CN113625172B - Analysis method for influence factors of lithium battery energy storage operation and maintenance optimization - Google Patents
Analysis method for influence factors of lithium battery energy storage operation and maintenance optimization Download PDFInfo
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- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 35
- 238000012423 maintenance Methods 0.000 title claims abstract description 27
- 238000005457 optimization Methods 0.000 title claims abstract description 23
- 238000004458 analytical method Methods 0.000 title claims abstract description 18
- 230000008901 benefit Effects 0.000 claims abstract description 57
- 230000036541 health Effects 0.000 claims abstract description 32
- 238000000034 method Methods 0.000 claims abstract description 22
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- 238000007599 discharging Methods 0.000 claims description 5
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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Abstract
The invention provides a lithium battery energy storage operation and maintenance optimization influence factor analysis method which comprises the steps of establishing a mathematical analysis model of a lithium battery energy storage system, wherein the analysis model considers that after the lithium battery energy storage system is put into operation, the battery health degree, the system energy loss rate and the discharge depth of the energy storage system are taken as key indexes for influencing operation and maintenance benefits. The invention constructs a mathematical model for analyzing the operation and maintenance optimization influence factors of the lithium battery energy storage system, and finds out the core influence factors by a control variable method to guide the operation and maintenance work of the lithium battery energy storage system. The model not only can improve the efficiency of operation and maintenance work and bring high income of operation, but also can help the energy storage system to operate more stably, relieve the power grid pressure and bring double promotion of economic benefit and social benefit to enterprises.
Description
Technical Field
The invention belongs to the technical field of electric power, and particularly relates to a method for analyzing an influence factor of lithium battery energy storage operation and maintenance optimization.
Background
With the rapid development of the economy in China, the power load demand is continuously increased, and the source-load contradiction is gradually activated. The rapid increase of life and production loads causes gradual increase of power grid peak Gu Chalv, so that the peak electricity consumption is relieved, the stability of the power distribution network is ensured, the power grid can realize the electricity price (namely peak-valley electricity price) in time intervals, and users are encouraged to stagger peak electricity consumption.
The lithium battery energy storage is the most mature and most common scheme in the domestic peak clipping and valley filling scene at present due to the comprehensive advantages of technical stability, system manufacturing cost, equipment cost and the like. In order to verify the input output rate, the investment operator of the lithium battery energy storage system usually sets an operation target at the beginning of the input operation, and then performs subsequent operation management. However, in the actual operation process, a series of operation problems such as inaccurate analysis, inaccurate problem positioning and the like generally exist, so that operation and maintenance measures are not timely and inaccurate, the operation benefit of the energy storage system is low, and double waste of enterprise resources and social resources is caused.
Disclosure of Invention
The invention aims to solve the technical problems and provides a method for analyzing the influence factors of the energy storage operation and maintenance optimization of a lithium battery.
In order to achieve the above purpose, the invention adopts the following technical scheme:
A lithium battery energy storage operation and maintenance optimization influence factor analysis method comprises the steps of establishing a lithium battery energy storage system mathematical analysis model, wherein the analysis model considers the battery health, the system energy loss rate and the discharge depth of an energy storage system as core factors influencing operation benefits after the lithium battery energy storage system is put into operation;
the method for establishing the mathematical analysis model of the lithium battery energy storage system comprises the following steps:
step 1, default charge-discharge strategy is one charge-discharge every day, valley charge-peak discharge is adopted, namely charging is carried out at the lowest electricity price, discharging is carried out at the highest electricity price, and known parameters of the lithium battery energy storage system at the beginning of operation are obtained, wherein the method comprises the following steps:
the initial capacity C of the system, the parameter is constant;
the initial cycle number of the system is x 0, and the parameter is constant;
System minimum available capacity C l;
charging electricity price P c, discharging electricity price P d, both of which are constant;
Step 2, setting a core factor theoretical value after system operation, wherein the core factor theoretical value is as follows:
the theoretical optimal depth of discharge is d, and the index is a fixed value;
the theoretical battery health degree is h, the index and the circulation times x have piecewise linear function relation, and the expression is as follows:
wherein the coefficient k and the constant b are both constant values, and the minimum value of h is equal to C/C l;
the theoretical system energy loss rate is eta, and the index is a fixed value;
step 3, calculating the theoretical battery health degree of the current day:
assuming that the system is in operation on a certain day, the known quantities are as follows:
the single-pass charge quantity Q 1 and the single-pass discharge quantity Q 2 are calculated according to the acquired data of the intelligent ammeter;
The increment of the battery SoC in the single-pass charging process calculated according to the collected data of the BMS system is delta S;
By the day, the increment of the accumulated charging SoC is Σs, and the accumulated cycle number=x0+ Σs/d;
Combining the value of the accumulated cycle times to obtain the theoretical battery health degree of the current day as h (x 0 plus sigma S/d);
And 4, converting the known quantity in the step 3 to obtain an actual value of the core factor, wherein the actual value is as follows:
the actual depth of discharge has a value of d 1 =Δs;
The value of the actual system energy loss rate is η1= (Q 1-Q2)/Q1;
The actual battery health is h 1＝(Q1+Q2)/(2 Δs C);
And 5, calculating the theoretical benefit and the actual benefit of the same day respectively, wherein the formula is as follows:
Theoretical benefit:
Bt＝Pd*[2*C*h(x0+∑S/d)*d*(1-η)/(2-η)]-Pc*[2*C*h(x0+∑S/d)*d*(2-η)];
Actual benefits:
Br＝Pd*[2*C*h1*d1*(1-η1)/(2-η1)]-Pc*[2*C*h1*d1*(2-η1)];
and 6, calculating theoretical benefits of each core factor after being lifted by a control variable method, wherein the formula is as follows:
theoretical benefit after depth of discharge boost:
B1＝Pd*[2*C*h1*d*(1-η1)/(2-η1)]-Pc*[2*C*h1*d*(2-η1)]；
Theoretical benefit after system energy loss rate is improved:
B2＝Pd*[2*C*h1*d1*(1-η)/(2-η)]-Pc*[2*C*h1*d1*(2-η)];
theoretical benefit after battery health improvement:
B3＝Pd*[2*C*h(x0+∑S/d)*d1*(1-η1)/(2-η1)]-Pc*[2*C*h(x0+∑S/d)*d1*(2-η1)];
step 7, the operation benefit brought by optimizing each factor respectively is improved as follows:
The operation benefit brought by the depth of discharge optimization is improved by delta B 1＝B1-Br;
The operation benefit brought by the optimization of the energy loss rate of the system is improved by delta B 2＝B2-Br;
the operation benefit brought by the battery health degree SoH optimization is improved by DeltaB 3＝B3-Br;
Through the operation results, the operation benefit improvement amplitude brought by the optimization of three core factors including the battery health degree, the system energy loss rate and the discharge depth is respectively analyzed.
Preferably, in the operation and maintenance operation of the lithium battery energy storage system, the following measures are taken to optimize the depth of discharge: and the user adjusts parameters of the charge-discharge strategy to enable the depth of discharge to reach a theoretical value.
Preferably, in the operation and maintenance work of the lithium battery energy storage system, the following measures are taken to optimize the energy loss rate of the system: by adjusting the working power of the PCS, BMS parameters, circuits and maintenance of electrical components, the energy loss is improved.
Preferably, in the operation and maintenance work of the lithium battery energy storage system, the following measures are taken to optimize the battery health: the short-plate battery is checked, the BMS balancing algorithm is adjusted, the consistency of the battery is improved, and the health degree of the battery is optimized.
Preferably, when the actual application scenario involves operation benefit analysis in a day 'two-charge-two-discharge' or accumulated time period, each charge-discharge cycle is calculated and re-summed respectively.
After the technical scheme is adopted, the invention has the following advantages:
the invention constructs a mathematical model for analyzing the operation and maintenance optimization influence factors of the lithium battery energy storage system, and finds out the core influence factors by a control variable method to guide the operation and maintenance work of the lithium battery energy storage system. The model not only can improve the efficiency of operation and maintenance work and bring high income of operation, but also can help the energy storage system to operate more stably, relieve the power grid pressure and bring double promotion of economic benefit and social benefit to enterprises.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
A lithium battery energy storage operation and maintenance optimization influence factor analysis method comprises the steps of establishing a lithium battery energy storage system mathematical analysis model, wherein the analysis model takes battery health (SoH), system energy loss rate (n) and depth of discharge (DoD) of an energy storage system as core factors influencing operation benefits after the lithium battery energy storage system is put into operation.
The mathematical model related by the invention is only used for the lithium battery energy storage system. In analyzing the influence factors of the operation benefits, unquantifiable/uncontrollable factors such as market price mechanism change, idle damage of the battery and the like are not considered to be taken as analysis factors.
The method for establishing the mathematical analysis model of the lithium battery energy storage system comprises the following steps:
Step 1, obtaining known parameters at the beginning of operation of a lithium battery energy storage system, wherein the known parameters comprise:
the initial capacity C of the system, the parameter is constant;
the initial cycle number of the system is x 0, and the parameter is constant;
System minimum available capacity C l;
The default charge-discharge strategy is charge-discharge (Gu Chongfeng discharge, i.e. charge at lowest electricity price and discharge at highest electricity price) per day;
The charge electricity price (Price of Charging) is P c, the discharge electricity price (Price of Discharging) is P d, and both are constant;
Step 2, setting a core factor theoretical value after system operation, wherein the core factor theoretical value is as follows:
the theoretical optimal depth of discharge (DoD) is d, and the index is a fixed value;
the theoretical battery health (SoH) is h, and the index has piecewise linear function relation with the cycle number (x), and the expression is as follows:
The functional expression of the battery health degree (h) and the cycle number (x) is provided by an equipment manufacturer, wherein the coefficient k and the constant b are constant values, and the minimum value of h is equal to C/C l;
the theoretical system energy loss rate is eta, and the index is a fixed value;
step 3, calculating the theoretical battery health (SoH) of the day:
assuming that the system is in operation on a certain day, the known quantities are as follows:
Calculating a single-pass charge quantity Q 1 and a single-pass discharge quantity Q 2 according to the acquired data of the intelligent electric meter;
calculating the increment delta S of the battery SoC in a single-pass charging process according to the acquired data of the BMS system;
By the day, the increment of the accumulated charging SoC is sigma S, and the accumulated cycle number=x 0 +sigma S/d;
Combining the value of the accumulated cycle times to obtain the theoretical battery health degree (SoH) of the current day as h (x 0 plus sigma S/d);
And 4, converting the known quantity in the step 3 to obtain an actual value of the core factor, wherein the actual value is as follows:
The actual depth of discharge (DoD) has a value of d 1 =Δs;
the value of the actual system energy loss rate (η 1) is η1= (Q 1-Q2)/Q1;
the actual cell health (SoH) is h 1＝(Q1+Q2)/(2 Δs C);
And 5, calculating the theoretical benefit (Theoretical Benefits) and the actual benefit (Real Benefits) of the current day respectively, wherein the formulas are as follows:
Theoretical benefit:
Bt＝Pd*[2*C*h(x0+∑S/d)*d*(1-η)/(2-η)]-Pc*[2*C*h(x0+∑S/d)*d*(2-η)];
Actual benefits:
Br＝Pd*[2*C*h1*d1*(1-η1)/(2-η1)]-Pc*[2*C*h1*d1*(2-η1)];
and 6, calculating theoretical benefits of each core factor after being lifted by a control variable method, wherein the formula is as follows:
theoretical benefit after depth of discharge (DoD) boost:
B1＝Pd*[2*C*h1*d*(1-η1)/(2-η1)]-Pc*[2*C*h1*d*(2-η1)]；
Theoretical benefit after the system energy loss rate (n) is improved:
B2＝Pd*[2*C*h1*d1*(1-η)/(2-η)]-Pc*[2*C*h1*d1*(2-η)];
Theoretical benefit after cell health (SoH) improvement:
B3＝Pd*[2*C*h(x0+∑S/d)*d1*(1-η1)/(2-η1)]-Pc*[2*C*h(x0+∑S/d)*d1*(2-η1)];
step 7, the operation benefit brought by optimizing each factor respectively is improved as follows:
The operation benefit brought by the depth of discharge optimization is improved by delta B 1＝B1-Br;
The operation benefit brought by the optimization of the energy loss rate of the system is improved by delta B 2＝B2-Br;
the operation benefit brought by the battery health degree SoH optimization is improved by DeltaB 3＝B3-Br;
In the operation and maintenance work of the actual lithium battery energy storage system, the following measures can be taken in the three optimization steps in the step 7:
optimizing the depth of discharge: the user can adjust the parameters of the charge-discharge strategy to enable the depth of discharge to reach a theoretical value;
Optimizing the energy loss rate of the system: the energy loss of the lithium battery energy storage system is concentrated in PCS links, the self loss of the battery, the loss of a transformer and the energy consumption of the internal load of the system; the user can improve the energy loss by adjusting the working power of the PCS, BMS parameters, circuits, maintenance of electrical components and the like;
Optimizing the battery health: the consistency of the battery can be improved by checking the short-plate battery, adjusting BMS balance algorithm and other measures, so that the health of the battery is optimized;
The operation process is based on a data model constructed by a 1-day 'one-charge-one-discharge' use scene, and when the actual application scene relates to operation benefit analysis in a one-day 'two-charge-two-discharge' or accumulated time period, each charge-discharge cycle is calculated and summed respectively.
Through the operation result, a user can analyze the operation benefit improvement amplitude brought by the optimization of three core factors of battery health (SoH), system energy loss rate (eta) and depth of discharge (DoD) respectively. The user can comprehensively consider the implementation cost of operation and maintenance and the self operation and maintenance technology capability, and take targeted operation and maintenance measures. In summary, the efficient operation of the user side energy storage system not only can bring economic benefit improvement, but also can help the large power grid to relieve peak voltage and improve the stability of the power grid.
In addition to the above preferred embodiments, the present invention has other embodiments, and various changes and modifications may be made by those skilled in the art without departing from the spirit of the invention, which is defined in the appended claims.
Claims (2)
1. The method is characterized by comprising the steps of establishing a mathematical analysis model of the lithium battery energy storage system, wherein the analysis model considers the battery health degree, the system energy loss rate and the discharge depth of the energy storage system as core factors for influencing the operation benefit after the operation of the lithium battery energy storage system;
the method for establishing the mathematical analysis model of the lithium battery energy storage system comprises the following steps:
step 1, default charge-discharge strategy is one charge-discharge every day, valley charge-peak discharge is adopted, namely charging is carried out at the lowest electricity price, discharging is carried out at the highest electricity price, and known parameters of the lithium battery energy storage system at the beginning of operation are obtained, wherein the method comprises the following steps:
the initial capacity C of the system, the parameter is constant;
the initial cycle number of the system is x 0, and the parameter is constant;
System minimum available capacity C l;
charging electricity price P c, discharging electricity price P d, both of which are constant;
Step 2, setting a core factor theoretical value after system operation, wherein the core factor theoretical value is as follows:
the theoretical optimal depth of discharge is d, and the index is a fixed value;
the theoretical battery health degree is h, the index and the circulation times x have piecewise linear function relation, and the expression is as follows:
wherein the coefficient k and the constant b are both constant values, and the minimum value of h is equal to C/C l;
the theoretical system energy loss rate is eta, and the index is a fixed value;
step 3, calculating the theoretical battery health degree of the current day:
assuming that the system is in operation on a certain day, the known quantities are as follows:
the single-pass charge quantity Q 1 and the single-pass discharge quantity Q 2 are calculated according to the acquired data of the intelligent ammeter;
The increment of the battery SoC in the single-pass charging process calculated according to the collected data of the BMS system is delta S;
By the day, the increment of the accumulated charging SoC is Σs, and the accumulated cycle number=x0+ Σs/d;
Combining the value of the accumulated cycle times to obtain the theoretical battery health degree of the current day as h (x 0 plus sigma S/d);
And 4, converting the known quantity in the step 3 to obtain an actual value of the core factor, wherein the actual value is as follows:
the actual depth of discharge has a value of d 1 =Δs;
The value of the actual system energy loss rate is η1= (Q 1-Q2)/Q1;
The actual battery health is h 1＝(Q1+Q2)/(2 Δs C);
And 5, calculating the theoretical benefit and the actual benefit of the same day respectively, wherein the formula is as follows:
Theoretical benefit:
Bt＝Pd*[2*C*h(x0+∑S/d)*d*(1-η)/(2-η)]-Pc*[2*C*h(x0+∑S/d)*d*(2-η)];
Actual benefits:
Br＝Pd*[2*C*h1*d1*(1-η1)/(2-η1)]-Pc*[2*C*h1*d1*(2-η1)];
and 6, calculating theoretical benefits of each core factor after being lifted by a control variable method, wherein the formula is as follows:
theoretical benefit after depth of discharge boost:
B1＝Pd*[2*C*h1*d*(1-η1)/(2-η1)]-Pc*[2*C*h1*d*(2-η1)]；
Theoretical benefit after system energy loss rate is improved:
B2＝Pd*[2*C*h1*d1*(1-η)/(2-η)]-Pc*[2*C*h1*d1*(2-η)];
theoretical benefit after battery health improvement:
B3＝Pd*[2*C*h(x0+ΣS/d)*d1*(1-η1)(2-η1)]-Pc*[2*C*h(x0+ΣS/d)*d1*(2-η1)];
step 7, the operation benefit brought by optimizing each factor respectively is improved as follows:
The operation benefit brought by the depth of discharge optimization is improved by delta B 1＝B1-Br;
The operation benefit brought by the optimization of the energy loss rate of the system is improved by delta B 2＝B2-Br;
the operation benefit brought by the battery health degree SoH optimization is improved by DeltaB 3＝B3-Br;
Through the operation results, the operation benefit improvement amplitude caused by the optimization of three core factors including the battery health degree, the system energy loss rate and the discharge depth is respectively analyzed;
In the operation and maintenance work of the lithium battery energy storage system, the following measures are taken to optimize the depth of discharge: the user adjusts parameters of the charge-discharge strategy to enable the depth of discharge to reach a theoretical value; the following measures are taken to optimize the system energy loss rate: the energy loss is improved by adjusting the working power of the PCS, BMS parameters, circuits and maintenance of electrical components; the following measures are taken to optimize the battery health: the short-plate battery is checked, the BMS balancing algorithm is adjusted, the consistency of the battery is improved, and the health degree of the battery is optimized.
2. The method for analyzing the influence factor of the energy storage operation and maintenance optimization of the lithium battery according to claim 1, wherein when the actual application scene relates to operation benefit analysis in a day 'two-charge two-discharge' or accumulated time period, each charge and discharge cycle is calculated and re-summed respectively.
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