Lithium battery, super-capacitor hybrid energy storage coupled with frequency modulation optimization method and system for thermal power

Abstract

The application discloses a lithium battery, super-capacitor hybrid energy storage coupled thermal power frequency modulation optimization method and system, relates to the technical field of power system frequency modulation, and comprises the following steps: S1: frequency modulation signal preprocessing, frequency modulation signal multi-time scale decomposition; S2: thermal power generating unit adjustable power evaluation; S3: hybrid energy storage power distribution; S4: adaptive control, hybrid energy storage adaptive droop control; S5: capacity optimization, hybrid energy storage capacity multi-objective optimization. The technical problem to be solved by the application is to provide a lithium battery, super-capacitor hybrid energy storage coupled thermal power frequency modulation optimization method and system, based on multi-time scale frequency modulation instruction dynamic decomposition, matching the response characteristics of thermal power generating units, lithium ion batteries and super-capacitors; considering the hybrid energy storage adaptive distribution strategy of state of charge (SOC) and power boundary; combining the multi-objective capacity optimization configuration of economy and frequency modulation performance, reducing the full life cycle cost of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system frequency modulation, in particular to a lithium battery, super-capacitor hybrid energy storage coupled thermal power frequency modulation optimization method and system. BACKGROUND

[0002] With the continuous increase of intermittent energy sources such as wind power and photovoltaic power in the power grid, the proportion of new energy in some regions has exceeded 30%, and the overall rotational inertia of the power grid has decreased significantly. The frequency fluctuation amplitude has expanded from the traditional ±0.1Hz to ±0.3Hz or more. Under this background, as the main frequency modulation power source of the power grid, the thermal power generator needs to be adjusted frequently, with more than 200 times per day, which leads to the intensification of the thermal stress cycle of the steam turbine and the increase of the service life by 40%. In addition, the coal consumption increases by 0.5g / kWh per peak regulation, and the operation economy is significantly reduced.

[0003] In the prior art, single lithium ion battery energy storage has a faster response speed, with a response time of ≤2s, which is better than the response time of thermal power generators (≥5s). However, in the 100ms level high-frequency small-capacity fluctuation scenario, its charge-discharge efficiency is lower than 85%. Although supercapacitors can respond to 10ms level high-frequency fluctuations, the charge-discharge efficiency is higher than 90%, but the energy density is only 5-10Wh / kg, which cannot support long-term frequency modulation requirements. Therefore, single type energy storage cannot meet the dual requirements of fast response and energy storage, and a hybrid energy storage coupling control method combining the advantages of lithium ion battery in low frequency and large capacity and supercapacitor in high frequency and small capacity is needed to realize the collaborative frequency modulation of thermal power generators and hybrid energy storage and cover the frequency modulation requirements of all time scales. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a lithium battery, super-capacitor hybrid energy storage coupled thermal power frequency modulation optimization method and system, which dynamically decomposes the frequency modulation instruction based on multiple time scales, matches the response characteristics of thermal power generators, lithium ion batteries and supercapacitors, considers the adaptive allocation strategy of hybrid energy storage considering state of charge (SOC) and power boundary, and optimizes the capacity configuration considering economy and frequency modulation performance to reduce the overall life cycle cost of the system.

[0005] The present application realizes the purpose of the application by adopting the following technical solutions:

[0006] The present application proposes a lithium battery, super-capacitor hybrid energy storage coupled thermal power frequency modulation optimization method, which comprises the following steps:

[0007] S1: frequency modulation signal preprocessing, multi-time scale decomposition of frequency modulation signal;

[0008] S2: thermal power generator adjustable power evaluation;

[0009] S3: hybrid energy storage power distribution;

[0010] S4: adaptive control, hybrid energy storage adaptive droop control;

[0011] S5: capacity optimization, hybrid energy storage capacity multi-objective optimization.

[0012] As a further limitation of the technical solution, the specific steps of S1 are:

[0013] Real-time acquisition of grid-connected point frequency by synchronous phasor measurement device , Calculate the frequency deviation:

[0014] ,

[0015] Where: is the rated frequency;

[0016] According to the difference in response time of three types of equipment, a double first-order low-pass filter is constructed:

[0017] Super capacitor handles high frequency components:

[0018] ,

[0019] Where: is the Laplace operator, representing the complex frequency variable in the frequency domain transformation; is the filter time constant;

[0020] Lithium ion battery handles medium frequency components:

[0021] ,

[0022] Where: is the filter time constant;

[0023] Thermal generator set handles low frequency components:

[0024] ,

[0025] Where: is the high frequency component (>2Hz) allocated by the super capacitor; is the medium frequency component (0.1-2Hz) allocated by the lithium ion battery.

[0026] As a further limitation of the technical solution, the specific steps of S2 are:

[0027] Considering the main steam pressure and valve position limit, the maximum adjustable power of the thermal generator set:

[0028] ,

[0029] Where: is the rated power; Limiting coefficient of valve position for real-time feedback of distributed control system Main steam pressure Thermal conversion efficiency

[0030] When the low-frequency component requires power Exceeds Power to be supplemented by hybrid energy storage

[0031] ,

[0032] Wherein: Actual output of thermal power unit, turbine power feedback value collected by distributed control system in real time

[0033] Power to be supplemented by hybrid energy storage As the upper limit of the total reference power of hybrid energy storage in S3, to avoid overload of thermal power unit.

[0034] As a further limitation of the technical solution, the specific steps of S3 are:

[0035] The power distribution error is expressed as:

[0036] ,

[0037] Wherein: Lithium battery power Super capacitor power

[0038] The state of charge of lithium ion battery, the state of charge of super capacitor, the distribution error As input, the distribution coefficient is generated by fuzzy rule ;

[0039] The distributed power is solved by using the barycenter method:

[0040] ,

[0041] The distribution result needs to meet the charge-discharge rate constraint.

[0042] As a further limitation of the technical solution, the specific steps of S4 are:

[0043] The droop coefficient is dynamically adjusted according to real-time SOC:

[0044] ,

[0045] Wherein: Initial droop coefficient of lithium ion battery State of charge adjustment factor of lithium ion battery The initial droop coefficient of lithium ion battery is State of Charge at time t; is the initial droop coefficient of the super capacitor; is the State of Charge adjustment factor of the super capacitor; is the State of Charge at time t of the super capacitor; State of Charge at time t;

[0046] The power is fed back to S3 for correcting the allocation error at the next time.

[0047] As a further limitation of the technical solution, the specific steps of S5 are:

[0048] A double-objective function is constructed:

[0049] ,

[0050] Wherein: is the initial investment; is the annual operating cost, ; is the replacement cost, , = 20 years, = 10 years; is the actual output power of the lithium ion battery (MW); is the actual output power of the super capacitor (MW);

[0051] The constraint conditions include:

[0052] The battery output power constraint condition is: , and the super capacitor output power constraint condition is: ;

[0053] State of Charge (SOC) constraint: lithium ion battery [0.2, 0.8], super capacitor [0.3, 0.9];

[0054] Rate constraint: lithium ion battery is 1 times, and super capacitor is 10 times.

[0055] In addition, the application proposes a lithium battery and super capacitor hybrid energy storage coupled thermal power frequency modulation optimization system, which is used to realize the lithium battery and super capacitor hybrid energy storage coupled thermal power frequency modulation optimization method.

[0056] As a further limitation of the technical solution, the signal detection module is composed of a PMU, a precision current / voltage sensor, and an ampere-hour integral method State of Charge monitoring unit, which is used to collect key parameters in real time.

[0057] As a further limitation of the technical solution, the coordination control module comprises a filter decomposition unit, a thermal power evaluation unit, a fuzzy distribution unit and an adaptive control unit.

[0058] As a further limitation of the technical solution, the hybrid energy storage module comprises a lithium ion battery pack and a super capacitor pack.

[0059] Compared with the related art, the lithium battery, super-capacitor hybrid energy storage coupled thermal power frequency modulation optimization method and system have the following beneficial effects:

[0060] 1. Full-scale coordinated frequency modulation and precise task division: The multi-time scale decomposition technology is innovatively adopted to dynamically separate the power grid frequency deviation into high-frequency, medium-frequency and low-frequency components, which are precisely responded by the super capacitor, lithium ion battery and thermal power generator, respectively. The three-level coordination mechanism fully utilizes the high-frequency rapid response capability of the super capacitor, the medium-frequency energy buffer advantage of the lithium ion battery and the low-frequency stable adjustment characteristics of the thermal power generator, forming a full-time scale frequency modulation capability covering milliseconds to seconds, significantly improving the adaptability of the system to complex frequency disturbances and realizing the complementary advantages and load balancing among different devices.

[0061] 2. Intelligent distribution and adaptive control to prolong device life: The fuzzy control and adaptive droop strategy are constructed based on the state of charge (SOC) of the energy storage device to dynamically adjust the power distribution coefficient and the droop control parameter in real time. The output power of the lithium ion battery and the super capacitor is intelligently matched through 27 fuzzy rules, and the droop coefficient is dynamically adjusted according to the real-time SOC to ensure that the energy storage device works in the high-efficiency and safe interval and to avoid overcharging and overdischarging. This mechanism effectively narrows the state of charge fluctuation range of the energy storage device, prolongs the cycle life of the lithium ion battery and the super capacitor, and reduces the equipment maintenance cost and replacement frequency.

[0062] 3. Multi-objective optimization to achieve economic performance balance: A dual-objective optimization model including the full life cycle cost and the frequency modulation performance is constructed, multiple restriction conditions such as power constraint, state of charge boundary and charge-discharge rate are considered, and the NSGA-Ⅱ algorithm is used to solve the optimal energy storage capacity configuration. This method breaks through the limitation of single index optimization, finds the best balance point between initial investment, operation and maintenance cost and frequency modulation efficiency, realizes the coordinated improvement of system economy and stability, and provides a solution with engineering feasibility and economic rationality for frequency control of high-proportion new energy power grids. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 The method flowchart of the present application. DETAILED DESCRIPTION

[0064] The present application will be further described below in conjunction with the drawings and embodiments.

[0065] Please refer to Figure 1 The embodiment proposes a lithium battery, super-capacitor hybrid energy storage coupled with thermal power frequency modulation optimization method, comprising the following steps:

[0066] S1: frequency modulation signal preprocessing, multi-time scale decomposition of frequency modulation signal;

[0067] S2: thermal power unit adjustable power evaluation;

[0068] S3: hybrid energy storage power distribution;

[0069] S4: adaptive control, hybrid energy storage adaptive droop control;

[0070] S5: capacity optimization, multi-objective optimization of hybrid energy storage capacity.

[0071] Define the whole process framework of frequency modulation optimization. As the general outline, the five-stage closed-loop logic from signal input to capacity design is clear, ensuring the coordination of lithium battery, super-capacitor and thermal power, and solving the problem of "how to divide the multi-time scale resources".

[0072] The specific steps of S1 are:

[0073] The frequency of the grid-connected point is obtained in real time by a synchronous phasor measurement device (Phasor Measurement Unit, PMU, sampling frequency 100Hz) The frequency deviation is calculated:

[0074] ,

[0075] Where: is the rated frequency;

[0076] According to the difference in response time of three types of equipment, a double first-order low-pass filter is constructed:

[0077] The super-capacitor (response time ≤0.1s) processes high-frequency components:

[0078] ,

[0079] Where: is the Laplace operator, representing the complex frequency variable in the frequency domain transformation; is the filter time constant, =0.05s;

[0080] The lithium ion battery (response time 0.5-2s) processes the medium frequency components:

[0081] ,

[0082] Where: is the filter time constant, = 1.5s;

[0083] Thermal power unit (response time ≥ 5s) handles low-frequency components:

[0084] ,

[0085] Wherein: is the high-frequency component (> 2Hz) allocated by the super capacitor; is the medium-frequency component (0.1-2Hz) allocated by the lithium-ion battery.

[0086] Filter time constant , According to the subsequent optimization of energy storage capacity dynamic adjustment.

[0087] The "frequency sieve" that realizes the frequency modulation demand, through the double filter, the grid frequency deviation is divided into three layers of tasks of high frequency (super capacitor pipe), medium frequency (lithium battery pipe) and low frequency (thermal power pipe), avoiding the misplacement of equipment response, dynamically adjusting the filter parameters to adapt to the energy storage aging or working condition change.

[0088] The specific steps of S2 are:

[0089] Considering the main steam pressure and valve position limit, the maximum adjustable power of thermal power unit:

[0090] ,

[0091] Wherein: is the rated power; is the valve position limit coefficient of the real-time feedback of the distributed control system (DCS); is the main steam pressure; is the thermal power conversion efficiency;

[0092] When the low-frequency component demand power exceeds , the power that the hybrid energy storage needs to supplement:

[0093] ,

[0094] Wherein: is the actual output of the thermal power unit (MW), which is the real-time turbine power feedback value collected by DCS;

[0095] The power that the hybrid energy storage needs to supplement as the upper limit of the total reference power of the hybrid energy storage in S3, to avoid the overload of the thermal power unit.

[0096] Quantify how much thermal power can be adjusted, real-time computer group in the current steam pressure / valve position under the maximum safe output, clear thermal power adjustment boundary, part of the automatic transfer by energy to bottom, prevent thermal power unit shutdown due to frequency regulation overload.

[0097] The specific steps of S3 are:

[0098] The power distribution error is expressed as:

[0099] ,

[0100] Where: is the lithium battery power; is the super capacitor power;

[0101] The input is the lithium ion battery state of charge (SOC) ), the super capacitor state of charge (SOC) ), and the distribution error ; ;

[0102] Typical rules are:

[0103] ,

[0104] When and and , (lithium ion battery charging priority); when and and , (super capacitor discharge priority);

[0105] : real-time state of charge of lithium ion battery (range [0.2, 0.8]).

[0106] : real-time state of charge of super capacitor (range [0.3, 0.9]).

[0107] is a physical quantity that measures the deviation between the total power that the hybrid energy storage system actually needs to bear and the sum of the current distribution power of lithium ion battery (lithium battery) and super capacitor (super capacitor), and is one of the three input variables for generating power distribution coefficient by fuzzy rules.

[0108] Calculation process:

[0109] Fuzzification: let With With Mapping to fuzzy sets (e.g. "low", "medium", "high").

[0110] Rule base: based on 27 fuzzy rules (e.g. if low and high and then ).

[0111] Defuzzification: using Centroid Method to calculate precise value .

[0112] Power distribution after defuzzification using Centroid Method:

[0113] ,

[0114] The distribution result needs to meet the charge and discharge rate constraints (lithium-ion battery ≤ 1C, super capacitor ≤ 10C).

[0115] Through the above power distribution strategy, the lithium-ion battery and super capacitor can work together under different working conditions and fully exert their respective advantages. At the same time, in order to further improve the control accuracy and stability, adaptive control will be introduced in the future.

[0116] Through fuzzy rule-based dynamic power distribution, super capacitor is preferentially used for high-frequency small wave (protecting lithium battery cycle life), and lithium battery is used for medium-frequency large wave (avoiding super capacitor energy depletion), and distribution error is real-time feedback correction, solving the conflict of "who does more and who does less".

[0117] The specific steps of S4 are:

[0118] According to the real-time state of charge (State of Charge, SOC) to dynamically adjust the droop coefficient:

[0119] ,

[0120] Wherein: is the initial droop coefficient of lithium-ion battery; is the state of charge adjustment factor of lithium-ion battery; is the state of charge of lithium-ion battery at time; is the initial droop coefficient of super capacitor; is the state of charge adjustment factor of super capacitor; is the state of charge of super capacitor at time;

[0121] The power is fed back to S3 to correct the distribution error at the next time.

[0122] According to the SOC real-time adjustment of the droop coefficient, the discharge intensity is reduced at low SOC (to prevent over-discharge), the charging intensity is reduced at high SOC (to prevent over-charging), the SOC is pulled back to the safety zone, and the energy storage life is prolonged.

[0123] The specific steps of S5 are:

[0124] A double-objective function is constructed:

[0125] ,

[0126] Wherein: is the initial investment; is the annual operating cost, ; is the replacement cost, , = 20 years, = 10 years; is the actual output power of the lithium ion battery (MW); is the actual output power of the super capacitor (MW);

[0127] The constraint conditions include:

[0128] The battery output power constraint condition is: , and the super capacitor output power constraint condition is: ;

[0129] The state of charge (SOC) constraint: lithium ion battery [0.2, 0.8], super capacitor [0.3, 0.9];

[0130] The rate constraint: the lithium ion battery is 1 times, and the super capacitor is 10 times.

[0131] A three-objective balance model of investment-life-frequency modulation loss is established, and the optimal power and energy ratio of lithium battery / super capacitor is calculated through algorithm, so as to avoid waste caused by buying too much and not enough caused by buying too little, and to forcibly meet the engineering constraints such as temperature and rate.

[0132] The embodiment also provides a lithium battery / super capacitor hybrid energy storage coupled thermal power frequency modulation optimization system, which is used to realize the above lithium battery / super capacitor hybrid energy storage coupled thermal power frequency modulation optimization method.

[0133] The lithium battery / super capacitor hybrid energy storage coupled thermal power frequency modulation optimization system includes a signal detection module, a coordination control module, a hybrid energy storage module, and a thermal power unit module.

[0134] The signal detection module is composed of a PMU (100 Hz sampling), a 0.1% precision current / voltage sensor, and an ampere-hour integral state of charge (SOC) monitoring unit (error <2%) for real-time acquisition of key parameters, including power grid frequency, thermal power unit power, and state of charge (SOC) of energy storage.

[0135] The coordinated control module includes a filter decomposition unit, a thermal power evaluation unit, a fuzzy distribution unit, and an adaptive control unit. The filter decomposition unit realizes multi-time scale decomposition using a MATLAB (Matrix Laboratory) real-time tool chain; the thermal power evaluation unit embeds a DCS (Distributed Control System) interface to obtain valve position and main steam pressure data in real time; the fuzzy distribution unit uses an FPGA (Field-Programmable Gate Array) to realize 10 ms period power distribution calculation; and the adaptive control unit uses a DSP (Digital Signal Processor) chip to realize 5 ms response droop control parameter adjustment.

[0136] The hybrid energy storage module includes a lithium ion battery pack and a super capacitor pack, which are connected in parallel to a 6kV auxiliary power bus through a 98% efficient bidirectional converter and a step-up transformer. In this embodiment, the lithium ion battery pack is 3.6MW / 14.6MWh (lithium iron phosphate, 5000 cycle life); and the super capacitor pack is 8.5MW / 1.3MWh (lithium ion super capacitor, 100000 cycle life).

[0137] The thermal power unit module includes a 600MW steam turbine (primary frequency modulation dead zone ±0.03Hz), a 17.5MPa main steam pressure boiler, and a DCS system for receiving low-frequency power instructions and adjusting the main steam valve opening.

[0138] The above only describes the embodiments of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for optimizing frequency modulation of thermal power by coupling lithium battery, super-capacitor hybrid energy storage, characterized in that, The method comprises the following steps: S1: pre-processing of the frequency modulation signal, multi-time scale decomposition of the frequency modulation signal; The specific steps of S1 are: Real-time acquisition of grid point frequency by synchronous phasor measurement device , calculate frequency deviation: ， wherein: is the nominal frequency; According to the differences in response time of the three types of equipment, a double first-order low-pass filter is constructed: The super capacitor processes high-frequency components: ， wherein: is the Laplace operator, representing the complex frequency variable in the frequency domain transform; is the filter time constant; The lithium ion battery processes medium-frequency components: ， wherein: is a filter time constant; The thermal power generator set processes low-frequency components: ， wherein: is the high frequency component allocated to the supercapacitor; is the medium frequency component allocated to the lithium-ion battery; S2: adjustable power evaluation of the thermal power generator set; The specific steps of S2 are: Considering the main steam pressure and valve position restrictions, the maximum adjustable power of the thermal power generator set is determined: ， wherein: is the rated power; is the valve position limit coefficient of the distributed control system real-time feedback; is the main steam pressure; is the thermal power conversion efficiency; When the low frequency component demands power exceeds , the power that the hybrid energy storage needs to supplement: ， wherein: is the actual output of the thermal power unit, and is the steam turbine power feedback value collected in real time by the distributed control system. Power to be supplemented for hybrid energy storage As an upper limit of the total reference power of hybrid energy storage in S3, to avoid overloading of thermal power units; S3: power distribution of the hybrid energy storage system; The specific steps of S3 are: The power distribution error is expressed as: ， wherein: is lithium battery power; is supercapacitor power; Lithium-ion battery state of charge, supercapacitor state of charge, allocation error For input, allocation coefficients are generated by fuzzy rules ; The power is distributed after the ambiguity is resolved by the barycenter method: ， The distribution result needs to satisfy the charge / discharge rate constraint: S4: adaptive control, adaptive droop control of the hybrid energy storage system; S5: capacity optimization, multi-objective optimization of the capacity of the hybrid energy storage system.

2. The method according to claim 1, wherein the method is characterized in that, The specific steps of S4 are: The droop coefficient is dynamically adjusted according to the real-time SOC: ， in: , which is the initial droop coefficient of the lithium-ion battery; , is the state-of-charge adjustment factor for lithium-ion batteries; For lithium-ion batteries The state of charge at any given moment; , where is the initial droop factor of the supercapacitor; , is the state-of-charge adjustment factor for supercapacitors; For supercapacitors in The state of charge at any given moment; The power is fed back to S3 to correct the distribution error at the next time.

3. The method of claim 2, wherein the method is characterized in that, The specific steps of S5 are: A double-objective function is constructed: ， Where: is the initial investment; is the annual operating cost, ; is the replacement cost, , = 20 years, = 10 years; is the actual output power of the lithium-ion battery; is the actual output power of the supercapacitor; The constraint conditions include: The battery output power constraint condition is: The super capacitor output power constraint condition is: ; State of Charge (SOC) constraint: lithium ion battery [0.2, 0.8], super capacitor [0.3, 0.9]; Rate constraint: 1 times for lithium ion battery, 10 times for super capacitor.

4. The lithium battery, super-capacitor hybrid energy storage coupled with frequency modulation optimization system of thermal power, characterized in that, The frequency modulation optimization system is used to implement the lithium battery, super capacitor hybrid energy storage coupling thermal power frequency modulation optimization method according to any one of claims 1 to 3, and the frequency modulation optimization system comprises a signal detection module, a coordination control module, a hybrid energy storage module, and a thermal power generator set module.

5. The lithium battery, super-capacitor hybrid energy storage coupled with frequency modulation optimized system of claim 4, wherein, The signal detection module is composed of a PMU, an accuracy current / voltage sensor, and an ampere-hour integral state of charge monitoring unit, and is used to collect key parameters in real time.

6. The lithium battery, super-capacitor hybrid energy storage coupled with frequency modulation optimized system of claim 5, wherein, The coordination control module comprises a filter decomposition unit, a thermal power evaluation unit, a fuzzy distribution unit, and an adaptive control unit.

7. The lithium battery, super-capacitor hybrid energy storage coupled with frequency modulation optimized system of claim 6, wherein, The hybrid energy storage module comprises a lithium ion battery pack and a super capacitor pack.

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