CN114154800A - Energy storage system optimization planning method and device for power transmission and distribution network cooperation - Google Patents

Energy storage system optimization planning method and device for power transmission and distribution network cooperation Download PDF

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CN114154800A
CN114154800A CN202111313668.8A CN202111313668A CN114154800A CN 114154800 A CN114154800 A CN 114154800A CN 202111313668 A CN202111313668 A CN 202111313668A CN 114154800 A CN114154800 A CN 114154800A
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胡泽春
蔡福霖
曹敏健
蔡德福
陈汝斯
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Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
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Abstract

The disclosure provides a power transmission and distribution network collaborative energy storage system optimization planning method and device, and belongs to the technical field of power transmission and distribution network and energy storage optimization planning. Wherein the method comprises the following steps: according to a preset typical daily operation scene set, establishing a joint optimization model for a power transmission network and a power distribution network which are configured with battery energy storage, wherein the joint optimization model comprises a power distribution network planning sub-model and a power transmission network planning sub-model; and solving the combined optimization model by adopting a distributed optimization algorithm to obtain a planning scheme of battery energy storage of the power transmission network and the power distribution network. The hybrid power transmission and distribution network is provided with the battery for energy storage, so that large-scale new energy consumption in new energy enrichment areas can be promoted, abandonment is reduced, and comprehensive operation benefits of the large-scale power transmission and distribution network can be improved through cooperative consideration of the power transmission and distribution network.

Description

Energy storage system optimization planning method and device for power transmission and distribution network cooperation
Technical Field
The disclosure relates to the technical field of power transmission and distribution network and energy storage optimization planning, in particular to a power transmission and distribution network collaborative energy storage system optimization planning method and device.
Background
New energy in China is continuously and rapidly developed, and high proportion of new energy gradually permeates into each level of power grid. However, the output of the new energy is mainly determined by weather and meteorological conditions which cannot be accurately predicted and are difficult to control from the outside, and the output of the new energy presents strong volatility and intermittency, thereby bringing great challenges to the stability and the safety of a power system. In recent years, the technical progress and the cost of energy storage are greatly developed, and the large-scale chemical energy storage can effectively smooth the output fluctuation of new energy, improve the peak regulation capability of a power grid, solve the problem of power transmission channel blockage and the like, realize the transfer of energy in time and space, and improve the stability and the safety of the power grid.
In recent years, with the fact that a large number of distributed power sources are connected into a power distribution network, a traditional passive power distribution network is gradually converted into a novel active power distribution network, interaction requirements among power transmission and distribution networks are more and more obvious, the trend among the power transmission and distribution networks gradually shows a bidirectional trend, and the coupling relation is also increased day by day. The traditional independent planning of the transmission and distribution network is difficult to coordinate resources and requirements on different levels, new energy cannot be fully consumed, unnecessary wind abandoning and light abandoning are easy to cause and the power grid is blocked, so that the operation cost of the power grid is increased; therefore, it is necessary to consider an optimized planning of the transmission and distribution network coordination.
In the cooperative optimization of the transmission and distribution network, the transmission and distribution network has larger differences in structure, parameters and analysis methods, and the distribution network has the characteristics of large quantity, wide distribution, more nodes and low magnitude, so that the centralized modeling of the transmission and distribution network is difficult, the model scale is too large, and the calculation time is too long; therefore, the transmission and distribution network collaborative optimization should adopt a distributed optimization algorithm, and the distributed algorithm can not only analyze and model the transmission and distribution network independently, but also reduce the complexity of the model, realize parallel operation and improve the computation speed. As a distributed optimization algorithm, the analysis target cascade method enables all layers of benefit agents to cooperatively optimize in the iterative process by setting punishment items of all layers on the basis of considering the autonomous operation characteristics, and can ensure the convergence of the convex optimization problem.
At present, for the problem of collaborative planning of transmission and distribution networks, the coordination of power generation resources among the transmission and distribution networks, the consumption of new energy power generation, and the reduction of the influence of new energy output fluctuation on the power grid are mainly included, but the configuration of energy storage cannot be considered while the collaborative optimization of the transmission and distribution networks is carried out. In addition, only a power distribution network or a power transmission network is independently considered for the optimization planning and operation of the power system considering energy storage, and the hierarchical optimization scheduling of the transmission and distribution network coordination problem by using a distributed algorithm is not considered.
Disclosure of Invention
The purpose of the disclosure is to provide a method and a device for optimizing and planning a power transmission and distribution network collaborative energy storage system to overcome the defects in the prior art. The hybrid power transmission and distribution network is provided with the battery for energy storage, so that large-scale new energy consumption in new energy enrichment areas can be promoted, abandonment is reduced, and comprehensive operation benefits of the large-scale power transmission and distribution network can be improved through cooperative consideration of the power transmission and distribution network.
An embodiment of the first aspect of the present disclosure provides an energy storage system optimization planning method for power transmission and distribution network coordination, including:
according to a preset typical daily operation scene set, establishing a joint optimization model for a power transmission network and a power distribution network which are configured with battery energy storage, wherein the joint optimization model comprises a power distribution network planning sub-model and a power transmission network planning sub-model;
and solving the combined optimization model by adopting a distributed optimization algorithm to obtain a planning scheme of battery energy storage of the power transmission network and the power distribution network.
In a specific embodiment of the present disclosure, the typical daily operating scenario set includes a new energy plant station output typical scenario set and a load typical scenario set, where:
sampling historical output data of new energy plants in the transmission and distribution network according to days, clustering the sampled daily historical output data of each new energy plant by using a K-means algorithm, and generating an output typical daily scene of each new energy plant; forming output typical daily scenes of all new energy plant stations into a new energy plant station output typical scene set;
sampling historical load data of a transmission and distribution network on a daily basis, wherein the sampling time period of the historical load data is consistent with the sampling time period of the historical output data of the new energy plant station; and clustering daily historical load data obtained by sampling by using a K-means algorithm to generate a load typical daily scene, and forming a load typical scene set by using all load typical daily scenes.
In a specific embodiment of the present disclosure, the power distribution network planning sub-model includes:
1) an objective function;
Figure BDA0003342957340000021
in the formula, subscript n is the serial number of the distribution network, Fdis,nRepresents the total cost, omega, of the nth distribution network investment and operationSRepresenting a typical daily running scene set, s ∈ ΩS，DsRepresents the number of days a typical daily operational scenario s occupies in a year; cinv,nRepresenting the investment cost of the nth distribution network,
Figure BDA0003342957340000022
representing the net surfing and electricity purchasing cost of the new energy of the nth power distribution network under the scene s,
Figure BDA0003342957340000023
representing the new energy abandon cost of the nth power distribution network under the scene s,
Figure BDA0003342957340000024
represents the penalty cost of sharing variable error between the transmission network and the nth distribution network under the scene s,
Figure BDA0003342957340000025
representing the cost of purchasing electricity from the nth distribution network to the transmission network under the scene s,
Figure BDA0003342957340000026
representing the operation and maintenance cost of the energy storage of the nth power distribution network under the scene s;
wherein the content of the first and second substances,
Figure BDA0003342957340000031
Figure BDA0003342957340000032
Figure BDA0003342957340000033
Figure BDA0003342957340000034
Figure BDA0003342957340000035
Figure BDA0003342957340000036
Figure BDA0003342957340000037
in the formula, omegaessSet of candidate configuration nodes, G, representing all stored energyinvA coefficient for converting the investment cost from the current value to the equal-year value in the planning period; c1,n,kRepresents the capital construction cost of energy storage of the kth energy storage configuration node in the nth distribution network, C2,n,kRepresents the cost of the unit energy capacity of the energy storage of the kth energy storage configuration node in the nth power distribution network, C3,n,kThe unit power capacity cost of energy storage of the kth energy storage configuration node in the nth power distribution network is represented;
ΩRethe node set of the new energy accessed to the power distribution network is represented, i is a node sequence number, and i belongs to omegaReT is the serial number of the sampling time point, T is the total sampling time period number of a typical day, and pis,n,tThe new energy grid-surfing electricity price of the t-th sampling point in the nth power distribution network under the scene s,
Figure BDA0003342957340000038
the renewable energy source internet active power of a node i in the nth power distribution network at the t-th sampling point under a scene s is obtained, and delta t is the length of a sampling period; beta is a penalty coefficient for abandoning the new energy,
Figure BDA0003342957340000039
generating power for the renewable energy source of a node i in the nth power distribution network at the t-th sampling point under the scene s; v. ofs,n,tAnd ws,n,tRespectively sharing a coefficient value and a weight value of a variable penalty function at a t sampling point in an nth power distribution network under a scene s;
Figure BDA00033429573400000310
the active power exchanged between the transmission and distribution networks on the transmission network side at the t sampling point of the nth distribution network under the scene s,
Figure BDA00033429573400000311
the active power exchanged between the transmission and distribution networks on the distribution network side when the nth distribution network is at the tth sampling point under the scene s is obtained;
Figure BDA00033429573400000312
the node marginal electricity of the nth power distribution network at the t-th sampling point under the scene s is obtained; picAnd pidThe unit power operation and maintenance costs during energy storage charging and discharging are respectively;
2) a constraint condition; the method comprises the following specific steps:
2-1) sharing upper and lower limit constraints of variables:
Figure BDA00033429573400000313
wherein the content of the first and second substances,
Figure BDA00033429573400000314
and
Figure BDA00033429573400000315
maximum and minimum values allowed by the transmission of active power between the nth power distribution network and the transmission network are respectively set;
2-2) active and reactive power output constraint of new energy:
Figure BDA0003342957340000041
Figure BDA0003342957340000042
Figure BDA0003342957340000043
in the formula (I), the compound is shown in the specification,
Figure BDA0003342957340000044
and
Figure BDA0003342957340000045
respectively representing the maximum value and the minimum value of the renewable energy source internet active power of the node i at the t-th sampling point under the scene s;
Figure BDA0003342957340000046
the method comprises the steps that renewable energy source online reactive power of a node i in an nth power distribution network at a t sampling point under a scene s is obtained;
Figure BDA0003342957340000047
and
Figure BDA0003342957340000048
respectively setting the maximum value and the minimum value of the renewable energy internet reactive power of the node i at the t-th sampling point under the scene s;
Figure BDA0003342957340000049
the renewable energy capacity of the node i in the nth power distribution network;
2-3) the investment operation constraint of the battery energy storage system:
Emin≤En,i≤Emax (12)
0≤Pn,i≤Pmax (13)
Figure BDA00033429573400000410
in the formula, En,iAnd Pn,iRespectively storing the battery energy in the built-in capacity and power of a node i in the nth distribution network, EmaxAnd EminMaximum and minimum projected capacity, P, of the battery's stored energy, respectivelymaxMaximum built-in power for battery energy storage, CmaxAnd CminRespectively the maximum multiplying power and the minimum multiplying power of the energy storage of the battery;
Figure BDA00033429573400000411
Figure BDA00033429573400000412
Figure BDA00033429573400000413
Figure BDA00033429573400000414
Figure BDA0003342957340000051
Figure BDA0003342957340000052
Figure BDA0003342957340000053
in the formula (I), the compound is shown in the specification,
Figure BDA0003342957340000054
storing a state mark 0-1 variable of charging active power of a battery at a t sampling point in an nth power distribution network node i under a scene s;
Figure BDA0003342957340000055
storing a state mark 0-1 variable of discharging active power of a battery at a t sampling point in an nth power distribution network node i under a scene s;
Figure BDA0003342957340000056
and
Figure BDA0003342957340000057
charging active power and discharging active power of a battery stored in an nth power distribution network node i at a tth sampling point under a scene s are respectively stored,
Figure BDA0003342957340000058
and
Figure BDA0003342957340000059
respectively storing reactive power absorbed and released at the t sampling point by a battery in the nth power distribution network node i under the scene s,
Figure BDA00033429573400000510
and
Figure BDA00033429573400000511
respectively obtaining the maximum value and the minimum value of the reactive power exchanged between the battery energy storage and the power grid in the nth power distribution network node i;
Figure BDA00033429573400000512
En,i·SOCmin≤Es,n,i,t≤En,i·SOCmax (23)
wherein E iss,n,i,tThe method comprises the steps that the storage electric quantity of a node i in an nth power distribution network at a t sampling point under a scene s is obtained; etacAnd ηdRespectively representing the charging efficiency and the discharging efficiency of the battery energy storage, SOCmaxAnd SOCminRespectively representing the upper limit and the lower limit of the state of charge of the battery energy storage operation;
Es,n,i,0＝Es,n,i,T＝En,i·SOCini (24)
in the formula, Es,n,i,0And Es,n,i,TRespectively storing the stored electric quantity and SOC of the initial sampling point and the ending sampling point of each day for the stored energy in the nth power distribution networkiniRepresenting the initial value of the state of charge of the battery energy storage operation;
2-4) optimal power flow constraint of the power distribution network:
Figure BDA00033429573400000513
Figure BDA00033429573400000514
Figure BDA00033429573400000515
Figure BDA0003342957340000061
Figure BDA0003342957340000062
Figure BDA0003342957340000063
Figure BDA0003342957340000064
Figure BDA0003342957340000065
in the formula, a corridor ij represents a power transmission line set from a node i to a node j;
Figure BDA0003342957340000066
and
Figure BDA0003342957340000067
respectively setting the active power and the reactive power of the l line on the ij in the nth distribution network at the t sampling point under the scene s;
Figure BDA0003342957340000068
the square of the current amplitude of the ith line at the t sampling point on the corridor ij in the nth power distribution network under the scene s is shown;
Figure BDA0003342957340000069
the current amplitude of the ith line on the ith line of the corridor ij in the nth power distribution network at the t sampling point under the scene s is obtained; vs,n,i,tThe voltage amplitude of an ith node in an nth power distribution network at a t sampling point under a scene s is shown;
Figure BDA00033429573400000610
and
Figure BDA00033429573400000611
are respectively the n-th distributionThe resistance and reactance of the l line on the corridor ij in the power grid;
Figure BDA00033429573400000612
and
Figure BDA00033429573400000613
the method comprises the steps that active load and reactive load of a jth node in an nth power distribution network at a tth sampling point under a scene s are measured;
Figure BDA00033429573400000614
the maximum value of the current of the l line on the corridor ij in the nth power distribution network,
Figure BDA00033429573400000615
and
Figure BDA00033429573400000616
the maximum voltage value of the ith node in the nth power distribution network.
In one embodiment of the present disclosure, the calculation expression of the coefficient of the investment cost in the planning period from the present value to the equal-year value is as follows:
Figure BDA00033429573400000617
wherein α represents a general discount rate, NyFor planning the years.
In a specific embodiment of the present disclosure, the power transmission network planning sub-model includes:
1) an objective function;
Figure BDA00033429573400000618
in the formula, FtransRepresents the total cost of power grid investment and operation; cinvRepresents the investment cost of the power transmission network;
Figure BDA00033429573400000619
representing the cost of electricity generation by the generators in the grid under scenario s,
Figure BDA00033429573400000620
representing the cost of purchasing electricity from the internet of new energy under the scene s,
Figure BDA00033429573400000621
represents the cost of new energy abandonment under the scene s,
Figure BDA0003342957340000071
representing the cost of the transmission network selling electricity to the distribution network under scenario s,
Figure BDA0003342957340000072
represents the punishment cost of sharing variable errors between the transmission and distribution networks under the scene s,
Figure BDA0003342957340000073
represents the operating cost of energy storage;
wherein the content of the first and second substances,
Figure BDA0003342957340000074
Figure BDA0003342957340000075
Figure BDA0003342957340000076
Figure BDA0003342957340000077
Figure BDA0003342957340000078
Figure BDA0003342957340000079
Figure BDA00033429573400000710
in the formula, C1,kCapital cost, C, of energy storage for the kth energy storage configuration node in the grid2,kConfiguring cost per energy capacity of node energy storage for kth energy storage in power transmission network, C3,kConfiguring cost per power capacity of node energy storage for kth energy storage in power transmission network, EkAnd PkRespectively configuring the built-in capacity and power of a kth energy storage configuration node for battery energy storage in the power transmission network; omegaGSet of nodes for all the transmission networks provided with generators, CG,i() is a function of the cost of power generation of the generator at node i in the grid,
Figure BDA00033429573400000711
the generated power of a generator in a node i in the power transmission network at a t-th sampling point under a scene s is obtained; pis,tThe new energy grid-connected electricity price of the t-th sampling point under the scene s,
Figure BDA00033429573400000712
the method comprises the steps that the renewable energy source internet active power of a node i in a power transmission network at a t-th sampling point under a scene s is obtained;
Figure BDA00033429573400000713
generating power for a renewable energy source of a node i in the power transmission network at the t-th sampling point under a scene s; omegaDThe method comprises the following steps of (1) forming a set by all power distribution networks;
Figure BDA00033429573400000714
and
Figure BDA00033429573400000715
charging active power and discharging active power of a battery stored at the t-th sampling point in a node k in the power transmission network under a scene s are respectively stored;
2) a constraint condition; the method comprises the following specific steps:
2-1) sharing upper and lower limit constraints of variables:
Figure BDA0003342957340000081
wherein the content of the first and second substances,
Figure BDA0003342957340000082
and
Figure BDA0003342957340000083
the maximum value and the minimum value of active power transmitted between the transmission network and the nth power distribution network are respectively obtained;
2-2) active and reactive power output constraint of new energy:
Figure BDA0003342957340000084
in the formula (I), the compound is shown in the specification,
Figure BDA0003342957340000085
and
Figure BDA0003342957340000086
respectively representing the maximum value and the minimum value of the renewable energy source network active power of the power transmission network node i at the t-th sampling point under the scene s,
Figure BDA0003342957340000087
the method comprises the steps that the renewable energy source internet active power of a node i in a power transmission network at a t-th sampling point under a scene s is obtained;
2-2-2-3) investment and operation constraints of the battery energy storage system:
Emin,trans≤Ek≤Emax,trans (43)
0≤Pk≤Pmax,trans (44)
Figure BDA0003342957340000088
in the formula, Emax,transAnd Emin,transMaximum and minimum projected capacity, P, of the stored energy of the battery in the gridmax,transEstablishing maximum operating power for the energy storage of the battery in the power transmission network; cmax,transAnd Cmin,transRespectively the maximum multiplying power and the minimum multiplying power of the battery energy storage in the power transmission network;
Figure BDA0003342957340000089
Figure BDA00033429573400000810
Figure BDA00033429573400000811
in the formula (I), the compound is shown in the specification,
Figure BDA00033429573400000812
the state flag of charging active power of a battery stored at the t-th sampling point in a node k in the power transmission network under a scene s is changed into 0-1;
Figure BDA00033429573400000813
a state flag 0-1 variable of the discharge active power of a battery stored in a node k in the power transmission network at the t-th sampling point under a scene s;
Figure BDA0003342957340000091
and
Figure BDA0003342957340000092
charging active power and discharging active power of a battery stored at the t-th sampling point in a node k in the power transmission network under a scene s are respectively stored;
Figure BDA0003342957340000093
Ek·SOCmin≤Es,k,t≤Ek·SOCmax (50)
wherein E iss,k,tThe storage electric quantity of a node k in the power transmission network at the t-th sampling point under a scene s is obtained;
Es,k,0＝Es,k,T＝Ek·SOCini (51)
in the formula, Es,k,0And Es,k,TRespectively storing the stored electric quantity of a node k in the power transmission network under a scene s at an initial sampling point and an ending sampling point every day;
2-4) generator single power constraint:
Figure BDA0003342957340000094
in the formula, Pi G,maxAnd Pi G,minRespectively the maximum generating power and the minimum generating power of a generator in a node i in the power transmission network;
2-5) optimal power flow constraint of the power transmission network:
Figure BDA0003342957340000095
Figure BDA0003342957340000096
Figure BDA0003342957340000097
in the formula (I), the compound is shown in the specification,
Figure BDA0003342957340000098
the marginal electricity price of a node i in the power transmission network at the t-th sampling point under a scene s is obtained;
Figure BDA0003342957340000099
for the l-th line susceptance, theta, between nodes i and j in the transmission networks,i,tAnd thetas,j,tThe phase angles of the node i and the node j in the power transmission network at the t-th sampling point under the scene s respectively,
Figure BDA00033429573400000910
the capacity of the ith line between nodes i and j in the grid.
In a specific embodiment of the present disclosure, the solving the joint optimization model by using a distributed optimization algorithm to obtain a planning scheme for battery energy storage of the power transmission network and the power distribution network includes:
1) setting the initial value of iteration times j as 0, and respectively setting penalty coefficients of consistency constraint of jth iteration
Figure BDA00033429573400000911
Weight of
Figure BDA00033429573400000912
Sharing variables with the grid side
Figure BDA00033429573400000913
An initial value of wherein
Figure BDA00033429573400000914
The initial value of (a) is taken as 0,
Figure BDA00033429573400000915
the initial value of (a) is taken as 1,
Figure BDA00033429573400000916
the initial value of (A) is 0;
2) will be provided with
Figure BDA00033429573400000917
As the current vs,n,tWill be
Figure BDA00033429573400000918
As the current ws,n,tWill be
Figure BDA00033429573400000919
As is present
Figure BDA00033429573400000920
Solving a power distribution network planning sub-model and a power transmission network planning sub-model in the joint optimization model, and obtaining F through solvingdis,nTotal cost of investment and operation of nth distribution network as jth iteration
Figure BDA0003342957340000101
Is obtained by solving FtransTotal cost of transmission network investment and operation as jth iteration
Figure BDA0003342957340000102
An initial value of (d);
the obtained updated v is solveds,n,t，ws,n,t，
Figure BDA0003342957340000103
Are respectively marked as
Figure BDA0003342957340000104
And
Figure BDA0003342957340000105
3-3) making j equal to j +1, and solving the node marginal electricity price of the t-th sampling point of the node i in the power transmission network under the scene s in the power transmission network planning sub-model
Figure BDA0003342957340000106
If the nth power distribution network is connected to the node i of the power transmission network, the node marginal electricity price of the nth power distribution network at the t sampling point under the scene s
Figure BDA0003342957340000107
Equal to the node marginal price of the node i at the t-th sampling point in the power transmission network under the scene s
Figure BDA0003342957340000108
4) Will be provided with
Figure BDA0003342957340000109
As the current vs,n,tWill be
Figure BDA00033429573400001010
As the current ws,n,tWill be
Figure BDA00033429573400001011
As is present
Figure BDA00033429573400001012
Sequentially solving each power distribution network planning sub-model, and obtaining the solution result
Figure BDA00033429573400001013
As
Figure BDA00033429573400001014
The active power exchanged between the transmission and distribution networks on the distribution network side at the t sampling point of the nth distribution network under the scene s during the jth iteration is calculated;
5) subjecting the product obtained in step 4)
Figure BDA00033429573400001015
The sub-model is brought into the sub-model and solved, and the sub-model obtained from the solution result is used
Figure BDA00033429573400001016
As
Figure BDA00033429573400001017
Figure BDA00033429573400001018
The active power exchanged between the transmission and distribution network of the transmission network and the transmission and distribution network of the nth distribution network at the tth sampling point of the scene s during the jth iteration is obtained;
6) whether the iteration converges is judged according to the equations (56) and (57):
Figure BDA00033429573400001019
Figure BDA00033429573400001020
wherein epsilon1The optimal error represents the relative error between the costs of the transmission and distribution network of two iterations; epsilon2The sharing error represents the error of active power transmission between the transmission and distribution network;
if the equations (56) and (57) are both satisfied, iteration is converged, and the built-in capacity and power E of the battery energy storage in the power distribution network obtained by solving in the jth iteration is usedn,iAnd Pn,iAnd the projected capacity and power E of the battery energy storage in the gridkAnd PkAs an optimization result of the energy storage planning, the planning is finished;
if either of equations (56) and (57) is not satisfied, the iterations do not converge and are updated according to equations (58) and (59)
Figure BDA00033429573400001021
And
Figure BDA00033429573400001022
then returning to the step 3-3) again;
Figure BDA0003342957340000111
Figure BDA0003342957340000112
and theta is a punished quadratic term iteration coefficient.
In a specific embodiment of the present disclosure, the optimal error is less than or equal to 0.01, the sharing error is less than or equal to 0.01, and the penalty quadratic term iteration coefficient is greater than or equal to 2.
An embodiment of a second aspect of the present disclosure provides an energy storage system optimization planning device for power transmission and distribution network coordination, including:
the optimization model building module is used for building a combined optimization model for the power transmission network and the power distribution network which are configured with the battery energy storage according to a preset typical daily operation scene set, wherein the combined optimization model comprises a power distribution network planning sub-model and a power transmission network planning sub-model;
and the energy storage planning module is used for solving the combined optimization model by adopting a distributed optimization algorithm to obtain a planning scheme of battery energy storage of the power transmission network and the power distribution network.
An embodiment of a third aspect of the present disclosure provides an electronic device, including:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a method of energy storage system optimization planning in conjunction with transmission and distribution networks as described above.
In an embodiment of a fourth aspect of the present disclosure, a computer-readable storage medium is provided, where the computer-readable storage medium stores computer instructions for causing a computer to execute the above-mentioned method for optimizing and planning an energy storage system in cooperation with a transmission and distribution network.
The characteristics and the beneficial effects of the disclosure are as follows:
1. the present disclosure contemplates configuring battery storage in hybrid transmission and distribution networks to facilitate large-scale new energy-rich areas
The new energy consumption is reduced, the abandonment is reduced, and the comprehensive operation benefit of the large-range transmission and distribution network can be improved by the cooperative consideration of the transmission and distribution network.
2. According to the method, uncertain factors such as new energy output and load change are considered through a typical scene clustering method, layered modeling and independent solution are carried out on the power transmission and distribution network through a target analysis cascaded distributed algorithm, the problem that the mixed power transmission and distribution network has many variables, is complex in condition and is difficult to solve is solved through the layered independent solution algorithm, the solution time of the model is guaranteed, the coupling relation between the power transmission and distribution network is guaranteed through sharing variable consistency punishment, the overall situation of the power transmission and distribution network is optimized in the iteration process while the respective operation characteristics and network constraints of the power transmission and distribution network are guaranteed, and the new energy consumption is achieved through determining the configuration of stored energy.
Drawings
Fig. 1 is an overall flowchart of an energy storage system optimization planning method for power transmission and distribution network coordination in the embodiment of the present disclosure.
Fig. 2 is a flowchart of a solution algorithm for power transmission and distribution network collaborative optimization according to an embodiment of the present disclosure.
Detailed Description
The present disclosure provides a power transmission and distribution network collaborative energy storage system optimization planning method and device, which are further described in detail below with reference to the accompanying drawings and specific embodiments.
An embodiment of the first aspect of the disclosure provides an energy storage system optimization planning method for power transmission and distribution network coordination, an overall process is shown in fig. 1, and the method includes the following steps:
1) the method comprises the steps that uncertainty factors such as new energy output and load change are considered by the multi-scene probability method, and a typical daily operation scene set is established, wherein the typical daily operation scene set comprises a new energy plant station output typical scene set and a load typical scene set; the specific method comprises the following steps:
1-1) sampling historical actual output data of all new energy plants in the transmission and distribution network on a daily basis; in the disclosure, a sampling period is at least one hour, and daily operation data of historical output of a new energy plant station of at least one year is required; clustering daily historical output data of each new energy plant station obtained by sampling by using a K-means algorithm to generate an output typical daily scene of each new energy plant station, wherein each new energy plant station can establish a plurality of typical daily scenes through clustering, and the typical daily scenes of each new energy plant station can be different in number;
forming output typical daily scenes of all new energy plant stations into a new energy plant station output typical scene set;
1-2) sampling historical actual load data of the transmission and distribution network day by day, wherein the sampling time period and the sampling period are consistent with those in the step 1-1); in the disclosure, the sampling period is at least one hour, and daily load data of at least one year is needed, and a specific embodiment of the disclosure adopts historical load data with the duration of one year and the sampling period of one hour; and clustering daily historical load data obtained by sampling by using a K-means algorithm to generate a load typical daily scene, and forming a load typical scene set by using all load typical daily scenes.
1-3) forming a typical daily operation scene set by the new energy plant station output typical scene set and the load typical scene set, and reducing the scale of the problem while considering the uncertainty of the new energy output and the load.
2) Establishing a combined optimization model of the power transmission and distribution network, wherein the combined optimization model comprises a power distribution network planning sub-model corresponding to each power distribution network and a power transmission network planning sub-model corresponding to the power transmission network; the method comprises the following specific steps:
2-1) respectively establishing a corresponding power distribution network planning sub-model for each power distribution network contained in the transmission and distribution network, wherein the model consists of an objective function and a constraint condition.
Wherein, for the nth distribution network, n is the distribution network sequence number, and concrete step is as follows:
2-1-1) determining an objective function of the nth power distribution network planning sub-model;
Figure BDA0003342957340000131
the objective function is the nth distribution network investment and operation total cost Fdis,nMinimization of (d);
wherein the investment cost of the nth power distribution network is the investment cost C of energy storageinv,n；ΩSA scene set is operated in a typical day, s is a scene sequence number, and s belongs to omegaS，DsThe number of days a typical daily operating scenario s occupies in a year;
under the scene s, the nth power distribution network operatesThe total line cost includes: net surfing and electricity purchasing cost of nth distribution network new energy
Figure BDA0003342957340000132
Nth new energy abandoning cost of power distribution network
Figure BDA0003342957340000133
Penalty cost for sharing variable error between transmission network and nth distribution network
Figure BDA0003342957340000134
Cost of purchasing electricity from nth distribution network to transmission network
Figure BDA0003342957340000135
Operation and maintenance cost of nth distribution network energy storage
Figure BDA0003342957340000136
Wherein the content of the first and second substances,
Figure BDA0003342957340000137
Figure BDA0003342957340000138
Figure BDA0003342957340000139
Figure BDA00033429573400001310
Figure BDA00033429573400001311
Figure BDA00033429573400001312
Figure BDA00033429573400001313
in formula (2), ΩessConfiguring a node set to be selected for all stored energy, configuring the serial number of the node to be selected for k stored energy, and enabling k to be omegaess，GinvIn order to convert the investment cost from a current value to a constant-year value in a planning period, the conversion coefficients of all stored energy in the transmission and distribution network are assumed to be consistent in the embodiment of the disclosure; c1,n,kCapital cost, C, of energy storage for the kth energy storage configuration node in the nth distribution network2,n,kConfiguring cost per energy capacity of node energy storage for kth energy storage in nth distribution network3,n,kConfiguring the unit power capacity cost of energy storage of a node for the kth energy storage in the nth power distribution network; en,iAnd Pn,iAnd respectively storing the built-in capacity and power of the node i in the nth power distribution network for the battery.
Wherein G isinvThe calculation expression of (a) is as follows:
Figure BDA0003342957340000141
wherein, alpha is the general mark rate, NyIn order to plan the age, it is assumed in the embodiment of the present disclosure that the planned ages of all stored energy in the transmission and distribution network are consistent.
In formula (3), ΩReA node set for accessing the new energy into the power distribution network, i is a node sequence number, and i belongs to omegaReT is the serial number of the sampling time point, T is the total sampling time period number of a typical day, and pis,n,tThe new energy grid-surfing electricity price of the t-th sampling point in the nth power distribution network under the scene s,
Figure BDA0003342957340000142
the renewable energy source internet active power of a node i in the nth power distribution network at the t-th sampling point under the scene s is represented by delta t, which is the length of the sampling period.
In the formula (4), beta is a penalty coefficient for abandoning new energyThe value range is 1 to 3, and in one specific embodiment of the disclosure, 1.5 is taken,
Figure BDA0003342957340000143
and generating power for the renewable energy source of the node i in the nth power distribution network at the t-th sampling point under the scene s.
In formula (5), vs,n,tAnd ws,n,tThe method comprises the steps of respectively sharing a coefficient value and a weight value of a variable penalty function at a t-th sampling point in an nth power distribution network under a scene s.
Figure BDA0003342957340000144
The active power exchanged between the transmission and distribution networks on the transmission network side at the t sampling point of the nth distribution network under the scene s,
Figure BDA0003342957340000145
the active power exchanged between the transmission and distribution networks on the distribution network side when the nth distribution network is at the tth sampling point under the scene s.
In the formula (6), the reaction mixture is,
Figure BDA0003342957340000146
and the node marginal electricity price of the nth power distribution network at the t sampling point under the scene s is obtained.
In the formula (7), picAnd pidThe unit power operation and maintenance costs during the charging and discharging of the stored energy are respectively.
2-1-2) determining the constraint condition of the nth power distribution network planning sub-model; the method comprises the following specific steps:
2-1-2-1) sharing upper and lower limit constraints of variables:
Figure BDA0003342957340000147
wherein the content of the first and second substances,
Figure BDA0003342957340000148
and
Figure BDA0003342957340000149
respectively the maximum value and the minimum value allowed by the transfer of active power between the nth power distribution network and the transmission network.
2-1-2-2) active and reactive power output constraint of new energy:
Figure BDA00033429573400001410
Figure BDA00033429573400001411
Figure BDA00033429573400001412
in the formula (I), the compound is shown in the specification,
Figure BDA00033429573400001413
and
Figure BDA00033429573400001414
respectively representing the maximum value and the minimum value of the renewable energy source internet active power of the node i at the t-th sampling point under the scene s;
Figure BDA0003342957340000151
the method comprises the steps that renewable energy source online reactive power of a node i in an nth power distribution network at a t sampling point under a scene s is obtained;
Figure BDA0003342957340000152
and
Figure BDA0003342957340000153
respectively setting the maximum value and the minimum value of the renewable energy internet reactive power of the node i at the t-th sampling point under the scene s;
Figure BDA0003342957340000154
is the renewable energy capacity of node i in the nth distribution network.
2-1-2-3) battery energy storage system investment operation constraints:
Emin≤En,i≤Emax (12)
0≤Pn,i≤Pmax (13)
Figure BDA0003342957340000155
in the formula, En,iAnd Pn,iRespectively storing the battery energy in the built-in capacity and power of a node i in the nth distribution network, EmaxAnd EminMaximum and minimum projected capacity, P, of the battery's stored energy, respectivelymaxAnd (4) the maximum built-in power for the energy storage of the battery. CmaxAnd CminThe maximum multiplying power and the minimum multiplying power of the energy storage of the battery are respectively.
Figure BDA0003342957340000156
Figure BDA0003342957340000157
Figure BDA0003342957340000158
Figure BDA0003342957340000159
Figure BDA00033429573400001510
Figure BDA00033429573400001511
Figure BDA00033429573400001512
In the formula (I), the compound is shown in the specification,
Figure BDA00033429573400001513
a state flag 0-1 variable of charging active power of a battery stored at a t sampling point in an nth power distribution network node i under a scene s is set, wherein 0 represents that the battery stored energy is not allowed to be charged, and 1 represents that the battery stored energy is allowed to be charged;
Figure BDA00033429573400001514
a state mark 0-1 variable of discharging active power of a battery stored in an nth power distribution network node i at a tth sampling point under a scene s is represented, wherein 0 represents that the battery stored energy is not allowed to discharge, and 1 represents that the battery stored energy is allowed to discharge;
Figure BDA0003342957340000161
and
Figure BDA0003342957340000162
charging active power and discharging active power of a battery stored in an nth power distribution network node i at a tth sampling point under a scene s are respectively stored,
Figure BDA0003342957340000163
and
Figure BDA0003342957340000164
respectively storing reactive power absorbed and released at the t sampling point by a battery in the nth power distribution network node i under the scene s,
Figure BDA0003342957340000165
and
Figure BDA0003342957340000166
and respectively the maximum value and the minimum value of the reactive power exchanged between the battery energy storage and the power grid in the nth power distribution network node i.
Figure BDA0003342957340000167
En,i·SOCmin≤Es,n,i,t≤En,i·SOCmax (23)
Wherein E iss,n,i,tThe method comprises the steps that the storage electric quantity of a node i in an nth power distribution network at a t sampling point under a scene s is obtained; etacAnd ηdRespectively representing the charging efficiency and the discharging efficiency of the battery energy storage, SOCmaxAnd SOCminRespectively representing the upper limit and the lower limit of the state of charge of the battery energy storage operation;
considering the continuity of the battery energy storage operation, the energy storage is returned to the initial energy storage state after one day of operation, namely:
Es,n,i,0＝Es,n,i,T＝En,i·SOCini (24)
in the formula, Es,n,i,0And Es,n,i,TRespectively storing the stored electric quantity and SOC of the node i in the nth power distribution network in the scene s at the initial sampling point and the ending sampling point of each dayiniAnd the initial value of the state of charge of the battery energy storage operation is represented.
2-1-2-4) optimal power flow constraint of the power distribution network:
Figure BDA0003342957340000168
Figure BDA0003342957340000169
Figure BDA00033429573400001610
Figure BDA00033429573400001611
Figure BDA00033429573400001612
Figure BDA00033429573400001613
Figure BDA00033429573400001614
Figure BDA0003342957340000171
in the formula, a corridor ij represents a power transmission line set from a node i to a node j;
Figure BDA0003342957340000172
and
Figure BDA0003342957340000173
respectively setting the active power and the reactive power of the l line on the ij in the nth distribution network at the t sampling point under the scene s;
Figure BDA0003342957340000174
the square of the current amplitude of the ith line at the t sampling point on the corridor ij in the nth power distribution network under the scene s is shown;
Figure BDA0003342957340000175
the current amplitude of the ith line on the ith line of the corridor ij in the nth power distribution network at the t sampling point under the scene s is obtained; vs,n,i,tThe voltage amplitude of the ith node in the nth power distribution network at the t-th sampling point under the scene s is shown.
Figure BDA0003342957340000176
And
Figure BDA0003342957340000177
respectively the resistance and reactance of the l line on the corridor ij in the nth power distribution network.
Figure BDA0003342957340000178
And
Figure BDA0003342957340000179
the method comprises the steps of obtaining the active load and the reactive load of a jth node in an nth power distribution network at a tth sampling point under a scene s.
Figure BDA00033429573400001710
The maximum value of the current of the l line on the corridor ij in the nth power distribution network,
Figure BDA00033429573400001711
and
Figure BDA00033429573400001712
the maximum voltage value of the ith node in the nth power distribution network.
2-2) establishing a transmission network planning sub-model for the transmission network in the transmission and distribution network, wherein the model consists of an objective function and constraint conditions. The method comprises the following specific steps:
2-2-1) determining an objective function of the power transmission network planning sub-model;
Figure BDA00033429573400001713
the objective function is the total investment and operation cost F of the power transmission networktransMinimization of (d);
in the formula, the investment cost of the power transmission network is the investment cost C of energy storageinv(ii) a The total cost of the operation of the power transmission network under the scene s comprises the following items: cost of generator generation in a power transmission grid
Figure BDA00033429573400001714
Cost for purchasing electricity by surfing Internet of new energy
Figure BDA00033429573400001715
Cost of new energy abandonment
Figure BDA00033429573400001716
Cost of selling electricity to distribution network by transmission network
Figure BDA00033429573400001717
Penalty cost for sharing variable error between transmission and distribution networks
Figure BDA00033429573400001718
Operating cost of stored energy
Figure BDA00033429573400001719
Wherein the content of the first and second substances,
Figure BDA00033429573400001720
Figure BDA00033429573400001721
Figure BDA00033429573400001722
Figure BDA0003342957340000181
Figure BDA0003342957340000182
Figure BDA0003342957340000183
Figure BDA0003342957340000184
in formula (34), C1,kConfiguring node for kth energy storage in power transmission networkCapital cost of point energy storage, C2,kConfiguring cost per energy capacity of node energy storage for kth energy storage in power transmission network, C3,kConfiguring cost per power capacity of node energy storage for kth energy storage in power transmission network, EkAnd PkAnd respectively configuring the built-in capacity and power of the node for the kth energy storage in the power transmission network for the battery energy storage.
In formula (35), ΩGSet of nodes for all the transmission networks provided with generators, CG,i() is a function of the cost of power generation for the generator at node i in the grid,
Figure BDA0003342957340000185
and the generated power of the generator in the node i in the power transmission network at the t sampling point under the scene s is shown.
In the formula (36), pis,tThe new energy grid-connected electricity price of the t-th sampling point under the scene s,
Figure BDA0003342957340000186
and the renewable energy source network active power of the node i in the power transmission network at the t-th sampling point under the scene s is obtained.
In the formula (37), the reaction mixture is,
Figure BDA0003342957340000187
and generating power for the renewable energy source of the node i in the power transmission network at the t-th sampling point under the scene s.
In formula (39), ΩDIs a set of all distribution networks.
In the formula (40), the reaction mixture is,
Figure BDA0003342957340000188
and
Figure BDA0003342957340000189
charging active power and discharging active power of a battery stored at the t-th sampling point in a node k in the power transmission network under a scene s are respectively stored;
2-2-2) determining constraint conditions of the power transmission network planning sub-model; the method comprises the following specific steps:
2-2-2-1) sharing upper and lower limit constraints of variables:
Figure BDA00033429573400001810
wherein the content of the first and second substances,
Figure BDA00033429573400001811
and
Figure BDA00033429573400001812
the maximum value and the minimum value of the active power transmitted between the transmission network and the nth distribution network are respectively.
2-2-2-2) active and reactive power output constraint of new energy:
Figure BDA0003342957340000191
in the formula (I), the compound is shown in the specification,
Figure BDA0003342957340000192
and
Figure BDA0003342957340000193
respectively representing the maximum value and the minimum value of the renewable energy source network active power of the power transmission network node i at the t-th sampling point under the scene s,
Figure BDA0003342957340000194
and the renewable energy source network active power of the node i in the power transmission network at the t-th sampling point under the scene s is obtained.
2-2-2-3) investment and operation constraints of the battery energy storage system:
Emin,trans≤Ek≤Emax,trans (43)
0≤Pk≤Pmax,trans (44)
Figure BDA0003342957340000195
in the formula, Emax,transAnd Emin,transMaximum and minimum projected capacity, P, of the stored energy of the battery in the gridmax,transEstablishing maximum operating power for the energy storage of the battery in the power transmission network; cmax,transAnd Cmin,transRespectively the maximum multiplying power and the minimum multiplying power of the battery energy storage in the power transmission network;
Figure BDA0003342957340000196
Figure BDA0003342957340000197
Figure BDA0003342957340000198
in the formula (I), the compound is shown in the specification,
Figure BDA0003342957340000199
a state flag 0-1 variable of charging active power of a battery energy storage at the t-th sampling point in a node k in the power transmission network under a scene s is set, wherein 0 represents that the battery energy storage is not allowed to be charged, and 1 represents that the battery energy storage is allowed to be charged;
Figure BDA00033429573400001910
a state mark 0-1 variable of discharge active power of a battery energy storage at the t-th sampling point in a node k in the power transmission network under a scene s is represented, wherein 0 represents that the battery energy storage does not allow discharge, and 1 represents that the battery energy storage allows discharge;
Figure BDA00033429573400001911
and
Figure BDA00033429573400001912
and respectively representing the charging active power and the discharging active power of the battery energy stored at the t-th sampling point in the node k in the power transmission network under the scene s.
Figure BDA00033429573400001913
Ek·SOCmin≤Es,k,t≤Ek·SOCmax (50)
Wherein E iss,k,tAnd the storage capacity of the node k in the power transmission network at the t-th sampling point under the scene s is shown.
Considering the continuity of the battery energy storage operation, the energy storage is returned to the initial energy storage state after one day of operation, namely:
Es,k,0＝Es,k,T＝Ek·SOCini (51)
in the formula, Es,k,0And Es,k,TAnd respectively storing the stored electric quantity of the node k in the power transmission network under the scene s at the initial sampling point and the ending sampling point every day.
2-2-2-4) generator single power constraint:
Figure BDA0003342957340000201
in the formula, Pi G,maxAnd Pi G,minThe maximum generated power and the minimum generated power of the generator in the node i in the transmission network are respectively.
2-2-2-5) optimal power flow constraint of the power transmission network:
Figure BDA0003342957340000202
Figure BDA0003342957340000203
Figure BDA0003342957340000204
in the formula (I), the compound is shown in the specification,
Figure BDA0003342957340000205
the node marginal electricity price of a node i in the power transmission network at the t-th sampling point under a scene s is a dual variable of a constraint formula (53);
Figure BDA0003342957340000206
for the l-th line susceptance, theta, between nodes i and j in the transmission networks,i,tAnd thetas,j,tAnd the phase angles of the node i and the node j in the power transmission network at the t-th sampling point under the scene s are respectively.
Figure BDA0003342957340000207
The capacity of the ith line between nodes i and j in the grid.
3) Solving the combined optimization model established in the step 2) to obtain an optimization planning scheme of battery energy storage in the power transmission and distribution network.
In the embodiment of the disclosure, in consideration of layered solution of the transmission and distribution network, and the consistency of boundary conditions needs to be ensured, a distributed optimization algorithm based on a target analysis cascade method is adopted to solve the joint optimization model, the overall flow is shown in fig. 2, and the specific steps are as follows:
3-1) setting the initial value of the iteration times j to be 0, and respectively setting penalty coefficients of consistency constraint of the jth iteration
Figure BDA0003342957340000208
Weight of
Figure BDA0003342957340000209
Sharing variables with the grid side
Figure BDA00033429573400002010
An initial value of wherein
Figure BDA00033429573400002011
The initial value of (a) is taken as 0,
Figure BDA00033429573400002012
the initial value of (a) is taken as 1,
Figure BDA00033429573400002013
the initial value of (2) is 0.
3-2) mixing
Figure BDA00033429573400002014
As the current vs,n,tWill be
Figure BDA00033429573400002015
As the current ws,n,tWill be
Figure BDA00033429573400002016
As is present
Figure BDA00033429573400002017
Solving each power distribution network planning sub-model and each power transmission network planning sub-model in the joint optimization model, and obtaining F through solvingdis,nTotal cost of investment and operation of nth distribution network as jth iteration
Figure BDA00033429573400002018
Is obtained by solving FtransTotal cost of transmission network investment and operation as jth iteration
Figure BDA0003342957340000211
The initial value of (c).
V after each submodel is updateds,n,t，ws,n,t，
Figure BDA0003342957340000212
Are respectively marked as
Figure BDA0003342957340000213
And
Figure BDA0003342957340000214
then the model is substituted into the later model to continue the iterative solution.
3-3) making j equal to j +1, and solving the node marginal electricity of the node i in the t-th sampling point in the power transmission network under the scene s in the power transmission network planning sub-modelPrice of
Figure BDA0003342957340000215
If the nth power distribution network is connected to the node i of the power transmission network, the node marginal electricity price of the nth power distribution network at the t sampling point under the scene s
Figure BDA0003342957340000216
Equal to the node marginal price of the node i at the t-th sampling point in the power transmission network under the scene s
Figure BDA0003342957340000217
3-4) mixing
Figure BDA0003342957340000218
As the current vs,n,tWill be
Figure BDA0003342957340000219
As the current ws,n,tWill be
Figure BDA00033429573400002110
As is present
Figure BDA00033429573400002111
Solving each power distribution network planning sub-model in sequence, and sharing variables of each power distribution network side in the solving result
Figure BDA00033429573400002112
As
Figure BDA00033429573400002113
Figure BDA00033429573400002114
The active power is the active power exchanged between the transmission and distribution networks on the distribution network side at the t sampling point of the nth distribution network under the scene s during the j iteration.
Wherein, when solving the power distribution network planning submodel at every turn, obtain including: battery energy storage capacity and power built-in at kth energy storage configuration node in power transmission network：EkAnd Pk(ii) a Under a scene s, the renewable energy source internet active power of a node i in the power transmission network at the t-th sampling point:
Figure BDA00033429573400002115
the active power exchanged between the transmission and distribution networks of the transmission network side when the nth distribution network is at the tth sampling point under the scene s:
Figure BDA00033429573400002116
3-5) subjecting the product obtained in step 3-4)
Figure BDA00033429573400002117
The sub-model is substituted into the sub-model, the sub-model is solved, and the solved shared variables of the power distribution networks corresponding to the power distribution networks are obtained
Figure BDA00033429573400002118
As updated
Figure BDA00033429573400002119
Figure BDA00033429573400002120
And the active power exchanged between the transmission network and the transmission and distribution network of the nth distribution network at the tth sampling point of the scene s during the jth iteration is obtained.
Wherein, when solving transmission network planning submodel each time, obtain including: the battery energy storage is in the built-in capacity and power of the kth energy storage configuration node in the power transmission network: ekAnd Pk(ii) a Under a scene s, the renewable energy source internet active power of a node i in the power transmission network at the t-th sampling point:
Figure BDA00033429573400002121
the active power exchanged between the transmission and distribution networks of the transmission network side when the nth distribution network is at the tth sampling point under the scene s:
Figure BDA00033429573400002122
3-6) determining whether the iteration converges according to equations (56) and (57):
Figure BDA00033429573400002123
Figure BDA0003342957340000221
wherein epsilon1The optimal error represents the relative error between the costs of the transmission and distribution network of two iterations, the value range is required to be less than or equal to 0.01, and the value range is 0.001 in one specific embodiment of the disclosure; epsilon2The error is a shared error, which represents the error of active power transmission between the transmission and distribution network, and the value range is required to be less than or equal to 0.01, which is 0.001 in one specific embodiment of the disclosure.
If the equations (56) and (57) are both satisfied, iteration converges, and the built-in capacity and power E of the battery energy storage in the power distribution network obtained by the jth iteration are usedn,iAnd Pn,iAnd the on-stream capacity and power E of the battery energy storage in the gridkAnd PkAs an optimization result of the energy storage planning, the planning is finished;
if either of equations (56) and (57) is not satisfied, the iterations do not converge and the coherency constraint penalty function coefficients are updated according to equations (58) and (59)
Figure BDA0003342957340000222
And weight
Figure BDA0003342957340000223
And then returns to step 3-3) again.
Figure BDA0003342957340000224
Figure BDA0003342957340000225
And theta is a punished quadratic term iteration coefficient, the value range is greater than or equal to 2, the punishment is increased faster when the value is larger, and 2 is taken in one specific embodiment of the disclosure.
In order to implement the foregoing embodiment, an embodiment of a second aspect of the present disclosure provides an energy storage system optimization planning apparatus for power transmission and distribution network coordination, including:
the optimization model building module is used for building a combined optimization model for the power transmission network and the power distribution network which are configured with the battery energy storage according to a preset typical daily operation scene set, wherein the combined optimization model comprises a power distribution network planning sub-model and a power transmission network planning sub-model;
and the energy storage planning module is used for solving the combined optimization model by adopting a distributed optimization algorithm to obtain a planning scheme of battery energy storage of the power transmission network and the power distribution network.
To achieve the above embodiments, an embodiment of a third aspect of the present disclosure provides an electronic device, including:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a method of energy storage system optimization planning in conjunction with transmission and distribution networks as described above.
In order to implement the foregoing embodiments, a fourth aspect of the present disclosure provides a computer-readable storage medium storing computer instructions, where the computer instructions are configured to cause a computer to execute the foregoing method for optimizing and planning an energy storage system in coordination with a transmission and distribution network.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device is caused to execute a method for optimizing and planning a power transmission and distribution network coordinated energy storage system of the embodiment.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims (10)

1. A power transmission and distribution network collaborative energy storage system optimization planning method is characterized by comprising the following steps:
according to a preset typical daily operation scene set, establishing a joint optimization model for a power transmission network and a power distribution network which are configured with battery energy storage, wherein the joint optimization model comprises a power distribution network planning sub-model and a power transmission network planning sub-model;
and solving the combined optimization model by adopting a distributed optimization algorithm to obtain a planning scheme of battery energy storage of the power transmission network and the power distribution network.
2. The method of claim 1, wherein the set of typical daily operational scenarios comprises a set of new energy plant standing output typical scenarios and a set of load typical scenarios, wherein:
sampling historical output data of new energy plants in the transmission and distribution network according to days, clustering the sampled daily historical output data of each new energy plant by using a K-means algorithm, and generating an output typical daily scene of each new energy plant; forming output typical daily scenes of all new energy plant stations into a new energy plant station output typical scene set;
sampling historical load data of a transmission and distribution network on a daily basis, wherein the sampling time period of the historical load data is consistent with the sampling time period of the historical output data of the new energy plant station; and clustering daily historical load data obtained by sampling by using a K-means algorithm to generate a load typical daily scene, and forming a load typical scene set by using all load typical daily scenes.
3. The method of claim 1, wherein the distribution network planning submodel comprises:
1) an objective function;
Figure FDA0003342957330000011
in the formula, subscript n is the serial number of the distribution network, Fdis,nRepresents the total cost, omega, of the nth distribution network investment and operationSRepresenting a typical daily running scene set, s ∈ ΩS，DsRepresents the number of days a typical daily operational scenario s occupies in a year; cinv,nRepresenting the investment cost of the nth distribution network,
Figure FDA0003342957330000012
representing the net surfing and electricity purchasing cost of the new energy of the nth power distribution network under the scene s,
Figure FDA0003342957330000013
representing new energy of nth power distribution network under scene sThe cost of the disposal of the source is reduced,
Figure FDA0003342957330000014
represents the penalty cost of sharing variable error between the transmission network and the nth distribution network under the scene s,
Figure FDA0003342957330000015
representing the cost of purchasing electricity from the nth distribution network to the transmission network under the scene s,
Figure FDA0003342957330000016
representing the operation and maintenance cost of the energy storage of the nth power distribution network under the scene s;
wherein the content of the first and second substances,
Figure FDA0003342957330000017
Figure FDA0003342957330000018
Figure FDA0003342957330000021
Figure FDA0003342957330000022
Figure FDA0003342957330000023
in the formula, omegaessSet of candidate configuration nodes, G, representing all stored energyinvA coefficient for converting the investment cost from the current value to the equal-year value in the planning period; c1,n,kRepresents the capital construction cost of energy storage of the kth energy storage configuration node in the nth distribution network, C2,n,kTo representCost per energy capacity of energy storage of kth energy storage configuration node in nth distribution network, C3,n,kThe unit power capacity cost of energy storage of the kth energy storage configuration node in the nth power distribution network is represented;
ΩRethe node set of the new energy accessed to the power distribution network is represented, i is a node sequence number, and i belongs to omegaReT is the serial number of the sampling time point, T is the total sampling time period number of a typical day, and pis,n,tThe new energy grid-surfing electricity price of the t-th sampling point in the nth power distribution network under the scene s,
Figure FDA0003342957330000024
the renewable energy source internet active power of a node i in the nth power distribution network at the t-th sampling point under a scene s is obtained, and delta t is the length of a sampling period; beta is a penalty coefficient for abandoning the new energy,
Figure FDA0003342957330000025
generating power for the renewable energy source of a node i in the nth power distribution network at the t-th sampling point under the scene s; v. ofs,n,tAnd ws,n,tRespectively sharing a coefficient value and a weight value of a variable penalty function at a t sampling point in an nth power distribution network under a scene s;
Figure FDA0003342957330000026
the active power exchanged between the transmission and distribution networks on the transmission network side at the t sampling point of the nth distribution network under the scene s,
Figure FDA0003342957330000027
the active power exchanged between the transmission and distribution networks on the distribution network side when the nth distribution network is at the tth sampling point under the scene s is obtained;
Figure FDA0003342957330000028
the node marginal electricity of the nth power distribution network at the t-th sampling point under the scene s is obtained; picAnd pidThe unit power operation and maintenance costs during energy storage charging and discharging are respectively;
2) a constraint condition; the method comprises the following specific steps:
2-1) sharing upper and lower limit constraints of variables:
Figure FDA0003342957330000029
wherein the content of the first and second substances,
Figure FDA00033429573300000210
and
Figure FDA00033429573300000211
maximum and minimum values allowed by the transmission of active power between the nth power distribution network and the transmission network are respectively set;
2-2) active and reactive power output constraint of new energy:
Figure FDA00033429573300000212
Figure FDA0003342957330000031
Figure FDA0003342957330000032
in the formula (I), the compound is shown in the specification,
Figure FDA0003342957330000033
and
Figure FDA0003342957330000034
respectively representing the maximum value and the minimum value of the renewable energy source internet active power of the node i at the t-th sampling point under the scene s;
Figure FDA0003342957330000035
in the nth distribution network under the scene s, the node i is in the tthRenewable energy online reactive power of the sampling point;
Figure FDA0003342957330000036
and
Figure FDA0003342957330000037
respectively setting the maximum value and the minimum value of the renewable energy internet reactive power of the node i at the t-th sampling point under the scene s;
Figure FDA0003342957330000038
the renewable energy capacity of the node i in the nth power distribution network;
2-3) the investment operation constraint of the battery energy storage system:
Emin≤En,i≤Emax (12)
0≤Pn,i≤Pmax (13)
Figure FDA0003342957330000039
in the formula, En,iAnd Pn,iRespectively storing the battery energy in the built-in capacity and power of a node i in the nth distribution network, EmaxAnd EminMaximum and minimum projected capacity, P, of the battery's stored energy, respectivelymaxMaximum built-in power for battery energy storage, CmaxAnd CminRespectively the maximum multiplying power and the minimum multiplying power of the energy storage of the battery;
Figure FDA00033429573300000310
Figure FDA00033429573300000311
Figure FDA00033429573300000312
Figure FDA00033429573300000313
Figure FDA00033429573300000314
Figure FDA00033429573300000315
Figure FDA00033429573300000316
in the formula (I), the compound is shown in the specification,
Figure FDA0003342957330000041
storing a state mark 0-1 variable of charging active power of a battery at a t sampling point in an nth power distribution network node i under a scene s;
Figure FDA0003342957330000042
storing a state mark 0-1 variable of discharging active power of a battery at a t sampling point in an nth power distribution network node i under a scene s;
Figure FDA0003342957330000043
and
Figure FDA0003342957330000044
charging active power and discharging active power of a battery stored in an nth power distribution network node i at a tth sampling point under a scene s are respectively stored,
Figure FDA0003342957330000045
and
Figure FDA0003342957330000046
respectively storing reactive power absorbed and released at the t sampling point by a battery in the nth power distribution network node i under the scene s,
Figure FDA0003342957330000047
and
Figure FDA0003342957330000048
respectively obtaining the maximum value and the minimum value of the reactive power exchanged between the battery energy storage and the power grid in the nth power distribution network node i;
Figure FDA0003342957330000049
En,i·SOCmin≤Es,n,i,t≤En,i·SOCmax (23)
wherein E iss,n,i,tThe method comprises the steps that the storage electric quantity of a node i in an nth power distribution network at a t sampling point under a scene s is obtained; etacAnd ηdRespectively representing the charging efficiency and the discharging efficiency of the battery energy storage, SOCmaxAnd SOCminRespectively representing the upper limit and the lower limit of the state of charge of the battery energy storage operation;
Es,n,i,0＝Es,n,i,T＝En,i·SOCini (24)
in the formula, Es,n,i,0And Es,n,i,TRespectively storing the stored electric quantity and SOC of the initial sampling point and the ending sampling point of each day for the stored energy in the nth power distribution networkiniRepresenting the initial value of the state of charge of the battery energy storage operation;
2-4) optimal power flow constraint of the power distribution network:
Figure FDA00033429573300000410
Figure FDA00033429573300000411
Figure FDA00033429573300000412
Figure FDA00033429573300000413
Figure FDA00033429573300000414
Figure FDA00033429573300000415
Figure FDA0003342957330000051
Figure FDA0003342957330000052
in the formula, a corridor ij represents a power transmission line set from a node i to a node j;
Figure FDA0003342957330000053
and
Figure FDA0003342957330000054
respectively setting the active power and the reactive power of the l line on the ij in the nth distribution network at the t sampling point under the scene s;
Figure FDA0003342957330000055
the square of the current amplitude of the ith line at the t sampling point on the corridor ij in the nth power distribution network under the scene s is shown;
Figure FDA0003342957330000056
the current amplitude of the ith line on the ith line of the corridor ij in the nth power distribution network at the t sampling point under the scene s is obtained; vs,n,i,tThe voltage amplitude of an ith node in an nth power distribution network at a t sampling point under a scene s is shown;
Figure FDA0003342957330000057
and
Figure FDA0003342957330000058
the resistance and the reactance of the l line on the corridor ij in the nth power distribution network are respectively;
Figure FDA0003342957330000059
and
Figure FDA00033429573300000510
the method comprises the steps that active load and reactive load of a jth node in an nth power distribution network at a tth sampling point under a scene s are measured;
Figure FDA00033429573300000511
the maximum value of the current of the l line on the corridor ij in the nth power distribution network,
Figure FDA00033429573300000512
and
Figure FDA00033429573300000513
the maximum voltage value of the ith node in the nth power distribution network.
4. The method of claim 3, wherein the coefficient for converting the investment cost from a present value to an equal year value during the planning period is calculated by the following expression:
Figure FDA00033429573300000514
wherein α represents a general discount rate, NyFor planning the years.
5. The method of claim 3, wherein the grid planning submodel comprises:
1) an objective function;
Figure FDA00033429573300000515
in the formula, FtransRepresents the total cost of power grid investment and operation; cinvRepresents the investment cost of the power transmission network;
Figure FDA00033429573300000516
representing the cost of electricity generation by the generators in the grid under scenario s,
Figure FDA00033429573300000517
representing the cost of purchasing electricity from the internet of new energy under the scene s,
Figure FDA00033429573300000518
represents the cost of new energy abandonment under the scene s,
Figure FDA00033429573300000519
representing the cost of the transmission network selling electricity to the distribution network under scenario s,
Figure FDA00033429573300000520
represents the punishment cost of sharing variable errors between the transmission and distribution networks under the scene s,
Figure FDA00033429573300000521
to representOperating costs of energy storage;
wherein the content of the first and second substances,
Figure FDA0003342957330000061
Figure FDA0003342957330000062
Figure FDA0003342957330000063
Figure FDA0003342957330000064
Figure FDA0003342957330000065
Figure FDA0003342957330000066
Figure FDA0003342957330000067
in the formula, C1,kCapital cost, C, of energy storage for the kth energy storage configuration node in the grid2,kConfiguring cost per energy capacity of node energy storage for kth energy storage in power transmission network, C3,kConfiguring cost per power capacity of node energy storage for kth energy storage in power transmission network, EkAnd PkRespectively configuring the built-in capacity and power of a kth energy storage configuration node for battery energy storage in the power transmission network; omegaGSet of nodes for all the transmission networks provided with generators, CG,iIs a middle section of a power transmission networkThe cost function of the power generation of the generator at point i,
Figure FDA0003342957330000068
the generated power of a generator in a node i in the power transmission network at a t-th sampling point under a scene s is obtained; pis,tThe new energy grid-connected electricity price of the t-th sampling point under the scene s,
Figure FDA0003342957330000069
the method comprises the steps that the renewable energy source internet active power of a node i in a power transmission network at a t-th sampling point under a scene s is obtained;
Figure FDA00033429573300000610
generating power for a renewable energy source of a node i in the power transmission network at the t-th sampling point under a scene s; omegaDThe method comprises the following steps of (1) forming a set by all power distribution networks;
Figure FDA00033429573300000611
and
Figure FDA00033429573300000612
charging active power and discharging active power of a battery stored at the t-th sampling point in a node k in the power transmission network under a scene s are respectively stored;
2) a constraint condition; the method comprises the following specific steps:
2-1) sharing upper and lower limit constraints of variables:
Figure FDA00033429573300000613
wherein the content of the first and second substances,
Figure FDA0003342957330000071
and
Figure FDA0003342957330000072
the maximum value and the minimum value of active power transmitted between the transmission network and the nth power distribution network are respectively obtained;
2-2) active and reactive power output constraint of new energy:
Figure FDA0003342957330000073
in the formula (I), the compound is shown in the specification,
Figure FDA0003342957330000074
and
Figure FDA0003342957330000075
respectively representing the maximum value and the minimum value of the renewable energy source network active power of the power transmission network node i at the t-th sampling point under the scene s,
Figure FDA0003342957330000076
the method comprises the steps that the renewable energy source internet active power of a node i in a power transmission network at a t-th sampling point under a scene s is obtained;
2-2-2-3) investment and operation constraints of the battery energy storage system:
Emin,trans≤Ek≤Emax,trans (43)
0≤Pk≤Pmax,trans (44)
Figure FDA0003342957330000077
in the formula, Emax,transAnd Emin,transMaximum and minimum projected capacity, P, of the stored energy of the battery in the gridmax,transEstablishing maximum operating power for the energy storage of the battery in the power transmission network; cmax,transAnd Cmin,transRespectively the maximum multiplying power and the minimum multiplying power of the battery energy storage in the power transmission network;
Figure FDA0003342957330000078
Figure FDA0003342957330000079
Figure FDA00033429573300000710
in the formula (I), the compound is shown in the specification,
Figure FDA00033429573300000711
the state flag of charging active power of a battery stored at the t-th sampling point in a node k in the power transmission network under a scene s is changed into 0-1;
Figure FDA00033429573300000712
a state flag 0-1 variable of the discharge active power of a battery stored in a node k in the power transmission network at the t-th sampling point under a scene s;
Figure FDA00033429573300000713
and
Figure FDA00033429573300000714
charging active power and discharging active power of a battery stored at the t-th sampling point in a node k in the power transmission network under a scene s are respectively stored;
Figure FDA00033429573300000715
Ek·SOCmin≤Es,k,t≤Ek·SOCmax (50)
wherein E iss,k,tThe storage electric quantity of a node k in the power transmission network at the t-th sampling point under a scene s is obtained;
Es,k,0＝Es,k,T＝Ek·SOCini (51)
in the formula, Es,k,0And Es,k,TRespectively storing the stored electric quantity of a node k in the power transmission network under a scene s at an initial sampling point and an ending sampling point every day;
2-4) generator single power constraint:
Figure FDA0003342957330000081
in the formula, Pi G,maxAnd Pi G,minRespectively the maximum generating power and the minimum generating power of a generator in a node i in the power transmission network;
2-5) optimal power flow constraint of the power transmission network:
Figure FDA0003342957330000082
Figure FDA0003342957330000083
Figure FDA0003342957330000084
in the formula (I), the compound is shown in the specification,
Figure FDA0003342957330000085
the marginal electricity price of a node i in the power transmission network at the t-th sampling point under a scene s is obtained;
Figure FDA0003342957330000086
for the l-th line susceptance, theta, between nodes i and j in the transmission networks,i,tAnd thetas,j,tThe phase angles of the node i and the node j in the power transmission network at the t-th sampling point under the scene s respectively,
Figure FDA0003342957330000087
for the capacity of the l-th line between nodes i and j in the power transmission networkAmount of the compound (A).
6. The method according to claim 5, wherein solving the joint optimization model using a distributed optimization algorithm to obtain a planning solution for battery energy storage of the transmission and distribution network comprises:
1) setting the initial value of iteration times j as 0, and respectively setting penalty coefficients of consistency constraint of jth iteration
Figure FDA0003342957330000088
Weight of
Figure FDA0003342957330000089
Sharing variables with the grid side
Figure FDA00033429573300000810
An initial value of wherein
Figure FDA00033429573300000811
The initial value of (a) is taken as 0,
Figure FDA00033429573300000812
the initial value of (a) is taken as 1,
Figure FDA00033429573300000813
the initial value of (A) is 0;
2) will be provided with
Figure FDA00033429573300000814
As the current vs,n,tWill be
Figure FDA00033429573300000815
As the current ws,n,tWill be
Figure FDA00033429573300000816
As is present
Figure FDA00033429573300000817
Solving a power distribution network planning sub-model and a power transmission network planning sub-model in the joint optimization model, and obtaining F through solvingdis,nTotal cost of investment and operation of nth distribution network as jth iteration
Figure FDA00033429573300000818
Is obtained by solving FtransTotal cost of transmission network investment and operation as jth iteration
Figure FDA00033429573300000819
An initial value of (d);
the obtained updated v is solveds,n,t，ws,n,t，
Figure FDA0003342957330000091
Are respectively marked as
Figure FDA0003342957330000092
And
Figure FDA0003342957330000093
3-3) making j equal to j +1, and solving the node marginal electricity price of the t-th sampling point of the node i in the power transmission network under the scene s in the power transmission network planning sub-model
Figure FDA0003342957330000094
If the nth power distribution network is connected to the node i of the power transmission network, the node marginal electricity price of the nth power distribution network at the t sampling point under the scene s
Figure FDA0003342957330000095
Equal to the node marginal price of the node i at the t-th sampling point in the power transmission network under the scene s
Figure FDA0003342957330000096
4) Will be provided with
Figure FDA0003342957330000097
As the current vs,n,tWill be
Figure FDA0003342957330000098
As the current ws,n,tWill be
Figure FDA0003342957330000099
As is present
Figure FDA00033429573300000910
Sequentially solving each power distribution network planning sub-model, and obtaining the solution result
Figure FDA00033429573300000911
As
Figure FDA00033429573300000912
Figure FDA00033429573300000913
The active power exchanged between the transmission and distribution networks on the distribution network side at the t sampling point of the nth distribution network under the scene s during the jth iteration is calculated;
5) subjecting the product obtained in step 4)
Figure FDA00033429573300000914
The sub-model is brought into the sub-model and solved, and the sub-model obtained from the solution result is used
Figure FDA00033429573300000915
As
Figure FDA00033429573300000916
The active power exchanged between the transmission and distribution network of the transmission network and the transmission and distribution network of the nth distribution network at the tth sampling point of the scene s during the jth iteration is obtained;
6) whether the iteration converges is judged according to the equations (56) and (57):
Figure FDA00033429573300000917
Figure FDA00033429573300000918
wherein epsilon1The optimal error represents the relative error between the costs of the transmission and distribution network of two iterations; epsilon2The sharing error represents the error of active power transmission between the transmission and distribution network;
if the equations (56) and (57) are both satisfied, iteration is converged, and the built-in capacity and power E of the battery energy storage in the power distribution network obtained by solving in the jth iteration is usedn,iAnd Pn,iAnd the projected capacity and power E of the battery energy storage in the gridkAnd PkAs an optimization result of the energy storage planning, the planning is finished;
if either of equations (56) and (57) is not satisfied, the iterations do not converge and are updated according to equations (58) and (59)
Figure FDA00033429573300000919
And
Figure FDA00033429573300000920
then returning to the step 3-3) again;
Figure FDA00033429573300000921
Figure FDA00033429573300000922
and theta is a punished quadratic term iteration coefficient.
7. The method of claim 6, wherein the optimal error is equal to or less than 0.01, the sharing error is equal to or less than 0.01, and the penalty quadratic term iteration coefficient is equal to or greater than 2.
8. The utility model provides a cooperative energy storage system optimization planning device of transmission and distribution network which characterized in that includes:
the optimization model building module is used for building a combined optimization model for the power transmission network and the power distribution network which are configured with the battery energy storage according to a preset typical daily operation scene set, wherein the combined optimization model comprises a power distribution network planning sub-model and a power transmission network planning sub-model;
and the energy storage planning module is used for solving the combined optimization model by adopting a distributed optimization algorithm to obtain a planning scheme of battery energy storage of the power transmission network and the power distribution network.
9. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the preceding claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-7.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114693095A (en) * 2022-03-21 2022-07-01 国网湖北省电力有限公司电力科学研究院 Distributed energy storage power station optimal configuration method applied to county power grid
CN116227953A (en) * 2023-05-10 2023-06-06 广东电网有限责任公司珠海供电局 Main-distribution collaborative distribution network planning method and system based on convolutional neural network
CN117422227A (en) * 2023-10-10 2024-01-19 国网山东省电力公司潍坊供电公司 Transmission and distribution network double-side energy storage collaborative planning method considering source network charge storage coupling characteristic

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114693095A (en) * 2022-03-21 2022-07-01 国网湖北省电力有限公司电力科学研究院 Distributed energy storage power station optimal configuration method applied to county power grid
CN114693095B (en) * 2022-03-21 2024-05-31 国网湖北省电力有限公司电力科学研究院 Distributed energy storage power station optimal configuration method applied to county power grid
CN116227953A (en) * 2023-05-10 2023-06-06 广东电网有限责任公司珠海供电局 Main-distribution collaborative distribution network planning method and system based on convolutional neural network
CN117422227A (en) * 2023-10-10 2024-01-19 国网山东省电力公司潍坊供电公司 Transmission and distribution network double-side energy storage collaborative planning method considering source network charge storage coupling characteristic
CN117422227B (en) * 2023-10-10 2024-05-24 国网山东省电力公司潍坊供电公司 Transmission and distribution network double-side energy storage collaborative planning method considering source network charge storage coupling characteristic

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