CN112290562B - Hybrid energy storage control system and method for stabilizing wind power fluctuation power multiple time scales - Google Patents
Hybrid energy storage control system and method for stabilizing wind power fluctuation power multiple time scales Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—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
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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
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- 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
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- 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
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Abstract
The invention discloses a hybrid energy storage multistage coordination control system and a control method for stabilizing wind power fluctuation power. The hybrid energy storage multistage coordination control system and the control method for stabilizing wind power fluctuation power, provided by the invention, utilize a plurality of time scales (before, in the day and in real time) to gradually reduce the influence of wind power prediction deviation on the control of the energy storage system, can meet the wind power stabilizing requirements under various different time scales, and ensure that the hybrid energy storage is always in a normal working state.
Description
Technical Field
The invention relates to the technical field of electric power systems and automation thereof, in particular to hybrid energy storage control, and especially relates to hybrid energy storage control for stabilizing wind power fluctuation power in multiple time scales.
Background
Wind power generation is an important renewable energy power generation mode, and is increasingly widely applied to power systems due to the advantages of high power generation efficiency, mature technology and the like. However, the uncertainty of wind energy resources causes intermittence and fluctuation of wind power generation power, which has adverse effects on the safety and reliability of a power grid, and restricts the development of wind power generation in a power system. The energy storage system can effectively alleviate adverse effects of wind power generation on a power grid, and is widely applied to wind power generation, wherein a reasonable control strategy and method are key to fully exerting the effect.
The wind power prediction precision is closely related to the time scale, the wind power prediction of a long time scale is difficult to meet the requirement of accurate energy storage control, and the wind power prediction of a short time scale can only ensure the optimal energy storage control in the time period, but cannot ensure that the energy storage is in a reasonable normal working state in a long time.
Disclosure of Invention
Aiming at different requirements of wind power stabilization on energy storage control under different time scales, the invention provides a hybrid energy storage multistage coordination control system and a control method suitable for stabilizing wind power fluctuation power in multiple time scales.
The technical scheme of the invention is as follows:
A hybrid energy storage multistage coordination control system for stabilizing wind power fluctuation power comprises a day-ahead hybrid energy storage control unit, a day-in hybrid energy storage control unit and a real-time hybrid energy storage control unit.
The solar hybrid energy storage control unit comprises a solar wind power prediction module, a second-order low-pass filter, an energy storage battery amplitude limiting link, a super-capacitor amplitude limiting link, an energy storage battery SOC optimizing link and a super-capacitor SOC optimizing link, wherein the solar wind power prediction module is connected with the second-order low-pass filter, the second-order low-pass filter is respectively connected with the energy storage battery amplitude limiting link and the super-capacitor amplitude limiting link, the energy storage battery amplitude limiting link is connected with the energy storage battery SOC optimizing link, and the super-capacitor amplitude limiting link is connected with the super-capacitor SOC optimizing link; the output of the energy storage battery SOC optimization link is connected to the energy storage battery SOC optimization link of the daily hybrid energy storage control unit, and the output of the super capacitor SOC optimization link is connected to the super capacitor SOC optimization link of the daily hybrid energy storage control unit; wherein:
The future wind power prediction module predicts wind power in the future 24 hours according to weather prediction in the future 24 hours, and transmits the wind power in the future 24 hours to the second-order low-pass filter.
The second-order low-pass filter controls the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor by changing f cap and f bat.
The limiting link and the SOC optimization link optimize control parameters f cap and f bat of the second-order low-pass filter, avoid overcharge and deep discharge, and send the SOC states of all stored energy in the future 24 hours to the intra-day hybrid energy storage control unit;
The solar hybrid energy storage control unit comprises a solar wind power prediction module, a second-order low-pass filter, an energy storage battery amplitude limiting link, a super-capacitor amplitude limiting link, an energy storage battery SOC optimizing link and a super-capacitor SOC optimizing link, wherein the solar wind power prediction module is connected with the second-order low-pass filter, the second-order low-pass filter is respectively connected with the energy storage battery amplitude limiting link and the super-capacitor amplitude limiting link, the energy storage battery amplitude limiting link is connected with the energy storage battery SOC optimizing link, and the super-capacitor amplitude limiting link is connected with the super-capacitor SOC optimizing link; the output of the energy storage battery SOC optimization link is connected to the energy storage battery SOC optimization link of the real-time hybrid energy storage control unit, and the output of the super capacitor SOC optimization link is connected to the super capacitor SOC optimization link of the real-time hybrid energy storage control unit; wherein:
The daily wind power prediction module predicts wind power in the future 4 hours according to weather prediction in the future 4 hours, and transmits the wind power in the future 4 hours to the second-order low-pass filter;
The second-order low-pass filter controls the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor by changing f cap and f bat.
The limiting link and the SOC optimization link optimize control parameters f cap and f bat of the second-order low-pass filter, the SOC optimization link ensures that all energy storage is in a normal working state in 4 hours in the future, and the SOC state of all energy storage after the period is over is as close as possible to the corresponding SOC state of all energy storage in the hybrid energy storage control before the day so as to keep the capacity of stabilizing wind power fluctuation power in the next period of the hybrid energy storage system, avoid overcharge and deep discharge, and send the SOC state of all energy storage in 4 hours in the future to the real-time hybrid energy storage control unit;
The real-time hybrid energy storage control unit comprises a real-time wind power prediction module, a second-order low-pass filter, an energy storage battery limiting link, a super-capacitor limiting link, an energy storage battery SOC optimizing link and a super-capacitor SOC optimizing link, wherein the real-time wind power prediction module is connected with the second-order low-pass filter, the second-order low-pass filter is respectively connected with the energy storage battery limiting link and the super-capacitor limiting link, the energy storage battery limiting link is connected with the energy storage battery SOC optimizing link, and the super-capacitor limiting link is connected with the super-capacitor SOC optimizing link; wherein:
the real-time wind power prediction module predicts wind power in 15 minutes according to weather prediction in 15 minutes in the future, and transmits the wind power in 15 minutes in the future to the second-order low-pass filter;
The second-order low-pass filter controls the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor by changing f cap and f bat.
The limiting link and the SOC optimization link optimize control parameters f cap and f bat of the second-order low-pass filter, the SOC optimization link ensures that all energy storage is in a normal working state within 15 minutes in the future, the SOC state of all energy storage after the period is over and the SOC state of all energy storage corresponding to the daily hybrid energy storage control are as close as possible, so that the capacity of stabilizing wind power fluctuation power of the hybrid energy storage system in the next period is maintained, overcharge and deep discharge are avoided, finally, the optimized control parameters f cap and f bat are sent to the second-order low-pass filter, and all energy storage output is controlled by the second-order low-pass filter.
When the hybrid energy storage system operates, the output power of the super capacitor, the output power of the energy storage battery and the wind power internet surfing power are respectively as follows:
Wherein: p w(s)、Pcap(s)、Pbat(s) and P g(s) are wind power on a complex frequency domain, supercapacitor output power, energy storage battery output power and stabilized wind power on-line power respectively; and f cap and f bat are frequencies corresponding to the filtering time constants of the super capacitor and the energy storage battery respectively, and the output power control of the super capacitor and the energy storage battery is realized by controlling f cap and f bat.
The control method based on the system comprises the following steps:
step 1: based on wind power prediction results within 24 hours in the future, a second-order low-pass filter is utilized to divide the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor, and the output of each energy storage is optimized, so that the problem of overcharge or deep discharge of each energy storage is guaranteed.
Step 1.1, dividing wind power fluctuation power in the future 24 hours into three parts of high frequency, secondary high frequency and low frequency according to frequency by utilizing a second-order low-pass filter, and stabilizing high frequency and secondary high frequency components by utilizing a super capacitor and an energy storage battery respectively by combining the characteristics of two types of energy storage.
And 1.2, taking 1 hour as a minimum time scale, taking the minimum wind power internet surfing fluctuation power, the minimum charging and discharging depth of the energy storage battery and the moderate level of the energy storage battery and the super capacitor as targets in 24 hours in the future, and establishing a multi-target optimization model by taking the state of charge of the energy storage battery and the super capacitor not exceeding the corresponding limit value.
The constraint conditions are as follows:
Wherein: p bat,c is the rated charge and discharge power of the energy storage battery; c soc,mod is the moderate state of charge of the energy storage device; Δt is the charge-discharge time interval of the energy storage device; m cap is the energy storage capacity of the super capacitor; c soc,cap and C soc,bat are the states of charge of the super capacitor and the energy storage battery respectively; c soc,cap,max and C soc,cap,min are the upper and lower limits of C soc,cap; c soc,bat,max and C soc,bat,min are the upper and lower limits of C soc,bat.
And 1.3, obtaining the weight of each target by using an analytic hierarchy process and a Bayesian theory, carrying out weighted summation on each target, converting the multi-target optimization model into a single-target optimization model, and solving by using a Lagrange multiplier method.
Step 2: based on wind power prediction results within 4 hours in the future, a second-order low-pass filter is utilized to divide the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor, and the output of each energy storage is optimized, so that the problem of overcharge or deep discharge of each energy storage is guaranteed.
And 2.1, dividing wind power fluctuation power in the future 4 hours into three parts of high frequency, secondary high frequency and low frequency according to frequency by utilizing a second-order low-pass filter, and stabilizing high frequency and secondary high frequency components by utilizing a super capacitor and an energy storage battery respectively by combining the characteristics of two types of energy storage.
And 2.2, taking 15 minutes as a minimum time scale, taking the minimum wind power internet surfing fluctuation power, the minimum charging and discharging depth of the energy storage battery, the moderate level of the energy storage battery and the super capacitor as well as the maximum approaching of each storage SOC in the hybrid energy storage system and each storage SOC in the hybrid energy storage control before the day after the period of time, and establishing a multi-target optimization model with the state of charge of the energy storage battery and the super capacitor not exceeding the corresponding limit value.
The constraint conditions are as follows:
And 2.3, obtaining the weight of each target by using an analytic hierarchy process and a Bayesian theory, carrying out weighted summation on each target, converting the multi-target optimization model into a single-target optimization model, and solving by using a Lagrange multiplier method.
Step 3: based on wind power prediction results within 15 minutes in the future, a second-order low-pass filter is utilized to divide the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor, and the output of each energy storage is optimized, so that the problem of overcharge or deep discharge of each energy storage is guaranteed.
And 3.1, dividing wind power fluctuation power in 15 minutes into three parts of high frequency, secondary high frequency and low frequency according to frequency by utilizing a second-order low-pass filter, and stabilizing high frequency and secondary high frequency components by utilizing a super capacitor and an energy storage battery respectively by combining the characteristics of two types of energy storage.
And 3.2, establishing a multi-objective optimization model by taking the minimum wind power on-line fluctuation power, the minimum charge and discharge depth of the energy storage battery, the moderate level of the energy storage battery and the super capacitor as well as the condition that the SOC of each energy storage in the hybrid energy storage system and the SOC of each energy storage in daily hybrid energy storage control are as close as possible after the period is finished, and taking the state of charge of the energy storage battery and the super capacitor not exceeding the corresponding limit value as far as possible.
The constraint conditions are as follows:
and 3.3, obtaining the weight of each target by using an analytic hierarchy process and a Bayesian theory, carrying out weighted summation on each target, converting the multi-target optimization model into a single-target optimization model, and solving by using a Lagrange multiplier method.
And 3.4, inputting control parameters f cap and f bat into the second-order low-pass filter, and controlling the output power of each energy storage by using the second-order low-pass filter.
The technical scheme provided by the invention has the beneficial effects that:
(1) According to the invention, adverse effects of wind power generation prediction errors of different time scales on energy storage control are reduced step by step through a multi-level coordination control strategy, meanwhile, the mixed energy storage is ensured to be in a reasonable working state within 24 hours, and the problem of poor optimization effects of other time periods due to excessive optimization within a short time period is avoided.
(2) The invention utilizes Bayesian theory to correct subjective weight, so that the target weight setting is more objective and reasonable.
Drawings
FIG. 1 is a hybrid energy storage multi-stage coordination control system for stabilizing wind power fluctuation power;
FIG. 2 is a graph of wind power grid-connected power spectrum with an energy storage system according to the present invention
FIG. 3 is a graph showing wind power distribution at a time scale of day and day
FIG. 4 is a graph of wind power versus absolute fluctuation after stabilization;
FIG. 5 shows the charge and discharge power of the energy storage battery and the super capacitor
FIG. 6 shows the state of charge of the energy storage battery and super-capacitor
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
A hybrid energy storage multistage coordination control system for stabilizing wind power fluctuation power comprises a day-ahead hybrid energy storage control unit, a day-in hybrid energy storage control unit and a real-time hybrid energy storage control unit.
The solar hybrid energy storage control unit comprises a solar wind power prediction module, a second-order low-pass filter, an energy storage battery amplitude limiting link, a super-capacitor amplitude limiting link, an energy storage battery SOC optimizing link and a super-capacitor SOC optimizing link, wherein the solar wind power prediction module is connected with the second-order low-pass filter, the second-order low-pass filter is respectively connected with the energy storage battery amplitude limiting link and the super-capacitor amplitude limiting link, the energy storage battery amplitude limiting link is connected with the energy storage battery SOC optimizing link, and the super-capacitor amplitude limiting link is connected with the super-capacitor SOC optimizing link; the output of the energy storage battery SOC optimization link is connected to the energy storage battery SOC optimization link of the daily hybrid energy storage control unit, and the output of the super capacitor SOC optimization link is connected to the super capacitor SOC optimization link of the daily hybrid energy storage control unit; wherein:
The future wind power prediction module predicts wind power in the future 24 hours according to weather prediction in the future 24 hours, and transmits the wind power in the future 24 hours to the second-order low-pass filter.
The second-order low-pass filter controls the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor by changing f cap and f bat.
The limiting link and the SOC optimization link optimize control parameters f cap and f bat of the second-order low-pass filter, avoid overcharge and deep discharge, and send the SOC states of all stored energy in the future 24 hours to the intra-day hybrid energy storage control unit;
The solar hybrid energy storage control unit comprises a solar wind power prediction module, a second-order low-pass filter, an energy storage battery amplitude limiting link, a super-capacitor amplitude limiting link, an energy storage battery SOC optimizing link and a super-capacitor SOC optimizing link, wherein the solar wind power prediction module is connected with the second-order low-pass filter, the second-order low-pass filter is respectively connected with the energy storage battery amplitude limiting link and the super-capacitor amplitude limiting link, the energy storage battery amplitude limiting link is connected with the energy storage battery SOC optimizing link, and the super-capacitor amplitude limiting link is connected with the super-capacitor SOC optimizing link; the output of the energy storage battery SOC optimization link is connected to the energy storage battery SOC optimization link of the real-time hybrid energy storage control unit, and the output of the super capacitor SOC optimization link is connected to the super capacitor SOC optimization link of the real-time hybrid energy storage control unit; wherein:
The daily wind power prediction module predicts wind power in the future 4 hours according to weather prediction in the future 4 hours, and transmits the wind power in the future 4 hours to the second-order low-pass filter;
The second-order low-pass filter controls the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor by changing f cap and f bat.
The limiting link and the SOC optimization link optimize control parameters f cap and f bat of the second-order low-pass filter, the SOC optimization link ensures that all energy storage is in a normal working state in 4 hours in the future, and the SOC state of all energy storage after the period is over is as close as possible to the corresponding SOC state of all energy storage in the hybrid energy storage control before the day so as to keep the capacity of stabilizing wind power fluctuation power in the next period of the hybrid energy storage system, avoid overcharge and deep discharge, and send the SOC state of all energy storage in 4 hours in the future to the real-time hybrid energy storage control unit;
The real-time hybrid energy storage control unit comprises a real-time wind power prediction module, a second-order low-pass filter, an energy storage battery limiting link, a super-capacitor limiting link, an energy storage battery SOC optimizing link and a super-capacitor SOC optimizing link, wherein the real-time wind power prediction module is connected with the second-order low-pass filter, the second-order low-pass filter is respectively connected with the energy storage battery limiting link and the super-capacitor limiting link, the energy storage battery limiting link is connected with the energy storage battery SOC optimizing link, and the super-capacitor limiting link is connected with the super-capacitor SOC optimizing link; wherein:
the real-time wind power prediction module predicts wind power in 15 minutes according to weather prediction in 15 minutes in the future, and transmits the wind power in 15 minutes in the future to the second-order low-pass filter;
The second-order low-pass filter controls the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor by changing f cap and f bat.
The limiting link and the SOC optimization link optimize control parameters f cap and f bat of the second-order low-pass filter, the SOC optimization link ensures that all energy storage is in a normal working state within 15 minutes in the future, the SOC state of all energy storage after the period is over and the SOC state of all energy storage corresponding to the daily hybrid energy storage control are as close as possible, so that the capacity of stabilizing wind power fluctuation power of the hybrid energy storage system in the next period is maintained, overcharge and deep discharge are avoided, finally, the optimized control parameters f cap and f bat are sent to the second-order low-pass filter, and all energy storage output is controlled by the second-order low-pass filter.
The wind power prediction module predicts wind power in the future 24 hours according to weather prediction in the future 24 hours, and transmits the wind power in the future 24 hours to the second-order low-pass filter.
And the second-order low-pass filter divides the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor according to the wind power within 24 hours in the future.
The limiting link and the SOC optimization link optimize control parameters f cap and f bat of the second-order low-pass filter, the minimum time scale is 1 hour, the minimum wind power internet surfing fluctuation power, the minimum charging and discharging depth of the energy storage battery and the moderate level of the energy storage battery and the super capacitor are used as targets in 24 hours in the future, and a multi-target optimization model is built according to the state of charge of the energy storage battery and the super capacitor not exceeding the corresponding limit value.
The constraint conditions are as follows:
Wherein: p bat,c is the rated charge and discharge power of the energy storage battery; c soc,mod is the moderate state of charge of the energy storage device; Δt is the charge-discharge time interval of the energy storage device; m cap is the energy storage capacity of the super capacitor; c soc,cap and C soc,bat are the states of charge of the super capacitor and the energy storage battery respectively; c soc,cap,max and C soc,cap,min are the upper and lower limits of C soc,cap; c soc,bat,max and C soc,bat,min are the upper and lower limits of C soc,bat. Solving the multi-target optimization model, firstly, obtaining each target weight by using an analytic hierarchy process and a Bayesian theory, carrying out weighted summation on each target, converting the multi-target optimization model into a single-target optimization model, and then, solving by using a CPLEX solver, wherein the specific steps of obtaining each target weight are as follows:
Step 1: the subjective weight of each target is obtained by using an analytic hierarchy process and is marked as omega '= [ omega' - 1,ω'2,…,ω'm ], and m is the number of optimization targets
Step 2: and correcting the subjective weight by using the Bayesian theory.
Step 2.1: and collecting each target value in n days to form a construction target data matrix Y.
Wherein: alpha i＝[yi1…yi3],yij (i.epsilon. {1,2, … n }, j.epsilon. {1,2, … m }) is the value of the ith target in the previous j days
Step 2.2: and calculating the weight of the benefit index of each sample by using Bayesian theory. The prior probability of the subjective weight being the target f j (j=1, 2, 3), i.e. P (f j)＝ω'j), is obtained by analytic hierarchy process.
At the target f j (j=1, 2, 3), the probability of the i-th sample α i is y ij, i.e., P (α i|fj)＝yij.
According to the bayesian theory, the j index weight of the i sample α i is:
(i＝1,2,…n；j＝1,2,…m)
Step 2.3: and (3) weighting and summing each target of the j-th sample according to omega' j (i) to obtain a comprehensive target value f (i).
And carrying out weighted summation on each target of each sample according to the subjective weight to obtain a comprehensive target value f' (i).
And (3) correcting the subjective weight to ensure that the smaller and better the comprehensive target value deviation of all samples are, and establishing a least square method optimization evaluation model to correct the target weight.
And solving the model by using a Lagrangian multiplier method to obtain the modified benefit index weight, wherein the modified benefit index weight is marked as omega= [ omega 1,…,ωm ].
The intra-day wind power prediction module predicts wind power in the future 4 hours according to weather prediction in the future 4 hours, and transmits the wind power in the future 4 hours to the second-order low-pass filter.
And the second-order low-pass filter divides the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor according to the wind power within 4 hours in the future. When the hybrid energy storage system operates, the output power of the super capacitor, the output power of the energy storage battery and the wind power internet surfing power are respectively as follows:
Wherein: p w(s)、Pcap(s)、Pbat(s) and P g(s) are wind power on a complex frequency domain, supercapacitor output power, energy storage battery output power and stabilized wind power on-line power respectively; and f cap and f bat are frequencies corresponding to the filtering time constants of the super capacitor and the energy storage battery respectively, and the output power control of the super capacitor and the energy storage battery is realized by controlling f cap and f bat.
The limiting link and the SOC optimization link optimize control parameters f cap and f bat of the second-order low-pass filter, 15 minutes is taken as a minimum time scale, the minimum wind power on-line fluctuation power, the minimum charging and discharging depth of the energy storage battery, the moderate level of the energy storage battery and the super capacitor are maintained, and after the period is finished, the SOC of each energy storage in the hybrid energy storage system is as close as possible to the SOC of each energy storage in the hybrid energy storage control before the day, and a multi-target optimization model is established by taking the state of charge of the energy storage battery and the super capacitor not exceeding the corresponding limit value.
The constraint conditions are as follows:
Wherein: p bat,c is the rated charge and discharge power of the energy storage battery; c soc,mod is the moderate state of charge of the energy storage device; Δt is the charge-discharge time interval of the energy storage device; m cap is the energy storage capacity of the super capacitor; c soc,cap and C soc,bat are the states of charge of the super capacitor and the energy storage battery respectively; c soc,cap,max and C soc,cap,min are the upper and lower limits of C soc,cap; c soc,bat,max and C soc,bat,min are the upper and lower limits of C soc,bat. Solving the multi-target optimization model, firstly, obtaining the weight of each target by using an analytic hierarchy process and a Bayesian theory, carrying out weighted summation on each target, converting the multi-target optimization model into a single-target optimization model, and then, solving by using a CPLEX solver.
The real-time hybrid energy storage control unit predicts wind power in 15 minutes according to weather prediction in 15 minutes, and transmits the wind power in 15 minutes to the second-order low-pass filter.
And the second-order low-pass filter divides the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor according to the wind power within 15 minutes in the future. When the hybrid energy storage system operates, the output power of the super capacitor, the output power of the energy storage battery and the wind power internet surfing power are respectively as follows:
The limiting link and the SOC optimization link optimize control parameters f cap and f bat of the second-order low-pass filter, and aim at minimum wind power on-line fluctuation power, minimum charging and discharging depth of the energy storage battery, moderate level of the energy storage battery and the super capacitor in 15 minutes in the future, and as close as possible of each stored SOC in the hybrid energy storage system and each stored SOC in the daily hybrid energy storage control after the period is finished, and establish a multi-target optimization model with the states of charge of the energy storage battery and the super capacitor not exceeding corresponding limit values.
The constraint conditions are as follows:
Wherein: p bat,c is the rated charge and discharge power of the energy storage battery; c soc,mod is the moderate state of charge of the energy storage device; Δt is the charge-discharge time interval of the energy storage device; m cap is the energy storage capacity of the super capacitor; c soc,cap and C soc,bat are the states of charge of the super capacitor and the energy storage battery respectively; c soc,cap,max and C soc,cap,min are the upper and lower limits of C soc,cap; c soc,bat,max and C soc,bat,min are the upper and lower limits of C soc,bat. Solving the multi-target optimization model, firstly, obtaining the weight of each target by using an analytic hierarchy process and a Bayesian theory, carrying out weighted summation on each target, converting the multi-target optimization model into a single-target optimization model, and then, solving by using a CPLEX solver. And finally, transmitting the optimized control parameters to a second-order low-pass filter to control the energy storage output.
Calculation and analysis
Because the wind power prediction precision and the prediction time span are inversely related, the wind power prediction error before the day is larger than the wind power prediction error in the day. Consider the power distribution of wind power for two time scales with uncertainty in wind power output, as shown in FIG. 3. The 95% confidence probability is adopted, the confidence interval of the power prediction before the day is larger, the wind power fluctuation is more serious, the accuracy of the power prediction in the day is improved greatly, and the method is also the reason for adopting multi-time scale energy storage coordination control.
The main parameters of the constructed energy storage battery and super capacitor system are shown in table 1. In a smooth output scene, the technical evaluation is mainly used for measuring the compensation effect of the energy storage device for smooth output, and the technical evaluation indexes mainly comprise the fluctuation rate, the absolute fluctuation rate and the like; taking the fluctuation rate and the absolute fluctuation rate as examples, the smaller the fluctuation rate and the absolute fluctuation rate, the smoother the wind power generation internet power curve, and the better the control effect of the hybrid energy storage system. The absolute fluctuation rate of the wind power on-line power stabilized by the hybrid energy storage system and the wind power on-line power not stabilized is shown in fig. 4. According to the graph, wind power can be effectively stabilized by controlling the energy storage system, and the absolute fluctuation rate of the wind power is reduced.
Table 1 main parameters of hybrid energy storage system
The charge-discharge power curve of the energy storage battery and the super capacitor is shown in fig. 5, wherein positive values represent the charge state, and negative values represent the discharge state. It can be seen that the super capacitor is mainly used for high-power rapid charge and discharge to compensate peak power, and the storage battery is longer in charge and discharge time and is mainly used for compensating stable area power. And calculating the charge states of all the stored energy of the hybrid energy storage system according to the discharge power of the energy storage battery and the super capacitor. As can be seen from fig. 6, the energy storage battery and the super capacitor are in a reasonable working range at all times, and the problem of overcharge or deep discharge is avoided.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (3)
1. A hybrid energy storage multistage coordination control method for stabilizing wind power fluctuation power is characterized by comprising the following steps of:
The wind power prediction module is responsible for predicting wind power in 24 hours in the future, the wind power in 24 hours in the future is transmitted to the second-order low-pass filter, the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor is determined by the second-order low-pass filter, parameters of the second-order low-pass filter are optimized through the limiting link and the SOC optimization link, and finally the SOC state of each energy storage in the 24 hours in the future is transmitted to the hybrid energy storage control unit in the future;
the solar wind power prediction module is responsible for predicting wind power in 4 hours in the future, the wind power in 4 hours in the future is transmitted to the second-order low-pass filter, the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor is determined by the second-order low-pass filter, parameters of the second-order low-pass filter are optimized through the limiting link and the SOC optimization link, the SOC optimization link receives the SOC state of each energy storage in the solar energy storage control sent by the solar hybrid energy storage control unit, the state of each energy storage in the 4 hours in the future is ensured to be in a normal working state, the SOC state of each energy storage after the period is finished is enabled to be as close as possible to the SOC state of each energy storage corresponding to the solar energy storage control, so that the capacity of stabilizing wind power fluctuation power in the next period of the hybrid energy storage system is maintained, and finally the SOC state of each energy storage in the 4 hours in the future is sent to the real-time hybrid energy storage control unit;
The real-time wind power prediction module is responsible for predicting wind power in 15 minutes in the future, the wind power in 15 minutes in the future is transmitted to the second-order low-pass filter, the wind power fluctuation power stabilizing range of the energy storage battery and the super capacitor is determined by the second-order low-pass filter, parameters of the second-order low-pass filter are optimized through the limiting link and the SOC optimization link, the SOC optimization link receives the SOC state of each energy storage in the daily energy storage control sent by the daily hybrid energy storage control unit, the SOC state of each energy storage in 15 minutes in the future is ensured to be in a normal working state, and the SOC state of each energy storage after the period is finished is enabled to be as close as possible to the SOC state of each energy storage corresponding to the daily hybrid energy storage control, so that the capacity of stabilizing wind power fluctuation power in the next period of the hybrid energy storage system is maintained, and finally the output power of the energy storage battery and the super capacitor is controlled by utilizing the optimized parameters of the second-order low-pass filter;
The method comprises the following steps:
Based on wind power prediction results in 24 hours in the future, dividing wind power fluctuation power stabilizing ranges of the energy storage battery and the super capacitor by using a second-order low-pass filter, and optimizing control parameters of the second-order low-pass filter;
The wind power fluctuation power in the future 24 hours is divided into three parts of high frequency, secondary high frequency and low frequency according to frequency by utilizing a second-order low-pass filter, the high frequency and secondary high frequency components are stabilized by utilizing a super capacitor and an energy storage battery respectively,
Taking 1 hour as a minimum time scale, taking the aims of minimum wind power internet surfing fluctuation power, minimum charging and discharging depth of the energy storage battery and moderate level maintenance of the energy storage battery and the super capacitor in 24 hours in the future, establishing a multi-objective optimization model by taking the states of charge of the energy storage battery and the super capacitor not exceeding corresponding limit values,
The constraint conditions are as follows:
Wherein: p bat,c is the rated charge and discharge power of the energy storage battery; c soc,mod is the moderate state of charge of the energy storage device; Δt is the charge-discharge time interval of the energy storage device; m cap is the energy storage capacity of the super capacitor; c soc,cap and C soc,bat are the states of charge of the super capacitor and the energy storage battery respectively; c soc,cap,max and C soc,cap,min are the upper and lower limits of C soc,cap; c soc,bat,max and C soc,bat,min are the upper and lower limits of C soc,bat;
Obtaining each target weight by using an analytic hierarchy process and a Bayesian theory, carrying out weighted summation on each target, converting a multi-target optimization model into a single-target optimization model, and solving by using a Lagrange multiplier method;
Based on wind power prediction results within 4 hours in the future, dividing wind power fluctuation power stabilizing ranges of the energy storage battery and the super capacitor by utilizing a second-order low-pass filter, and optimizing each energy storage output;
Dividing wind power fluctuation power in the future 4 hours into three parts of high frequency, secondary high frequency and low frequency according to frequency by utilizing a second-order low-pass filter, and stabilizing high frequency and secondary high frequency components by utilizing a super capacitor and an energy storage battery respectively;
taking 15 minutes as a minimum time scale, taking the minimum wind power Internet surfing fluctuation power, the minimum charging and discharging depth of an energy storage battery and the minimum charging and discharging depth of the energy storage battery as well as the moderate level of the energy storage battery and a super capacitor in the future 4 hours, and taking the SOC of each energy storage in the hybrid energy storage system and the SOC of each energy storage in the hybrid energy storage control before the day as targets as close as possible after the period is finished, and establishing a multi-target optimization model with the state of charge of the energy storage battery and the super capacitor not exceeding the corresponding limit value;
The constraint conditions are as follows:
Obtaining each target weight by using an analytic hierarchy process and a Bayesian theory, carrying out weighted summation on each target, converting a multi-target optimization model into a single-target optimization model, and solving by using a Lagrange multiplier method;
based on wind power prediction results within 15 minutes in the future, dividing wind power fluctuation power stabilizing ranges of the energy storage battery and the super capacitor by utilizing a second-order low-pass filter, and optimizing each energy storage output;
The wind power fluctuation power within 15 minutes in the future is divided into three parts of high frequency, secondary high frequency and low frequency according to frequency by utilizing a second-order low-pass filter, and the high frequency and secondary high frequency components are stabilized by utilizing a super capacitor and an energy storage battery respectively by combining the characteristics of two types of energy storage;
The method comprises the steps of establishing a multi-objective optimization model by taking the minimum wind power internet fluctuation power, the minimum charging and discharging depth of an energy storage battery, the moderate level of the energy storage battery and a super capacitor in 15 minutes in the future and the fact that the SOC of each energy storage in a hybrid energy storage system and the SOC of each energy storage in daily hybrid energy storage control are as close as possible after the period is finished, and taking the state of charge of the energy storage battery and the super capacitor not exceeding the corresponding limit value of the state of charge;
The constraint conditions are as follows:
Obtaining each target weight by using an analytic hierarchy process and a Bayesian theory, carrying out weighted summation on each target, converting a multi-target optimization model into a single-target optimization model, and solving by using a Lagrange multiplier method;
inputting control parameters f cap and f bat into a second-order low-pass filter, and controlling the output power of each energy storage by using the second-order low-pass filter;
The method for acquiring each target weight by using the analytic hierarchy process and the Bayesian weight comprises the following steps:
Obtaining subjective weights of all targets by using an analytic hierarchy process, wherein the subjective weights are marked as omega '= [ omega' - 1,ω′2,…,ω′m ], and m is the number of optimization targets;
correcting subjective weights by using a Bayesian theory;
Collecting each target value in n days to form a constructed target data matrix Y;
wherein: alpha i＝[yi1…yi3],yij (i ε {1,2, … n }, j ε {1,2, … m }) is the value of the ith target on the previous j days;
The weight of the benefit index of each sample is calculated by using the bayesian theory, and the prior probability of the subjective weight as the target f j (j=1, 2, 3), namely P (f j)＝ω′j,
At the target f j (j=1, 2, 3), the probability of the i-th sample a i is y ij, i.e. P (a i|fj)＝yij,
According to the bayesian theory, the j index weight of the i sample α i is:
the targets of the j-th sample are weighted and summed according to omega' j (i) to obtain a comprehensive target value f (i),
The respective targets of each sample are weighted and summed according to subjective weights to obtain a comprehensive target value f' (i),
The subjective weight is corrected, the smaller and the better the comprehensive target value deviation of all samples are, for this purpose, the following least square method optimization evaluation model is established to correct the target weight,
And solving the model by using a Lagrangian multiplier method to obtain the modified benefit index weight, wherein the modified benefit index weight is marked as omega= [ omega 1,…,ωm ].
2. The control method according to claim 1, characterized in that: when the hybrid energy storage system operates, the output power of the super capacitor, the output power of the energy storage battery and the wind power internet surfing power are respectively as follows:
Wherein: p w(s)、Pcap(s)、Pbat(s) and P g(s) are wind power on a complex frequency domain, supercapacitor output power, energy storage battery output power and stabilized wind power on-line power respectively; and f cap and f bat are frequencies corresponding to the filtering time constants of the super capacitor and the energy storage battery respectively, and the output power control of the super capacitor and the energy storage battery is realized by controlling f cap and f bat.
3. The control system of the hybrid energy storage multistage coordination control method for stabilizing wind power fluctuation power according to claim 1, wherein the control system comprises: comprises a day-ahead hybrid energy storage control unit, a day-in hybrid energy storage control unit and a real-time hybrid energy storage control unit,
The solar hybrid energy storage control unit comprises a solar wind power prediction module, a second-order low-pass filter, an energy storage battery amplitude limiting link, a super-capacitor amplitude limiting link, an energy storage battery SOC optimizing link and a super-capacitor SOC optimizing link, wherein the solar wind power prediction module is connected with the second-order low-pass filter, the second-order low-pass filter is respectively connected with the energy storage battery amplitude limiting link and the super-capacitor amplitude limiting link, the energy storage battery amplitude limiting link is connected with the energy storage battery SOC optimizing link, and the super-capacitor amplitude limiting link is connected with the super-capacitor SOC optimizing link; the output of the energy storage battery SOC optimization link is connected to the energy storage battery SOC optimization link of the daily hybrid energy storage control unit, and the output of the super capacitor SOC optimization link is connected to the super capacitor SOC optimization link of the daily hybrid energy storage control unit;
The solar hybrid energy storage control unit comprises a solar wind power prediction module, a second-order low-pass filter, an energy storage battery amplitude limiting link, a super-capacitor amplitude limiting link, an energy storage battery SOC optimizing link and a super-capacitor SOC optimizing link, wherein the solar wind power prediction module is connected with the second-order low-pass filter, the second-order low-pass filter is respectively connected with the energy storage battery amplitude limiting link and the super-capacitor amplitude limiting link, the energy storage battery amplitude limiting link is connected with the energy storage battery SOC optimizing link, and the super-capacitor amplitude limiting link is connected with the super-capacitor SOC optimizing link; the output of the energy storage battery SOC optimization link is connected to the energy storage battery SOC optimization link of the real-time hybrid energy storage control unit, and the output of the super capacitor SOC optimization link is connected to the super capacitor SOC optimization link of the real-time hybrid energy storage control unit;
The real-time hybrid energy storage control unit comprises a real-time wind power prediction module, a second-order low-pass filter, an energy storage battery limiting link, a super-capacitor limiting link, an energy storage battery SOC optimizing link and a super-capacitor SOC optimizing link, wherein the real-time wind power prediction module is connected with the second-order low-pass filter, the second-order low-pass filter is respectively connected with the energy storage battery limiting link and the super-capacitor limiting link, the energy storage battery limiting link is connected with the energy storage battery SOC optimizing link, and the super-capacitor limiting link is connected with the super-capacitor SOC optimizing link.
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