CN109713666B - K-means clustering-based distributed energy storage economy regulation and control method in power market - Google Patents
K-means clustering-based distributed energy storage economy regulation and control method in power market Download PDFInfo
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Abstract
The invention discloses a K-means cluster-based distributed energy storage economy regulation and control method in a power market, which comprises the following steps of: step 1: and collecting power grid data, load data and distributed energy storage data. Step 2: and establishing a distributed energy storage economy model, and analyzing the operation cost and the income of the model. And step 3: and inputting parameters of distributed energy storage, and determining an initial clustering center. And 4, step 4: and clustering and grouping the distributed energy storage by using a K-means clustering algorithm. And 5: the method takes improving the economy of the distributed energy storage and saving the adjustable potential of the distributed energy storage to the maximum extent as a target function, and takes the grouping completed by clustering as a control unit to optimize the charging and discharging power of the distributed energy storage. Step 6: and issuing a charge and discharge instruction of distributed energy storage. The invention can balance the difference of service life loss among the distributed energy storage individuals in the operation process, reduce the total operation cost and improve the overall regulation and control potential of the distributed energy storage in the operation process.
Description
Technical Field
The invention belongs to the technical field of energy storage, relates to distributed energy storage, and particularly relates to a K-means cluster-based economic regulation and control method for distributed energy storage in an electric power market.
Background
Over the last few years, with the steady increase of peak load demand and the large-scale use of intermittent renewable energy resources, the power grid has become more vulnerable. The distributed energy storage can convert the electric energy into a more stable form to be stored in the device and released when needed. The characteristic enables the distributed energy storage to simultaneously play the roles of 'source' and 'load' in the power grid, and can effectively solve the problems brought to the power system by the access of the distributed power supply and the rapid increase of the load.
One important reason for the spread of distributed energy storage is its high operating cost. The document published by foreign scholars in the journal of Scientific & Technical Information Technical Reports [ Energy Storage Systems cost update: a study for the DOE Energy Storage Systems Program ] shows that the cost of using independent distributed Energy Storage is very high and that the independent distributed Energy Storage is not economically feasible. Therefore, it would be desirable to provide a reasonable solution to reduce the cost of using energy storage devices. In terms of single distributed energy storage, the capacity is limited, the power is small, and in contrast, the aggregated distributed energy storage has considerable quantity, flexible scheduling mode and huge scheduling potential for participating in the system, so that a proper regulation and control strategy is required to control the distributed energy storage. A document [ Ledwich g.timing belts of energy storage for aggregation of storage applications in electric distribution networks in Queensland ] published by researchers at conference Decision and Control proposes a novel distributed algorithm for optimizing an energy sharing network containing stored energy, and improving schedulability potential by improving cooperation among consumers and resource sharing to the maximum extent, however, the algorithm only simply considers the dispersed stored energy as a whole, does not consider optimization inside the distributed stored energy, and cannot fully utilize the schedulable potential of the distributed stored energy. Therefore, a resource scheduling scheme that can minimize the cost of distributed energy storage operation and maximize its economic benefits is necessary.
The invention content is as follows:
in order to overcome the defects of the prior art, the invention provides a distributed energy storage economy regulation and control method in the power market based on K-means clustering.
The invention adopts the following technical scheme:
a distributed energy storage economy regulation and control method under a power market based on K-means clustering comprises the following steps:
step 1: and collecting power grid data, load data and distributed energy storage data.
Step 2: and establishing a distributed energy storage economy model, and analyzing the operation cost and the income of the model.
And step 3: and inputting parameters of distributed energy storage, and determining an initial clustering center.
And 4, step 4: and clustering and grouping the distributed energy storage by using a K-means clustering algorithm.
And 5: the method takes improving the economy of distributed energy storage and storing the adjustable potential of the distributed energy storage as a target function to the maximum extent, takes the grouping completed by clustering as a control unit, and optimizes the charging and discharging power of the distributed energy storage.
Step 6: and issuing a charge and discharge instruction of distributed energy storage.
In step 5, the distributed energy storage operation objective function is:
f＝max(w 1 f 1 +w 2 f 2 )
wherein, the first and the second end of the pipe are connected with each other,
f 2 ＝min(min(|p max,c (t)|,|p max,d (t)|))，t＝1,2,...,24
in the formula (f) 1 Economic profit representing energy storage, f 2 Indicating the controllable potential of stored energy, w 1 ,w 2 Is weight, because the highest gain is the main target when the energy storage operates, w is taken as value 1 ＞w 2 ，
Represents the charging power of the ith distributed energy storage at the moment t,
the power discharge power of the ith distributed energy storage at the moment t is shown, R (t) shows the electricity price of a power grid at the moment t, delta t shows a preset time interval, B cap Distributed energy storage participationThe revenue obtained by the grid assistance service is,
is the life loss of the ith energy storage day, p max,c (t),p max,d And (t) respectively representing the maximum charge and discharge potential of the distributed energy storage at the moment t.
f 1 Item I of (1)
Is the energy sale income, the second item
Is the cost of purchasing energy, the two jointly constitute the operating profit of distributed energy storage, item B cap The fourth item is the income obtained by helping the peak clipping and valley filling of the power grid
Life loss generated during energy storage operation; f. of 2 Representing the schedulable potential of the distributed energy storage at each moment.
The constraints of the objective function are:
(1) distributed stored energy and power constraints:
e i ＝SoC i ×E i
wherein the content of the first and second substances,
is the upper and lower bounds of the energy storage capacity,
is the maximum charge-discharge power of the stored energy,upper energy boundary
Corresponding to the fastest path of absorbed energy, and a lower energy boundary
Corresponding to the fastest path to consume energy. SoC (system on chip) i Is the state of charge of the ith cell, E i Is the capacity of the ith battery.
The relationship between capacity and power is:
e i (t+1)＝e i (t)+p i (t)Δt
wherein e is i (t),e i (t +1) represents the energy inside the energy storage i at the moment t and the next moment, p (t) represents the power of the energy storage i at the moment t, and Δ t represents the time interval.
(2) Distributed energy storage current maximum charge-discharge power:
potential charge and discharge of energy storage
Can be calculated by the following formula:
wherein the content of the first and second substances,
the chargeable capacity and dischargeable capacity of the energy storage at time t are shown,
representing the maximum and minimum values of the internal energy reserve of the stored energy i.
Evaluating best chargeable potential through current distributed energy storage aggregation populationHigh charging power P max,c (t), the maximum charging power should last at least for one scheduled time interval Δ t.
e pc (t)+P max,c (t)×Δt≤e max
The distributed energy storage maximum charging power can be expressed as:
wherein, P + Indicating the charge limit of the stored energy itself, e max Represents the maximum energy reserve of the stored energy, and e (t) represents the energy reserve of the stored energy at time t.
Estimating maximum discharge power P through dischargeable potential of distributed energy storage aggregation totality max,d (t), the maximum discharge power should last at least for one scheduled time interval Δ t.
e pd (t)+P max,d (t)×Δt≥e min
The distributed energy storage maximum discharge power can be expressed as:
wherein, P - Indicating the discharge limit of the stored energy itself, e min Representing the minimum energy reserve of stored energy.
Therefore, the current maximum charge-discharge power of the distributed storage is:
(3) power constraint of a power grid:
wherein P is max ,P min The maximum charge and discharge power acceptable by the power grid is shown.
The distributed energy storage economy model in the step 2 is as follows:
currently, energy storage gains come from two parts. One part of the electric quantity benefit is the electric quantity benefit, and the other part of the electric quantity benefit is the benefit obtained by assisting the peak clipping and valley filling of the power grid through the service power grid. These two part benefits can be expressed as:
B mar ＝p sell e sell -p buy e buy
B cap ＝b cap P cap
wherein, B mar Representing the electric charge gain of stored energy, p sell ,e sell Indicating the price and quantity of electricity sold, p buy ,e buy Indicating the price and quantity of electricity purchased, B cap Representing the gain of energy storage participating in the auxiliary service, b cap Patches representing unit peak shaver, P cap Represents the total amount of peak shaver.
The total revenue from energy storage can be expressed as the sum of these two partial revenue:
B t ＝B mar +B cap
the battery energy storage is composed of an energy storage device and an energy conversion device, so the cost of the energy storage device can be expressed as:
C t ＝C bat +C PCS
wherein C is bat Representing the cost of the energy storage device, C PCS Representing the cost of the energy conversion device.
C bat ＝C e E t
C PCS ＝C p P t
Wherein, C e Indicating energy storage devicesPrice per unit capacity (yuan/kW. h), E t Representing the total capacity (kW. h), C of the energy storage installation p Representing the price per unit power (U/kW), P, of the energy conversion device t Representing the total power of the installation.
To a depth of discharge D a The life loss cost generated by one cycle of (a) is:
wherein N' a Is carried out to a depth of discharge D a The reduced cycle number in the charge/discharge behavior of (1).
In the market economic environment, the net daily gain B of distributed energy storage d Comprises the following steps:
where n represents the actual number of cycles.
The K-means cluster-based distributed energy storage economy regulation and control strategy in the power market in the step 5 is as follows:
in actual operation, the free charge and discharge of the released energy storage not only can cause unnecessary loss of the service life of the energy storage, but also can reduce the schedulable potential; and the cost and the workload for controlling the devices one by one are high. Therefore, clustering grouping is carried out on the distributed energy storage participating in operation, and the aggregation group is used as a unit to participate in regulation and control.
The clustering grouping of the large amount of distributed energy storage aims to cluster the distributed energy storage with similar regulation and control potential and operable space into a group, so that the demand response regulation and control capability of the energy storage is fully exerted in the scheduling process. The running state of the energy storage participating in the power system scheduling is a multidimensional variable, and comprises power, a charge state, available capacity and the like.
The K-means clustering is a commonly used clustering algorithm at present, has the advantages of simple and clear clustering idea and high operability, and is widely applied to load classification of a power system.
During regulation and control, distributed energy storage is required to be clustered into K groups of data with similar schedulable potential:
wherein, with D k Set of samples representing the Kth cluster group, C k Representing the clustering center of the Kth clustering group, the clustering objective function is the minimum mean square error of the whole cluster:
for the practical problem of large-amount energy storage load clustering, the actual scheduling situation deviates the expected regulation and control target due to too few classification numbers, and the too large classification number is beneficial to the consistency and similarity of the regulation and control potentials in each clustering group, but the regulation and control difficulty of aggregators is increased. Therefore, in the actual regulation and control process, the load agent can determine the maximum K value range based on the self regulation and control capability and further determine the optimal clustering group number by an exhaustion method.
In summary, the clustering grouping method based on the energy storage regulation potential and the parameter difference has the following process.
Step 1: the number of clusters K is determined.
And 2, step: and sequencing all the distributed energy storage parameters to determine an initial clustering center.
And step 3: starting from the distributed energy storage with the number i being 1, the distance from the distributed energy storage to the Kth clustering center is obtained, and the samples are clustered into the groups closest to the clustering centers.
And 4, step 4: the parameters of each cluster group are summed and averaged and updated to a new cluster center.
And 5: and (4) repeating the steps 3 to 4 until the clustering center meets the iteration convergence condition.
Before each scheduling is started, an aggregator needs to obtain load requirements and renewable energy generated energy, calculate the current adjustable potential of energy storage, formulate an energy storage operation target, and optimally adjust the output of a plurality of energy storage aggregation groups governed by the aggregator to maximally reach the operation target.
The invention has the advantages and beneficial effects that:
1. the invention provides a K-means clustering-based distributed energy storage optimization regulation and control model in a power market, which can balance the difference of service life loss among distributed energy storage individuals in the operation process, reduce the total operation cost and improve the overall regulation and control potential of distributed energy storage in the operation process.
2. The method is based on a typical daily load curve, and adopts a K-means cluster-based regulation and control method to optimize the charge and discharge power of distributed energy storage. The benefits of the power grid and the energy storage users are taken into consideration, the load peak-valley difference of the power distribution network is reduced, and the economic benefit of the energy storage users is improved.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of the basic principle of the regulation strategy proposed by the present invention;
FIG. 3 is a graph comparing net daily gains for a single stored energy using different values of K;
FIG. 4 is a graph comparing the life loss resulting from a one-day operation of distributed energy storage before and after use of the proposed strategy;
fig. 5 is a graph comparing the potential of distributed energy storage regulation at a typical time of the day before and after the test using the proposed strategy.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 shows an optimized operation method of distributed energy storage based on time-of-use electricity prices, which includes the following steps:
step 1: and collecting power grid data, load data and distributed energy storage data.
Step 2: and establishing a distributed energy storage economic model, and analyzing the operation cost and the profit of the model.
And 3, step 3: and inputting parameters of distributed energy storage, and determining an initial clustering center.
And 4, step 4: and clustering and grouping the distributed energy storage by using a K-means clustering algorithm.
And 5: the method takes improving the economy of distributed energy storage and storing the adjustable potential of the distributed energy storage as a target function to the maximum extent, takes the grouping completed by clustering as a control unit, and optimizes the charging and discharging power of the distributed energy storage.
Step 6: and issuing a charge and discharge instruction of distributed energy storage.
In step 5, the distributed energy storage operation objective function is:
f＝max(w 1 f 1 +w 2 f 2 )
wherein the content of the first and second substances,
f 2 ＝min(min(|p max,c (t)|,|p max,d (t)|))，t＝1,2,...,24
in the formula (f) 1 Representing economic benefit of stored energy, f 2 Indicating the controllable potential of stored energy, w 1 ,w 2 Is weight, because the highest gain is the main target when the energy storage operates, w is taken as value 1 ＞w 2 ，
Represents the charging power of the ith distributed energy storage at the moment t,
representing the discharge power of the ith distributed energy storage at the moment t, R (t) representing the electricity price of a power grid at the moment t, delta t representing a preset time interval, B cap The distributed energy storage participates in the income obtained by the auxiliary service of the power grid,
is the life loss of the ith energy storage day, p max,c (t),p max,d And (t) respectively representing the maximum charge and discharge potential of the distributed energy storage at the moment t.
f 1 Item I of (1)
Is the energy sale income, the second item
Is the cost of purchasing energy, the two jointly constitute the operating profit of distributed energy storage, item B cap The fourth item is the income obtained by helping the peak clipping and valley filling of the power grid
Life loss generated during energy storage operation; f. of 2 Representing the schedulable potential of the distributed energy storage at each moment.
The constraints of the objective function are:
(1) distributed stored energy and power constraints:
e i ＝SoC i ×E i
wherein the content of the first and second substances,
is the upper and lower bounds of the energy storage capacity,
is the maximum charge-discharge power of stored energy, the upper energy boundary
Corresponding to the fastest path of absorbed energy, and a lower energy boundary
Corresponding to the fastest path to consume energy. SoC (system on chip) i Is the state of charge of the ith cell, E i Is the capacity of the ith battery.
The relationship between capacity and power is:
e i (t+1)＝e i (t)+p i (t)Δt
(2) distributed energy storage current maximum charge-discharge power:
potential charge and discharge of energy storage
Can be calculated by the following formula:
estimating maximum charging power P through chargeable potential of current distributed energy storage aggregation totality max,c (t), the maximum charging power should last at least for one scheduled time interval Δ t.
e pc (t)+P max,c (t)×Δt≤e max
The distributed energy storage maximum charging power can be expressed as:
estimating maximum discharge power P through dischargeable potential of distributed energy storage aggregation totality max,d (t), the maximum discharge power should last at least for one scheduled time interval Δ t.
e pd (t)+P max,d (t)×Δt≥e min
The distributed energy storage maximum discharge power can be expressed as:
therefore, the current maximum charge-discharge power of the distributed storage is:
(3) power grid power constraint:
wherein P is max ,P min The maximum charge and discharge power acceptable by the power grid is shown.
The distributed energy storage economy model in the step 2 is as follows:
currently, energy storage gains come from two parts. One part of the electric quantity benefit is the electric quantity benefit, and the other part of the electric quantity benefit is the benefit obtained by assisting the peak clipping and valley filling of the power grid through the service power grid. These two part gains can be expressed as:
B mar ＝p sell e sell -p buy e buy
B cap ＝b cap P cap
the total revenue from energy storage can be expressed as the sum of these two partial revenue:
B t ＝B mar +B cap
the battery energy storage is composed of an energy storage device and an energy conversion device, so the cost of the energy storage device can be expressed as:
C t ＝C bat +C PCS
wherein C is bat Representing the cost of the energy storage device, C PCS Representing the cost of the energy conversion device.
C bat ＝C e E t
C PCS ＝C p P t
Wherein, C e Representing the price per unit capacity (yuan/kW.h), E of the energy storage device t Representing the total capacity (kW. h), C of the energy storage installation p Representing the price per unit power (U/kW), P, of the energy conversion device t Representing the total power of the installation.
To a depth of discharge D a The life loss cost generated by one cycle of (a) is:
wherein N' a Is carried out to a depth of discharge D a The reduced cycle number in the charge/discharge behavior of (1).
In the market economic environment, the net daily gain B of distributed energy storage d Comprises the following steps:
where n represents the actual number of cycles.
The K-means cluster-based distributed energy storage economy regulation and control strategy in the power market is characterized by comprising the following steps:
in actual operation, the free charge and discharge of the released energy storage not only leads to unnecessary loss of the service life of the energy storage, but also reduces the scheduling potential, as shown in fig. 5; and the cost and the workload for controlling the devices one by one are high. Therefore, clustering grouping is carried out on the distributed energy storage participating in operation, and the aggregation group is taken as a unit to participate in regulation and control.
The basic purpose of clustering and grouping a large amount of distributed energy storage is to cluster distributed energy storage with similar regulation and control potential and operable space into a group, so that the demand response regulation and control capability of the energy storage is fully exerted in the scheduling process. The running state of the energy storage participating in the power system scheduling is a multidimensional variable, and comprises power, a charge state, available capacity and the like.
The K-means clustering is a commonly used clustering algorithm at present, has the advantages of simple and clear clustering idea and high operability, and is widely applied to load classification of a power system.
During regulation and control, the distributed energy storage is required to be clustered into K groups of data with similar schedulable potential, and D is used k Set of samples representing the Kth cluster group, C k Representing the clustering center of the Kth clustering group, the clustering objective function is the minimum mean square error of the whole cluster:
for the practical problem of large-amount energy storage load clustering, the actual scheduling situation deviates the expected regulation and control target due to too few classification numbers, and the too large classification number is beneficial to the consistency and similarity of the regulation and control potentials in each clustering group, but the regulation and control difficulty of aggregators is increased. Therefore, in the actual regulation and control process, the load agent can determine the maximum K value range based on the self regulation and control capability and further determine the optimal clustering group number by an exhaustion method.
In summary, the clustering grouping method based on the energy storage regulation potential and the parameter difference has the following process.
Step 1: the number of clusters K is determined.
Step 2: and sequencing all the distributed energy storage parameters to determine an initial clustering center.
And step 3: starting from the distributed energy storage with the number i being 1, the distance from the distributed energy storage to the Kth clustering center is obtained, and the samples are clustered into the groups closest to the clustering centers.
And 4, step 4: the parameters of each cluster group are summed and averaged and updated to a new cluster center.
And 5: and (4) repeating the steps 3 to 4 until the clustering center meets the iteration convergence condition.
Before each scheduling is started, an aggregator needs to obtain load requirements and renewable energy generated energy, calculate the current adjustable potential of energy storage, formulate an energy storage operation target, and optimally adjust the output of a plurality of energy storage aggregation groups governed by the aggregator to maximally reach the operation target.
Example analysis
The system is provided with 500 energy storages for polymerization analysis, the capacity of the energy storages is 150 energy storages of 100MW & h, 150 energy storages of 150MW & h and 200 energy storages of 200MW & h respectively, the rated charge and discharge power is 1MW, the charge and discharge efficiency is 95%, parameters of each energy storage during access are shown in figure 5, the unit power price of the energy conversion device is 3224 × 103 (yuan/(MW)), the unit capacity price of the energy storage device is 1085 × 103 (yuan/(MW & h)), and the power grid power selling price is selected from the Beijing market peak-valley time-share power price standard (see table 1). According to the provision of 'notice on developing electric power demand response market trial work' in China, compensation cost of no more than 30 yuan is given at most when 1 kilowatt load is responded, the limit standard for a certain province in China is 8 times, the compensation for each kilowatt is 3.75 yuan, and the compensation is performed 2 times in a peak period of one day. In order to protect the service life of the battery, 0.9 is generally taken as the maximum value of the state of charge, and 0.1 is taken as the minimum value of the state of charge during energy storage operation. The energy storage capacity is calculated according to an exponential model, the battery capacity is reduced to 60% scrappage, and the total cycle number is 2511 times.
TABLE 1 Peking City peak-valley time-of-use electricity price standard
Assuming that an energy storage aggregator needs to regulate and control 500 energy storages in the control range of the energy storage aggregator, and the initial charge state of the distributed energy storage is randomly selected from 0.1-0.9. The 500 energy stores were grouped using the clustering method of step 4 and the results are shown in table 2.
TABLE 2 distributed energy storage economic benefits
It can be seen that the proposed method can effectively reduce the total cost during energy storage operation and improve the economic benefit of energy storage. Meanwhile, because the energy storage parameters in the aggregation groups are similar, each group has similar operable space, and the decision making of an aggregator is facilitated. When the grouping number reaches 10, the net profit of the grouping number energy storage is improved, the operation complexity increased from the net profit is greatly improved, and the experiment selects to divide the energy storage into 10 groups for regulation and control.
As can be seen from the comparison of fig. 3, when the number of packets reaches 10, the net benefit of individual energy storage is reduced, the profit fluctuation between energy storage is increased, but the total benefit of energy storage is greatly improved. This is because young, state-of-charge, stored energy will act more frequently by optimizing the control of the regulation strategy, and these stored energy inevitably assume a major profitable role.
Energy storage the life loss resulting from one day of operation was observed each time, see figure 4. It can be seen that the service life loss of the stored energy is relatively large without considering the operation strategy of optimal regulation, which is not beneficial to the long-term operation of the distributed stored energy. In contrast, the life loss of the stored energy is relatively small in consideration of the operation strategy of optimizing regulation, and the regulation strategy provided by the invention is favorable for the cost of distributed energy storage compression and improves the economic benefit.
Comparing the potential of energy storage regulation before and after polymerization: the unclustered energy storage operation is rigid, the schedulable potential is small, the clustered energy storage operation is flexible, and the schedulable potential is large because the available capacity of some energy storage is insufficient, the charging (discharging) capability can be temporarily lost before the energy storage is discharged (charged) again after the energy storage is used, so that the schedulable potential is reduced, and a aggregator can not meet the requirement of the power system in serious cases. As can be seen from the observation of fig. 5, on the day 11, since each stored energy is almost fully charged, a large part of the randomly-operated stored energy loses the charging capability, and the schedulable potential is greatly reduced; and the energy storage after polymerization is charged by preferentially using the small group with larger residual capacity, so that the schedulable potential is saved, and a certain charging capacity is provided at the moment.
In summary, the energy storage aggregation model provided herein can effectively optimize and improve the economic benefit of the energy storage device after aggregation, and simultaneously, the charge and discharge potential of distributed energy storage is greatly improved.
It should be understood that the present embodiments are illustrative only and not intended to limit the scope of the present invention, and that modifications and variations made by those skilled in the art in light of the teachings of the present invention without departing from the scope thereof should be construed as being within the full scope of the invention.
Claims (1)
1. A distributed energy storage economy regulation and control method under a power market based on K-means clustering is characterized by comprising the following steps: the method comprises the following steps:
step 1: collecting power grid data, load data and distributed energy storage data;
step 2: establishing a distributed energy storage economy model, and analyzing the operation cost and the income of the model;
and step 3: inputting parameters of distributed energy storage, and randomly determining an initial clustering center;
and 4, step 4: clustering and grouping the distributed energy storage by using a K-means clustering algorithm;
and 5: optimizing the charging and discharging power of the distributed energy storage by taking the clustering-completed group as a control unit and taking the improvement of the economy of the distributed energy storage and the maximum preservation of the adjustable potential as a target function;
step 6: issuing charging and discharging instructions of distributed energy storage;
in step 5, the distributed energy storage operation objective function is:
f＝max(w 1 f 1 +w 2 f 2 )
wherein:
f 2 ＝min(min(|p max,c (t)|,|p max,d (t)|))，t＝1,2,...,24
in the formula (f) 1 Representing economic benefits of distributed energy storage, f 2 Representing the controllable potential of the distributed energy storage at each moment, w 1 ,w 2 Is weight, and w is the value-taking time because the distributed energy storage operation takes the highest gain as the main target 1 ＞w 2 ，
Indicating the discharge power of the ith distributed energy storage at time t,
represents the charging power of the ith distributed energy storage at the moment t, R (t) represents the electricity price of a power grid at the moment t, delta t represents a preset time interval, B cap Representing the income obtained by the distributed energy storage participating in the auxiliary service of the power grid,
is the life loss of the ith distributed energy storage for one day, p max,c (t),p max,d (t) respectively representing the maximum charging power and the maximum discharging power of distributed energy storage at the time t;
f 1 item I of (1)
Is the energy sale revenue, item two
Is the cost of purchasing energy, the two jointly constitute the operating profit of distributed energy storage, item B cap The fourth item is the income obtained by helping the peak clipping and valley filling of the power grid
Life loss generated during distributed energy storage operation;
the distributed energy storage energy and power constraints of the objective function are:
e i ＝SoC i ×E i
wherein e is i Is the energy inside the ith distributed storage runtime,
upper and lower bounds, p, of distributed stored energy, respectively i Is the power at the time of the ith distributed energy storage operation,
maximum charge and discharge power of distributed energy storage, upper energy boundary
Corresponding to the fastest path to absorb energy, lower energy boundary
Corresponding to the fastest path to consume energy; SoC (system on chip) i Is the state of charge of the ith distributed energy storage, E i Is the capacity of the ith distributed energy storage;
the maximum charging power of the distributed energy storage at the moment t is represented as:
wherein, P + Representing the charge limit of the distributed energy storage itself, e max Representing the maximum energy reserve, e, of the distributed energy storage pc (t) represents the charging potential of the distributed energy storage at time t;
the maximum discharge power of the distributed energy storage at the moment t is represented as:
wherein, P - Represents the discharge limit of the distributed energy storage itself, e min Representing the minimum energy reserve, e, of the distributed energy store pd (t) represents the discharge potential of the distributed energy storage at time t;
power grid power constraint:
step 2, the distributed energy storage economic model is as follows:
total revenue B for distributed energy storage t Is shown as
B t ＝B mar +B cap
B mar ＝p sell e sell -p buy e buy
B cap ＝b cap P cap
Wherein, B mar Representing the electric yield, p, of distributed energy storage sell ,e sell Indicating the price and quantity of electricity sold, p buy ,e buy Indicating the price and quantity of electricity purchased, B cap Representing the gains of the distributed energy storage participating in the grid auxiliary service, b cap Patches representing unit peak shaver, P cap Represents the total peak shaver amount;
cost C of distributed energy storage device t Expressed as:
C t ＝C bat +C PCS
wherein C is bat Representing the cost of the distributed energy storage, C PCS Indicating energy conversion deviceThe cost of (a);
C bat ＝C e E t
C PCS ＝C p P t
wherein, C e Represents the price per unit capacity of the distributed energy storage device, E t Representing the total capacity of the distributed energy storage installation, C p Representing the price per unit power, P, of the energy conversion device t Representing the total power of the installed energy conversion devices;
to a depth of discharge D a Life loss cost C generated by one cycle of loss Expressed as:
net daily gain B of distributed energy storage d Comprises the following steps:
wherein n represents the actual number of cycles;
the method for clustering and grouping the distributed energy storage by using the K-means clustering algorithm in the step 4 comprises the following steps:
step (1): determining the clustering number K;
step (2): sequencing all distributed energy storage parameters to determine an initial clustering center;
and (3): starting from the distributed energy storage with the number i being 1, calculating the distance from the distributed energy storage to the Kth clustering center, and clustering samples into the closest group to the clustering center;
and (4): calculating the sum and the average of the parameters of each clustering group, and updating the parameter sum and the average to be a new clustering center;
and (5): and (4) repeating the steps (3) to (4) until the clustering center meets the iteration convergence condition.
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