CN114611787A - Method for determining optimal chemical energy storage capacity of multi-target offshore wind farm - Google Patents

Method for determining optimal chemical energy storage capacity of multi-target offshore wind farm Download PDF

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CN114611787A
CN114611787A CN202210223726.6A CN202210223726A CN114611787A CN 114611787 A CN114611787 A CN 114611787A CN 202210223726 A CN202210223726 A CN 202210223726A CN 114611787 A CN114611787 A CN 114611787A
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fan
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黄阮明
边晓燕
王晓晖
费斐
李灏恩
吴恩琦
宋天立
戚宇辰
朱昌辉
杨云轶
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Shanghai University of Electric Power
State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention relates to a method for determining optimal chemical energy storage capacity of a multi-target offshore wind farm, which comprises the following steps: 1) considering wind condition-wind speed uncertainty of an offshore wind power plant and wake effect between fans, constructing a fan running state matrix and calculating actual output of the wind power plant, namely output power Pout(V); 2) constructing a probability-power difference curve according to the power difference between the day-ahead power generation plan and the actual output of the wind power plant, and combining the probability-power difference curve with the storage under multiple time scalesDetermining the rated power of the stored energy under three time scales of short, medium and long by using the cost-power curve, selecting an evaluation target and evaluating the rated power; 3) and obtaining the optimal chemical energy storage scheme of the multi-target offshore wind farm based on an analytic hierarchy process according to the evaluation result. Compared with the prior art, the method has the advantages of strong flexibility, consideration of short-term, medium-term and long-term multi-time scales, multiple evaluation targets and the like.

Description

Method for determining optimal chemical energy storage capacity of multi-target offshore wind farm
Technical Field
The invention relates to the technical field of power systems, in particular to a method for determining optimal chemical energy storage capacity of a multi-target offshore wind farm.
Background
Much research is currently conducted in determining the optimal capacity of the offshore wind farm BESS, and the problems of expected electrical generation difference (EENS), load loss hours (LOLH), net present value and air curtailment of the BESS need to be considered when determining the optimal BESS capacity of a large-scale offshore wind farm.
However, many studies are currently focused on the economy and operability, and little attention is paid to the reliability problem of determining the BESS capacity, the problems related to the operation and reliability, such as energy curtailment and LOLH problems depending on the electric power market policy, which have different priorities from region to region, which are important parameters for selecting a proper size of the BESS, but few studies are concerned, and furthermore, the uncertainty of the wind speed and the loss of wake effects play an important role in determining the BESS capacity, which may result in overestimating the BESS capacity if the two factors are ignored, and the performance and life of the battery may be reduced as the number of charge/discharge cycles increases, and thus, it is necessary to consider the degradation problem of the battery in the planning stage. At the same time, fans are prone to random failures, resulting in a loss of the expected supply energy, and therefore also having a large impact on the choice of BESS.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for determining the optimal chemical energy storage capacity of a multi-target offshore wind farm.
The purpose of the invention can be realized by the following technical scheme:
a method for determining the optimal chemical energy storage capacity of a multi-target offshore wind farm comprises the following steps:
1) considering wind condition-wind speed uncertainty of an offshore wind power plant and wake effect between fans, constructing a fan running state matrix and calculating actual output of the wind power plant, namely output power Pout(V)；
2) Constructing a probability-power difference curve according to the power difference between the day-ahead power generation plan and the actual output of the wind power plant, determining the rated power of energy storage under three time scales of short, medium and long by combining the energy storage cost-power curve under the multiple time scales, selecting an evaluation target and evaluating the rated power;
3) and obtaining the optimal chemical energy storage scheme of the multi-target offshore wind farm based on an analytic hierarchy process according to the evaluation result.
In the step 1), the output power P of the fan in each running stateoutThe expression of (V) is:
Figure BDA0003538429770000021
wherein V is the windward speed of the fan, etaoi、ηjAre all conversion coefficients, ViBeing ith fansWind speed, V, of the wind0For cutting into wind speed, V1To cut off the wind speed, V2Rated wind speed, V3For cutting out the wind speed, PratedAt rated power, VjThe wind speed of the fan in the jth state is shown, and m is the total state number of the fan.
In an offshore wind farm, considering the wake superposition effect generated by an upstream fan, the calculation formula of the windward speed V of the fan is as follows:
Figure BDA0003538429770000022
Figure BDA0003538429770000023
wherein, VoFor free wind speed, VlIs the windward speed of the first downstream fan in the wind power plant, n is the total number of fans in the wind power plant,
Figure BDA0003538429770000024
for superimposing area A on the wakeshwSwept area a with the wind wheelrThe ratio of (a) to (b).
In the step 1), a running state matrix S of the fann×mThe method comprises state values of m states of n fans, wherein each state value is obtained through a Monte Carlo simulation sequence and only has two values of 0 and 1, wherein 1 represents that the fan is in an operating state, and 0 represents that the fan is in a shutdown maintenance state.
And the operation or shutdown state of each fan is obtained by inverse transformation according to the shutdown state duration, and then:
Tdown＝-MTTR×ln(r)
wherein, TdownFor the duration of the shutdown state of the wind turbine, ln represents a natural logarithmic function, MTTR is the average time required to repair the wind turbine in the shutdown state, i.e., the recovery time, r → unif (0,1), i.e., obeys a uniform distribution between 0 and 1, and when the shutdown time of the wind turbine is greater than the duration of the state, the state value of the state is 0.
The step 2) specifically comprises the following steps:
21) acquiring the occurrence probability of each operating state of the offshore wind farm, taking the occurrence probability as the probability of the corresponding power difference in the state, and acquiring the power difference in the state according to the output power of the offshore wind farm in any state to construct a probability-power difference curve;
22) preliminarily selecting the energy storage total power value which meets the requirement through a preset cost range and a preset probability range by combining a probability-power difference curve and cost-power curves under three time scales of short, medium and long;
23) and determining the number of the energy storage batteries under three time scales of short, medium and long according to the total energy storage power value meeting the requirement to form alternative schemes, calculating specific values of the four targets according to the four evaluation targets respectively and through scene design, and obtaining the evaluation target value of each alternative scheme.
In the step 21), for each iteration of the sequence Monte Carlo simulation, the occurrence probability P of each state in m operating states of the offshore wind farmrComprises the following steps:
Figure BDA0003538429770000031
wherein z is the number of times of occurrence of a certain state of a certain fan in the simulation process, and noIs the total number of iterations in the sequential monte carlo simulation process.
In the step 21), under any state, the output power P of the offshore wind farmojComprises the following steps:
Figure BDA0003538429770000032
wherein, SWij,kThe state of the ith fan in the kth iteration of the sequence Monte Carlo simulation process in the jth state, j is 1,2, …, m, and is obtained by a fan operation state matrix, noIs the total number of iterations in the sequential monte carlo simulation process.
In the step 23), the four evaluation targets are respectively net present value, expected power generation difference, load loss hours and air curtailment quantity, the designed scene comprises that the wind power plant is not combined with an energy storage system, the wind power plant is combined with a short-time scale energy storage system, the wind power plant is combined with a medium-time scale energy storage system and a wind power plant is combined with a long-time energy storage system, and the expression of the net present value NPV is as follows:
Figure BDA0003538429770000033
Figure BDA0003538429770000034
Figure BDA0003538429770000035
wherein L islifeFor the service life of the energy storage battery, I is the charging and discharging times of the battery, L is the degradation index of the energy storage battery, and NbNumber of energy storage cells, PL, required for wind farmlifeFor the operation life of the offshore wind farm, fix represents rounding towards zero, k is the energy storage quantity, CbatteryFor energy storage battery cost, d is discount rate;
the expression of the expected power generation difference is as follows:
Figure BDA0003538429770000041
wherein, POWF,jIn order to not consider random faults of the fan, namely when the fan runs the state matrix Sn×mIs all 1, POTo take account of the actual output of the wind farm at random faults, Pb,jFor the energy storage power in the jth state, the value of C is 1 to indicate charging, the value of C is 2 to indicate discharging, and T ismDuration of fan state;
the expression of the load loss hours LOLH is as follows:
Figure BDA0003538429770000042
wherein, Pd,jFor planned output of wind farm in jth state, ROWFThe rated power value is the rated power value of the offshore wind plant;
the air abandoning quantity PcThe expression of (a) is:
Figure BDA0003538429770000043
the step 3) specifically comprises the following steps:
31) obtaining a score matrix G of each alternative scheme under the net present value, the expected power generation difference, the load loss hours and the air curtailment quantity;
32) constructing a weight vector W' according to the relative importance of the preference targets;
33) and sequencing the alternatives to obtain the ranking zeta of the alternatives which is G multiplied by W', so as to determine the optimal capacity scheme of the energy storage coordination.
The step 31) is specifically as follows:
311) establishing a comparison matrix V of all possible alternative schemes u under four evaluation targetsgAnd g is 1,2, 3, 4;
312) constructing a score vector W for each target according to the comparison matrixi gA scoring matrix G is then formed.
Compared with the prior art, the invention has the following advantages:
the invention provides a method for determining the optimal chemical energy storage capacity of a multi-target offshore wind farm so as to realize the economic and reliable operation of a large-scale offshore wind farm, in this context, a number of objectives, such as net present value, expected difference in power generation, number of hours of load loss and air curtailment, when the above objects are evaluated, random faults of the fans, wake effect loss among the fans and uncertainty of the wind speed are considered, and chemical energy storage on short, medium and long-term multiple time scales is considered, depending on the charge/discharge time, in order to enable offshore wind farm developers to make appropriate decisions, the invention provides a hierarchical analysis capacity selection strategy, according to the predefined target priority, different energy storage is evaluated according to different scenes, and the invention can provide the flexibility of selecting the most appropriate alternative scheme according to local requirements for a decision maker.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a probability-power difference curve versus energy storage cost-power curve at short, medium, and long time scales.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
The invention provides a method for determining optimal chemical energy storage capacity of a multi-target offshore wind farm, which comprises the steps of calculating the wake effect among fans through a sequence Monte Carlo simulation method, establishing an uncertainty model of wind speed, establishing a running state matrix of the fans, deducing a probability distribution function of mismatch between actual output of a wind farm and a day-ahead power generation plan, solving according to cost functions of chemical energy storage under different time scales, and determining the chemical energy storage capacity; under the condition of considering a plurality of targets, a scoring matrix is established according to the weight relation of different targets, all possible schemes are ranked, and finally the chemical energy storage capacity under the specific target is determined.
The method comprises the following specific steps
Step 1, taking account of wake effect among fans, establishing a wind speed uncertainty model taking account of the wake effect, constructing a fan running state matrix and calculating output power (actual output) of the fans in each running state based on a sequence Monte Carlo simulation method;
step 2, predicting a day-ahead power generation plan of the wind power plant based on historical data of the wind power plant, obtaining a 'probability-power difference' curve of the actual output and the day-ahead power generation plan by combining with the actual output of the wind power plant in the step 1, determining energy storage rated power under short-term, medium-term and long-term time scales respectively, defining four targets including a net present value, an expected power generation difference, a load loss hour number and a wind abandoning amount, calculating specific values of the four targets through scene design, and evaluating different types of energy storage;
and 3, providing a multi-target offshore wind farm optimal chemical energy storage scheme based on an analytic hierarchy process, establishing a hierarchical analysis (analytic hierarchy process) selection strategy, considering a plurality of targets including a net present value, an expected power generation difference, a load loss hour and a wind abandoning amount, establishing a score matrix according to the weight relation of different targets, giving target weights based on an objective attitude, and respectively selecting short-term, medium-term and long-term time scale energy storage capacity selection schemes.
In step 1, complicated wind conditions-wind speed uncertainty and wake effect between wind turbines in an offshore wind farm are considered through sequential monte carlo simulation, and the implementation flow is as shown in fig. 1, and specifically comprises the following steps:
1) the method comprises the following steps of establishing an offshore wind farm uncertainty model through sequence sampling of running states of fans, enabling each fan to be in a complete running state or a fault shutdown state under any wind speed, respectively representing by '1' and '0', and during the whole research period, obtaining the running or shutdown state of each fan in an offshore wind farm according to the duration inverse transformation of the shutdown state, wherein the running or shutdown state is realized through the following formula:
Tdown＝-MTTR×ln(r) (1)
in the formula, TdownFor the shutdown time of the fan, ln represents a natural logarithmic function, and the MTTR is the average time required to repair the fan in the shutdown state, i.e., the recovery time, r → unif (0,1), i.e., obeying an even distribution between 0 and 1.
Considering n fans in an offshore wind farm (each state in the invention is continuously generated in 10 minutes), for example, when the repair time is longer than 10 minutes, the operating state value of the fan is 0, the operating state of each fan is updated in sequence, m operating states of the n fans are obtained through sequential Monte Carlo simulation, and therefore the operating state matrix S of the fans in the wind farm is obtainedn×mThe running state matrix Sn×mThe element values in (1) are only 0 and 1, 0 represents that the fan is in a shutdown state, and 1 represents that the fan is in an operation state.
2) The output power of the fans in the wind power plant can be obviously reduced under the influence of wake flow, the wake flow effect influenced by a single fan is calculated through a Jensen wake flow model and linear expansion behind the fans, and the wind speed at the downstream distance x of the fans is given by the following formula:
Figure BDA0003538429770000061
in the formula, VoIs the free wind speed, r is the fan radius, rxIs the wake radius at distance x, CtIs the thrust coefficient.
In an offshore wind farm, a downstream fan is affected by multiple wake flows of an upstream fan, and considering the wake flow superposition effect generated by the upstream fan, the wind speed V of any one downstream fan can be expressed as follows:
Figure BDA0003538429770000062
in the formula (I), the compound is shown in the specification,
Figure BDA0003538429770000063
Ashwfor wake overlap area, ArIs swept area of the wind wheel, VlIs the wind speed of the downstream wind turbine in the wind farm, and n is the total number of the wind turbines in the wind farm.
The output power of the fan is as follows:
Figure BDA0003538429770000071
wherein V is the windward speed of the fan, etaoi、ηjAre all conversion coefficients, ViIs the windward speed of the ith fan, V0For cutting into wind speed, V1To cut off the wind speed, V2Rated wind speed, V3For cutting out the wind speed, PratedTo ratedPower, VjThe wind speed of the fan in the jth state.
In step 2, according to the actual output of the wind power plant and the day-ahead power generation plan, a 'probability-power difference' curve of the difference between the actual output of the wind power plant and the day-ahead power generation plan is obtained, energy storage 'cost-power curves' under short-term, medium-term and long-term multiple time scales are combined, and the intersection point of the curves is determined to be the rated power of energy storage under three time scales, namely short, medium and long. And providing four targets of net present value, expected power generation difference, load loss hours and air abandoning amount, and performing performance evaluation on the energy storage type by calculating the four target values under three time scales of energy storage. The method specifically comprises the following steps:
1) for each iteration of the sequence monte carlo simulation provided in step 1, the offshore wind farm has m operating states, and the occurrence probability of each state is as follows:
Figure BDA0003538429770000072
wherein z is the number of times of occurrence of a certain state of a certain fan in the simulation process, and noIs the total number of iterations in the sequential monte carlo simulation process. The difference between the actual wind power production and the day-ahead power generation plan is estimated at each state, there will be a corresponding power difference at each state, and then the probability of each state occurring is equal to the probability of the power difference.
Then, in any state, the output power of the offshore wind farm is:
Figure BDA0003538429770000073
in the formula, SWij,kFor the state of the ith fan at the kth iteration of the sequential monte carlo simulation process in the jth (j ═ 1,2, …, m) state, it is obtained from the fan operating state matrix S as "1" or "0". Equation (6) may then relate the output power of the wind farm to the fan on/off state.
And predicting a day-ahead power generation plan according to historical data of the wind power plant, taking an output error band of +/-6% to consider uncertainty of wind speed due to intermittency of wind power generation, and constraining by applying deviation penalty to a wind power plant operator when a difference exists between actual power generation amount of the offshore wind power plant and day-ahead predicted power generation amount. Thus, the power margin can be minimized by determining the stored energy power.
Through the process, a probability-power difference curve (the larger the difference is, the lower the occurrence probability) between the actual output of the wind power plant and the day-ahead power generation plan can be obtained, and a power value which meets the requirements of low battery cost (preset cost range) and small power difference occurrence probability (preset probability range) is obtained as a reference to participate in the cooperative operation of the offshore wind power plant by combining an energy storage cost-power curve (the larger the energy storage power is, the higher the energy storage cost is) under short-term, medium-term and long-term multiple time scales as shown in fig. 2.
2) Under the obtained energy storage rated power under the short, medium and long time scales, four targets of net present value, expected power generation difference, load loss hours and air abandoning amount are provided, and the performance performances of the energy storage types under the three time scales on the four targets are compared.
(1) Net present value (net present value)
The net present value when the offshore wind power plant and the energy storage are cooperatively allocated meets the following requirements:
Figure BDA0003538429770000081
Figure BDA0003538429770000082
Figure BDA0003538429770000083
in the formula, LlifeFor the service life of the energy storage battery, I is the charging and discharging times of the battery, L is the degradation index of the energy storage battery, and NbThe number of energy storage batteries required by the wind power plant, PLlife is the operation life of the offshore wind power plant, fix represents rounding towards zero,k is the amount of stored energy, CbatteryIs the battery cost, d is the discount rate, and this patent is got 6%.
(2) Expected difference in power generation
The expected power generation margin is an important objective to measure the energy loss of the offshore wind field.
Figure BDA0003538429770000084
In the formula, POWF,jFor wind farm output power without taking into account random failure of the wind turbine, i.e. the wind turbine operating state matrix Sn×mAll the elements of (1); pOThe actual output of the wind power plant is considered when random faults occur; pb,jThe energy storage power in the jth state; c, taking '1' to indicate charging, and taking '2' to indicate discharging; t ismThe duration of the fan state.
(3) Hours of load loss (LOLH)
The number of hours of load loss, which is a target of operational reliability, is defined as the ratio of the amount of electricity (MWh) that cannot be supplied by the offshore wind farm to the rated power value.
Figure BDA0003538429770000085
In the formula, Pd,jIs the planned output of the wind farm in the jth state, ROWFThe rated power value is the rated power value of the offshore wind plant.
(4) Abandon the blast volume
Due to uncertainty in the prediction, the output power of a large offshore wind farm may be higher over a considerable period of time compared to a day-ahead power generation plan, which in turn may result in a reduction P in wind power generationcLeading to lost revenue for offshore wind farms. The air rejection is therefore also taken into account by the goal, which is defined as follows:
Figure BDA0003538429770000091
the performance of the stored energy was evaluated by four objectives: designing four scenes such as a wind power plant, a wind power plant combined energy storage system, a short time scale energy storage system, a wind power plant combined time scale energy storage system and a wind power plant combined long time energy storage system, coordinating the determined short, medium and long time scale energy storage with an offshore wind power plant respectively, calculating specific values of four targets under the four scenes, obtaining the number of required short, medium and long energy storage batteries according to the output condition of the offshore wind power plant, comparing the number of batteries required by the short, medium and long type energy storage (the battery cost of the batteries is considered in the net present value) and the variation of the four targets based on the reference scene, and reflecting which time scale energy storage type is selected through the variation.
3. In step 3, the four targets of the net present value, the expected power generation difference, the load loss hours and the air curtailment amount proposed in the foregoing may represent priority targets in terms of economy and operational reliability, but the scheme selection for the coordination of the wind farm and the energy storage systems of different time scales may differ according to the wind farm developer (it is assumed that a short-term energy storage scheme may be selected in order to achieve NPV optimization and a long-term energy storage scheme may be selected in order to achieve LOLH minimum according to the result of step 2), so that flexibility of scheme selection is provided for the developer based on an analytic hierarchy process, and the most appropriate scheme is selected according to the priority of the desired target. The method comprises the following specific steps:
31) calculating a score matrix G of the energy storage type selection scheme under three time scales of short, medium and long under the net present value, the expected power generation difference, the load loss hours and the air abandoning amount;
32) calculating a weight vector of the preference target according to the relative importance of the preference target;
33) and sequencing all the possible capacity selection schemes to determine the optimal capacity scheme of the energy storage coordination. Let m and u be the target number and potential energy storage scheme number considered, respectively, as follows:
(1) determining a score matrix G: establishing a comparison matrix V of all possible alternative schemes u (namely the number of energy storage schemes) under four targetsg，g＝1、2…4，VgIs the u x u matrix of the g-th object, each element in the matrix is like
Figure BDA0003538429770000092
Representing the relative importance of the c-th alternative compared to the d-th alternative, if the importance of the c-th alternative is the same as the d-th alternative, then
Figure BDA0003538429770000093
If the c-th scheme is more important than the d-th scheme
Figure BDA0003538429770000094
Vice versa, VgThe elements of the matrix should satisfy:
Figure BDA0003538429770000095
in the comparison matrix (V)g) On the basis of the above-mentioned data, a score vector W of each target is establishedi g：
Figure BDA0003538429770000101
Wherein i is 1,2, …, u,
Figure BDA0003538429770000102
(2) calculating a weight vector of the target: first, an m × m combination matrix A is formed, where each column of A is V in (1)g(g ═ 1,2, …, m), the elements in a depend on the selection priority of the different targets, embodied in the form of assignments: the assignment 1 indicates that object 1 is as important as object 2, i.e. a12＝a21The value 3 indicates that object 1 is more important than object 2, i.e. a, 112＝3，a21When 1/3, the value 5 indicates that object 1 is more important than object 2, i.e., a12＝5，a211/5 … and so on. To avoid windfarm businessmen from wandering between target 1 and target 2If the specific gravity cannot be determined, the assignment 2, 4, 6 … follows the AND matrix VgThe elements are the same feature and the matrix a is normalized. Calculating a weight vector W':
Figure BDA0003538429770000103
wherein i, j is 1,2, …, m.
(3) And (3) sequencing the schemes, and after obtaining the score matrix G and the weight vector W', obtaining the ranking zeta of the alternative schemes:
ζ＝G×W′ (17)

Claims (10)

1. a method for determining the optimal chemical energy storage capacity of a multi-target offshore wind farm is characterized by comprising the following steps:
1) considering wind condition-wind speed uncertainty of an offshore wind power plant and wake effect between fans, constructing a fan running state matrix and calculating actual output of the wind power plant, namely output power Pout(V)；
2) Constructing a probability-power difference curve according to the power difference between the day-ahead power generation plan and the actual output of the wind power plant, determining the rated power of energy storage under three time scales of short, medium and long by combining the energy storage cost-power curve under the multiple time scales, selecting an evaluation target and evaluating the rated power;
3) and obtaining the optimal chemical energy storage scheme of the multi-target offshore wind farm based on an analytic hierarchy process according to the evaluation result.
2. The method for determining the optimal chemical energy storage capacity of the multi-target offshore wind farm according to claim 1, wherein in the step 1), the output power P of the fan in each operation stateoutThe expression of (V) is:
Figure FDA0003538429760000011
wherein V is the windward speed of the fan, etaoi、ηjAre all conversion coefficients, ViThe windward speed of the ith fan, V0For cutting into wind speed, V1To cut off the wind speed, V2Rated wind speed, V3For cutting out the wind speed, PratedAt rated power, VjThe wind speed of the fan in the jth state is shown, and m is the total state number of the fan;
in an offshore wind farm, considering a wake superposition effect generated by an upstream fan, a calculation formula of the windward speed V of the fan is as follows:
Figure FDA0003538429760000012
Figure FDA0003538429760000013
wherein, VoFor free wind speed, VlIs the windward speed of the first downstream fan in the wind farm, n is the total number of the fans in the wind farm,
Figure FDA0003538429760000014
for superimposing area A on the wakeshwSwept area a with the wind wheelrThe ratio of (a) to (b).
3. The method for determining the optimal chemical energy storage capacity of the multi-target offshore wind farm according to claim 1, wherein in the step 1), the running state matrix S of the fan is adoptedn×mThe method comprises state values of m states of n fans, wherein each state value is obtained through sequence Monte Carlo simulation, and the state values are only 0 and 1, wherein 1 represents that the fans are in an operating state, and 0 represents that the fans are in a shutdown maintenance state.
4. The method for determining the optimal chemical energy storage capacity of the multi-target offshore wind farm according to claim 3, wherein the operation or shutdown state of each fan is obtained by inverse transformation of the shutdown state duration, and then:
Tdown＝-MTTR×ln(r)
wherein, TdownFor the duration of the shutdown state of the wind turbine, ln represents a natural logarithmic function, MTTR is the average time required to repair the wind turbine in the shutdown state, i.e., the recovery time, r → unif (0,1), i.e., obeys a uniform distribution between 0 and 1, and when the shutdown time of the wind turbine is greater than the duration of the state, the state value of the state is 0.
5. The method for determining the optimal chemical energy storage capacity of the multi-target offshore wind farm according to claim 3, wherein the step 2) specifically comprises the following steps:
21) acquiring the occurrence probability of each operating state of the offshore wind farm, taking the occurrence probability as the probability of the corresponding power difference in the state, and acquiring the power difference in the state according to the output power of the offshore wind farm in any state to construct a probability-power difference curve;
22) preliminarily selecting the energy storage total power value which meets the requirement through a preset cost range and a preset probability range by combining a probability-power difference curve and cost-power curves under three time scales of short, medium and long;
23) and determining the number of the energy storage batteries under three time scales of short, medium and long according to the total energy storage power value meeting the requirement to form alternative schemes, calculating specific values of the four targets according to the four evaluation targets respectively and through scene design, and obtaining the evaluation target value of each alternative scheme.
6. The method as claimed in claim 5, wherein in the step 21), for each iteration of the Monte Carlo simulation, the probability P of occurrence of each state in m operating states of the offshore wind farm is determinedrComprises the following steps:
Figure FDA0003538429760000021
wherein z is the number of times of occurrence of a certain state of a certain fan in the simulation process, and noIs the total number of iterations in the sequential monte carlo simulation process.
7. The method for determining the optimal chemical energy storage capacity of the multi-target offshore wind farm according to claim 5, wherein in the step 21), the output power P of the offshore wind farm is determined in any stateojComprises the following steps:
Figure FDA0003538429760000022
wherein, SWij,kThe state of the ith fan in the kth iteration of the sequence Monte Carlo simulation process in the jth state, j is 1,2, …, m, and is obtained by a fan operation state matrix, noIs the total number of iterations in the sequential monte carlo simulation process.
8. The method for determining the optimal chemical energy storage capacity of the multi-target offshore wind farm according to claim 5, wherein in the step 23), the four evaluation targets are a net present value, an expected power generation difference, a load loss hour and a wind curtailment quantity respectively, the designed scene comprises that a wind farm does not combine an energy storage system, the wind farm combines a short-time scale energy storage system, the wind farm combines a medium-time scale energy storage system and the wind farm combines a long-time energy storage system, and the expression of the net present value NPV is as follows:
Figure FDA0003538429760000031
Figure FDA0003538429760000032
Figure FDA0003538429760000033
wherein L islifeFor the service life of the energy storage battery, I is the charging and discharging times of the battery, L is the degradation index of the energy storage battery, and NbNumber of energy storage cells, PL, required for wind farmlifeFor the operation life of the offshore wind farm, fix represents rounding towards zero, k is the energy storage quantity, CbatteryFor energy storage battery cost, d is discount rate;
the expression of the expected power generation difference is as follows:
Figure FDA0003538429760000034
wherein, POWF,jIn order to not consider random faults of the fan, namely when the fan runs the state matrix Sn×mIs all 1, POTo take account of the actual output of the wind farm at random faults, Pb,jFor the energy storage power in the jth state, the value of C is 1 to indicate charging, the value of C is 2 to indicate discharging, and T ismThe duration of the fan state;
the expression of the load loss hours LOLH is as follows:
Figure FDA0003538429760000035
wherein, Pd,jFor planned output of wind farm in jth state, ROWFThe rated power value is the rated power value of the offshore wind plant;
the air abandoning quantity PcThe expression of (a) is:
Figure FDA0003538429760000036
9. the method for determining the optimal chemical energy storage capacity of the multi-target offshore wind farm according to claim 8, wherein the step 3) specifically comprises the following steps:
31) obtaining a score matrix G of each alternative scheme under the net present value, the expected power generation difference, the load loss hours and the air curtailment quantity;
32) constructing a weight vector W' according to the relative importance of the preference targets;
33) and sequencing the alternatives to obtain the ranking zeta of the alternatives which is G multiplied by W', so as to determine the optimal capacity scheme of the energy storage coordination.
10. The method for determining the optimal chemical energy storage capacity of the multi-target offshore wind farm according to claim 8, wherein the step 31) comprises the following specific steps:
311) establishing a comparison matrix V of all possible alternative schemes u under four evaluation targetsgAnd g is 1,2, 3, 4;
312) constructing a score vector W for each target according to the comparison matrixi gAnd then forming a scoring matrix G.
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