are comprehensively considered with the sales income of hydrogen and oxygen obtained after the water electrolysis conversion, the optimal capacity configuration of the water electrolysis device is researched, the economic benefit of the water electrolysis device is maximized, and the wind power utilization efficiency is improved. The method reduces the influence of intermittence and fluctuation of wind power output on a power grid, converts redundant wind power into hydrogen through the water electrolysis device to the maximum extent, and improves the energy utilization efficiency.
Description
Technical Field
The invention relates to a P2H optimal capacity determination method for stabilizing wind power fluctuation, in particular to a method for determining the optimal capacity for stabilizing wind power fluctuation by using a power-to-hydrogen device, and belongs to the technical field of wind power grid-connected operation.
Background
Due to problems of fossil energy crisis, climate change, and severe environmental pollution, energy systems need to be turned to a more sustainable direction. Large-scale renewable energy can successfully achieve this goal, but environmentally friendly energy systems face severe problems and challenges. Renewable energy sources are random, intermittent and threaten the safety and stability of energy systems. Energy storage devices are important to improve the stability and safety of energy systems. However, existing energy storage devices are not capable of long-term, large-scale storage. In addition, fossil fuels, particularly hydrogen and natural gas, have the property of storing energy on a large scale and play a very important role in the energy conversion process. Therefore, the electric gas transfer device is produced. The device can convert the surplus electric power into hydrogen or natural gas for industrial production or injection into the existing natural gas network and gas storage, can improve the use efficiency of natural gas infrastructure, and avoids the increase of sinking cost. In addition, the electric gas conversion device can improve the flexibility of the electric power and natural gas system through different mechanisms.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for determining the optimal P2H capacity for stabilizing wind power fluctuation is characterized in that the output of a wind power plant is stabilized by using a water electrolysis device in the electricity-to-gas conversion, the technical standard of the national power grid company on the wind power grid is met, meanwhile, the wind power which cannot be connected to the grid for meeting the grid-connection standard is used for electrolyzing water, and the wind energy is converted into hydrogen for industrial production or injected into a natural gas pipeline, so that the energy utilization efficiency is improved.
The invention adopts the following technical scheme for solving the technical problems:
a P2H optimal capacity determination method for stabilizing wind power fluctuation comprises the following steps:
step 1, establishing a mathematical model for the original wind power output according to the regulation of a national power grid on the power grid accessed to a wind power plant, and judging the redundant wind power output required to be absorbed by a water electrolysis device according to the mathematical model;
step 2, modeling the optimal capacity of the water electrolysis device for stabilizing wind power fluctuation in consideration of the investment cost, the operation cost and the water electrolysis income of the water electrolysis device; the model is as follows:
maxf＝Prev-Cinv-Copt
wherein, PrevSales revenue for hydrogen and oxygen for electrolyzed water; cinvInvestment cost for water electrolysis equipment; coptThe operating cost of the water electrolysis device comprises the electricity consumption cost and the operating maintenance cost; f is the income of the electrolytic water device for absorbing the surplus wind power output;
step 3, considering the economy of the water electrolysis device, and establishing a mathematical model of the sales income of the hydrogen and the oxygen of the electrolyzed water;
step 4, considering the relation between the investment cost of the water electrolysis device and the optimal capacity of the water electrolysis device, and establishing a mathematical model of the investment cost of the water electrolysis device;
step 5, considering the relation between the operation maintenance cost of the water electrolysis device and the optimal capacity and electricity consumption cost of the water electrolysis device, and establishing a mathematical model of the operation cost of the water electrolysis device;
step 6, establishing a relational expression between the unit investment cost and the optimal capacity of the water electrolysis device;
step 7, establishing a relational expression between the operation maintenance coefficient and the optimal capacity of the water electrolysis device;
step 8, restraining the upper limit and the lower limit of the wind power output consumed by the electrolyzed water at each moment;
and 9, performing simulation analysis on the model obtained in the steps 2 to 8 by using matlab and lingo to determine the optimal capacity of the electrolytic water device for stabilizing wind power fluctuation.
As a preferred embodiment of the present invention, the mathematical model in step 1 is as follows:
Pw.t＝Pw.t.1_10+Pew.t+Pabn.t
wherein, Pw.tThe original wind power output P at the moment t without stabilizationw.t.1_10For the wind farm output, P, at time t meeting the national grid wind farm access standardw.t-1.1_10The power output P of the wind power plant which accords with the national grid wind power plant access standard at the time of t-1ew.tWind power output, P, for consumption by the electrolysis water at time tabn.tWind power output, delta P, abandoned at time t1_minIs the maximum limit value of wind power output fluctuation within 1 minute, delta P10_minThe maximum limit value of the wind power output fluctuation within 10 minutes is shown as i, and the sampling point is shown as i.
As a preferred embodiment of the present invention, the mathematical model of sales income of hydrogen and oxygen of electrolyzed water in step 3 is as follows:
wherein, PrevFor the sales revenue of hydrogen and oxygen for the electrolysis of water, eta is the efficiency of the water electrolysis plant, Pew.tFor the electric power to be taken up at time t,
respectively the conversion coefficient of converting electric energy into hydrogen and oxygen,
the selling prices of hydrogen and oxygen respectively.
As a preferable scheme of the invention, the mathematical model of the investment cost of the water electrolysis device in the step 4 is as follows:
wherein, CinvFor the investment cost of the water electrolysis plant, Cet_cFor optimal capacity of the water electrolysis plant, Cost _ PtH is the unit investment Cost of the water electrolysis plant, roFor interest rate, n is the service life of the water electrolysis device, and τ is the time conversion coefficient.
As a preferable scheme of the invention, the mathematical model of the operation cost of the water electrolysis device in the step 5 is as follows:
wherein, CoptFor the running cost of the water electrolysis apparatus, CinvFor the investment cost of the water electrolysis plant, alpha is the operation and maintenance factor of the water electrolysis plant, EpCost per unit electricity consumption, P, for water electrolysis installationsew.tThe electric power consumed for time t.
As a preferred embodiment of the present invention, the relationship between the unit investment cost and the optimal capacity of the water electrolysis device in step 6 is as follows:
Cost_PtH＝0.0468*Cet_c^4-3.3109*Cet_c^3
+54.8285*Cet_c^2-331.3499*Cet_c+1254.7
wherein Cost _ PtH is the unit investment Cost of the water electrolysis device, Cet_cIs the optimal capacity of the water electrolysis device.
As a preferred scheme of the invention, the relation between the operation maintenance coefficient and the optimal capacity of the water electrolysis device in the step 7 is as follows:
wherein alpha is the operation and maintenance coefficient of the water electrolysis device, Cet_cIs the optimal capacity of the water electrolysis device.
As a preferred scheme of the present invention, in step 8, the upper and lower limits of the wind power output consumed by the electrolyzed water at each time are constrained as follows:
0≤Pew.t≤min(Pw.t-Pw.t.1_10,Cet_c)
wherein, Pew.tElectric power taken up for time t, Pw.tThe original wind power output P at the moment t without stabilizationw.t.1_10For wind farm output at time t meeting the national grid wind farm access standard, Cet_cIs the optimal capacity of the water electrolysis device.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the invention starts from the actual condition specified by the access technology of the national power grid wind power station, improves the wind energy utilization efficiency while meeting the requirement of wind power output grid connection, maximizes the economic benefit of the water electrolysis device, lays a foundation for the development of energy Internet, greatly promotes the electrolysis water to eliminate the wind power output, and reduces the use cost of the electrolysis water.
Detailed Description
The following detailed description of embodiments of the invention is intended to be illustrative, but not limiting, of the invention.
A P2H optimal capacity research method for stabilizing wind power fluctuation is characterized in that according to the technical specification of accessing a national grid wind farm to a power grid, the method comprises the requirements of wind power 1-minute maximum power change rate and 10-minute maximum power on two time scales, and the wind power output which needs to be eliminated by a water electrolysis device for meeting the requirements is utilized for water electrolysis, and the method specifically comprises the following steps:
converting a technical specification of a national power grid for accessing a wind power plant into a mathematical model, respectively corresponding to a 1-minute smoothing specification and a 10-minute smoothing specification for 2 time scales, firstly carrying out stabilization according to a 1-minute smoothing requirement, and then carrying out stabilization according to a 10-minute smoothing requirement, wherein the time sequence is a stabilization time sequence of wind power stabilization;
Pw.t＝Pw.t.1_10+Pew.t+Pabn.t(1)
Pw.tthe original wind power output P at the moment t without stabilizationw.t.1_10For the wind farm output, P, at time t meeting the national grid wind farm access standardw.t-1.1_10The power output P of the wind power plant which accords with the national grid wind power plant access standard at the time of t-1ew.tWind power output, P, for consumption by the electrolysis water at time tabn.tWind power abandoned at time t, Δ P1_minIs the maximum limit value of wind power output fluctuation, delta P, of 1 minute10_minThe maximum limit value of the wind power output fluctuation within 10 minutes is shown as i, and the sampling point is shown as i.
Step two, on the basis of the step one, the wind power output P is generatedw.t-Pw.t.1_10The consumption is carried out so that P meets the goal of optimal economy of the electrolyzed waterew.tAs large as possible, Pabn.tAs small as possible. And modeling the optimal capacity of the electrolytic water device for stabilizing the wind power fluctuation by considering the investment cost, the operation cost (mainly comprising the electricity utilization cost and the operation maintenance cost) and the electrolytic water profit of the electrolytic water device. Sales revenue P of hydrogen and oxygen for electrolyzed water as objective functionrevLess the investment cost C of the water electrolysis plantinvIn the absence of operating costs C of the water electrolysis unitopt：
maxf＝Prev-Cinv-Copt(4)
Step three, researching the economy of the water electrolysis device, and establishing a mathematical model of the income of the water electrolysis device:
eta is the efficiency, P, of the electric gas-converting apparatusew.tFor the electric power to be taken up at time t,
and
respectively representing the conversion coefficients of electrical energy into hydrogen and oxygen,
and
the selling prices for hydrogen and oxygen, respectively.
Step four, researching the relation between the investment cost of the water electrolysis device and the installation capacity of the water electrolysis device, and establishing a mathematical model of the investment cost of the water electrolysis device:
Cet_cfor optimal installation capacity of the water electrolysis apparatus, Cost _ PtH is the unit investment Cost of the water electrolysis apparatus, roFor interest rate, n is suitAnd the service life is set, and tau is a time conversion coefficient.
And step five, researching the relation between the operation maintenance cost of the electrolyzed water and the installation capacity and the electricity consumption cost of the electrolyzed water device, and establishing a mathematical model of the operation cost of the electrolyzed water device:
alpha% is the operation and maintenance coefficient of the water electrolysis device, which has a nonlinear relation with the installation capacity, EpIs the unit electricity cost of the device.
Step six, establishing a mathematical model between the unit investment cost and the optimal installation capacity of the water electrolysis device:
step seven, establishing a mathematical model between the operation maintenance coefficient and the optimal installation capacity of the water electrolysis device:
step eight, performing mathematical modeling on a constraint condition for determining the optimal installation capacity of the electrolyzed water by taking the optimal economical efficiency of the electrolyzed water as a target, namely constraining the upper limit and the lower limit of the wind power consumed by the electrolyzed water in each time period:
0≤Pew.t≤min(Pw.t-Pw.t.1_10,Cet_c) (10)
and step nine, carrying out simulation analysis on the model, and determining the determination method of the optimal installation capacity of the water electrolysis device (P2H) in practical application.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.
Claims (3)
1. A P2H optimal capacity determination method for stabilizing wind power fluctuation is characterized by comprising the following steps:
step 1, establishing a mathematical model for the original wind power output according to the regulation of a national power grid on the power grid accessed to a wind power plant, and judging the redundant wind power output required to be absorbed by a water electrolysis device according to the mathematical model;
step 2, modeling the optimal capacity of the water electrolysis device for stabilizing wind power fluctuation in consideration of the investment cost, the operation cost and the water electrolysis income of the water electrolysis device; the model is as follows:
maxf＝Prev-Cinv-Copt
wherein, PrevSales revenue for hydrogen and oxygen for electrolyzed water; cinvInvestment cost for water electrolysis equipment; coptThe operating cost of the water electrolysis device comprises the electricity consumption cost and the operating maintenance cost; f is the income of the electrolytic water device for absorbing the surplus wind power output;
step 3, considering the economy of the water electrolysis device, and establishing a mathematical model of the sales income of the hydrogen and the oxygen of the electrolyzed water;
the mathematical model of the sales income of the hydrogen and the oxygen of the electrolyzed water is as follows:
wherein, PrevFor the sales revenue of hydrogen and oxygen for the electrolysis of water, eta is the efficiency of the water electrolysis plant, Pew.tFor the electric power to be taken up at time t,
respectively the conversion coefficient of converting electric energy into hydrogen and oxygen,
are each hydrogenThe selling price of oxygen;
step 4, considering the relation between the investment cost of the water electrolysis device and the optimal capacity of the water electrolysis device, and establishing a mathematical model of the investment cost of the water electrolysis device;
the mathematical model of the investment cost of the water electrolysis device is as follows:
wherein, CinvFor the investment cost of the water electrolysis plant, Cet_cFor optimal capacity of the water electrolysis plant, Cost _ PtH is the unit investment Cost of the water electrolysis plant, roThe interest rate is, n is the service life of the water electrolysis device, and tau is a time conversion coefficient;
step 5, considering the relation between the operation maintenance cost of the water electrolysis device and the optimal capacity and electricity consumption cost of the water electrolysis device, and establishing a mathematical model of the operation cost of the water electrolysis device;
the mathematical model of the operating cost of the water electrolysis device is as follows:
wherein, CoptFor the running cost of the water electrolysis apparatus, CinvFor the investment cost of the water electrolysis plant, alpha is the operation and maintenance factor of the water electrolysis plant, EpCost per unit electricity consumption, P, for water electrolysis installationsew.tThe electric power consumed for the time t;
step 6, establishing a mathematical model between the unit investment cost and the optimal capacity of the water electrolysis device;
the mathematical model between the unit investment cost and the optimal capacity of the water electrolysis device is as follows:
Cost_PtH＝0.0468*Cet_c^4-3.3109*Cet_c^3+54.8285*Cet_c^2-331.3499*Cet_c+1254.7
wherein Cost _ PtH is the unit investment Cost of the water electrolysis device, Cet_cThe optimal capacity of the water electrolysis device;
step 7, establishing a mathematical model between the operation maintenance coefficient and the optimal capacity of the water electrolysis device;
the mathematical model between the operation maintenance coefficient and the optimal capacity of the water electrolysis device is as follows:
wherein alpha is the operation and maintenance coefficient of the water electrolysis device, Cet_cThe optimal capacity of the water electrolysis device;
step 8, establishing a mathematical model for the upper limit and the lower limit of the wind power output consumed by the electrolyzed water at each moment;
and 9, performing simulation analysis on the model obtained in the steps 2 to 8 by using matlab and lingo to determine the optimal capacity of the electrolytic water device for stabilizing wind power fluctuation.
2. The method for determining the optimal capacity of P2H for stabilizing wind power fluctuation according to claim 1, wherein the mathematical model in step 1 is as follows:
Pw.t＝Pw.t.1_10+Pew.t+Pabn.t
wherein, Pw.tThe original wind power output P at the moment t without stabilizationw.t.1_10For the wind farm output, P, at time t meeting the national grid wind farm access standardw.t-1.1_10The power output P of the wind power plant which accords with the national grid wind power plant access standard at the time of t-1ew.tWind power output, P, for consumption by the electrolysis water at time tabn.tWind power output, delta P, abandoned at time t1_minThe wind power output fluctuation is the largest within 1 minuteLarge limit value, Δ P10_minThe maximum limit value of the wind power output fluctuation within 10 minutes is shown as i, and the sampling point is shown as i.
3. The method for determining the optimal capacity of P2H for stabilizing wind power fluctuation according to claim 1, wherein the mathematical model for the upper and lower limits of wind power output consumed by electrolyzed water at each moment is established in step 8 as follows:
0≤Pew.t≤min(Pw.t-Pw.t.1_10,Cet_c)
wherein, Pew.tElectric power taken up for time t, Pw.tThe original wind power output P at the moment t without stabilizationw.t.1_10For wind farm output at time t meeting the national grid wind farm access standard, Cet_cIs the optimal capacity of the water electrolysis device.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103346562A (en) * | 2013-07-11 | 2013-10-09 | 江苏省电力设计院 | Multi-time scale microgrid energy control method considering demand response |
| 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 |
| CN106786764A (en) * | 2017-01-13 | 2017-05-31 | 东北电力大学 | A kind of utilization hydrogen generating system wind-powered electricity generation of dissolving abandons the hydrogen manufacturing capacity configuration optimizing method of wind |
| CN107147109A (en) * | 2017-05-10 | 2017-09-08 | 袁铁江 | The energy storage of wind-powered electricity generation hydrogen distributes control method with coal chemical industry multipotency coupled system gas |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103346562A (en) * | 2013-07-11 | 2013-10-09 | 江苏省电力设计院 | Multi-time scale microgrid energy control method considering demand response |
| 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 |
| CN106786764A (en) * | 2017-01-13 | 2017-05-31 | 东北电力大学 | A kind of utilization hydrogen generating system wind-powered electricity generation of dissolving abandons the hydrogen manufacturing capacity configuration optimizing method of wind |
| CN107147109A (en) * | 2017-05-10 | 2017-09-08 | 袁铁江 | The energy storage of wind-powered electricity generation hydrogen distributes control method with coal chemical industry multipotency coupled system gas |
Non-Patent Citations (1)
| Title |
|---|
| "Integrated Modeling and Assessment of the Operational Impact of Power-to-Gas (P2G) on Electrical and Gas Transmission Networks";Stephen Clegg;《IEEE TRANSACTIONS ON SUSTAINABLE ENERGY》;20151031;第6卷(第4期);全文 * |
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