CN114285086A - Electrochemical energy storage power station energy scheduling method and system based on improved ant colony algorithm - Google Patents
Electrochemical energy storage power station energy scheduling method and system based on improved ant colony algorithm Download PDFInfo
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Abstract
The invention provides an electrochemical energy storage energy scheduling method and system based on an improved ant colony algorithm. The method comprises the steps of firstly extracting measured and calculated data in a certain scene from an electrochemical energy storage information acquisition system, then determining the weight of each data in an energy scheduling method by using grey correlation analysis, establishing an energy scheduling optimization model aiming at the lowest cost, high operation efficiency, low loss rate and stable output factors, finally solving the energy scheduling model by using an ant colony algorithm, improving the ant colony algorithm by introducing corresponding strategies, realizing reasonable scheduling of the electrochemical energy storage power station in different time periods, prolonging the service life of the electrochemical energy storage power station and promoting sustainable development of the energy storage power station.
Description
Technical Field
The invention belongs to the technical field of electrochemical energy storage, and particularly relates to an electrochemical energy storage power station energy scheduling method and system based on an improved ant colony algorithm.
Background
The electrochemical energy storage has the advantages of high controllability and high module degree, and has the advantages of high energy density, high conversion efficiency, short construction period, convenience in installation, wide application range and great popularization value. With the continuous development of technical means, electrochemical energy storage is being widely applied to various fields, especially electric vehicles and power systems.
The capacity and the power configuration of the energy storage battery depend on different micro-grid structures and operation modes, and are also limited by the operation mode of the energy storage system. The control strategy of the energy storage system in the power grid/microgrid is one of the hot spots of the related researches of the power grid/microgrid at present. As a main means for realizing the large capacity of the energy storage device, the large-scale integration of the energy storage system is a necessary condition for realizing a large-scale energy storage power station, and the importance of the operation control technology of the energy storage system in further improving the efficiency and reducing the cost is increasing day by day. Therefore, an efficient and reasonable system internal power distribution strategy is provided for the energy storage power station, and is worth discussing.
At present, most of electrochemical energy storage systems only manage and control a single target or obtain a specific numerical value of energy management of an energy storage power station in a complex mode, and the accuracy is not high. The energy storage power station power distribution model is rarely solved by an optimization method considering multi-objective factors.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide an electrochemical energy storage power station energy scheduling method based on an improved ant colony algorithm, consider an electrochemical energy storage system energy management strategy of multi-objective factors, and aim to solve the problem of unreasonable management and control in the prior art and prolong the residual service life of an energy storage power station.
The invention adopts the following technical scheme. The invention provides an electrochemical energy storage power station energy scheduling method based on an improved ant colony algorithm, which is characterized by comprising the following steps of:
step 2, performing characteristic selection processing on the energy storage power station by adopting a principal component analysis method, and screening to obtain characteristic data;
step 3, judging the demand instruction in three preset intervals, executing step 4 if the demand instruction is in a first interval, executing step 5 if the demand instruction is in a second interval, and executing step 7 if the demand instruction is in a third interval;
step 4, distributing the battery clusters according to the overall SOC values of the battery clusters in proportion to obtain the output condition of each battery cluster, and continuing to execute step 8;
step 5, determining the weight of each characteristic data obtained in the step 2 in the energy dispatching method by adopting grey correlation analysis, and establishing an energy dispatching multi-target function according to the weight to obtain an energy dispatching model;
step 6, solving an energy scheduling model by adopting an ant colony algorithm to obtain the output condition of each battery cluster;
step 7, each battery cluster runs at the highest power;
and 8, outputting a power condition distribution instruction of each battery cluster according to the actual load requirement.
Preferably, in step 1, the acquired data is preprocessed, and missing data is supplemented through neighbor values or mean values; outlier data is removed by curve fitting or clustering algorithms.
Preferably, step 2 specifically comprises: analyzing the data collected in the step 1 by adopting a principal component analysis method, wherein the screened data comprises the following data: state of charge c of each cell cluster1Output power c of each battery cluster4Loss of power conversion device c5Charge and discharge rate of battery c6Variation of power per unit time c13。
Preferably, in step 3, the first interval means that the demand instruction RI is less than or equal to 30% of the rated power of the energy storage power station;
the second interval refers to that 30% of rated power of the energy storage power station is less than RI < 90% of rated power of the energy storage power station;
the third interval means that RI is more than or equal to 90 percent of rated power of the energy storage power station.
Preferably, step 5 specifically includes:
step 5.1, determining the association degree of the cost factor, the equipment loss factor and the output stability factor with the operation efficiency factor by adopting grey correlation analysis, and further calculating the weight of each factor in the energy scheduling method through normalization;
step 5.2, combining the weight obtained in the step 5.1, and establishing an energy scheduling multi-objective function with the lowest cost, high operation efficiency, small equipment loss rate and stable output; as expressed in the following formula,
min f＝ω1f1+ω2f2+ω3f3+ω4f4
in the formula:
f denotes an energy scheduling multi-objective function, f1Representing an objective function of the running cost, f2Representing an objective function of operating efficiency, f3Representing the equipment loss objective function，f4Representing an output stabilization objective function;
ω1weight value, omega, representing line cost objective function2Weight value, ω, representing an objective function of operating efficiency3Weight value, omega, representing the equipment loss objective function4A weight value representing an output stabilization objective function;
and 5.3, establishing boundary conditions.
Further preferably, step 5.1 specifically comprises:
step 5.1.1, carrying out standardization processing on the brought data; carrying out standardized processing on data of cost factors, equipment loss factors, output stability factors and operation efficiency factors;
step 5.1.2, respectively forming 3 comparison curves by the cost factor, the equipment loss factor and the output stability factor; forming a reference curve by the operating efficiency factors;
step 5.1.3, calculating the correlation coefficient of each data and the operation efficiency of the energy storage power station; as expressed in the following formula,
in the formula:
Δ min represents the minimum difference of the comparison curve and the reference curve,
Δ max represents the maximum difference of the comparison curve and the reference curve,
a represents a resolution coefficient and a represents a resolution coefficient,
Δi0(k) representing the absolute difference between each point on the comparison curve and the corresponding point on the reference curve,
βi0representing a correlation coefficient;
step 5.1.4, calculating the correlation degree of each data and the operation efficiency of the energy storage power station; as expressed in the following formula,
in the formula:
nirepresenting the comparison series of the factors with the reference series n0The degree of association of (a) with (b),
n represents the number of the associated factors;
and 5.1.5, sequencing the results according to the degree of association, and determining the weight of various data in the operating efficiency of the energy storage power station.
Preferably, in step 5.2,
for running cost objective function f1The expression is given by the following formula,
in the formula:
m1represents the electricity purchase cost saved by changing the control strategy,
m2representing reduced operation and maintenance costs of changing the control strategy,
m3representing the gains obtained by the energy storage power station under the change of the control strategy,
g represents the age limit;
for the running efficiency objective function f2The expression is given by the following formula,
in the formula:
d represents the number of the battery clusters in the energy storage power station,
Ebwhich represents the efficiency of the operation of the battery cluster,
E2indicating the efficiency of operation of the dc boost circuit,
E3representing the operation efficiency of the energy storage converter;
for the device loss objective function f3As expressed in the following formula,
in the formula:
λ1represents the loss coefficient of the electrical equipment for switching the charge-discharge state of the battery cluster,
N1represents the number of times of switching the charge and discharge states of the battery cluster,
λ2the loss factor of the energy storage converter equipment is shown,
N2indicating the number of times the energy storage converter device switches,
λ3the coefficient of the on-time is represented,
U1the running time of the energy storage battery cluster is shown,
u represents the total time of the statistics;
for the output stabilization objective function, expressed in the following formula,
in the formula:
o represents the total time for calculating the amplitude of the output power variation, and the unit represents second,
Ptbrepresents the output power of the b-th cell cluster at time t,
Δ t represents a preset unit time.
Preferably, step 6 specifically includes:
step 6.1, initializing information;
step 6.2, randomly selecting an ant starting point, and moving to a remote place according to the concentration of the released pheromone of the ants;
step 6.3, according to the initial pheromone, the ant state transition probability is calculated and expressed by the following formula,
in the formula:
p (i, r) represents the transfer probability value of ant r at the ith iteration,
max (V) represents the maximum value of the pheromone,
v (r) pheromone representing ant r;
6.4, calculating a distance calculation formula for the movement of the ants;
step 6.5, calculating the fitness of the ants and the food, namely a multi-objective function value, and sequencing according to the fitness; the fitness of ants to food is expressed by the following formula,
S＝f’-Pnew
in the formula:
f' is the objective function value at a single iteration,
Pnewis the length of the ants moving in the direction of the movement,
the meaning of S expression is that the lower the fitness value is, the higher the fitness is;
6.6, updating the pheromone concentration of each ant according to the sequence of the step 5.5; the pheromone concentration is updated by the formula,
in the formula:
z is pheromone volatilization factor;
step 6.7, judging whether a termination condition is met; if yes, ending the calculation process; if not, continuing to perform iterative optimization from step 5.8;
and 6.8, outputting the objective function, finding out and outputting the position corresponding to the ant with the highest adaptability value, and obtaining the output condition of each battery cluster.
Preferably, in step 6.4,
(1) if the state transition probability is less than or equal to the preset transition probability constant, the distance calculation formula is as follows:
Pnew＝Pold+δ·θ·Tp
in the formula:
Pnewis the position to be moved and is,
Poldis the current position of the ant and is the current position of the ant,
delta is the inverse of the current number of iterations,
theta is the step size to be moved,
Tpis [ -1,1 [ ]]A random number in between;
(2) if the state transition probability is greater than the preset transition probability constant, the distance calculation formula is as follows:
Pnew＝Pold+Q·Tp
in the formula:
q is the size of the independent variable interval.
The invention provides an electrochemical energy storage power station energy scheduling system based on an improved ant colony algorithm, which comprises: the ant colony optimization system comprises a data acquisition module, a feature processing module, a demand instruction judging module, an improved ant colony algorithm module and an output instruction issuing module, and is characterized in that:
the data acquisition module is used for extracting data measured and calculated in a certain scene from an electrochemical energy storage information acquisition system and sending the data to the feature processing module;
the characteristic processing module adopts a principal component analysis method to perform characteristic selection processing on the energy storage power station, screens characteristic data and sends the characteristic data to the improved ant colony algorithm module;
the demand instruction judging module is used for judging and executing the demand instruction in three preset intervals;
the improved ant colony algorithm module is used for determining the weight of the energy scheduling method occupied by each feature data obtained by the feature processing module through grey correlation analysis, establishing an energy scheduling multi-target function according to the weight to obtain an energy scheduling model, solving the energy scheduling model through the ant colony algorithm to obtain the output condition of each battery cluster, and sending the output condition to the output instruction issuing module
And the output instruction issuing module outputs an output condition distribution instruction of each battery cluster according to the actual load requirement.
Compared with the prior art, the invention has the beneficial effects that at least: the method comprises the steps of firstly extracting measured and calculated data in a certain scene from an electrochemical energy storage information acquisition system, then determining the weight of each data in an energy scheduling method by using grey correlation analysis, establishing an energy scheduling optimization model aiming at the factors of lowest cost, high operation efficiency, low loss rate and the like, finally solving the energy scheduling model by using an ant colony algorithm, and improving the ant colony algorithm by introducing a corresponding strategy, so that the electrochemical energy storage power station is reasonably scheduled in different time periods, the service life of the electrochemical energy storage power station is prolonged, and the sustainable development of the energy storage power station is promoted. Meanwhile, the operation efficiency of the whole battery energy storage power station is improved, and the service life of the battery is prolonged.
Drawings
Fig. 1 is a flow chart of a specific process of power distribution of an energy storage power station according to the present invention.
Fig. 2 is a flow chart of a model for solving power distribution by using the improved ant colony algorithm according to the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, embodiment 1 of the present invention provides an electrochemical energy storage power station energy scheduling method based on an improved ant colony algorithm, including the following steps:
In a preferred but non-limiting embodiment of the invention, preferably in a scenario of peak clipping and valley filling or new energy fluctuation flattening, the collected data comprises: state of Charge (SOC) c of each battery cluster1Internal resistance of battery c2Terminal voltage c of battery3Output power c of each battery cluster4Loss of power conversion device c5Charge and discharge rate of battery c6Temperature of battery c7Battery cycle life c8Battery capacity c9Cutoff voltage c for charging and discharging of battery10Cutoff current c for charging and discharging of battery11Self discharge rate of battery c12。
In a further preferred embodiment, the acquired data is preprocessed, missing data is filled up, and abnormal data is removed. Further preferably, missing data is filled in by neighbor values or mean values; outlier data is removed by curve fitting or clustering algorithms such as, but not limited to, K-means, mean shift clustering, and the like.
And 2, performing characteristic selection processing on the energy storage power station by adopting a principal component analysis method. And according to the screened features, establishing boundary conditions for the energy management model of the energy storage power station under different demand instructions.
It can be understood that the principal component analysis is a multivariate statistical method for examining the correlation among a plurality of variables, and how to reveal the internal structure among the plurality of variables through a few principal components is researched, namely, the few principal components are derived from the original variables, so that the few principal components keep the information of the original variables as much as possible and are not related to each other. Generally, the mathematical treatment is to linearly combine the original P indexes to form a new comprehensive index.
In a preferred but non-limiting embodiment of the invention, step 2 comprises in particular:
step 2.1, analyzing the data collected in the step 1 by adopting a principal component analysis method, wherein the screened data comprises the following steps: state of charge c of each cell cluster1Output power c of each battery cluster4Loss of power conversion device c5Charge and discharge rate of battery c6Variation of power per unit time c13。
And 2.2, according to the characteristics screened out in the step 2.1, establishing boundary conditions for an energy management model of the energy storage power station under the scene demand instruction.
And 3, judging the demand instruction in three preset intervals, executing the step 4 if the demand instruction is in a first interval, executing the steps 5 and 6 if the demand instruction is in a second interval, and executing the step 7 if the demand instruction is in a third interval.
Further, the first interval means that the demand instruction RI is less than or equal to 30% of the rated power of the energy storage power station; the second interval refers to that 30% of rated power of the energy storage power station is less than RI < 90% of rated power of the energy storage power station; the third interval means that RI is more than or equal to 90 percent of rated power of the energy storage power station. It should be noted that the interval boundary given in this embodiment is only a preferred but non-limiting embodiment, and as long as the proposed improved ant colony algorithm of the present invention is implemented for any at least one interval, it falls within the scope of the inventive concept of the present invention, for example, but not limited to, dividing into two, four or more intervals, wherein one interval implements the proposed improved ant colony algorithm of the present invention.
And 4, distributing the battery clusters according to the proportion according to the total SOC value of the battery clusters, and continuously executing the step 7.
And 5, determining the weight of each data obtained in the step 2 in the energy scheduling method by adopting grey correlation analysis, and establishing an energy scheduling multi-target function according to the weight to obtain an energy scheduling model. In a further preferred embodiment, step 5 comprises:
and 5.1, determining the association degree of the cost factor, the equipment loss factor and the output stability factor with the operation efficiency factor by adopting grey correlation analysis, and further calculating the weight of each factor in the energy scheduling method by normalization. Further preferably, step 5.1 specifically comprises:
step 5.1.1, carrying out standardization processing on the brought data; namely, the data of the cost factor, the equipment loss factor, the output stability factor and the operation efficiency factor are subjected to standardization processing.
Step 5.1.2, respectively forming 3 comparison curves by the cost factor, the equipment loss factor and the output stability factor; the operating efficiency factor is formed into a reference curve.
Step 5.1.3, calculating the correlation coefficient of each data and the operation efficiency of the energy storage power station; as expressed in the following formula,
in the formula:
Δ min represents the minimum difference of the comparison curve and the reference curve,
Δ max represents the maximum difference of the comparison curve and the reference curve,
a represents a resolution coefficient and a represents a resolution coefficient,
Δi0(k) showing comparative curvesThe absolute difference between each point on the line and the corresponding point on the reference curve,
βi0representing the correlation coefficient.
Step 5.1.4, calculating the correlation degree of each data and the operation efficiency of the energy storage power station; as expressed in the following formula,
in the formula:
nirepresenting the comparison series of the factors with the reference series n0The degree of association of (a) with (b),
n represents the number of associated factors.
And 5.1.5, sequencing the results according to the degree of association, and determining the weight of various data in the operating efficiency of the energy storage power station. The data weight with high relevance is greater than the data weight with low relevance, and the sum of the weights is 1.
ω1Weight value, omega, representing line cost objective function2Weight value, ω, representing an objective function of operating efficiency3Weight value, omega, representing the equipment loss objective function4Represents a weight value of the output stabilization objective function, and1+ω2+ω3+ω3＝1。
and 5.2, combining the weight obtained in the step 5.1 to establish an energy scheduling multi-objective function with the advantages of lowest cost, high operation efficiency, small equipment loss rate and stable output. Specifically, the following formula is used to represent,
min f＝ω1f1+ω2f2+ω3f3+ω4f4
in the formula:
f denotes an energy scheduling multi-objective function, f1Representing an objective function of the running cost, f2Representing an objective function of operating efficiency, f3Representing the equipment loss objective function, f4Representing an output stabilization objective function;
more specifically, the objective function f for the running cost1In the following formulaIt is shown that,
in the formula:
m1represents the electricity purchase cost saved by changing the control strategy,
m2representing reduced operation and maintenance costs of changing the control strategy,
m3representing the gains obtained by the energy storage power station under the change of the control strategy,
g represents the age.
Wherein, as an option, changing the control strategy may improve an existing control strategy with the control strategy of the present invention.
More specifically, for the operating efficiency objective function f2The expression is given by the following formula,
in the formula:
d represents the number of the battery clusters in the energy storage power station,
Ebwhich represents the efficiency of the operation of the battery cluster,
E2indicating the efficiency of operation of the dc boost circuit,
E3and the operation efficiency of the energy storage converter is shown.
More specifically, for the device loss objective function f3As expressed in the following formula,
in the formula:
λ1represents the loss coefficient of the electrical equipment for switching the charge-discharge state of the battery cluster,
N1represents the number of times of switching the charge and discharge states of the battery cluster,
λ2show storeThe current can be transformed to the loss factor of the device,
N2indicating the number of times the energy storage converter device switches,
λ3the coefficient of the on-time is represented,
U1the running time of the energy storage battery cluster is shown,
u represents the total time of the statistics.
More specifically, with respect to the output stabilization objective function, as expressed in the following formula,
in the formula:
o represents the total time for calculating the amplitude of the output power variation, and the unit represents second,
Ptbrepresents the output power of the b-th cell cluster at time t,
Δ t represents a preset unit time.
And 5.3, establishing boundary conditions. Specifically, the method comprises the following steps:
and 5.3.1, restricting the demand of the energy storage power station, wherein the maximum output of the energy storage power station cannot be greater than the maximum output of the energy storage power station.
And 5.3.2, SOC constraint, wherein the SOC is more than or equal to 10% and less than or equal to 100%.
Step 5.3.3, the charge and discharge power of the battery cluster is restricted within the time period t,
in the formula:
Pch、Pdchrespectively representing the charging and discharging power of the battery cluster during the period t,
Step 5.3.4, the output power of the energy storage battery cluster is restricted,
Hmin(t)≤Hj(t)≤Hmax(t)
in the formula:
Hj(t) represents the output power of the jth cell cluster,
Hmin(t) and HmaxAnd (t) respectively represents the minimum and maximum output power of the energy storage battery cluster.
Step 5.3.5, restraining the charge and discharge multiplying power of the battery cluster,
0.2C≤Cb≤C
in the formula:
Cbrepresents the actual charge-discharge rate of the battery cluster,
0.2C respectively represents the lower limit of the rated charge-discharge rate of the battery cluster,
and C represents the upper limit of the rated charge-discharge rate of the battery cluster.
And 6, as shown in fig. 2, solving the energy scheduling model by adopting an ant colony algorithm. In a further embodiment of the present invention, step 6 specifically comprises:
step 6.1, information is initialized. Setting initial values for parameters in the algorithm, including: the ant starting point, the pheromone, the ant transfer probability constant, the pheromone volatilization factor, the number of ants, the iteration times and the step length to be moved.
And 6.2, randomly selecting an ant starting point, and moving to a remote place according to the concentration of the released pheromone of the ants.
Step 6.3, according to the initial pheromone, the ant state transition probability is calculated and expressed by the following formula,
in the formula:
p (i, r) represents the transfer probability value of ant r at the ith iteration,
max (V) represents the maximum value of the pheromone,
v (r) represents pheromone of ant r.
And 6.4, calculating a distance calculation formula for the movement of the ants. More specifically, the present invention is to provide a novel,
(1) if the state transition probability is less than or equal to the preset transition probability constant, the distance calculation formula is as follows:
Pnew＝Pold+δ·θ·Tp
in the formula:
Pnewis the position to be moved and is,
Poldis the current position of the ant and is the current position of the ant,
delta is the inverse of the current number of iterations,
theta is the step size to be moved,
Tpis [ -1,1 [ ]]A random number in between.
(2) If the state transition probability is greater than the preset transition probability constant, the distance calculation formula is as follows:
Pnew＝Pold+Q·Tp
in the formula:
q is the size of the independent variable interval.
And 6.5, calculating the fitness of the ants and the food, namely a multi-objective function value, and sequencing according to the fitness. The fitness of ants to food is expressed by the following formula,
S＝f’-Pnew
in the formula:
f' is the objective function value at a single iteration,
Pnewis the length of the ants moving in the direction of the movement,
the meaning of S expression is that the lower the fitness value, the higher the fitness.
And 6.6, updating the pheromone concentration of each ant according to the sequence of the step 5.5. The pheromone concentration is updated by the formula,
in the formula:
z is pheromone volatilization factor.
And 6.7, judging whether the termination condition is met. If yes, ending the calculation process; if not, the iterative optimization is continued from step 5.8.
And 6.8, outputting the objective function, finding out and outputting the position corresponding to the ant with the highest adaptability value, and obtaining the output condition of each battery cluster.
And 7, operating each battery cluster at the highest power, namely operating at the maximum power.
And 8, outputting a power condition distribution instruction of each battery cluster according to the actual load requirement.
The embodiment 2 of the present invention provides an electrochemical energy storage power station energy scheduling system based on an improved ant colony algorithm, and the electrochemical energy storage power station energy scheduling system based on the improved ant colony algorithm includes: data acquisition module, characteristic processing module, demand instruction decision module, improvement ant colony algorithm module and output instruction issue module, wherein:
the data acquisition module is used for extracting data measured and calculated in a certain scene from an electrochemical energy storage information acquisition system and sending the data to the feature processing module;
the characteristic processing module adopts a principal component analysis method to perform characteristic selection processing on the energy storage power station, screens characteristic data and sends the characteristic data to the improved ant colony algorithm module;
the demand instruction judging module is used for judging and executing the demand instruction in three preset intervals;
the improved ant colony algorithm module is used for determining the weight of the energy scheduling method occupied by each feature data obtained by the feature processing module through grey correlation analysis, establishing an energy scheduling multi-target function according to the weight to obtain an energy scheduling model, solving the energy scheduling model through the ant colony algorithm to obtain the output condition of each battery cluster, and sending the output condition to the output instruction issuing module
And the output instruction issuing module outputs an output condition distribution instruction of each battery cluster according to the actual load requirement.
Compared with the prior art, the invention has the beneficial effects that at least: the method comprises the steps of firstly extracting measured and calculated data in a certain scene from an electrochemical energy storage information acquisition system, then determining the weight of each data in an energy scheduling method by using grey correlation analysis, establishing an energy scheduling optimization model aiming at the factors of lowest cost, high operation efficiency, low loss rate and the like, finally solving the energy scheduling model by using an ant colony algorithm, and improving the ant colony algorithm by introducing a corresponding strategy, so that the electrochemical energy storage power station is reasonably scheduled in different time periods, the service life of the electrochemical energy storage power station is prolonged, and the sustainable development of the energy storage power station is promoted. Meanwhile, the operation efficiency of the whole battery energy storage power station is improved, and the service life of the battery is prolonged.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (10)
1. The electrochemical energy storage power station energy scheduling method based on the improved ant colony algorithm is characterized by comprising the following steps of:
step 1, extracting data measured and calculated in a certain scene from an information acquisition system of electrochemical energy storage;
step 2, performing characteristic selection processing on the energy storage power station by adopting a principal component analysis method, and screening to obtain characteristic data;
step 3, judging the demand instruction in three preset intervals, executing step 4 if the demand instruction is in a first interval, executing step 5 if the demand instruction is in a second interval, and executing step 7 if the demand instruction is in a third interval;
step 4, distributing the battery clusters according to the overall SOC values of the battery clusters in proportion to obtain the output condition of each battery cluster, and continuing to execute step 8;
step 5, determining the weight of each characteristic data obtained in the step 2 in the energy dispatching method by adopting grey correlation analysis, and establishing an energy dispatching multi-target function according to the weight to obtain an energy dispatching model;
step 6, solving an energy scheduling model by adopting an ant colony algorithm to obtain the output condition of each battery cluster;
step 7, each battery cluster runs at the highest power;
and 8, outputting a power condition distribution instruction of each battery cluster according to the actual load requirement.
2. The electrochemical energy storage power station energy scheduling method based on the improved ant colony algorithm as claimed in claim 1, characterized in that:
step 1, preprocessing acquired data, and supplementing missing data through neighbor values or mean values; outlier data is removed by curve fitting or clustering algorithms.
3. The electrochemical energy storage power station energy scheduling method based on the improved ant colony algorithm as claimed in claim 2, characterized in that:
the step 2 specifically comprises the following steps: analyzing the data collected in the step 1 by adopting a principal component analysis method, wherein the screened data comprises the following data: state of charge c of each cell cluster1Output power c of each battery cluster4Loss of power conversion device c5Charge and discharge rate of battery c6Variation of power per unit time c13。
4. The improved ant colony algorithm-based electrochemical energy storage power station energy scheduling method according to any one of claims 1 to 3, characterized in that:
in the step 3, a first interval means that the demand instruction RI is less than or equal to 30% of the rated power of the energy storage power station;
the second interval refers to that the rated power of the energy storage power station is more than 30% < RI < 90% of the rated power of the energy storage power station;
the third interval means that RI is more than or equal to 90 percent of rated power of the energy storage power station.
5. The electrochemical energy storage power station energy scheduling method based on the improved ant colony algorithm as claimed in claim 3, characterized in that:
the step 5 specifically comprises the following steps:
step 5.1, determining the association degree of the cost factor, the equipment loss factor and the output stability factor with the operation efficiency factor by adopting grey correlation analysis, and further calculating the weight of each factor in the energy scheduling method through normalization;
step 5.2, combining the weight obtained in the step 5.1, and establishing an energy scheduling multi-objective function with the lowest cost, high operation efficiency, small equipment loss rate and stable output; as expressed in the following formula,
minf＝ω1f1+ω2f2+ω3f3+ω4f4
in the formula:
f denotes an energy scheduling multi-objective function, f1Representing an objective function of the running cost, f2Representing an objective function of operating efficiency, f3Representing the equipment loss objective function, f4Representing an output stabilization objective function;
ω1weight value, omega, representing line cost objective function2Weight value, ω, representing an objective function of operating efficiency3Weight value, omega, representing the equipment loss objective function4A weight value representing an output stabilization objective function;
and 5.3, establishing boundary conditions.
6. The electrochemical energy storage power station energy scheduling method based on the improved ant colony algorithm as claimed in claim 5, wherein:
the step 5.1 specifically comprises the following steps:
step 5.1.1, carrying out standardization processing on the brought data; carrying out standardized processing on data of cost factors, equipment loss factors, output stability factors and operation efficiency factors;
step 5.1.2, respectively forming 3 comparison curves by the cost factor, the equipment loss factor and the output stability factor; forming a reference curve by the operating efficiency factors;
step 5.1.3, calculating the correlation coefficient of each data and the operation efficiency of the energy storage power station; as expressed in the following formula,
in the formula:
Δ min represents the minimum difference of the comparison curve and the reference curve,
Δ max represents the maximum difference of the comparison curve and the reference curve,
a represents a resolution coefficient and a represents a resolution coefficient,
Δi0(k) representing the absolute difference between each point on the comparison curve and the corresponding point on the reference curve,
βi0representing a correlation coefficient;
step 5.1.4, calculating the correlation degree of each data and the operation efficiency of the energy storage power station; as expressed in the following formula,
in the formula:
nirepresenting the comparison series of the factors with the reference series n0The degree of association of (a) with (b),
n represents the number of the associated factors;
and 5.1.5, sequencing the results according to the degree of association, and determining the weight of various data in the operating efficiency of the energy storage power station.
7. The electrochemical energy storage power station energy scheduling method based on the improved ant colony algorithm as claimed in claim 5 or 6, wherein:
in the step 5.2, the process is carried out,
for running cost objective function f1The expression is given by the following formula,
in the formula:
m1represents the electricity purchase cost saved by changing the control strategy,
m2representing reduced operation and maintenance costs of changing the control strategy,
m3representing the gains obtained by the energy storage power station under the change of the control strategy,
g represents the age limit;
for the running efficiency objective function f2The expression is given by the following formula,
in the formula:
d represents the number of the battery clusters in the energy storage power station,
Ebwhich represents the efficiency of the operation of the battery cluster,
E2indicating the efficiency of operation of the dc boost circuit,
E3representing the operation efficiency of the energy storage converter;
for the device loss objective function f3As expressed in the following formula,
in the formula:
λ1represents the loss coefficient of the electrical equipment for switching the charge-discharge state of the battery cluster,
N1represents the number of times of switching the charge and discharge states of the battery cluster,
λ2the loss factor of the energy storage converter equipment is shown,
N2indicating the number of times the energy storage converter device switches,
λ3the coefficient of the on-time is represented,
U1the running time of the energy storage battery cluster is shown,
u represents the total time of the statistics;
for the output stabilization objective function, expressed in the following formula,
in the formula:
o represents the total time for calculating the amplitude of the output power variation, and the unit represents second,
Ptbrepresents the output power of the b-th cell cluster at time t,
Δ t represents a preset unit time.
8. The electrochemical energy storage power station energy scheduling method based on the improved ant colony algorithm as claimed in claim 5 or 6, wherein:
the step 6 specifically comprises the following steps:
step 6.1, initializing information;
step 6.2, randomly selecting an ant starting point, and moving to a remote place according to the concentration of the released pheromone of the ants;
step 6.3, according to the initial pheromone, the ant state transition probability is calculated and expressed by the following formula,
in the formula:
p (i, r) represents the transfer probability value of ant r at the ith iteration,
max (V) represents the maximum value of the pheromone,
v (r) pheromone representing ant r;
6.4, calculating a distance calculation formula for the movement of the ants;
step 6.5, calculating the fitness of the ants and the food, namely a multi-objective function value, and sequencing according to the fitness; the fitness of ants to food is expressed by the following formula,
S＝f′-Pnew
in the formula:
f' is the objective function value at a single iteration,
Pnewis the length of the ants moving in the direction of the movement,
the meaning of S expression is that the lower the fitness value is, the higher the fitness is;
6.6, updating the pheromone concentration of each ant according to the sequence of the step 5.5; the pheromone concentration is updated by the formula,
in the formula:
z is pheromone volatilization factor;
step 6.7, judging whether a termination condition is met; if yes, ending the calculation process; if not, continuing to perform iterative optimization from step 5.8;
and 6.8, outputting the objective function, finding out and outputting the position corresponding to the ant with the highest adaptability value, and obtaining the output condition of each battery cluster.
9. The electrochemical energy storage power station energy scheduling method based on the improved ant colony algorithm as claimed in claim 8, wherein:
in a step 6.4, the process is carried out,
(1) if the state transition probability is less than or equal to the preset transition probability constant, the distance calculation formula is as follows:
Pnew＝Pold+δ·θ·Tp
in the formula:
Pnewis the position to be moved and is,
Poldis an antThe current position of the mobile phone is determined,
delta is the inverse of the current number of iterations,
theta is the step size to be moved,
Tpis [ -1,1 [ ]]A random number in between;
(2) if the state transition probability is greater than the preset transition probability constant, the distance calculation formula is as follows:
Pnew＝Pold+Q·Tp
in the formula:
q is the size of the independent variable interval.
10. The electrochemical energy storage power station energy scheduling system based on the improved ant colony algorithm runs the electrochemical energy storage power station energy scheduling method based on the improved ant colony algorithm according to any one of claims 1 to 9, and comprises the following steps: the ant colony optimization system comprises a data acquisition module, a feature processing module, a demand instruction judging module, an improved ant colony algorithm module and an output instruction issuing module, and is characterized in that:
the data acquisition module is used for extracting data measured and calculated in a certain scene from an electrochemical energy storage information acquisition system and sending the data to the feature processing module;
the characteristic processing module adopts a principal component analysis method to perform characteristic selection processing on the energy storage power station, screens characteristic data and sends the characteristic data to the improved ant colony algorithm module;
the demand instruction judging module is used for judging and executing the demand instruction in three preset intervals;
the improved ant colony algorithm module is used for determining the weight of the energy scheduling method occupied by each feature data obtained by the feature processing module through grey correlation analysis, establishing an energy scheduling multi-target function according to the weight to obtain an energy scheduling model, solving the energy scheduling model through the ant colony algorithm to obtain the output condition of each battery cluster, and sending the output condition to the output instruction issuing module
And the output instruction issuing module outputs an output condition distribution instruction of each battery cluster according to the actual load requirement.
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