CN113507110A - Distributed energy storage cluster optimization control method for improving new energy consumption in power distribution network - Google Patents

Distributed energy storage cluster optimization control method for improving new energy consumption in power distribution network Download PDF

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CN113507110A
CN113507110A CN202110682188.2A CN202110682188A CN113507110A CN 113507110 A CN113507110 A CN 113507110A CN 202110682188 A CN202110682188 A CN 202110682188A CN 113507110 A CN113507110 A CN 113507110A
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energy storage
cluster
distribution network
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李翠萍
李军徽
东哲民
高崑齐
朱星旭
马得轩
孙大朋
闫佳琪
陈钊
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Northeast Electric Power University
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Northeast Dianli University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract

The invention relates to a distributed energy storage cluster optimization control method for improving new energy consumption in a power distribution network, which aims at the problem of resource waste caused by poor space-time matching of distributed power supply output and load, the cluster is applied to energy storage control of the power distribution network, and by adopting power distribution network cluster division indexes and a cluster division method, establishing a distributed energy storage cluster control method and establishing evaluation index steps of the control method, regional division is carried out on the power distribution network to analyze regional source-load matching relation, so that energy storage power of each region is determined, and optimal energy storage time sequence output is determined according to economy; the distribution network is divided in a cluster mode by adopting the electrical index of the active balance degree, so that the balance between regional load and power supply and demand can be improved, the advantage that distributed energy storage participates in power grid regulation is played, and the energy consumption in the region is promoted. Has the advantages of scientific and reasonable method, strong applicability, good effect and the like.

Description

Distributed energy storage cluster optimization control method for improving new energy consumption in power distribution network
Technical Field
The invention belongs to the field of distributed energy storage, and particularly relates to a distributed energy storage cluster optimization control method for improving new energy consumption in a power distribution network.
Background
With the development of power distribution network technology, the proportion of load and renewable energy sources connected to the power distribution network is changing continuously. In order to absorb renewable energy, adjust load and optimize power distribution network power distribution, the distributed power supply device and the distributed energy storage technology are applied more and more widely in the field of power distribution networks by virtue of the advantages of the distributed power supply device and the distributed energy storage technology. The problem that the output intermittency of the distributed power supply is poor in time-space matching with the load of a power grid is more and more obvious when the access scale of the distributed power supply is continuously increased, and the consumption capacity of the power distribution network to the distributed power supply is gradually reduced. If the output of the distributed power supply is limited, large resource waste is caused, the effect of optimizing the access position and capacity of the distributed power supply is limited, and local consumption of the distributed power supply is difficult to realize. The distributed power supply has the characteristics of large access quantity, scattered positions and the like, local power excess easily occurs, the phenomenon of power transmission to a superior power grid is caused, and adverse effects are brought to the voltage quality, the relay protection setting and the like.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a distributed energy storage cluster optimization control method which is scientific, reasonable, high in applicability and good in effect and can improve the consumption of new energy in a power distribution network. The method aims to control the power grid in a partitioned mode through a cluster control mode, further realize local consumption of regional resources and reduce the influence of inter-regional power flow on the power grid.
The technical scheme adopted for realizing the purpose of the invention is as follows: a distributed energy storage cluster optimization control method for improving new energy consumption in a power distribution network is characterized by comprising the following steps:
step 1, distribution network cluster division index and cluster division method
1) For the index of the power distribution network cluster division,
the modularity index defined based on the electric distance weight is adopted to measure the structural strength of the power network division,
the modularity index defined based on the electrical distance weight is as follows:
Figure BDA0003123231930000011
in the formula: f. of1Is the modularity based on the electrical distance, rho is the system modularity, m is the sum of all the side weights in the network, dijIs the electrical distance between node i and node j, kiIs the sum of the edge weights of the edges connected to the inode, kjIs the sum of the edge weights of the edges connected to the j node, and
Figure BDA0003123231930000012
corresponding to dijThe specific calculation formula is as follows:
Figure BDA0003123231930000021
Figure BDA0003123231930000022
in the formula: sijObtaining the voltage sensitivity of each node by inverting the Jacobian matrix in load flow calculation for the elements in the ith row and the j column in the sensitivity matrix;
Figure BDA0003123231930000023
representing the maximum value in the jth column element in the sensitivity matrix; n is the number of network nodes;
measuring the balance capability of the source load active power in the cluster by adopting the active power balance degree; in order to express the matching degree of the internal source loads of the cluster under a certain time scale, an active power balance degree index is defined in a form based on net power, and the method specifically comprises the following steps:
Figure BDA0003123231930000024
Figure BDA0003123231930000025
in the formula: pckIs the ckThe active balance index of each cluster; t is the time scale of the scene, and is taken as 96; pclu(t) is the total net power value of all clusters at time t; pclu(t)ckIs the ckNet power value of each cluster at t moment;
Figure BDA0003123231930000026
the active balance degree index of the divided clusters; c is the total number of clusters;
2) for the power distribution network cluster division method, the factors of the overall modularity and the active power balance degree of the integrated system take the division mode of the system as variables, and on the basis of the autonomous regulation and control of each cluster area, an integrated index power grid cluster division model is established as follows:
F＝λ1f1+λ2f2 (7)
in the formula: λ 1 and λ 2 are weight coefficients of different indexes, where λ 1+ λ 2 is 1, and λ 1 is 0.5;
step 2, establishing a distributed energy storage cluster control method
1) The optimal daily operating benefit of the stored energy under the set configuration is taken as an optimization target, and an objective function is established as follows:
maxFP＝FDG+Floss (8)
in the formula: fPIncome from stored energy operation, FDGAdditional consumption of new energy for energy storage, FlossNetwork loss revenue brought to energy storage operation;
wherein, the energy storage consumes the electricity selling income F brought by the new energyDGThe electric power selling income brought by absorbing and transmitting power for energy storage and releasing in the peak load period is as follows:
FDG＝Fsale-Fbuy (9)
Figure BDA0003123231930000031
Figure BDA0003123231930000032
in the formula: fDGThe electricity selling income brought by new energy is consumed for energy storage; fsaleThe electricity selling income brought by the stored energy and the released electric energy; fbuyThe electricity purchasing cost for energy storage charging is 0 when the energy storage operation is in a new energy consumption area; pess,k,c(t) represents the charging power of the stored energy k at time t; pess,k,d(t) represents the discharge power of the stored energy k at time t; m (t) is the time-of-use electricity price of purchasing electricity from the main grid at the moment t; n is a radical ofEThe DES access total number;
network loss gain F brought by energy storage operationlossThe method specifically comprises the following steps:
Floss＝Floss1-Floss2 (12)
Figure BDA0003123231930000033
Figure BDA0003123231930000034
in the formula: ploss,n(t) is the original network active power loss, P, of the nth branch of the distribution networkloss-Ess,n(t) network active loss after the branch is accessed in the stored energy, NLIs the total number of branches; the network loss gain is the gain brought by the reduction of the system network loss before and after the energy storage access;
constraint conditions of the objective function mainly come from power distribution network and energy storage operation, including power flow constraint, voltage constraint, energy storage charge state constraint, power and capacity constraint, and specifically include:
constraint of tidal current equation
Figure BDA0003123231930000035
In the formula: pi(t)、Qi(t) injecting active and reactive power of the node i at the moment t; u shapei(t)、Uj(t) is the voltage amplitude of the node i and j at the time t; gij、BijRespectively representing the real part and the imaginary part of the j element in the ith row and the j element in the node admittance matrix; deltaij(t) is the phase angle difference of the nodes i and j at the moment t, and N is the number of system nodes;
(ii) ESS State of Charge constraint
SOC，min≤SOC(t)≤SOC，max (16)
SOC(0)＝SOC(T) (17)
-PESS,N≤PESS(t)≤PESS,N (18)
In the formula: sOC,minThe lower limit of the energy storage charge state is taken as 0.1; sOC,maxThe upper limit of the energy storage charge state is 0.9; sOC(t) is the state of charge of the stored energy at time t; the initial state of charge is equal to the end-cycle state of charge, 0.45 is selected; pESS(t) is the energy storage power at time t; pESS,NRated power for energy storage;
third, node voltage constraint
Umin≤Un,t≤Umax (19)
In the formula: u shapeminFor allowing minimum value of node voltage, UmaxFor the maximum allowed node voltage, Un,tSetting the voltage constraint range of the node of the power distribution network to be 0.95U for the voltage value of the node n at the time tN-1.05UN；
Proportional constraint of distributed power supply consumption
The maximum value of the new energy consumption ratio is regulated to be 1, the minimum value is regulated to be 0,
0≤ηDG1 (20) wherein: etaDGThe proportion is consumed for the distributed power supply;
step 3, establishing evaluation indexes of the control method
Distributed consumption ratio
Defining the consumption proportion eta of the distributed power supply as the electric quantity E of the distributed power supply with additional consumptionadAnd E should be discarded without taking measuresabThe ratio of the amounts of electricity of;
Figure BDA0003123231930000041
number of voltage out-of-limit nodes
The node voltage of the power distribution network is one of main factors for limiting the consumption of the distributed power supply, and the node voltage is out-of-limit for consuming new energy and is characterized as follows:
Figure BDA0003123231930000042
in the formula, NeThe total number of the out-of-limit nodes of the power grid voltage is obtained; n is the total number of nodes; the out-of-limit condition L of the n node voltage in the time scale TnIs 1, otherwise is 0;
③ load peak-valley difference
The energy storage is utilized to absorb the reverse power of the distribution network, the reverse power is released during the load peak to play a role in peak clipping and valley filling, and the maximum value P of the load power on a net load curve is defineds.maxAnd a minimum value Ps.minThe difference of (A) is the load peak-to-valley difference Pfg；
Pfg＝Ps,max-Ps,min (23)
Degree of load fluctuation
Defining the average value of the load power difference values of all adjacent moments in one day as the load fluctuation degree;
Figure BDA0003123231930000051
in the formula: pcgTo the extent of load fluctuations, PtLoad power at time t.
The invention relates to a distributed energy storage cluster optimization control method for improving new energy consumption in a power distribution network, which aims at the problem of resource waste caused by poor space-time matching of distributed power supply output and load, the cluster is applied to energy storage control of the power distribution network, and by adopting power distribution network cluster division indexes and a cluster division method, establishing a distributed energy storage cluster control method and establishing evaluation index steps of the control method, regional division is carried out on the power distribution network to analyze regional source-load matching relation, so that energy storage power of each region is determined, and optimal energy storage time sequence output is determined according to economy; the distribution network is divided in a cluster mode by adopting the electrical index of the active balance degree, so that the balance between regional load and power supply and demand can be improved, the advantage that distributed energy storage participates in power grid regulation is played, and the energy consumption in the region is promoted. Has the advantages of scientific and reasonable method, strong applicability, good effect and the like.
Drawings
FIG. 1 is a diagram of a power distribution network architecture;
FIG. 2 is a power distribution network solution flow diagram;
FIG. 3 is a flow chart of a cluster energy storage control method implementation;
FIG. 4 is a distributed power access diagram;
FIG. 5 is a diagram of a distribution network cluster partitioning result;
FIG. 6 is a power diagram of a power supply load prior to an energy storage access;
FIG. 7 is a graph of net power of clusters before energy storage access;
FIG. 8 is a diagram of energy storage output conditions of nodes in the scheme 2;
FIG. 9 is a diagram of system load power before and after energy storage participation adjustment in different scenarios;
FIG. 10 is a diagram of system network loss under different scenarios;
FIG. 11 is a graph of the voltage levels at various nodes at various times for scenario 1;
FIG. 12 is a graph of the voltage levels at various nodes at various times for scenario 2.
Detailed Description
The present invention will now be described in further detail with reference to specific embodiments thereof, which are illustrated by the following examples, but it should be understood that the scope of the present invention is not limited thereto.
The invention discloses a distributed energy storage cluster optimization control method for improving new energy consumption in a power distribution network, which comprises the following steps: firstly, establishing a comprehensive performance index which gives consideration to both system modularity and active power balance, and providing a cluster division method based on a genetic algorithm; then, performing peak clipping and valley filling on the distribution network and the cluster layer by layer, and sequentially determining cluster energy storage and node energy storage power; under the constraint conditions of power distribution system safety, energy storage system operation and the like, a cluster energy storage economic model is constructed, the proportion of new energy which can be consumed under the existing energy storage configuration and the daily operation income of the energy storage system are analyzed, and the optimal time sequence output of each node energy storage is determined, and the method specifically comprises the following steps:
step 1, power distribution network cluster division
The power distribution network cluster division comprises a cluster division index and a cluster division method.
And for the cluster division index, the modularity index and the active power balance index are integrated to perform cluster division on the power distribution network so as to ensure that the method can effectively improve the new energy consumption proportion. The modularity is a common index for measuring the structural strength of the network, and the structural strength of the power network division is measured by mostly adopting the modularity index based on the electrical distance in the power distribution network. The active power balance degree refers to the balance capability of the active power of the source charge in the cluster.
The modularity index defined based on the electrical distance weight is as follows:
Figure BDA0003123231930000061
in the formula: f. of1Is the modularity based on the electrical distance, rho is the system modularity, m is the sum of all the side weights in the network, dijIs the electrical distance between node i and node j, kiIs the sum of the edge weights of the edges connected to the inode, kjIs the sum of the edge weights of the edges connected to the j node.
And is
Figure BDA0003123231930000062
Corresponding to dijThe specific calculation formula is as follows:
Figure BDA0003123231930000063
Figure BDA0003123231930000064
in the formula: sijObtaining the voltage sensitivity of each node by inverting the Jacobian matrix in load flow calculation for the elements in the ith row and the j column in the sensitivity matrix;
Figure BDA0003123231930000065
representing the maximum value in the jth column element in the sensitivity matrix; n is the number of network nodes.
In order to express the matching degree of the internal source loads of the cluster under a certain time scale, an active power balance degree index is defined in a form based on net power, and the method specifically comprises the following steps:
Figure BDA0003123231930000066
Figure BDA0003123231930000071
in the formula: pckIs the ckThe active balance index of each cluster; t is the time scale of the scene, here taken as 96; pclu(t) is the total net power value of all clusters at time t; pclu(t)ckIs the ckAnd (4) the net power value at the t moment of each cluster.
Figure BDA0003123231930000074
The active balance degree index of the divided clusters; and c is the total number of clusters.
For the cluster division method, factors such as the overall modularity and the active balance of the system are integrated, the division mode of the system is used as a variable, and on the basis of realizing autonomous regulation and control of each cluster area as far as possible, a power grid cluster division model considering the integrated indexes is established as follows.
F＝λ1f1+λ2f2 (7)
In the formula: λ 1 and λ 2 are weighting coefficients of different indexes, where λ 1+ λ 2 is 1, and λ 1 is λ 2 is 0.5.
The invention adopts a genetic algorithm to solve the established cluster partitioning model. And (3) adopting a binary coding form, and respectively setting the positions of all genes as 0 or 1 to represent the connection condition of all branches of the power distribution network. Taking the IEEE33 distribution system as an example, each gene position of a certain chromosome is defined to be 00001000000000000000000010000000, and the division situation of the corresponding distribution network is shown in fig. 1. The solution flow is shown in fig. 2.
Step 2, establishing a distributed energy storage cluster control method
The optimal daily operating benefit of the stored energy under the set configuration is taken as an optimization target, and an objective function is established as follows:
maxFP＝FDG+Floss (8)
in the formula: fPIncome from stored energy operation, FDGAdditional consumption of new energy for energy storage, FlossAnd network loss and gain brought to energy storage operation.
Wherein, the energy storage consumes the electricity selling income F brought by the new energyDGThe energy is stored, the power is absorbed and sent back, and the electricity is released in the peak load period to bring electricity selling benefits. The method specifically comprises the following steps:
FDG＝Fsale-Fbuy (9)
Figure BDA0003123231930000072
Figure BDA0003123231930000073
in the formula: fDGThe electricity selling income brought by new energy is consumed for energy storage; fsaleThe electricity selling income brought by the stored energy and the released electric energy; fbuyThe electricity purchasing cost for energy storage charging is 0 when the energy storage operation is in a new energy consumption area; pess,k,c(t) represents the charging power of the stored energy k at time t; pess,k,d(t) represents the discharge power of the stored energy k at time t; m (t) is the time-of-use electricity price of purchasing electricity from the main grid at the moment t; n is a radical ofETotal number for DES accesses.
Network loss gain F brought by energy storage operationlossThe method specifically comprises the following steps:
Floss＝Floss1-Floss2 (12)
Figure BDA0003123231930000081
Figure BDA0003123231930000082
in the formula: ploss,n(t) is the original network active power loss, P, of the nth branch of the distribution networkloss-Ess,n(t) network active loss after the branch is accessed in the stored energy, NLIs the total number of branches. The network loss gain is the gain brought by the reduction of the system network loss before and after the energy storage access.
Constraint conditions of the objective function mainly come from power distribution network and energy storage operation, including power flow constraint, voltage constraint, energy storage charge state constraint and power and capacity constraint. The method comprises the following specific steps:
1) flow equation constraints
Figure BDA0003123231930000083
In the formula: pi(t)、Qi(t) injecting active and reactive power of the node i at the moment t; u shapei(t)、Uj(t) is the voltage amplitude of the node i and j at the time t; gij、BijRespectively representing the real part and the imaginary part of the j element in the ith row and the j element in the node admittance matrix; deltaijAnd (t) is the phase angle difference of the nodes i and j at the time t, and N is the number of system nodes.
2) ESS state of charge constraint
SOC，min≤SOC(t)≤SOC，max (16)
SOC(0)＝SOC(T) (17)
-PESS,N≤PESS(t)≤PESS,N (18)
In the formula: sOC,minThe lower limit of the energy storage charge state is 0.1; sOC,maxFor the upper limit of the energy storage state of charge, this is taken to be 0.9; sOC(t) is the state of charge of the stored energy at time t; the initial state of charge is equal to the end-of-cycle state of charge, which is 0.45; pESS(t) is the energy storage power at time t; pESS,NRated power for storing energy.
3) Node voltage constraint
Umin≤Un,t≤Umax (19)
In the formula: u shapeminFor allowing minimum value of node voltage, UmaxFor the maximum allowed node voltage, Un,tIs the voltage value of the node n at the time t. Setting the voltage constraint range of the nodes of the power distribution network to be 0.95UN-1.05UN。
4) Distributed power supply consumption proportional constraint
The maximum value of the new energy consumption ratio is 1, and the minimum value is 0.
0≤ηDG1 (20) wherein: etaDGThe scale is taken up for the distributed power supply.
Step 3, establishing evaluation indexes of the control method
In order to reflect that the control method of the invention has good effectiveness and advancement, the following indexes are established for evaluating the control method:
1) distributed ratio of consumption
Defining the consumption proportion eta of the distributed power supply as the electric quantity E of the distributed power supply with additional consumptionadAnd E should be discarded without taking measuresabThe ratio of the electric quantity of (c).
Figure BDA0003123231930000091
2) Number of voltage out-of-limit nodes
The node voltage of the power distribution network is one of main factors for limiting the consumption of the distributed power supply, and the problem that the node voltage is out of limit and the like possibly caused by excessive consumption of new energy is solved, so that the power supply quality is reduced. It is characterized as:
Figure BDA0003123231930000092
in the formula, NeThe total number of the out-of-limit nodes of the power grid voltage is obtained; n is the total number of nodes; the out-of-limit condition L of the n node voltage in the time scale TnIs 1, otherwise is 0.
3) Peak to valley difference of load
The energy storage is utilized to absorb the reverse power of the distribution network, the reverse power is released during the load peak to play a role in peak clipping and valley filling, and the maximum value P of the load power on a net load curve is defineds.maxAnd a minimum value Ps.minThe difference of (A) is the load peak-to-valley difference Pfg。
Pfg＝Ps,max-Ps,min (23)
4) Degree of load fluctuation
And defining the average value of the load power difference values of adjacent moments in one day as the load fluctuation degree.
Figure BDA0003123231930000093
In the formula: pcgTo the extent of load fluctuations, PtLoad power at time t.
After the distribution network cluster is divided, the distributed power supply is consumed by using the energy storage, and a cluster energy storage control method is constructed, wherein a specific implementation flow is shown in fig. 3.
Step 4, the control method of the invention is utilized to simulate and analyze the concrete examples
By adopting an IEEE33 node power distribution network example system, the distributed power supply access nodes are 4, 7, 8, 14, 24, 25, 30 and 32, the specific access situation is shown in FIG. 4, and the nodes 14, 32, 30, 8, 7, 4, 25 and 24 are accessed to EV; the number of energy storage installations is set to 6.
The method provided in section 1 is adopted to perform cluster division, the optimal chromosome solution is 00010010000000000000000000000000, and the division result after decoding is shown in fig. 5.
In order to verify the advantages of the scheme of the invention, the scheme is compared with the traditional scheme, and the technical effects and the economic benefits of energy storage operation of the two schemes are compared.
Scheme 1: according to the traditional scheme, the total energy storage power is determined according to the total power supply load of the power distribution network, power distribution is carried out according to the energy storage capacity of each node, an energy storage operation income set is formed, and the optimal time sequence output of energy storage is determined.
Scheme 2: according to the scheme, the comprehensive performance indexes are considered to divide the power distribution network into groups, and the energy storage control method is adopted to optimize the energy storage time sequence output of each group.
The net power of each cluster before the energy storage is accessed is shown in fig. 6 and 7. Due to the fact that the access proportion of the distributed power supply is high, the situation that the output of the distributed power supply is larger than the load occurs near 8:00-11:00 and 14:00-16:00, and the phenomenon that active power is transmitted to a superior power grid in a backward mode is caused. The cluster 1 has larger power margin in the time period of 12:00-16:00, the cluster 2 has power margin in the time period of 4:00-10:00 and around 15:00, the cluster 3 has power margin in the time period of 12:00-18:00, and the loads of all the clusters reach the peak value around 21: 00.
The reverse power is respectively absorbed according to different energy storage control modes, and the energy storage output of each node in the scheme 2 is shown in fig. 8.
The system load before and after the energy storage participation adjustment of different schemes is shown in fig. 9, and as the scheme 2 discharges and releases the energy storage capacity before the energy storage and charging, the valley filling line value is smaller compared with the scheme 1 in the subsequent absorption of the backward power.
The system network loss at each time under different schemes is shown in fig. 10. The network loss of the system is larger in the time periods of 12:00-16:00 and 20:00-22:00 before regulation, the network loss caused by larger power flow caused by the reverse power of the system in the former time period is increased, and the network loss caused by the late peak load surge in the latter time period is increased. In the scheme of the invention, the network loss at the moment is reduced by the energy storage and discharge of the nodes 14 and 18 near 10 am, the energy storage and charging network loss of each node is reduced in the midday period, and the peak proportion of each cluster load is considered in the energy storage and discharge period in the late peak period, so that the load at the time period is relatively flat and the network loss is relatively low.
The system node voltages at each time point in case 1 and case 2 are shown in fig. 11 and fig. 12. Because both schemes consider voltage constraint, the problem of node voltage out-of-limit does not occur. Therefore, the control method is real and effective, and the consumption of new energy in the power distribution network can be improved by adopting a distributed energy storage cluster optimization control method.
The embodiments of the invention are not intended to be exhaustive or to limit the scope of the claims, and those skilled in the art will be able to conceive of other substantially equivalent alternatives, without inventive step, based on the teachings of the embodiments of the present invention.

Claims (1)

1. A distributed energy storage cluster optimization control method for improving new energy consumption in a power distribution network is characterized by comprising the following steps:
step 1, distribution network cluster division index and cluster division method
1) For the index of the power distribution network cluster division,
the modularity index defined based on the electric distance weight is adopted to measure the structural strength of the power network division,
the modularity index defined based on the electrical distance weight is as follows:
Figure FDA0003123231920000011
in the formula: f. of1Is the modularity based on the electrical distance, rho is the system modularity, m is the sum of all the side weights in the network, dijIs the electrical distance between node i and node j, kiIs the sum of the edge weights of the edges connected to the inode, kjIs the sum of the edge weights of the edges connected to the j node, and
Figure FDA0003123231920000012
corresponding to dijThe specific calculation formula is as follows:
Figure FDA0003123231920000013
Figure FDA0003123231920000014
in the formula: sijObtaining the voltage sensitivity of each node by inverting the Jacobian matrix in load flow calculation for the elements in the ith row and the j column in the sensitivity matrix;
Figure FDA0003123231920000015
representing the maximum value in the jth column element in the sensitivity matrix; n is a networkThe number of nodes;
measuring the balance capability of the source load active power in the cluster by adopting the active power balance degree; in order to express the matching degree of the internal source loads of the cluster under a certain time scale, an active power balance degree index is defined in a form based on net power, and the method specifically comprises the following steps:
Figure FDA0003123231920000016
Figure FDA0003123231920000017
in the formula: pckIs the ckThe active balance index of each cluster; t is the time scale of the scene, and is taken as 96; pclu(t) is the total net power value of all clusters at time t; pclu(t)ckIs the ckNet power value of each cluster at t moment;
Figure FDA0003123231920000018
the active balance degree index of the divided clusters; c is the total number of clusters;
2) for the power distribution network cluster division method, the factors of the overall modularity and the active power balance degree of the integrated system take the division mode of the system as variables, and on the basis of the autonomous regulation and control of each cluster area, an integrated index power grid cluster division model is established as follows:
F＝λ1f1+λ2f2 (7)
in the formula: λ 1 and λ 2 are weight coefficients of different indexes, where λ 1+ λ 2 is 1, and λ 1 is 0.5;
step 2, establishing a distributed energy storage cluster control method
1) The optimal daily operating benefit of the stored energy under the set configuration is taken as an optimization target, and an objective function is established as follows:
maxFP＝FDG+Floss (8)
in the formula: fPIncome from stored energy operation, FDGAdditional consumption of new energy for energy storage, FlossNetwork loss revenue brought to energy storage operation;
wherein, the energy storage consumes the electricity selling income F brought by the new energyDGThe electric power selling income brought by absorbing and transmitting power for energy storage and releasing in the peak load period is as follows:
FDG＝Fsale-Fbuy (9)
Figure FDA0003123231920000021
Figure FDA0003123231920000022
in the formula: fDGThe electricity selling income brought by new energy is consumed for energy storage; fsaleThe electricity selling income brought by the stored energy and the released electric energy; fbuyThe electricity purchasing cost for energy storage charging is 0 when the energy storage operation is in a new energy consumption area; pess,k,c(t) represents the charging power of the stored energy k at time t; pess,k,d(t) represents the discharge power of the stored energy k at time t; m (t) is the time-of-use electricity price of purchasing electricity from the main grid at the moment t; n is a radical ofEThe DES access total number;
network loss gain F brought by energy storage operationlossThe method specifically comprises the following steps:
Floss＝Floss1-Floss2 (12)
Figure FDA0003123231920000023
Figure FDA0003123231920000024
in the formula: ploss,n(t) is the original network active power loss, P, of the nth branch of the distribution networkloss-Ess,n(t) network active loss after the branch is accessed in the stored energy, NLIs the total number of branches; the network loss gain is the gain brought by the reduction of the system network loss before and after the energy storage access;
constraint conditions of the objective function mainly come from power distribution network and energy storage operation, including power flow constraint, voltage constraint, energy storage charge state constraint, power and capacity constraint, and specifically include:
constraint of tidal current equation
Figure FDA0003123231920000031
In the formula: pi(t)、Qi(t) injecting active and reactive power of the node i at the moment t; u shapei(t)、Uj(t) is the voltage amplitude of the node i and j at the time t; gij、BijRespectively representing the real part and the imaginary part of the j element in the ith row and the j element in the node admittance matrix; deltaij(t) is the phase angle difference of the nodes i and j at the moment t, and N is the number of system nodes;
(ii) ESS State of Charge constraint
SOC，min≤SOC(t)≤SOC，max (16)
SOC(0)＝SOC(T) (17)
-PESS，N≤PESS(t)≤PESS，N (18)
In the formula: sOC，minThe lower limit of the energy storage charge state is taken as 0.1; sOC，maxThe upper limit of the energy storage charge state is 0.9; sOC(t) is the state of charge of the stored energy at time t; the initial state of charge is equal to the end-cycle state of charge, 0.45 is selected; pESS(t) is the energy storage power at time t; pESS，NRated power for energy storage;
third, node voltage constraint
Umin≤Un，t≤Umax (19)
In the formula: u shapeminFor allowing minimum value of node voltage, UmaxFor the maximum allowed node voltage, Un，tSetting the voltage constraint range of the node of the power distribution network to be 0.95U for the voltage value of the node n at the time tN-1.05UN；
Proportional constraint of distributed power supply consumption
The maximum value of the new energy consumption ratio is regulated to be 1, the minimum value is regulated to be 0,
0≤ηDG≤1 (20)
in the formula: etaDGThe proportion is consumed for the distributed power supply;
step 3, establishing evaluation indexes of the control method
Distributed consumption ratio
Defining the consumption proportion eta of the distributed power supply as the electric quantity E of the distributed power supply with additional consumptionadAnd E should be discarded without taking measuresabThe ratio of the amounts of electricity of;
Figure FDA0003123231920000032
number of voltage out-of-limit nodes
The node voltage of the power distribution network is one of main factors for limiting the consumption of the distributed power supply, and the node voltage is out-of-limit for consuming new energy and is characterized as follows:
Figure FDA0003123231920000041
in the formula, NeThe total number of the out-of-limit nodes of the power grid voltage is obtained; n is the total number of nodes; the out-of-limit condition L of the n node voltage in the time scale TnIs 1, otherwise is 0;
③ load peak-valley difference
The energy storage is utilized to absorb the reverse power of the distribution network, the reverse power is released during the load peak to play a role in peak clipping and valley filling, and the maximum value P of the load power on a net load curve is defineds.maxAnd a minimum value Ps.minThe difference of (A) is the load peak-to-valley difference Pfg；
Pfg＝Ps，max-Ps，min (23)
Degree of load fluctuation
Defining the average value of the load power difference values of all adjacent moments in one day as the load fluctuation degree;
Figure FDA0003123231920000042
in the formula: pcgTo the extent of load fluctuations, PtLoad power at time t.
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