CN115829112A - Power distribution network operation constraint-based producer and consumer distributed transaction double-layer optimization method - Google Patents

Power distribution network operation constraint-based producer and consumer distributed transaction double-layer optimization method Download PDF

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CN115829112A
CN115829112A CN202211490271.0A CN202211490271A CN115829112A CN 115829112 A CN115829112 A CN 115829112A CN 202211490271 A CN202211490271 A CN 202211490271A CN 115829112 A CN115829112 A CN 115829112A
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producer
representing
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power
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邓星
杨梓俊
王立峰
张景晨
路晓敏
张剑楠
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Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention belongs to the field of power markets, and particularly relates to a distributed trading double-layer optimization method for a producer and a consumer based on power distribution network operation constraint, which comprises the following steps of: step 1: constructing a distributed trading double-layer optimization model of a prosumer, which specifically comprises the following steps: step 101: establishing an upper-layer distributed transaction model of the producers and the consumers; step 102: establishing a lower-layer power distribution network optimization model; step 2: carrying out distributed solving on the distributed trading model of the upper layer of the prosumers and consumers to obtain distributed trading volume of each prosumer and consumer; inputting the distributed transaction amount of each producer and consumer into a lower-layer power distribution network optimization model to update bilateral transaction coefficients among the producers and consumers, and returning the bilateral transaction coefficients to an upper-layer producer and consumer distributed transaction model; and (4) iteratively solving a double-layer optimization model, minimizing the transaction cost of each producer and consumer and the operation cost of the power distribution network, and obtaining a distributed transaction strategy among a plurality of producers and consumers under the operation constraint of the power distribution network. The invention updates bilateral transaction coefficients among the producers and the consumers and reduces the cost of the producers and the consumers.

Description

Power distribution network operation constraint-based producer and consumer distributed transaction double-layer optimization method
Technical Field
The invention belongs to the field of electric power markets, and particularly relates to a distributed trading double-layer optimization method for a producer and a consumer based on operation constraint of a power distribution network.
Background
The energy structure in the world is mainly fossil energy, but under the large background of shortage of fossil energy and continuous aggravation of environmental pollution problems, distributed resources such as photovoltaic power generation, energy storage batteries, electric vehicles and the like become a new direction for developing low-carbon energy in countries in the world. The distributed resources have the characteristics of small pollution, safe and reliable operation, high energy conversion rate, small and exquisite equipment and easiness in installation, and the load flexibility of a user side is greatly improved. Along with the permeability of distributed resources in the power distribution network is continuously increased, a user side is gradually changed into a producer and a consumer with controllable load from a traditional electric energy consumer with uncontrollable load, the producer and the consumer have the capability of autonomous power generation, power generation and utilization management and energy storage management are carried out, and the self-production and self-consumption of electric energy are realized.
The traditional centralized scheduling method has the defects of large information amount, large calculation amount, poor stability and incapability of protecting the privacy of users, and can not support a plurality of producers and consumers to participate in distributed point-to-point transactions. Distributed transaction can enable producers and consumers to carry out electric energy transaction through information interaction, new energy consumption on the spot is achieved, flexibility of resources on the producer and consumer side can be mined, and operation stability of a power system is improved. Most researches relate to the influence of the operation constraint of the power distribution network on distributed transactions of the producers and the consumers; in view of the above, providing a distributed trading double-layer optimization method for producers and consumers based on power distribution network operation constraints becomes an urgent problem to be solved in the field.
Disclosure of Invention
The invention aims to provide a distributed trading double-layer optimization method for producing and eliminating persons based on power distribution network operation constraints, which is used for updating bilateral trading coefficients among the producing and eliminating persons and reducing the cost of the producing and eliminating persons.
In order to solve the technical problems, the technical scheme of the invention is as follows: a distributed trading double-layer optimization method for a producer and a consumer based on power distribution network operation constraint comprises the following steps:
step 1: constructing a distributed trading double-layer optimization model of a prosumer, which specifically comprises the following steps:
step 101: establishing an upper-layer distributed transaction model of the producers and the consumers;
step 102: establishing a lower-layer power distribution network optimization model;
and 2, step: solving the double-layer optimization model to obtain a distributed transaction strategy among the producers and the consumers, which comprises the following steps:
step 201: carrying out distributed solving on the distributed trading model of the upper layer of the prosumers and consumers to obtain distributed trading volume of each prosumer and consumer; inputting the distributed transaction amount of each producer and consumer into a lower-layer power distribution network optimization model to update bilateral transaction coefficients among the producers and consumers, and returning the bilateral transaction coefficients to an upper-layer producer and consumer distributed transaction model;
step 202: and (4) iteratively solving a double-layer optimization model, minimizing the transaction cost of each producer and consumer and the operation cost of the power distribution network, and obtaining a distributed transaction strategy among a plurality of producers and consumers under the operation constraint of the power distribution network.
The invention has the following beneficial effects:
the invention provides a distributed trading double-layer optimization method for producers and consumers based on power distribution network operation constraints, which widens trading channels among the producers and consumers, realizes balance of internal supply and demand and new energy consumption, reduces the cost per se, and promotes more producers and consumers to enter the market to participate in trading more actively. The invention is based on the optimization of the power distribution system, and the power distribution network optimization model can optimize and update the bilateral transaction coefficient between the producers and the consumers, thereby reducing the cost of the producers and the consumers. The distributed trading model of the upper layer of the stethoscopes is solved by adopting an alternating direction multiplier method, and compared with a centralized solving method, the distributed trading model of the upper layer of the stethoscopes can protect the privacy of the stethoscopes and reduce communication pressure.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of a two-layer model structure;
FIG. 3 is a topology diagram of an improved IEEE33 node power distribution system;
fig. 4 (1) is the internal energy management situation of the producer and the consumer under the photovoltaic low-output scene 2;
fig. 4 (2) shows the internal energy management of the producer and the consumer in the photovoltaic high-output scene 7;
FIG. 5 (1) shows the energy trade scenario of the buyer 1;
FIG. 5 (2) is an energy transaction scenario for the prenatal or post-mortem 2;
fig. 5 (3) is an energy transaction situation of the prenatal and post-mortem 3;
fig. 6 shows the price of electricity purchased and sold by the power grid and the trade price between the producer and the consumer.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1 and 2, the present invention is a distributed double-layer optimization method for a distributed transaction of a producer and a consumer based on the operation constraint of a power distribution network, comprising the following steps:
step 1: constructing a distributed trading double-layer optimization model of a prosumer, which specifically comprises the following steps:
step 101: establishing an upper-layer distributed transaction model of the producers and the consumers;
step 102: establishing a lower-layer power distribution network optimization model;
step 2: solving a double-layer optimization model to obtain a distributed trading strategy among the producers and the consumers, which comprises the following steps:
step 201: carrying out distributed solving on the distributed trading model of the upper layer of the prosumers and consumers to obtain distributed trading volume of each prosumer and consumer; inputting the distributed transaction amount of each producer and consumer into a lower-layer power distribution network optimization model to update bilateral transaction coefficients among the producers and consumers, and returning the bilateral transaction coefficients to an upper-layer producer and consumer distributed transaction model;
step 202: and (4) iteratively solving a double-layer optimization model, minimizing the transaction cost of each producer and consumer and the operation cost of the power distribution network, and obtaining a distributed transaction strategy among a plurality of producers and consumers under the operation constraint of the power distribution network.
The input data of the invention are the data of the purchase and sale electricity price, the basic load of each producer and consumer, the internal photovoltaic output, the parameters of the fuel cell, the energy storage parameters and the like. Firstly, constructing a distributed transaction upper layer model of a producer and a consumer aiming at different distributed resources; secondly, based on the influence of the operation of the power distribution network on distributed trading of the producers and the consumers, a double-layer optimization model is constructed, wherein the upper layer model is a distributed trading model of the producers and the consumers, and the lower layer model is a power distribution network optimization model; and finally, solving a double-layer optimization model through iteration to minimize the transaction cost of each producer and consumer and the operation cost of the power distribution network, so that distributed transaction among a plurality of producers and consumers under the operation constraint of the power distribution network is realized. The method fully excavates the scheduling potential of flexible resources in the producers and the consumers, stabilizes the uncertainty of the output of the new energy, promotes the local consumption of the new energy, reduces the transaction cost of the producers and the consumers, stimulates more producers and the consumers to participate in market transaction, and improves the economy and the safety of the operation of the system.
The above steps will be specifically described below.
Step 101 comprises the steps of:
step 1011: aiming at the minimum cost of the producer and the consumer participating in the distributed trade in the power market, an objective function of a producer and the consumer distributed trade model is established, and the formula is expressed as follows:
Figure BDA0003964634560000031
in the formula: subscript x represents parity; the subscript t denotes the trade period; subscript s denotes the photovoltaic contribution scenario; rho s Representing the probability of each scene; c all Represents the total cost of the producer and consumer;
Figure BDA0003964634560000032
representing the cost of the producer x trading with the grid at time t;
Figure BDA0003964634560000033
indicates that the person of birth or consumption x is inEnergy storage cost at a moment t scene s;
Figure BDA0003964634560000034
representing the air conditioning load cost of the producer x under the scene s at the moment t;
Figure BDA0003964634560000035
representing the flexible load cost of the producer x at the moment t scene s;
Figure BDA0003964634560000036
represents the fuel cell cost of the producer x at the time tset; c x,t Representing the distributed transaction cost of the destroyer x with other destroyers at time t;
step 1012: and establishing constraint conditions of the distributed transaction model of the producer and the consumer, wherein the constraint conditions comprise energy storage constraint, central air conditioning constraint, flexible load constraint, fuel cell constraint and distributed transaction constraint.
Specifically, in step 1011,
the trading cost formula with the power grid is expressed as follows:
Figure BDA0003964634560000037
in the formula:
Figure BDA0003964634560000038
representing the cost of the producer x trading with the grid at time t;
Figure BDA0003964634560000039
representing the electricity purchase price of the electricity market in the time period t;
Figure BDA00039646345600000310
representing the price of electricity purchased and sold by the electricity market in the time period t;
Figure BDA00039646345600000311
representing the virtual power plant x purchasing power from the electricity market during a time period t;
Figure BDA00039646345600000312
representing the amount of electricity sold by the virtual power plant x from the electricity marketplace over a period of time t;
the energy storage cost formula is expressed as follows:
Figure BDA00039646345600000313
in the formula:
Figure BDA0003964634560000041
representing the energy storage cost of the producer x under the scene s at the moment t;
Figure BDA0003964634560000042
representing the amount of charge of the stored energy of the producer x during the scene s time period t;
Figure BDA0003964634560000043
representing the discharge amount of the stored energy of the producer x in the scene s time period t; epsilon c A charge dissipation factor representing stored energy; epsilon d A discharge dissipation factor representing stored energy;
the central air conditioner and flexible load invocation cost formulas are expressed as follows:
Figure BDA0003964634560000044
Figure BDA0003964634560000045
in the formula:
Figure BDA0003964634560000046
representing the air conditioning load cost of the producer x under the scene s at the moment t;
Figure BDA0003964634560000047
representing the flexible load cost of the producer x at the moment t scene s; m and alpha areA user discomfort level coefficient;
Figure BDA0003964634560000048
is the indoor temperature of the victim x within the scene s time period t;
Figure BDA0003964634560000049
the most comfortable temperature is felt by the user in the patient x;
Figure BDA00039646345600000410
the flexible load value of the user in the scene s time period t is shown as a producer x;
Figure BDA00039646345600000411
a load reference value of a user in a time period t for a person x who is born or who goes away;
the fuel cell cost equation is expressed as follows:
Figure BDA00039646345600000412
in the formula:
Figure BDA00039646345600000413
represents the fuel cell cost of the producer x at the time tset; c. C on Is the operating cost factor of the fuel cell;
Figure BDA00039646345600000414
generating power of the fuel cell for the producer x in the scene s time period t;
the distributed transaction cost formula is expressed as follows:
Figure BDA00039646345600000415
in the formula: c x,t Representing the distributed transaction cost of the destroyer x with other destroyers at time t; c. C x,y Representing bilateral trading coefficients of a destroyer x and a destroyer y; p x,y,t Representing the time t between the person x and yAn amount of energy traded.
Specifically, in step 1012,
the fuel cell constraint equation is expressed as follows:
Figure BDA00039646345600000416
Figure BDA00039646345600000417
in the formula:
Figure BDA00039646345600000418
represents the minimum value of fuel cell power in the producer x;
Figure BDA00039646345600000419
represents the maximum value of fuel cell power in the producer x;
Figure BDA00039646345600000420
generating power of the fuel cell for the producer x in the scene s time period t;
Figure BDA00039646345600000421
representing the upward slope rate of the fuel cell in the person x with the birth or the disappearance;
Figure BDA00039646345600000422
representing the downward ramp rate of the fuel cell in the deputy x;
the energy storage constraint equation is expressed as follows:
Figure BDA00039646345600000423
Figure BDA0003964634560000051
Figure BDA0003964634560000052
S min ≤S x,s,t ≤S max
in the formula: p is c,max Represents a maximum value of the stored energy charging power; p d,max Represents the maximum value of the stored energy discharge power;
Figure BDA0003964634560000053
representing the amount of charge of the stored energy of the producer x during the scene s time period t;
Figure BDA0003964634560000054
representing the discharge amount of the stored energy of the producer x in the scene s time period t; s x,s,t The charge state of the energy storage of the producer x in the scene s time period t is shown;
Figure BDA0003964634560000055
representing the minimum electric storage quantity of the energy storage system in the X puerpera and the X;
Figure BDA0003964634560000056
representing the maximum electric storage capacity of the energy storage system in the patient x;
Figure BDA0003964634560000057
representing the charging efficiency of the stored energy in the xian x;
Figure BDA0003964634560000058
represents the discharge efficiency of the stored energy in the parity x;
the central air-conditioning constraint formula is expressed as follows:
Figure BDA0003964634560000059
in the formula:
Figure BDA00039646345600000510
is the indoor temperature of the victim x within the scene s time period t;
Figure BDA00039646345600000511
represents the outdoor temperature of the victim x at the scene s time period t; Δ t represents a time interval; c x 、R x Representing a physical parameter of the air conditioner; eta represents the energy efficiency ratio of the refrigerating unit;
Figure BDA00039646345600000512
representing the operating power of an internal building air conditioner of a prosumer x in a scene s time period t;
based on the user comfort, limiting the indoor temperature within an adjustable temperature range:
Figure BDA00039646345600000513
in the formula: t is in,min Is the minimum value of the room temperature, T in,max Is the maximum indoor temperature;
the flexible load constraint equation is expressed as follows:
Figure BDA00039646345600000514
Figure BDA00039646345600000515
in the formula:
Figure BDA00039646345600000516
a flexible load value of a user in a scene s time period t for a prosumer x;
Figure BDA00039646345600000517
a load reference value of a user in a time period t for a prenatal and Xiaoer x;
Figure BDA00039646345600000518
representing the flexible load minimum of the parity x in the time period t;
Figure BDA00039646345600000519
representing the flexible load maximum of the person x in the period t;
the distributed trade balance constraint formula is expressed as follows:
P x,y,t +P y,x,t ＝0 y≠x
Figure BDA00039646345600000520
in the formula: p is x,y,t Trading amount of energy for both the parity x and parity y over time period t; p y,x,t The energy trading volume for the parity y and parity x over time period t; g x,s,t Photovoltaic output of a producer and a consumer x in a time period t under a scene s;
Figure BDA00039646345600000521
representing the amount of electricity purchased by a producer x from the grid during a time period t under a scene s;
Figure BDA0003964634560000061
representing the amount of electricity sold from the grid by producer x for a period t under scene s.
Step 102 comprises the steps of:
step 1021: the method comprises the following steps of establishing an objective function of a power distribution network optimization model by taking the lowest operation cost of the power distribution network as a target, and expressing the formula as follows:
Figure BDA0003964634560000062
in the formula: the subscript t denotes the trade period; subscript i represents a distribution network node;
Figure BDA0003964634560000063
the electricity purchasing cost to the upper-level power grid is set for the time period t;
Figure BDA0003964634560000064
load shedding cost of the node i in the time period t;
step 1022: establishing model constraints of a power distribution network optimization model, wherein the model constraints comprise a branch flow model, upper and lower voltage amplitude limit constraints, branch capacity constraints and power balance constraints;
step 1023: and defining the marginal electricity price of the node according to an active power balance equation of the power distribution network node.
Specifically, in step 1021,
electricity purchase and sale costs and distributed transaction costs:
Figure BDA0003964634560000065
in the formula:
Figure BDA0003964634560000066
representing the cost of trading the producer x with the power grid at the moment t in the upper layer model; c x,t Representing the distributed transaction cost of the steward x and other stewards at the moment t in the upper model;
the electricity purchasing cost to the superior power grid is as follows:
Figure BDA0003964634560000067
in the formula:
Figure BDA0003964634560000068
a wholesale price representing time period t; p t sub Purchasing active power to the upper-level power grid for a time period t;
the cost of the power cut load:
Figure BDA0003964634560000069
in the formula:
Figure BDA00039646345600000610
a unit power-cut load cost coefficient representing a time period t;
Figure BDA00039646345600000611
representing the required power of the node i in the time period t;
Figure BDA00039646345600000612
representing the clipping power of node i during time period t.
Specifically, in step 1022,
a branch flow model:
Figure BDA00039646345600000613
Figure BDA00039646345600000614
in the formula: subscripts i, j, k denote distribution network nodes; p i,t Representing the injection active power of the node i in the time period t; q i,t Represents the reactive power injected at node i for time period t; p is ij,t Representing the active power of the branch ij over time period t; q ij,t Represents the reactive power of time period t on branch ij; p ki,t The active power of the branch ki in the time period t is represented; q ki,t Represents the reactive power of time period t on branch ki; f (i) represents the set of end nodes of the branch with node i as the head end; t (i) represents a set of head-end nodes for a branch with node i as the end node; l ij,t Represents the square of the current amplitude over branch ij over time period t; l ki,t The square of the current amplitude of the time period t on the branch ki is represented; r is a radical of hydrogen ij Represents the resistance of branch ij; x is the number of ij Represents the reactance of branch ij; r is ki The resistance of branch ki is represented; x is the number of ki Represents the reactance of branch ki; v i,t Represents the square of the voltage magnitude over time period t at node i; v j,t Represents the square of the voltage magnitude over time period t at node j; n is a radical of s Representing a set of system nodes; b s Representing a set of system branches;
and (3) limiting the upper limit and the lower limit of the voltage amplitude:
V i min ≤V i,t ≤V i max
in the formula: v i min 、V i max Respectively representing the minimum value and the maximum value of the voltage amplitude of the node i;
branch capacity constraint:
Figure BDA0003964634560000071
and power balance constraint:
Figure BDA0003964634560000072
Figure BDA0003964634560000073
Figure BDA0003964634560000074
Figure BDA0003964634560000075
in the formula: p i,t Representing the injected active power of the node i in the time period t;
Figure BDA0003964634560000076
representing the amount of electricity sold by the producer or consumer connected to the node i during the time period t;
Figure BDA0003964634560000077
representing the amount of electricity purchased by the parity person connected to node i during time period t;
Figure BDA0003964634560000078
an amount of power representing a distributed transaction between a ceU connected to node i and other ceUs during time period t; q i,t Represents the reactive power injected at node i for time period t;
Figure BDA0003964634560000079
representing the reactive power purchased from the upper-level power grid in a time period t; tan ψ represents a load power factor tangent value.
Specifically, in step 1023,
the active power balance equation of the power distribution network nodes is as follows:
Figure BDA00039646345600000710
defining the Lagrange dual multiplier of the formula as the marginal electricity price of the power distribution network node, and obtaining the power distribution network node marginal electricity price lambda of the node i in the time period t i,t ；
If the prosumer x accesses node i and the prosumer y accesses node j, then λ x,t ＝λ i,t 、λ y,t ＝λ j,t Then the bilateral transaction coefficient between the destroyer x and the destroyer y is expressed as:
c ij,t ＝(λ i,t -λ j,t )/2。
step 201 comprises the following steps:
step 2011: initializing bilateral transaction coefficients between upper-layer distributed transaction model solving iteration number k =0, upper-layer distributed transaction model solving iteration number n =0 and the parity person
Figure BDA0003964634560000081
Step 2012: adopting an ADMM algorithm to perform distributed solving on the upper-layer distributed trading model of the buyers and the sellers to obtain distributed trading volume of each buyer and the seller; in this embodiment, step 2012 includes the following steps:
step 20121: adopting an ADMM distributed algorithm to solve the upper layer producer and consumer distributed transaction model, wherein a Lagrange function is as follows:
Figure BDA0003964634560000082
in the formula: c all A total cost of participating in the distributed energy transaction for the producer and consumer;；u i,j,t for dual variables, defined as the energy trade P of the parity x and parity y over time period t x,y,t The price of (c); x x,y,t Is an auxiliary variable; rho is a penalty factor, namely step length;
step 20122: for energy transaction P i,j,t Auxiliary variable X i,j,t And iterating the dual variables, wherein the formula is expressed as follows:
Figure BDA0003964634560000083
Figure BDA0003964634560000084
Figure BDA0003964634560000085
in the formula: k is the number of iterations; the superscripts k, k +1 respectively represent the kth and k +1 th iteration;
step 20123: calculating the original residual error and the dual residual error after each iteration, and expressing the formula as follows:
Figure BDA0003964634560000086
in the formula:
Figure BDA0003964634560000087
respectively an original residual error and a dual residual error of the energy of a producer and a consumer x and a producer and a consumer y in the (k + 1) th iteration distributed transaction in a time period t;
step 20124: judging whether the ADMM algorithm is converged or not through an iteration stopping condition until the iteration stopping condition is met, and stopping iteration of the upper-layer producer and consumer distributed transaction model; the iteration stop condition is formulated as follows:
Figure BDA0003964634560000091
in the formula: epsilon pri And ε dual The tolerance upper limit of the original residual and the dual residual are respectively.
Step 2013: obtaining the purchase and sale electric quantity of each producer
Figure BDA0003964634560000092
And purchase and sell the electricity quantity for each producer and consumer
Figure BDA0003964634560000093
And distributed transaction amount P x,y,t Inputting the data into a lower-layer power distribution network optimization model to obtain node marginal electricity price lambda of each node i,t Thereby obtaining updated bilateral transaction coefficients between the abortive and the victims
Figure BDA0003964634560000094
And returning the bilateral transaction coefficient to the upper producer and consumer distributed transaction model.
Step 202 specifically includes: repeating the steps 2012-2013 to iteratively solve until the bilateral transaction coefficients reach the iteration condition, and obtaining the distributed transaction strategies of the producers and the consumers output by the distributed transaction model of the current upper layer; in this step, the iteration conditions are:
Figure BDA0003964634560000095
wherein:
Figure BDA0003964634560000096
bilateral transaction coefficients, ε, representing the n, n +1 th iterations between the parity x and parity y, respectively c Indicating an upper tolerance limit. In the embodiment, GAMS software is adopted to solve the model, and a distributed trading strategy of the birth-stills under the double-layer optimization model is obtained.
In the embodiment, the effectiveness of the method is verified by a simulation example formed by three producers and consumers combined in a power distribution network. Wherein, 3 producers and consumers all include photovoltaic, energy storage system, air conditioner load and flexible load, and wherein producer and consumer 1 still includes fuel cell. The parameters of the fuel cell, the energy storage, the central air conditioner and the photovoltaic system are shown in table 1. The time-of-use average purchase price was used as shown in table 2.
TABLE 1 parameters of the equipments of the producers and the consumers
Figure BDA0003964634560000097
TABLE 2 purchase and sale price of electricity to the electric network
Figure BDA0003964634560000098
Taking the producer 1 as an example, the internal energy management situation based on photovoltaic uncertainty output is shown in fig. 4 (1) and 4 (2). And comparing the photovoltaic low-output scene 2 with the photovoltaic high-output scene 7, and analyzing the internal multi-load scheduling of the producer and the destroyer under uncertain photovoltaic output. When the electricity purchase price is lower (1; when the electricity purchasing price is higher (12. As can be seen from the figure, the photovoltaic output low time (1 00-7) and the electricity consumption valley time (15 00-17). Thus, the flexibility of adjusting air conditioning loads and compliance loads and fully utilizing stored energy can be utilized to stabilize uncertain photovoltaic output. In the scene 2 and the scene 7, the net load of the producer and the consumer is the same, so that the load regulation can be performed through the inside of the producer and the consumer, uncertain photovoltaic consumption is realized, and the deterministic transaction amount is obtained.
The specific transaction of the prosumers and consummated persons is shown in fig. 5 (1), (2), and (3), wherein the positive direction indicates that the prosumers and consummated persons buy the electric energy, and the negative direction indicates that the prosumers and consummated persons sell the electric energy. The transaction amount of the producer and the consumer is positive or negative, which indicates that the producer and the consumer can participate in the transaction as a buyer or a seller, and the buying and selling identities are endogenous. As can be seen from comparison of fig. 5 (1), (2), and (3), the producer and the consumer 1 have large photovoltaic output and include a fuel cell, and therefore participate in the energy transaction mainly in the "power source dominated" seller status, and the producer and the consumer 2 and 3 participate in the energy transaction mainly in the "load dominated" buyer status. In the photovoltaic low-output period, the output of three producers and consumers can not meet the requirement of internal load, and electricity needs to be purchased from a power grid. And three producers and consumers only need to carry out distributed transaction to meet the load requirement in the photovoltaic high-output period, and do not need to purchase electricity to the power grid, so that the distributed transaction amount is increased. As can be seen from fig. 6, the distributed transaction price is between the power grid transaction electricity purchasing and selling prices, and the distributed transaction of the producers and the consumers can reduce the transaction cost, so that more producers and consumers are encouraged to participate in the distributed transaction, and the electric energy sharing is realized.
The transaction costs for each of the producers and the digesters are shown in table 3. Compared with direct trading with a power grid, the cost of the 'power supply leading type' producer and consumer 1 is increased to a certain extent under the condition that the producer and consumer participate in the distributed trading, but the cost of the 'load leading type' producer and consumer 2 and 3 is obviously reduced, and the total cost of participating in the distributed trading is lower. And under the double-layer model, the bilateral transaction coefficient between the producers and the consumers is updated, and the distributed transaction amount is improved and the cost is reduced due to the reduction of the bilateral transaction coefficient, thereby proving the economical efficiency of the double-layer optimization model.
TABLE 3 trade cost comparison
Figure BDA0003964634560000101
The parts not involved in the present invention are the same as or implemented using the prior art.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (10)

1. A distributed trading double-layer optimization method for a producer and a consumer based on power distribution network operation constraint is characterized by comprising the following steps: the method comprises the following steps:
step 1: constructing a distributed trading double-layer optimization model of a prosumer, which specifically comprises the following steps:
step 101: establishing an upper layer producer and consumer distributed transaction model;
step 102: establishing a lower-layer power distribution network optimization model;
step 2: solving the double-layer optimization model to obtain a distributed trading strategy among the producers and the consumers; the method specifically comprises the following steps:
step 201: carrying out distributed solving on the distributed trading model of the upper layer of the prosumers and consumers to obtain distributed trading volume of each prosumer and consumer; inputting the distributed transaction amount of each producer and consumer into a lower-layer power distribution network optimization model to update bilateral transaction coefficients among the producers and consumers, and returning the bilateral transaction coefficients to an upper-layer producer and consumer distributed transaction model;
step 202: and (4) iteratively solving a double-layer optimization model, minimizing the transaction cost of each producer and consumer and the operation cost of the power distribution network, and obtaining a distributed transaction strategy among a plurality of producers and consumers under the operation constraint of the power distribution network.
2. The distributed trading double-layer optimization method for the producers and the consumers based on the operation constraint of the power distribution network as claimed in claim 1, wherein: step 101 comprises the steps of:
step 1011: aiming at the minimum cost of the producer and the consumer participating in the distributed trade in the power market, an objective function of a producer and the consumer distributed trade model is established, and the formula is expressed as follows:
Figure FDA0003964634550000011
in the formula: subscript x represents parity; the subscript t represents the transaction period; subscript s denotes the photovoltaic contribution scenario; rho s Representing the probability of each scene; c all Represents the total cost of the producer and consumer;
Figure FDA0003964634550000012
representing the cost of the producer x trading with the grid at time t;
Figure FDA0003964634550000013
indicative of birthThe energy storage cost of the consumer x at the moment t scene s;
Figure FDA0003964634550000014
representing the air conditioning load cost of the producer x under the scene s at the moment t;
Figure FDA0003964634550000015
representing the flexible load cost of the producer x at the moment t scene s;
Figure FDA0003964634550000016
represents the fuel cell cost of the producer x at time tset; c x,t Representing the distributed transaction cost of the destroyer x with other destroyers at time t;
step 1012: and establishing constraint conditions of the distributed transaction model of the producer and the consumer, wherein the constraint conditions comprise energy storage constraint, central air conditioning constraint, flexible load constraint, fuel cell constraint and distributed transaction constraint.
3. The distributed trading double-layer optimization method for the producers and the consumers based on the operation constraint of the power distribution network as claimed in claim 2, wherein: in a step 1011, the process proceeds to,
the trading cost formula with the power grid is expressed as follows:
Figure FDA0003964634550000017
in the formula:
Figure FDA0003964634550000018
representing the cost of the producer x trading with the grid at time t;
Figure FDA0003964634550000019
representing the electricity purchase price of the electricity market in the time period t;
Figure FDA00039646345500000110
representing a time period tThe price of electricity purchased and sold in the electricity market;
Figure FDA00039646345500000111
representing the virtual power plant x purchasing power from the electricity market during a time period t;
Figure FDA0003964634550000021
representing the amount of electricity sold by the virtual power plant x from the electricity marketplace over a period of time t;
the energy storage cost formula is expressed as follows:
Figure FDA0003964634550000022
in the formula:
Figure FDA0003964634550000023
representing the energy storage cost of the producer x under the scene s at the moment t;
Figure FDA0003964634550000024
representing the amount of charge of the stored energy of the producer x during the scene s time period t;
Figure FDA0003964634550000025
representing the discharge amount of the stored energy of the producer x in the scene s time period t; epsilon c A charge dissipation factor representing stored energy; epsilon d A discharge dissipation factor representing stored energy;
the central air conditioner and flexible load invocation cost formulas are expressed as follows:
Figure FDA0003964634550000026
Figure FDA0003964634550000027
in the formula:
Figure FDA0003964634550000028
representing the air conditioning load cost of the producer x under the scene s at the moment t;
Figure FDA0003964634550000029
representing the flexible load cost of the producer x at the moment t scene s; m and alpha are the uncomfortable coefficients of the user;
Figure FDA00039646345500000210
is the indoor temperature of the victim x within the scene s time period t;
Figure FDA00039646345500000211
the most comfortable temperature is felt by the user in the patient x;
Figure FDA00039646345500000212
the flexible load value of the user in the scene s time interval t for the producer and the consumer x;
Figure FDA00039646345500000213
a load reference value of a user in a time period t for a prenatal and Xiaoer x;
the fuel cell cost equation is expressed as follows:
Figure FDA00039646345500000214
in the formula:
Figure FDA00039646345500000215
represents the fuel cell cost of the producer x at the time tset; c. C on Is the operating cost factor of the fuel cell;
Figure FDA00039646345500000216
generating power of the fuel cell for the producer x in the scene s time period t;
the distributed transaction cost formula is expressed as follows:
Figure FDA00039646345500000217
in the formula: c x,t Representing the distributed transaction cost of the destroyer x with other destroyers at time t; c. C x,y Representing bilateral trading coefficients of a destroyer x and a destroyer y; p x,y,t Representing the amount of energy traded between the steward x and the steward y at time t.
4. The distributed trading double-layer optimization method for the producers and the consumers based on the operation constraint of the power distribution network as claimed in claim 2, wherein: in the step 1012, the process is executed,
the fuel cell constraint equation is expressed as follows:
Figure FDA00039646345500000218
Figure FDA00039646345500000219
in the formula:
Figure FDA0003964634550000031
represents the minimum value of fuel cell power in the producer x;
Figure FDA0003964634550000032
represents the maximum value of fuel cell power in the producer x;
Figure FDA0003964634550000033
generating power of the fuel cell for the producer x in the scene s time period t;
Figure FDA0003964634550000034
direction of fuel cell in the producer xThe rate of uphill climb;
Figure FDA0003964634550000035
representing the downward ramp rate of the fuel cell in the deputy x;
the energy storage constraint equation is expressed as follows:
Figure FDA0003964634550000036
Figure FDA0003964634550000037
Figure FDA0003964634550000038
S min ≤S x,s,t ≤S max ；
in the formula: p is c,max Represents a maximum value of the stored energy charging power; p d,max Represents the maximum value of the stored energy discharge power;
Figure FDA0003964634550000039
representing the amount of charge of the stored energy of the producer x during the scene s time period t;
Figure FDA00039646345500000310
representing the discharge amount of the stored energy of the producer x in the scene s time period t; s x,s,t The charge state of the energy storage of the producer x in the scene s time period t is shown;
Figure FDA00039646345500000311
representing the minimum electric storage quantity of the energy storage system in the X puerpera and the X;
Figure FDA00039646345500000312
representing the maximum electric storage capacity of the energy storage system in the patient x;
Figure FDA00039646345500000313
representing the charging efficiency of the stored energy in the xian x;
Figure FDA00039646345500000314
represents the discharge efficiency of the stored energy in the patient x;
the central air-conditioning constraint formula is expressed as follows:
Figure FDA00039646345500000315
in the formula:
Figure FDA00039646345500000316
is the indoor temperature of the victim x within the scene s time period t;
Figure FDA00039646345500000317
represents the outdoor temperature of the victim x at the scene s time period t; Δ t represents a time interval; c x 、R x Representing a physical parameter of the air conditioner; eta represents the energy efficiency ratio of the refrigerating unit;
Figure FDA00039646345500000318
representing the operating power of an internal building air conditioner of a prosumer x in a scene s time period t;
based on the user comfort, limiting the indoor temperature within an adjustable temperature range:
Figure FDA00039646345500000319
in the formula: t is in,min Is the minimum value of the room temperature, T in,max Is the maximum indoor temperature;
the flexible load constraint equation is expressed as follows:
Figure FDA00039646345500000320
Figure FDA00039646345500000321
in the formula:
Figure FDA00039646345500000322
a flexible load value of a user in a scene s time period t for a prosumer x;
Figure FDA00039646345500000323
a load reference value of a user in a time period t for a prenatal and Xiaoer x;
Figure FDA00039646345500000324
representing the minimum value of the flexible load of the patient x in the time period t;
Figure FDA00039646345500000325
representing the flexible load maximum of the person x in the period t;
the distributed trade balance constraint formula is expressed as follows:
P x,y,t +P y,x,t ＝0y≠x；
Figure FDA0003964634550000041
in the formula: p x,y,t Trading amount of energy for both the parity x and parity y over time period t; p y,x,t Representing the amount of energy traded between the parity y and parity x at time t; g x,s,t Photovoltaic output of a producer and a consumer x in a time period t under a scene s;
Figure FDA0003964634550000042
representing the amount of electricity purchased by a producer x from the grid during a time period t under a scene s;
Figure FDA0003964634550000043
representing the amount of electricity sold from the grid by producer x for a period t under scene s.
5. The distributed trading double-layer optimization method for the producers and the consumers based on the operation constraint of the power distribution network as claimed in claim 1, wherein: step 102 comprises the steps of:
step 1021: the method comprises the following steps of establishing an objective function of a power distribution network optimization model by taking the lowest operation cost of the power distribution network as a target, and expressing the formula as follows:
Figure FDA0003964634550000044
in the formula: the subscript t represents the transaction period; subscript i represents a distribution network node;
Figure FDA0003964634550000045
the electricity purchasing cost to the upper-level power grid is set for the time period t;
Figure FDA0003964634550000046
load shedding cost for the node i in the time period t;
step 1022: establishing model constraints of a power distribution network optimization model, wherein the model constraints comprise a branch flow model, upper and lower voltage amplitude limit constraints, branch capacity constraints and power balance constraints;
step 1023: and defining the marginal electricity price of the node according to an active power balance equation of the power distribution network node.
6. The distributed trading double-layer optimization method for the producers and the consumers based on the operation constraint of the power distribution network, according to claim 5, is characterized in that: in a step 1021, the method further comprises,
electricity purchase and sale costs and distributed transaction costs:
Figure FDA0003964634550000047
in the formula:
Figure FDA0003964634550000048
representing the cost of trading the producer x with the power grid at the moment t in the upper layer model; c x,t Representing the distributed transaction cost of the destroyer x and other destroyers at the moment t in the upper model;
the electricity purchasing cost to the superior power grid is as follows:
Figure FDA0003964634550000049
in the formula:
Figure FDA00039646345500000410
a wholesale price representing time period t;
Figure FDA00039646345500000411
purchasing active power to the upper-level power grid for a time period t;
the cost of the power cut load:
Figure FDA0003964634550000051
in the formula:
Figure FDA0003964634550000052
a unit power-cut load cost coefficient representing a time period t;
Figure FDA0003964634550000053
representing the required power of the node i in the time period t;
Figure FDA0003964634550000054
representing the clipping power of node i during time period t.
7. The distributed producer and consumer transaction double-layer optimization method based on distribution network operation constraints as recited in claim 5, wherein: in step 1022, the process proceeds to step,
branch power flow model:
Figure FDA0003964634550000055
Figure FDA0003964634550000056
in the formula: subscripts i, j, k denote distribution network nodes; p i,t Representing the injected active power of the node i in the time period t; q i,t Represents the reactive power injected at node i for time period t; p ij,t Representing the active power of the branch ij over time period t; q ij,t Represents the reactive power of time period t on branch ij; p is ki,t The active power of the branch ki in the time period t is represented; q ki,t Represents the reactive power of time period t on branch ki; f (i) represents the set of end nodes of the branch with node i as the head end; t (i) represents a set of head-end nodes for a branch with node i as the end node; l. the ij,t Represents the square of the current amplitude over branch ij over time period t; l ki,t Represents the square of the current amplitude over time period t on branch ki; r is a radical of hydrogen ij Represents the resistance of branch ij; x is the number of ij Represents the reactance of branch ij; r is ki The resistance of branch ki is represented; x is the number of ki The reactance of the branch ki is represented; v i,t Represents the square of the voltage magnitude over time period t at node i; v j,t Represents the square of the amplitude of the voltage at node j over time period t; n is a radical of s Representing a set of system nodes; b is s Representing a set of system branches;
and (3) limiting the upper limit and the lower limit of the voltage amplitude:
V i min ≤V i,t ≤V i max ；
in the formula: v i min 、V i max Respectively representing the minimum value and the maximum value of the voltage amplitude of the node i;
branch capacity constraint:
Figure FDA0003964634550000057
and (3) power balance constraint:
Figure FDA0003964634550000058
Figure FDA0003964634550000059
Figure FDA0003964634550000061
Figure FDA0003964634550000062
in the formula: p i,t Representing the injected active power of the node i in the time period t;
Figure FDA0003964634550000063
representing the amount of electricity sold by the producer or consumer connected to the node i during the time period t;
Figure FDA0003964634550000064
representing the amount of electricity purchased by the parity person connected to node i during time period t;
Figure FDA0003964634550000065
representing the amount of power for a distributed transaction between a producer connected to node i and other producers during time period t; q i,t Represents the reactive power injected at node i for time period t;
Figure FDA0003964634550000066
representing the reactive power purchased from the upper-level power grid in a time period t; tan ψ represents a load power factor tangent value.
8. The distributed trading double-layer optimization method for the producers and the consumers based on the operation constraint of the power distribution network, according to claim 5, is characterized in that: in step 1023, the process is carried out,
the active power balance equation of the power distribution network nodes is as follows:
Figure FDA0003964634550000067
defining the Lagrange dual multiplier of the formula as the marginal electricity price of the power distribution network node, and obtaining the power distribution network node marginal electricity price lambda of the node i in the time period t i,t ；
If the prosumer x accesses node i and the prosumer y accesses node j, then λ x,t ＝λ i,t 、λ y,t ＝λ j,t Then the bilateral transaction coefficient between the destroyer x and the destroyer y is expressed as:
c ij,t ＝(λ i,t -λ j,t )/2。
9. the distributed trading double-layer optimization method for the producers and the consumers based on the operation constraint of the power distribution network as claimed in claim 1, wherein: step 201 specifically includes:
step 2011: initializing bilateral transaction coefficients between the upper-layer prosumer distributed transaction model solution iteration times k =0, the prosumer distributed transaction double-layer optimization model solution iteration times n =0 and the prosumers and cons
Figure FDA0003964634550000068
Step 2012: adopting an ADMM algorithm to perform distributed solving on the upper-layer distributed trading model of the buyers and the sellers to obtain distributed trading volume of each buyer and the seller; the method specifically comprises the following steps:
step 20121: adopting an ADMM distributed algorithm to solve the upper layer producer and consumer distributed transaction model, wherein a Lagrange function is as follows:
Figure FDA0003964634550000069
in the formula: c all A total cost of participating in the distributed energy transaction for the producer and consumer; (ii) a u. of i,j,t For dual variables, defined as the energy trade P of the parity x and parity y over time period t x,y,t The price of (c); x x,y,t Is an auxiliary variable; rho is a penalty factor, namely step length;
step 20122: to energy trade P i,j,t Auxiliary variable X i,j,t And iterating the dual variables, wherein the formula is expressed as follows:
Figure FDA0003964634550000071
Figure FDA0003964634550000072
Figure FDA0003964634550000073
in the formula: k is the number of iterations; the superscripts k, k +1 respectively represent the kth and k +1 th iteration;
step 20123: calculating the original residual error and the dual residual error after each iteration, and expressing the formula as follows:
Figure FDA0003964634550000074
in the formula:
Figure FDA0003964634550000075
respectively an original residual error and a dual residual error of the energy of a producer and a consumer x and a producer and a consumer y in the (k + 1) th iteration distributed transaction in a time period t;
step 20124: judging whether the ADMM algorithm is converged or not through an iteration stopping condition until the iteration stopping condition is met, and stopping iteration of the upper-layer producer and consumer distributed transaction model; the iteration stop condition is formulated as follows:
Figure FDA0003964634550000076
in the formula: epsilon pri And ε dual Tolerance upper limits of original residual errors and dual residual errors are respectively set;
step 2013: and acquiring the purchasing and selling electric quantity of each producer and consumer, inputting the purchasing and selling electric quantity and the distributed transaction quantity of each producer and consumer into the lower-layer power distribution network optimization model to obtain the node marginal electricity price of each node, thereby obtaining an updated bilateral transaction coefficient between the producers and consumers, and returning the bilateral transaction coefficient to the upper-layer producer and consumer distributed transaction model.
10. The distributed trading double-layer optimization method for producers and consumers based on distribution network operation constraints according to claim 9, wherein the method comprises the following steps:
step 202 specifically includes: repeating the steps 2012-2013 to iteratively solve until the bilateral transaction coefficient reaches the iteration condition, namely the bilateral transaction coefficient difference value of the n, n +1 th iteration between the producer x and the producer y
Figure FDA0003964634550000077
And when the tolerance upper limit is less than or equal to the preset tolerance upper limit, acquiring a distributed trading strategy of the producers and the consumers output by the current upper-layer distributed trading model.
CN202211490271.0A 2022-11-25 2022-11-25 Power distribution network operation constraint-based producer and consumer distributed transaction double-layer optimization method Pending CN115829112A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116187099A (en) * 2023-04-24 2023-05-30 山东理工大学 User side energy storage configuration method based on double-layer iteration
CN116187099B (en) * 2023-04-24 2023-07-28 山东理工大学 User side energy storage configuration method based on double-layer iteration

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