CN111552912B - Double-layer economic optimization method for micro-grid connection - Google Patents

Double-layer economic optimization method for micro-grid connection Download PDF

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CN111552912B
CN111552912B CN202010318493.9A CN202010318493A CN111552912B CN 111552912 B CN111552912 B CN 111552912B CN 202010318493 A CN202010318493 A CN 202010318493A CN 111552912 B CN111552912 B CN 111552912B
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王灿
应宇辰
董庆国
余宏亮
杨楠
刘颂凯
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Abstract

A double-layer economic optimization method for micro-grid connection comprises the following steps: step1: constructing a lower micro-grid optimization model; step2: solving the electricity purchasing/selling potential and electricity purchasing/selling price information of each micro-grid participating in upper layer transaction; step3: constructing an upper layer optimization model of the energy transmission path based on graph theory; step4: and solving a lower-layer micro-grid optimization model and an upper-layer optimization model based on a JAYA-Dijkstra algorithm to obtain an optimal transmission path of the output power of each micro-grid. The double-layer economic optimization method for the grid connection of the micro-grids can effectively improve the economic benefit of the multi-micro-grid.

Description

Double-layer economic optimization method for micro-grid connection
Technical Field
The invention relates to the technical field of micro-grid optimization, in particular to a double-layer economic optimization method for micro-grid connection.
Background
With the continuous exhaustion of global fossil energy, the development of renewable distributed energy has attracted widespread attention. Micro-grids are rapidly developing as an effective way of in-situ consumption of new energy power generation, and multi-micro-grids composed of multiple micro-grids through energy mutual-match are attracting attention. The economical efficiency of the operation of the multi-micro-grid is a key factor attracting wide users and increasing permeability in a large-scale power system, but the economic benefit of the multi-micro-grid is not well reflected at present. The uncertainty of the economic benefit of multiple micro-grids must be an important reason for preventing the development of micro-grids. Thus, there is a need for further enhancement and penetration in the study of the economic operation of multiple micro-networks.
Through the search of the prior art literature, the literature Co-optimization for Distribution networks withmulti-microgrids based on a two-stage optimization model with dynamicelectricity pricing (Hu, X.and T.Liu.Co-optimization for Distribution networks withmulti-microgrids based on a two-stage optimization model with dynamicelectricity pricing [ J ]. IET GenerationTransmission & Distribution,2017,11 (9): 2251-2259.) presents an optimized model between a multi-microgrid and a Distribution network that can reduce the operating cost and smooth load profile of the microgrid. However, the model does not consider that the running cost can be reduced by electric energy transaction among the micro-grids; document Interactive model for energymanagement of clustered microgrids (T.Lu, Z.Wang, Q.Ai, and W.Lee. Interaction model for energymanagement of clustered microgrids [ J ]. IEEE Transactions on Industrial Electronics Applications,2017,53 (3): 1739-1750.) constructs an interaction model for microgrid energy management. The upper layer part of the model is used for coordinating the operation between the power distribution network and the multi-micro network, and the lower layer part adopts cooperative game to coordinate the transaction between the sub-micro networks. But the model does not take into account the generation cost of the microgrid itself; documents Optimal power dispatch ofmulti-microgrids at future smart distribution grid (N.Nikmehr, S.N.Pavadanegh.Optimal power dispatch ofmulti-microgrids at future smart distribution grid [ J ]. IEEE Transactions onSmartGrid,2017,6 (4): 1648-1657.) construct a stochastic probability model between load and power sources that determines the optimal operating state of each sub-microgrid by analyzing economic transactions between the distribution network and the microgrid. But the object of the document study is a non-energy-storing micro-grid; the literature (Wu Gong, sun Ruisong, cai Gaoyuan. Dynamic economic dispatch study of Multi-microgrid interconnected systems. Solar school report, 2018,39 (5): 1-8.) proposes a particle swarm optimization algorithm combining Monte Carlo simulation, by which the results of the interconnected system dispatch optimization are analyzed. But does not take into account the power loss in the energy mutual-match process between the sub-micro networks.
Disclosure of Invention
In order to overcome the defects, the invention provides a double-layer economic optimization method for grid connection of micro-grids, which can effectively improve the economic benefit of the micro-grids in terms of reducing the running cost of the micro-grids.
The technical scheme adopted by the invention is as follows:
a double-layer economic optimization method for micro-grid connection comprises the following steps:
step1: constructing a lower micro-grid optimization model;
step2: solving the electricity purchasing/selling potential and electricity purchasing/selling price information of each micro-grid participating in upper layer transaction;
step3: constructing an upper layer optimization model of an energy transmission path;
step4: and solving a lower-layer micro-grid optimization model and an upper-layer optimization model based on a JAYA-Dijkstra algorithm to obtain an optimal transmission path of the output power of each micro-grid.
The invention relates to a double-layer economic optimization method for grid connection of micro-grids, which is divided into two layers of control, wherein the lower-layer micro-grid optimization model control performs economic optimization by simultaneously considering the generation cost of each sub-micro-grid and the real-time electricity price of a power distribution network; the upper optimizing model generates an energy trading object and an optimal trading path between the sub-micro grids based on the electricity purchase/selling price and the electricity generation/purchase potential uploaded by the bottom sub-micro grids so as to realize further economic optimization. The method can effectively improve the economic benefit of the multi-micro power grid.
Drawings
Fig. 1 is a system architecture diagram of a multi-microgrid system.
FIG. 2 is a flow chart of a solution of a double-layer optimization model proposed by the present invention.
Fig. 3 is an optimized schedule operation diagram of the sub-micro grid 5 of fig. 1.
Detailed Description
A double-layer economic optimization method for micro-grid connection comprises the following steps:
step1: constructing a lower micro-grid optimization model;
step2: solving the electricity purchasing/selling potential and electricity purchasing/selling price information of each micro-grid participating in upper layer transaction;
step3: constructing an upper layer optimization model of an energy transmission path;
step4: and solving a lower-layer micro-grid optimization model and an upper-layer optimization model based on a JAYA-Dijkstra algorithm to obtain an optimal transmission path of the output power of each micro-grid.
In the step (1) of the above-mentioned process,
establishing an underlying micro-grid optimization model, wherein the economic total cost formula is as follows:
Figure BDA0002460442100000031
wherein ,
f is the total cost of the lower optimization model operation;
Figure BDA0002460442100000032
SU i,t 、SD i,t the fuel cost, the maintenance cost, the starting cost and the stopping cost of the diesel generator are respectively; />
Figure BDA0002460442100000033
The maintenance cost of the energy storage battery i in the t period;
P EX,t for the power of the interconnection line of the sub-micro network, when P EX,t At positive values ρ EX,t Taking electricity purchase price; when P EX,t When negative, ρ EX,t And taking the price of electricity.
In the step (1) of the above-mentioned process,
solving the running cost of each device in the lower micro-grid optimization model:
the fuel cost, maintenance cost, starting cost and stopping cost of the diesel generator are given as the formulas
Figure BDA0002460442100000034
Figure BDA0002460442100000035
Figure BDA0002460442100000036
wherein ,
Figure BDA0002460442100000037
fuel cost for diesel generators;
Figure BDA0002460442100000038
maintenance costs for diesel generators;
U i,t the diesel generator is in an i-start and stop state at the t period;
U i,t-1 the diesel generator i is in an on-off state for the period t-1;
a i 、b i 、c i fuel cost factor for diesel generator i;
Figure BDA0002460442100000039
maintaining a cost coefficient for the operation of the diesel generator i;
P i,t the output power of the diesel generator i is t time period;
Figure BDA0002460442100000041
respectively representing the start-up and stop costs of the diesel generator i at unit moment;
SU i,t the starting cost of the diesel generator is;
SD i,t is the shutdown cost of the diesel generator.
The operation and maintenance cost of the energy storage battery is as follows:
Figure BDA0002460442100000042
wherein ,
Figure BDA0002460442100000043
the operation and maintenance cost of the energy storage battery i in the t period;
Figure BDA0002460442100000044
maintaining a cost coefficient for the operation of the energy storage battery i in the t period;
P BATi,t and the output power of the energy storage battery i is t time periods.
In the step (1) of the above-mentioned process,
solving the output constraint of each device in the lower micro-grid optimization model as follows:
the output constraint expression of the energy storage battery and the diesel generator is as follows:
Figure BDA0002460442100000045
P i min ≤P i,t ≤P t max
wherein ,
Figure BDA0002460442100000046
representing the minimum and maximum output power of the energy storage battery i respectively;
P BAT,t the output power of the energy storage battery is t time periods;
P i min 、P i max respectively representing the minimum output power and the maximum output power of the diesel generator i;
P i,t the output power of the diesel generator i is t time period.
The climbing rate constraint expression of the diesel generating set is as follows:
Figure BDA0002460442100000047
wherein ,
Figure BDA0002460442100000048
respectively representing the minimum climbing rate and the maximum climbing rate of the diesel generating set i; />
P i,t The output power of the diesel generator i is t time period;
P i,t-1 is the output power of the diesel generator i in the t-1 period.
The state of charge constraint expression of the energy storage battery is:
Figure BDA0002460442100000051
Figure BDA0002460442100000052
wherein ,
Figure BDA0002460442100000053
respectively representing the minimum charge state and the maximum charge state of the energy storage battery i;
SOC i,t the state of charge of the energy storage battery i in the t period;
E i,t the total energy of the energy storage battery i in the t period;
E i,t-1 the total energy of the energy storage battery i in the t-1 period;
E i C the rated capacity of the energy storage battery i;
P BAT,t the output power of the energy storage battery is t time periods;
Figure BDA0002460442100000055
the charge and discharge efficiencies of the energy storage battery i are respectively.
The system power constraint expression is:
Figure BDA0002460442100000056
Figure BDA0002460442100000057
wherein ,
P PVi,t 、P LD,t respectively representing the output power and the load power of the photovoltaic power generation i in the t period;
P BATi,t the output power of the energy storage battery i is t time periods;
P i,t the output power of the diesel generator i is t time period;
Figure BDA0002460442100000058
respectively minimum and maximum values of the tie line power;
P EX,t and interconnecting the line power for the period t.
In the step2, the purchase/selling potential calculation formula of each sub-micro-network is constructed as follows:
Figure BDA0002460442100000061
Figure BDA0002460442100000062
wherein ,
P i max maximum generated power of the generator i;
when P k,t When the power is more than 0, the maximum power of the power-selling micro-grid
Figure BDA0002460442100000063
When P k,t <Maximum purchase power of purchase power micro-grid at 0
Figure BDA0002460442100000064
In the step2, the quotation of each sub-micro-net is solved:
Figure BDA0002460442100000065
wherein ,
f (t) is the economic cost of the microgrid at the t period;
|f(t)-ρ EX,t P EX,t i is the net power generation cost of the microgrid;
Figure BDA0002460442100000066
net power generation for the microgrid;
when the micro grid k is an electricity purchasing micro grid, P k,t The electricity price is electricity purchasing price; when the micro-grid k is an electricity selling micro-grid, P k,t The electricity price is electricity selling price.
In the step (3) of the above-mentioned process,
describing topological relation among sub-micro networks in the multi-micro network based on graph theory:
the graph theory is divided into directed graphs and undirected graphs. If the edge between the two nodes connected in the network is directed, then graph G is a directed graph; otherwise, the graph G is an undirected graph. Because the energy among the sub-micro networks can mutually balance, the topological relation among the sub-micro networks of the multi-micro network is described by adopting an undirected graph G= { V, E, A }. V= { V 1 ,v 2 ,v 3 …v n The node set formed by n micro-grids is represented, E is an edge set of a plurality of micro-grids, and A= { a ij } n×n Is an adjacency matrix of a multi-microgrid, wherein a ij The method comprises the following steps:
Figure BDA0002460442100000071
wherein ,
w ij for node v i and vj Representing the impedance of the transmission line between the sub-micro networks.
In the step (3) of the above-mentioned process,
an upper layer optimization model of an energy transfer path is built, and n pairs of transaction objects among the sub-micro networks in different time periods are as follows:
S t ＝{E p1,s2 ,…E pi,si …,E pn,sn }
wherein ,
S t sum of sub-micro network transaction objects in t time period；
E pi,si And (5) the transaction electric quantity of the ith pair of transaction objects in the t period.
In the step3, a network loss calculation formula under the lowest network loss path in the upper layer optimization model is established:
Figure BDA0002460442100000072
wherein ,
E loss the network loss quantity under the lowest network loss path is obtained;
R pi,si 、U pi,si respectively representing the impedance and the voltage level of the lowest loss path;
E pi,si is the transaction electric quantity;
e is the amount of power that has been transferred between other sub-micro networks in the time path.
In the step (3) of the above-mentioned process,
solving an objective function of multi-microgrid energy mutual economy in different time periods in an upper-layer optimization model, wherein the objective function is as follows:
Figure BDA0002460442100000073
wherein ,
g t is an objective function of energy mutual aid of multiple micro-networks;
m is transaction logarithm in a period of time of a multi-microgrid t;
ρ pi,t 、ρ si,t respectively quoting the electricity purchasing micro-grid pi and the electricity selling micro-grid si in the period t;
E pi,si,t 、E loss,i,t and respectively obtaining the transaction electric quantity and the network loss quantity of the i pairs of transactions in the t period.
In the step4, JAYA-Dijkstra algorithm flow:
the JAYA algorithm solves the bottom optimization model as follows:
s1: initializing population size, variable number and optimizing space, and setting iteration termination standard as maximum iteration times.
S2: and obtaining the optimal solution and the worst solution of the population in the ith iteration.
S3: in the i+1th iteration, the population is updated by:
X i+1,j,k ＝X i,j,k +r i,j,1 (X i,j,best -|X i,j,k |)-r i,j,2 (X i,j,w o rst -|X i,j,k |)
wherein ,
X i,j,best and Xi,j,worst The optimal and worst two values in the ith iteration for variable k;
X i+1,j,k is the value after the update;
r i,j,1 and ri,j,2 Is that the jth variable in the ith iteration is in interval 0,1]Is a random number.
S4: judging update solution X i+1,j,k Whether the objective function can be used is better, if so, the updated solution is accepted, otherwise, the original solution is maintained.
S5: s2 to S4 are repeated until the termination condition is satisfied.
The Dijkstra algorithm solves the upper layer optimization model flow as follows:
step1: based on the multi-microgrid graph theory model g= { V, E, a }. Setting two sets S and T to make the set S store the found starting point v s To the node of the optimal loss path between the sub-micro networks directly connected with the node. The optimal network loss path is the path with the minimum network loss (obtained by a network loss calculation formula) when the transmitted electric quantity E of each line is considered between the directly connected sub-micro networks. And storing the nodes of the current optimal network loss path and all nodes except the initial node in the set T.
Step2: selecting a node v with the minimum current network loss from T j It is added to the set S such that s= { v j }. While removing the node from T such that t=v-V s -S。
Step3: v is set as j Step2 is repeated as a starting point until all nodes are traversed, i.e. when
Figure BDA0002460442100000091
When (1).
Step4: and selecting an optimal network loss path from the starting node to other nodes from the set S to be used as the lowest energy transmission path between the electronic sales micro-network and the electronic purchase micro-network.
Step5: and obtaining the network loss quantity of the sub-micro network transaction pair under the transmission path with the lowest energy according to the network loss calculation formula.
Step6: and judging whether all transaction pairs are matched with the transaction paths, if not, updating the transmitted electric quantity E of each line, and repeating Step1 to match the lowest network loss path for the next pair of transaction objects until all sub-micro network transaction pairs in the period of t are traversed.
The JAYA algorithm solves an optimization model of the lower-layer micro-grid, so that the running cost of each sub-micro-grid of the lower layer is optimal; then, solving an upper-layer optimization model through a Dijkstra algorithm, and searching an optimal sub-micro-network transaction pair path, so that the whole operation cost of the multi-micro-network is optimal.
Fig. 1 is a system architecture diagram of a multi-microgrid system. The multi-microgrid is composed of six sub-microgrids, and each sub-microgrid is provided with a diesel generator, an energy storage battery and photovoltaic power generation. Energy mutual-aid can be achieved among all the subnetworks through connecting lines, and economic operation of the integral multi-subnetwork is achieved through optimal electricity purchase/selling.
FIG. 2 is a flow chart of a solution of a double-layer optimization model proposed by the present invention. Firstly, parameters of electrical equipment in each sub-micro-network are acquired, and then an economic optimization model is built for the sub-micro-network. Solving a bottom layer model through a JAYA algorithm, calculating the electricity purchasing/selling potential and electricity purchasing/selling price of each sub-micro-grid participating in an upper layer optimization model, generating a micro-grid transaction object according to information uploaded by the bottom layer micro-grid and time sequence, and generating a lowest network loss path for sub-micro-grid transaction pairs based on a Dikstra algorithm. And finally, outputting the optimal output value of each device in the multi-micro network, the energy transaction value among the sub-micro networks and the total income of the multi-micro network.
Table 1 transaction information table for each sub-micro grid in 13 th period
Figure BDA0002460442100000092
Table 1 is a transaction information table for each sub-micro grid during period 13. It can be seen from table 1 that MG1, MG4, MG5 are the electricity selling micro-grids and MG2, MG3, MG6 are the electricity purchasing micro-grids during period 13.
Table 2 table of upper layer optimization results for multiple micro-grids using different methods during time period 13
Figure BDA0002460442100000093
Figure BDA0002460442100000101
Table 2 is a table of the upper layer optimization results for the multiple microgrid when different methods were employed during time period 13. As can be seen from table 2, in period 13, the economic cost under the conventional method is 153.57525 yuan. Compared with the economic cost of the traditional method, in the 13 th period, the economic cost of the method provided by the invention is 123.6670 yuan, and the overall benefit is improved by 19.46%. The comparison result shows that the method provided by the invention can effectively reduce the economic cost of multiple micro-grids and has better economic benefit.
Fig. 3 is a sub-microgrid 5 optimization scheduling operation diagram. As can be seen from fig. 3, during the period of 0:00 to 4:00, the diesel generator within the sub-micro-grid is in a shutdown state, and the sub-micro-grid purchases electric energy from the distribution network at a lower price to meet the load demand inside the sub-micro-grid, and charges the energy storage battery. In the peak period of 7:00 to 11:00, the diesel generator generates electricity to meet the load demand in the sub-micro network, electricity is sold to the distribution network to obtain profit, and the energy storage battery plays a role in peak clipping and valley filling in the process. The diesel generator is in a shutdown state in more time periods and can participate in the transaction process in the upper-layer multi-microgrid.

Claims (6)

1. A double-layer economic optimization method for micro-grid connection is characterized by comprising the following steps:
step1: constructing a lower micro-grid optimization model;
in the step1, a lower layer micro-grid optimization model is established, and the economic total cost formula is as follows:
Figure FDA0004176968030000011
wherein ,
f is the total cost of the operation of the lower micro-grid optimization model;
Figure FDA0004176968030000012
SU i,t 、SD i,t the fuel cost, the maintenance cost, the starting cost and the stopping cost of the diesel generator are respectively;
Figure FDA0004176968030000013
the maintenance cost of the energy storage battery i in the t period;
P EX,t for the power of the interconnection line of the sub-micro network, when P EX,t At positive values ρ EX,t Taking electricity purchase price; when P EX,t When negative, ρ EX,t Taking electricity selling price;
step2: solving the electricity purchasing/selling potential and electricity purchasing/selling price information of each micro-grid participating in upper layer transaction;
step3: constructing an upper layer optimization model of an energy transmission path;
in the step3, an upper layer optimization model of the energy transfer path is constructed, and n pairs of transaction objects among the sub-micro networks in different time periods are as follows:
S t ＝{E p1,s2 ,…E pi,si …,E pn,sn }
wherein ,
S t the sum of sub-micro network transaction objects in the t period is the sum of sub-micro network transaction objects;
E pi,si the transaction electric quantity of the ith pair of transaction objects in the t period;
establishing a network loss calculation formula under the lowest network loss path in the upper-layer optimization model:
Figure FDA0004176968030000014
wherein ,
E loss the network loss quantity under the lowest network loss path is obtained;
R pi,si 、U pi,si respectively representing the impedance and the voltage level of the lowest loss path;
E pi,si is the transaction electric quantity;
e, the transaction electric quantity among other sub-micro networks which are already transmitted in the time path is used;
solving an objective function of multi-microgrid energy mutual economy in different time periods in an upper-layer optimization model, wherein the objective function is as follows:
Figure FDA0004176968030000021
wherein ,
g t is an objective function of energy mutual aid of multiple micro-networks;
m is transaction logarithm in a period of time of a multi-microgrid t;
ρ pi,t 、ρ si,t respectively quoting the electricity purchasing micro-grid pi and the electricity selling micro-grid si in the period t;
E pi,si,t 、E loss,i,t respectively obtaining transaction electric quantity and network loss quantity of i pairs of transactions in t time periods;
step4: and solving a lower-layer micro-grid optimization model and an upper-layer optimization model based on a JAYA-Dijkstra algorithm to obtain an optimal transmission path of the output power of each micro-grid.
2. The double-layer economic optimization method for micro-grid connection according to claim 1, wherein the method comprises the following steps: in the step1, the running cost of each device in the lower-layer micro-grid optimization model is solved:
the fuel cost, maintenance cost, starting cost and stopping cost of the diesel generator are as follows:
Figure FDA0004176968030000022
Figure FDA0004176968030000023
Figure FDA0004176968030000024
wherein ,
Figure FDA0004176968030000025
fuel cost for diesel generators; />
Figure FDA0004176968030000026
Maintenance costs for diesel generators;
U i,t the diesel generator is in an i-start and stop state at the t period;
U i,t-1 the diesel generator i is in an on-off state for the period t-1;
a i 、b i 、c i fuel cost factor for diesel generator i;
Figure FDA0004176968030000027
maintaining a cost coefficient for the operation of the diesel generator i;
P i,t the output power of the diesel generator i is t time period;
Figure FDA0004176968030000031
respectively representing the starting and stopping costs of the diesel generator i at unit moment;
SU i,t the starting cost of the diesel generator is;
SD i,t the shutdown cost of the diesel generator is set;
the operation and maintenance cost of the energy storage battery is as follows:
Figure FDA0004176968030000032
wherein ,
Figure FDA0004176968030000033
the operation and maintenance cost of the energy storage battery i in the t period;
Figure FDA0004176968030000034
maintaining a cost coefficient for the operation of the energy storage battery i in the t period;
P BATi,t the output power of the energy storage battery i is t time periods;
solving the output constraint of each device in the lower micro-grid optimization model as follows:
the output constraint expression of the energy storage battery and the diesel generator is as follows:
Figure FDA0004176968030000035
Figure FDA0004176968030000036
wherein ,
Figure FDA0004176968030000037
representing the minimum and maximum output power of the energy storage battery i respectively;
P BAT,t the output power of the energy storage battery is t time periods;
P i min 、P i max separate tableThe minimum output power and the maximum output power of the diesel generator i are shown;
P i,t the output power of the diesel generator i is t time period;
the climbing rate constraint expression of the diesel generating set is as follows:
Figure FDA0004176968030000038
wherein ,
Figure FDA0004176968030000039
respectively representing the minimum climbing rate and the maximum climbing rate of the diesel generating set i; />
P i,t The output power of the diesel generator i is t time period;
P i,t-1 the output power of the diesel generator i is t-1 time period;
the state of charge constraint expression of the energy storage battery is:
Figure FDA0004176968030000041
Figure FDA0004176968030000042
wherein ,
Figure FDA0004176968030000043
respectively representing the minimum charge state and the maximum charge state of the energy storage battery i;
SOC i,t the state of charge of the energy storage battery i in the t period;
E i,t the total energy of the energy storage battery i in the t period;
E i,t-1 the total energy of the energy storage battery i in the t-1 period;
Figure FDA0004176968030000044
the rated capacity of the energy storage battery i;
P BAT,t the output power of the energy storage battery is t time periods;
Figure FDA0004176968030000045
respectively the charge and discharge efficiencies of the energy storage battery i;
the system power constraint expression is:
Figure FDA0004176968030000046
Figure FDA0004176968030000047
wherein ,
P PVi,t 、P LD,t respectively representing the output power and the load power of the photovoltaic power generation i in the t period;
P BATi,t the output power of the energy storage battery i is t time periods;
P i,t the output power of the diesel generator i is t time period;
Figure FDA0004176968030000048
respectively minimum and maximum values of the tie line power;
P EX,t and interconnecting the line power for the period t.
3. The double-layer economic optimization method for micro-grid connection according to claim 1, wherein the method comprises the following steps: in the step2, the purchase/selling potential calculation formula of each sub-micro-grid is constructed as follows:
Figure FDA0004176968030000051
Figure FDA0004176968030000052
wherein ,
P i max maximum output power of the diesel generator i;
when P k,t >At 0, the maximum electricity selling power of the electricity selling micro-grid
Figure FDA0004176968030000053
/>
When P k,t <Maximum purchase power of purchase power micro-grid at 0
Figure FDA0004176968030000054
4. A dual-layer economic optimization method for micro-grid connection according to claim 3, wherein: in the step2, the quotation of each sub-micro-net is solved:
Figure FDA0004176968030000055
wherein ,
f (t) is the economic cost of the microgrid at the t period;
|f(t)-ρ EX,t P EX,t i is the net power generation cost of the microgrid;
Figure FDA0004176968030000056
net power generation for the microgrid;
when the micro grid k is an electricity purchasing micro grid, ρ k,t For electricity purchase price, when the micro-grid k is an electricity selling micro-grid, ρ k,t The electricity price is electricity selling price.
5. The double-layer economic optimization method for micro-grid connection according to claim 1, wherein the method comprises the following steps: in the step3, the connection relationship between the sub-micro networks in the multi-micro network is described based on graph theory:
describing the topological relation among all sub-micro networks of the multi-micro network by adopting an undirected graph G= { V, E, A }; v= { V 1 ,v 2 ,v 3 …v n The node set formed by n micro-grids is represented, E is an edge set of a plurality of micro-grids, and A= { a ij } n×n Is an adjacency matrix of a multi-microgrid, wherein a ij The method comprises the following steps:
Figure FDA0004176968030000061
wherein ,wij For node v i and vj Representing the impedance of the transmission line between the sub-micro networks.
6. The double-layer economic optimization method for micro-grid connection according to claim 1, wherein the method comprises the following steps: in the step4, the JAYA-Dijkstra algorithm flow is as follows:
JAYA algorithm flow:
s1: initializing population size, variable number and optimizing space, and setting iteration termination criteria as maximum iteration times;
s2: obtaining an optimal solution and a worst solution of the population in the ith iteration;
s3: in the i+1th iteration, the population is updated by:
X i+1,j,k ＝X i,j,k +r i,j,1 (X i,j,best -|X i,j,k |)-r i,j,2 (X i,j,worst -|X i,j,k |)
wherein ,
X i,j,best and Xi,j,worst The optimal and worst two values in the ith iteration for variable k;
X i+1,j,k is the value after the update;
r i,j,1 and ri,j,2 Is that the jth variable in the ith iteration is in interval 0,1]Two random numbers in (a);
s4: judging update solution X i+1,j,k Whether the objective function can be used is better, if so, the updating solution is accepted, otherwise, the original solution is kept;
s5: repeating S2 to S4 until the termination condition is met;
dijkstra algorithm flow:
step1: based on the multi-microgrid graph theory model g= { V, E, a }; setting two sets S and T to make the set S store the found starting point v s To the node of the optimal network loss path between the sub-micro networks directly connected with the node; the optimal network loss path, namely, when the transmitted electric quantity E of each line is considered between the directly connected sub-micro networks, the path with the minimum network loss is obtained by a network loss calculation formula; and storing nodes of the current optimal network loss path and all nodes except the initial node in the set T;
step2: selecting a node v with the minimum current network loss from T j It is added to the set S such that s= { v j -a }; while removing the node from T such that t=v-V s -S；
Step3: v is set as j Step2 is repeated as a starting point until all nodes are traversed, i.e. when
Figure FDA0004176968030000071
When in use;
step4: selecting an optimal network loss path from the initial node to other nodes from the set S to be used as an energy minimum transmission path between the electronic sales micro-grid and the electronic purchase micro-grid;
step5: obtaining the network loss quantity of the sub-micro network transaction pair under the transmission path with the lowest energy according to a network loss calculation formula;
step6: and judging whether all transaction pairs are matched with the transaction paths, if not, updating the transmitted electric quantity E of each line, and repeating Step1 to match the lowest network loss path for the next pair of transaction objects until all sub-micro network transaction pairs in the period of t are traversed.
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