CN115659603A - Non-iterative P2P energy-consumption market decentralized clearing method - Google Patents

Non-iterative P2P energy-consumption market decentralized clearing method Download PDF

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CN115659603A
CN115659603A CN202211193797.2A CN202211193797A CN115659603A CN 115659603 A CN115659603 A CN 115659603A CN 202211193797 A CN202211193797 A CN 202211193797A CN 115659603 A CN115659603 A CN 115659603A
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energy
transaction
power
seller
buyer
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徐青山
夏元兴
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Southeast University
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Southeast University
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Abstract

The invention discloses a decentralized clearing method of a non-iterative P2P energy market, which comprises the following steps: obtaining usage data, the usage data comprising: user load, real-time electricity price, power network parameters, operation constraints of each distributed power supply and utility data of each market participant, and sending the obtained use data to an optimization model; the interaction between buyers and sellers of various buyers and consumers is described by using a Stackelberg game, each seller in the microgrid issues a transaction price first, and then the buyers determine the own purchase electric quantity according to the transaction price issued by the seller, wherein the specific model is as follows: constructing a double-layer optimization transaction by using a Stackelberg game, and solving a piecewise constant relation of upper and lower layers of optimization variables based on the optimal reaction of an energy buyer; and screening the safety constraint on the corresponding section constant relation.

Description

Non-iterative P2P energy-consumption market decentralized clearing method
Technical Field
The invention belongs to the technical field of electric power market trading models, and particularly relates to a decentralized clearing method for a non-iterative P2P energy-consuming market.
Background
In recent years, rapid development of distributed energy at a user end promotes the change of energy consumption modes, and on one hand, rapid development of Photovoltaic (PV) and wind power generation technologies provides opportunities for a power grid to improve local network problems (such as voltage fluctuation) in a flexible mode; on the other hand, the energy end user can also reduce the power generation cost of renewable energy by sharing the surplus energy of local power generation. The invention introduces the concept of energy producers and consumers to describe terminal users with energy sharing capability, each energy producer and consumer needs to reach a transaction agreement with other energy consumers to complete local P2P energy transaction in a decentralized form, and each microgrid can also reach a transaction agreement with other microgrids to make up for the excess and shortage of power of own community. A decentralized structured marketplace for trading can protect user privacy, promote efficient execution of trades, but can result in repeated iterations of the market clearing process. Therefore, establishing a non-iterative P2P (peer-to-peer) energy trading clearing method through model equivalence and scene reduction has become a current research focus, the existing P2P energy markets are mostly in a decentralized clearing mode, clearing needs to be performed by using a decentralized solving algorithm, the calculation difficulty is high, calculation convergence is not guaranteed, the solving time is too long, and the time requirement of rapid clearing of market trading cannot be met.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide a non-iterative P2P energy market decentralized clearing method.
The purpose of the invention can be realized by the following technical scheme: a non-iterative P2P energy market decentralization clearing method comprises the following steps:
obtaining usage data, the usage data comprising: the method comprises the following steps of (1) sending user load, real-time electricity price, power network parameters, operation constraints of distributed power supplies and utility data of market participants to an optimization model, wherein the optimization model is used for clearing and calculating the whole market;
describing the interaction between buyers and sellers of various buyers and sellers by using a Steckelberg game model, wherein each seller in the microgrid issues a transaction price, and then the buyers determine the own purchase electric quantity according to the transaction price issued by the seller:
constructing a double-layer optimized transaction by using a Stackelberg game model, and solving a piecewise constant relation of upper and lower layers of optimized variables based on the optimal reaction of an energy buyer;
and screening the safety constraint on the constant relation of the corresponding sections to obtain marginal transaction constant sections.
Preferably, the user load includes year-round load data of the user, the real-time electricity price adopts national uniform peak-valley average three-time electricity price, the power network parameters include resistance and reactance parameters of each branch of the corresponding network, transmission capacity of each branch, and upper and lower limits of node voltage of each node, the operation constraint of each distributed power supply includes an output power range of the distributed power supply, and the utility data of each market participant includes parameters of utility functions of different market main bodies.
Preferably, the user-agreed data acquisition interval is a minimum of 15 minutes.
Preferably, the interaction between buyers and sellers of various producers and consumers is described by using a siphe berg Stackelberg game model, and the process that the seller issues a transaction price first and then the buyer determines the own purchase power according to the transaction price issued by the seller in each microgrid comprises the following steps:
the energy seller directly sells electricity to an energy buyer in the same microgrid, and the rest electric energy is sold to a superior power grid, so that the transaction model of the energy seller is established as follows:
Figure BDA0003870036910000021
Figure BDA0003870036910000022
Figure BDA0003870036910000023
Figure BDA0003870036910000024
in the formula (I), the compound is shown in the specification,
Figure BDA0003870036910000025
the energy transaction price sold to buyer j for seller i,
Figure BDA0003870036910000026
trading the amount of power for the energy sold by seller i to buyer j,
Figure BDA0003870036910000031
the price of the power on the internet sold to the superior power grid by the energy seller,
Figure BDA0003870036910000032
the total generated energy of an energy seller i, delta t is the time step of transaction, and J belongs to J s Showing that the energy buyer corresponding to the energy seller i belongs to the transaction time interval of the whole electric power market for the T E T,
Figure BDA0003870036910000033
for the time-of-use electricity prices of the whole power grid,
Figure BDA0003870036910000034
representing grid operating constraints in the microgrid;
the energy buyer meets own power consumption requirement through P2P transaction in the microgrid, and then purchases the rest electric energy from the superior power grid, so the energy buyer is modeled as follows:
Figure BDA0003870036910000035
Figure BDA0003870036910000036
Figure BDA0003870036910000037
Figure BDA0003870036910000038
Figure BDA0003870036910000039
in the formula (I), the compound is shown in the specification,
Figure BDA00038700369100000310
is the value of the total load of the user,
Figure BDA00038700369100000311
is a base value of time-of-use electricity price, k t Is the rate of change of the time-of-use electricity prices,
Figure BDA00038700369100000312
for active power at the end of the line, active power at the end of the line
Figure BDA00038700369100000313
Subject to physical constraints;
after the internal transaction of each microgrid is completed, each microgrid manager carries out continuous bilateral auction to make up for the excess and shortage of energy in the internal transaction, and modeling of the bilateral auction is as follows:
Figure BDA00038700369100000314
Figure BDA00038700369100000315
Figure BDA00038700369100000316
Figure BDA00038700369100000317
Figure BDA00038700369100000318
Figure BDA0003870036910000041
in the formula (I), the compound is shown in the specification,
Figure BDA0003870036910000042
a collection of micro-grids representing energy vendors,
Figure BDA0003870036910000043
a collection of micro-grids representing energy buyers,
Figure BDA0003870036910000044
representing the revenue function of all energy vendors,
Figure BDA0003870036910000045
representing the revenue function of all energy buyers,
Figure BDA0003870036910000046
for the total generated output of the energy seller s,
Figure BDA0003870036910000047
for the amount of power of the transaction from the energy seller s to the buyer b,
Figure BDA0003870036910000048
is the total power load of the energy buyer b,
Figure BDA0003870036910000049
power on microgrid interconnections at the generating and consuming ends respectively, the interconnections satisfying network flow constraints
Figure BDA00038700369100000410
For the total electricity usage efficiency of the energy buyer b,
Figure BDA00038700369100000411
for the clearing of the prices for the two-sided continuous auctions,
Figure BDA00038700369100000412
the total power generation cost of the energy seller s;
after the energy producers and consumers in each microgrid complete the transaction in pairs, each microgrid needs to bid for market operators according to the power excess and shortage of each microgrid, and when the lowest price received by the energy seller is lower than the highest price output by the energy buyer, the transaction is established.
Preferably, the total load value of said user
Figure BDA00038700369100000413
Base value of time-of-use electricity price
Figure BDA00038700369100000414
Is in direct proportion.
Preferably, the process of constructing a double-layer optimized transaction by using the Stackelberg game model and solving the piecewise constant relation of the upper and lower layers of optimized variables based on the optimal response of the energy buyer includes the following steps:
reconstructing the model of the original energy buyer into a matrix form as follows:
Figure BDA00038700369100000415
Ap≤b:γ
p≥0
in the formula, λ and θ are respectively corresponding price and constant coefficient parameters, p is the electric quantity of the transaction, a and b are respectively constant matrixes constrained by inequalities, and γ is a corresponding lagrange dual variable;
the reconstructed matrix form is dualized as follows:
Figure BDA00038700369100000416
Figure BDA00038700369100000417
γ≤0
writing the dual form into a uniform form as follows:
Figure BDA00038700369100000418
Figure BDA0003870036910000051
Figure BDA0003870036910000052
the optimal solution can be obtained only at the extreme point of the feasible region, the invention takes the constraint forming the extreme point as an equality, and the residual constraint as an inequality, when
Figure BDA0003870036910000053
The optimal solution is unchanged.
Preferably, the process of screening the security constraints on the corresponding piecewise constant relationships includes the following steps:
linearizing the operational constraints of the entire power network:
Figure BDA0003870036910000054
Figure BDA0003870036910000055
Figure BDA0003870036910000056
Figure BDA0003870036910000057
Figure BDA0003870036910000058
Figure BDA0003870036910000059
Figure BDA00038700369100000510
Figure BDA00038700369100000511
in the formula (I), the compound is shown in the specification,
Figure BDA00038700369100000512
indicating that the transaction is on a marginal segment of play,
Figure BDA00038700369100000513
for the upper limit of the transaction amount of the transaction section s,
Figure BDA00038700369100000514
is the upper limit of the reactive power corresponding to the transaction section s,
Figure BDA00038700369100000515
is the generated power of the node k and,
Figure BDA00038700369100000516
is the load power of the node k and,
Figure BDA00038700369100000517
for the amount of transaction power to be at the upper limit,
Figure BDA00038700369100000518
is the reactive power for the generation of the node k,
Figure BDA00038700369100000519
is the reactive load power of the node k,
Figure BDA00038700369100000520
is the reactive transaction electric quantity at the upper limit, N is the linear segment of the corresponding transaction electric quantity, N is the paragraph number corresponding to the linear segment,
Figure BDA00038700369100000521
in order to be the upper limit of the transaction electric quantity,
Figure BDA00038700369100000522
for conductance between buyer and seller k and l,
Figure BDA00038700369100000523
respectively representing node voltage magnitudes for buyer and seller k and l,
Figure BDA00038700369100000524
for the susceptance between the buyer and seller k and l,
Figure BDA00038700369100000525
respectively representing the node voltage phase angles of the buyer and the seller k and l,
Figure BDA0003870036910000061
a lower limit and an upper limit representing the square of the voltage amplitude of the node k, respectively;
the operating constraints are modeled using the large M method as follows:
Figure BDA0003870036910000062
Figure BDA0003870036910000063
Figure BDA0003870036910000064
Figure BDA0003870036910000065
Figure BDA0003870036910000066
Figure BDA0003870036910000067
Figure BDA0003870036910000068
Figure BDA0003870036910000069
Figure BDA00038700369100000610
0≤α n ≤M(1-t n ),n∈[1,N]
0≤β k,- ≤M(1-t k,- ),0≤β k,+ ≤M(1-t k,+ )
0≤γ p,- ≤M(1-s p,- ),0≤γ p,+ ≤M(1-s p,+ )
0≤η q,- ≤M(1-s q,- ),0≤η q,+ ≤M(1-s q,+ )
Figure BDA00038700369100000611
Figure BDA00038700369100000612
Figure BDA00038700369100000613
Figure BDA00038700369100000614
t n 、t k,- and t k,+ Respectively, introduced auxiliary Boolean-type variables, s p,- 、s p,+ 、s q,- And s q,+ Respectively, the introduced auxiliary Boolean variables, wherein M is a positive number and is used for linearizing the original constraint;
based on the above-introduced auxiliary boolean variables and auxiliary boolean variables, marginal transaction segments and corresponding security constraints are modeled as follows:
Figure BDA0003870036910000071
in the formula, the flag bit corresponding to each transaction segment is maximized when the target function psi m And if the value of the objective function is 0, identifying the marginal transaction section and the safety constraint acting on the whole market optimization model, and identifying the marginal transaction section and the safety constraint in the whole clearing model.
Preferably, the auxiliary boolean variable is used to indicate whether an nth segment complex power constraint, an upper limit constraint of a kth node voltage amplitude, and a lower limit constraint of a kth node voltage amplitude are active, and the auxiliary boolean variable is used to indicate a lower limit and an upper limit of a live transaction electric quantity segment, and a lower limit and an upper limit of a reactive transaction electric quantity segment.
An apparatus, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a non-iterative P2P marketable decentralized closeout method as described above.
A storage medium containing computer executable instructions which, when executed by a computer processor, are operable to perform a non-iterative P2P energy marketplace decentralized closeout method as described above.
The invention has the beneficial effects that:
the method utilizes the characteristics of the optimal reaction model of the energy consumer in each microgrid in the Stackelberg game to establish the transaction electric quantity model with the segment constant, and uses a large M method to identify the acting safety constraint and the corresponding transaction electric quantity segment, thereby ensuring the decentralized degree of the P2P transaction participated by the user, improving the efficiency and the accuracy of the optimization algorithm and having stronger use value.
Drawings
In order to more clearly illustrate the embodiments or prior art solutions of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic diagram of a decentralized P2P multi-level marketplace according to the present invention;
fig. 2 is a schematic diagram illustrating an influence of a microgrid internal trading price segment on the whole P2P multi-level trading market according to an embodiment of the present invention;
FIG. 3 is a flow chart of the problem that needs to be pre-solved by the asymmetric algorithm of the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a non-iterative P2P energy market decentralized cleaning method includes the following steps:
(1) Acquiring user load, real-time electricity price, power network parameters, operation constraints of each distributed power supply and utility data of each market participant, and transmitting the collected data into an optimization model;
further, the user load data comprises the load data of the user all year round, and the data acquisition interval is minimum 15 minutes;
further, the real-time electricity price adopts the national uniform peak-valley average three-time electricity price;
further, the power network parameters are resistance reactance parameters of each branch of the corresponding network, transmission capacity of each branch, and upper and lower limits of node voltage of each node;
further, the operating constraints of each distributed power source include the output power range of the distributed power source;
further, the utility data of each market participant comprises parameters of utility functions of different market subjects.
(2) Considering the flexible electricity generating and consuming behavior of each energy producer and consumer inside the microgrid, the invention describes the interaction between buyers and sellers of each producer and consumer by using a Stackelberg game, each seller in the microgrid issues a transaction price first, and then the buyers determine the own purchase electricity quantity according to the transaction price issued by the seller, and the specific model is as follows:
(21) The energy seller needs to sell electricity to the energy buyer in the same microgrid directly, and the rest electric energy needs to be sold to the superior power grid, so that a transaction model can be established as follows:
Figure BDA0003870036910000091
Figure BDA0003870036910000092
Figure BDA0003870036910000093
Figure BDA0003870036910000094
in the formula (I), the compound is shown in the specification,
Figure BDA0003870036910000095
the energy transaction price sold by seller i to buyer j,
Figure BDA0003870036910000096
trading the amount of power for the energy sold by seller i to buyer j,
Figure BDA0003870036910000097
the price of the on-line electricity sold to the superior power grid by the energy seller,
Figure BDA0003870036910000098
the total generated energy of an energy seller i, delta t is the time step of transaction, and J belongs to J s Showing an energy buyer corresponding to the energy seller i, wherein the T belongs to T and is the transaction time interval of the whole electric power market,
Figure BDA0003870036910000099
for the time-of-use electricity prices of the whole power grid,
Figure BDA00038700369100000910
representing grid operating constraints in the microgrid. The objective function is to maximize the electricity selling income of each seller, the first constraint represents that all the sold electric energy does not exceed the total power generation amount of the sellers, and the second constraint represents that the transaction price is between the online price and the time-of-use price, so as toAnd the third constraint represents that the electric energy of the transaction is constrained by the network.
(22) The energy buyer can meet own power consumption requirements through P2P transaction in the microgrid, and then purchase the rest electric energy from the superior power grid, so that the buyer can be modeled as follows:
Figure BDA00038700369100000911
Figure BDA00038700369100000912
Figure BDA00038700369100000913
Figure BDA00038700369100000914
Figure BDA00038700369100000915
in the formula (I), the compound is shown in the specification,
Figure BDA00038700369100000916
is the value of the total load of the user,
Figure BDA00038700369100000917
is the base value of the time-of-use electricity price, the higher the load is, the higher the corresponding time-of-use electricity price is, k t Is the rate of change of the time-of-use electricity prices,
Figure BDA00038700369100000918
the active power at the end of the line, which is also subject to physical constraints, the meaning of the remaining variables in this model is identical to the corresponding meaning of the variables in (21). The model objective is to minimize the total cost of energy buyers, firstThe bar constraint represents that the total transaction amount of the energy buyer is smaller than the total load of the energy buyer, the second bar constraint represents that the electricity purchasing amount of the energy buyer is not negative, the third constraint represents a time-of-use electricity price calculation mode of the whole power grid, and the fourth constraint represents that the electric energy of the transaction is constrained by the network.
(23) After the internal transaction of each microgrid is completed, each microgrid manager can perform continuous bilateral auction to make up for the excess and shortage of energy in the internal transaction, and the modeling of the bilateral auction is as follows:
Figure BDA0003870036910000101
Figure BDA0003870036910000102
Figure BDA0003870036910000103
Figure BDA0003870036910000104
Figure BDA0003870036910000105
Figure BDA0003870036910000106
in the formula (I), the compound is shown in the specification,
Figure BDA0003870036910000107
represents a collection of micro-grids of energy vendors,
Figure BDA0003870036910000108
a collection of micro-grids representing energy buyers,
Figure BDA0003870036910000109
representing the revenue function of all energy vendors,
Figure BDA00038700369100001010
representing the revenue function of all energy buyers,
Figure BDA00038700369100001011
for the total generated output of the energy seller s,
Figure BDA00038700369100001012
for the amount of power of the transaction from the energy seller s to the buyer b,
Figure BDA00038700369100001013
is the total power load of the energy buyer b,
Figure BDA00038700369100001014
power on microgrid interconnections at the generating and consuming ends respectively, the interconnections satisfying network flow constraints
Figure BDA00038700369100001015
Figure BDA00038700369100001016
For the total electricity usage benefit of the energy buyer b,
Figure BDA00038700369100001017
for clearing prices for two-sided continuous auctions,
Figure BDA00038700369100001018
the total power generation cost of the energy seller s.
(24) After the energy producers and consumers in each microgrid complete the transaction in pairs, each microgrid needs to bid for market operators according to the power excess and shortage of each microgrid, and the transaction is established when the lowest price which the seller is willing to receive is lower than the highest price which the buyer is willing to pay.
(3) Because each energy buyer in the Stackelberg game needs to determine the transaction electric quantity according to the price issued by the seller, the whole transaction forms a double-layer optimization form.
(31) In order to facilitate analysis of transaction results of the energy buyers, the model of the original energy buyer is reconstructed into a matrix form as follows:
Figure BDA0003870036910000111
Ap≤b:γ
p≥0
in the formula, λ and θ are corresponding price and constant coefficient parameters respectively, p is the electric quantity of the transaction, a and b are constant matrixes constrained by inequalities respectively, and γ is a corresponding lagrange dual variable.
(32) To separate decision variables for both buyers and sellers, the present invention pairs the problem as follows:
Figure BDA0003870036910000112
Figure BDA0003870036910000113
γ≤0
the meanings of the variables in the formula (iii) correspond to the meanings of the variables in (31).
(33) Writing the constraints of the dual problem in (32) to a uniform form as follows:
Figure BDA0003870036910000114
Figure BDA0003870036910000115
Figure BDA0003870036910000116
because the dual problem is a convex linear problem, the optimal solution can be obtained only at the extreme point of a feasible domain, the invention writes out the constraint forming the extreme point as an equation, and the rest constraint as an inequality as long as
Figure BDA0003870036910000117
The optimal solution will not change and therefore the optimal point of the whole problem will not move, and the transaction power purchased by the buyer is a piecewise constant function of the transaction price as shown in fig. 2.
(4) After the segments of the transaction electric quantity are obtained, the invention also screens the safety constraints on the corresponding segments, so that only the electric network constraints which influence the optimization result can be included in the transaction result in the whole solving process;
(41) The invention firstly linearizes the operation constraint of the whole power network, and the linearized mathematical model is as follows:
Figure BDA0003870036910000121
Figure BDA0003870036910000122
Figure BDA0003870036910000123
Figure BDA0003870036910000124
Figure BDA0003870036910000125
Figure BDA0003870036910000126
Figure BDA0003870036910000127
Figure BDA0003870036910000128
in the formula (I), the compound is shown in the specification,
Figure BDA0003870036910000129
indicating that the transaction is on a marginal segment of play,
Figure BDA00038700369100001210
for the upper limit of the transaction amount of the transaction section s,
Figure BDA00038700369100001211
is the upper limit of the reactive power corresponding to the transaction section s,
Figure BDA00038700369100001212
is the generated power of the node k and,
Figure BDA00038700369100001213
is the load power of the node k and,
Figure BDA00038700369100001214
in order for the amount of transaction power to be at the upper limit,
Figure BDA00038700369100001215
is the reactive power generated at node k,
Figure BDA00038700369100001216
is the reactive load power of the node k,
Figure BDA00038700369100001217
is idle at an upper limitTrading electric quantity, N is a linear segment of the corresponding trading electric quantity, N is a paragraph number corresponding to the linear segment,
Figure BDA00038700369100001218
in order to be the upper limit of the transaction electric quantity,
Figure BDA00038700369100001219
for conductance between buyer and seller k and l,
Figure BDA00038700369100001220
respectively representing the node voltage amplitudes of both parties k and l,
Figure BDA00038700369100001221
for the susceptance between buyer and seller k and l,
Figure BDA00038700369100001222
respectively representing the node voltage phase angles of the buyer and the seller k and l,
Figure BDA00038700369100001223
respectively representing the lower and upper limits of the square of the voltage magnitude at node k.
(42) In order to screen out the acting marginal subsection electric quantity and the corresponding safety constraint, the invention models the original constraint into the following form by using a large M method:
Figure BDA00038700369100001224
Figure BDA0003870036910000131
Figure BDA0003870036910000132
Figure BDA0003870036910000133
Figure BDA0003870036910000134
Figure BDA0003870036910000135
Figure BDA0003870036910000136
Figure BDA0003870036910000137
Figure BDA0003870036910000138
0≤α n ≤M(1-t n ),n∈[1,N]
0≤β k,- ≤M(1-t k,- ),0≤β k,+ ≤M(1-t k,+ )
0≤γ p,- ≤M(1-s p,- ),0≤γ p,+ ≤M(1-s p,+ )
0≤η q,- ≤M(1-s q,- ),0≤η q,+ ≤M(1-s q,+ )
Figure BDA0003870036910000139
Figure BDA00038700369100001310
Figure BDA00038700369100001311
Figure BDA00038700369100001312
wherein the meanings of each variable are the same as those of the corresponding variable in (41), and t n 、t k,- And t k,+ Respectively an introduced auxiliary Boolean type variable for indicating whether the nth segment complex power constraint, the upper limit constraint of the voltage amplitude of the kth node and the lower limit constraint of the voltage amplitude of the kth node are acted or not, s p,- 、s p,+ 、s q,- And s q,+ The introduced auxiliary Boolean variables are used for representing the lower limit and the upper limit of a working trading electric quantity section and the lower limit and the upper limit of a non-working trading electric quantity section, and M is a positive number large enough for linearizing the original constraint.
(43) From the seven flags introduced above, the marginal transaction segments and corresponding security constraints can be modeled as follows:
Figure BDA0003870036910000141
in the formula, the flag bit corresponding to each transaction segment is maximized when the target function psi m When the value is not less than 0, the marginal transaction section and the safety constraint which are acted on the whole market clearing model are identified, so that the model is firstly optimized in a circulating mode until the calculated objective function value is 0, and the marginal transaction section and the safety constraint in the whole market clearing model are identified.
(5) As shown in FIG. 3, the present invention first requires a pre-solution to the problem before non-iterative transaction clearing can be performed. According to the invention, the Boolean optimization model corresponding to the model (43) is optimized, after the marginal transaction section in each microgrid is identified, decentralized P2P transaction in the microgrid is cleared, and then the excess and shortage of external power of each microgrid are solved. After the power excess and the shortage of each microgrid are obtained, a microgrid manager can bid respective power requirements and electricity selling quantity, and then a market manager can carry out bilateral matching and clearing.
An apparatus, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a non-iterative P2P marketable decentralized closeout method as described above.
A storage medium containing computer executable instructions which, when executed by a computer processor, are operable to perform a non-iterative P2P energy marketplace decentralized closeout method as described above.
The method is suitable for the distribution network-level P2P trading market formed by multiple micro-grids with high renewable energy permeability, analyzes the optimal reaction of each producer and consumer in the micro-grid to the trading from the perspective of energy consumers, optimizes the continuous bilateral matching scheme of each micro-grid P2P trading from the perspective of the total system, provides theoretical guidance for saving energy, reducing carbon emission and improving the market clearing calculation efficiency, and effectively promotes the application of the P2P trading in the distribution network system.
In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (10)

1. A non-iterative P2P energy market decentralization clearing method is characterized by comprising the following steps:
obtaining usage data, the usage data comprising: the method comprises the following steps of (1) sending user load, real-time electricity price, power network parameters, operation constraints of distributed power supplies and utility data of market participants to an optimization model, wherein the optimization model is used for clearing and calculating the whole market;
describing the interaction between buyers and sellers of various buyers and sellers by using a Steckelberg game model, wherein each seller in the microgrid issues a transaction price, and then the buyers determine the own purchase electric quantity according to the transaction price issued by the seller:
constructing a double-layer optimized transaction by using a Stackelberg game model, and solving a piecewise constant relation of upper and lower layers of optimized variables based on the optimal reaction of an energy buyer;
and screening the safety constraint on the constant relation of the corresponding sections to obtain marginal transaction constant sections.
2. The method of claim 1, wherein the user load comprises year-round load data of the user, the real-time electricity price is a state-uniform peak-valley-average three-time electricity price, the power network parameters are a resistance-reactance parameter corresponding to each branch of the network, a transmission capacity of each branch, and a node voltage upper limit and a node voltage lower limit of each node, the operation constraints of each distributed power source comprise a power output range of the distributed power source, and the utility data of each market participant comprises parameters of utility functions of different market subjects.
3. The non-iterative P2P marketable decentralized closeout method according to claim 2, wherein said user load data collection interval is a minimum of 15 minutes.
4. The non-iterative P2P marketable decentralized closeout method according to claim 1, wherein the interaction between each buyer and seller of the producer is described by using a schobenberg Stackelberg game model, and the process of issuing a transaction price by each seller in the microgrid first and then determining the purchase power of the buyer according to the transaction price issued by the seller comprises the following steps:
the energy seller directly sells electricity to an energy buyer in the same microgrid, and the rest electric energy is sold to a superior power grid, so that a transaction model of the energy seller is established as follows:
Figure FDA0003870036900000021
Figure FDA0003870036900000022
Figure FDA0003870036900000023
Figure FDA0003870036900000024
in the formula (I), the compound is shown in the specification,
Figure FDA0003870036900000025
the energy transaction price sold to buyer j for seller i,
Figure FDA0003870036900000026
trading the amount of power for the energy sold by seller i to buyer j,
Figure FDA0003870036900000027
sold to energy sellerThe grid-connected electricity price of the secondary power grid,
Figure FDA0003870036900000028
the total generated energy of an energy seller i, delta t is the time step of transaction, and J belongs to J s Showing that the energy buyer corresponding to the energy seller i belongs to the transaction time interval of the whole electric power market for the T E T,
Figure FDA0003870036900000029
for the time-of-use electricity prices of the whole power grid,
Figure FDA00038700369000000210
representing grid operating constraints in the microgrid;
the energy buyer meets own power consumption requirements through P2P transaction in the microgrid, and then purchases the residual electric energy from the superior power grid, so the energy buyer is modeled as follows:
Figure FDA00038700369000000211
Figure FDA00038700369000000212
Figure FDA00038700369000000213
Figure FDA00038700369000000214
Figure FDA00038700369000000215
in the formula (I), the compound is shown in the specification,
Figure FDA00038700369000000216
is the value of the total load of the user,
Figure FDA00038700369000000217
is a base value of time-of-use electricity price, k t Is the rate of change of the time-of-use electricity prices,
Figure FDA00038700369000000218
for active power at the end of the line, active power at the end of the line
Figure FDA00038700369000000219
Subject to physical constraints;
after the internal transaction of each microgrid is finished, each microgrid manager carries out continuous bilateral auction to make up for the excess and shortage of energy in the internal transaction, and modeling of the bilateral auction is as follows:
Figure FDA0003870036900000031
Figure FDA0003870036900000032
Figure FDA0003870036900000033
Figure FDA0003870036900000034
Figure FDA0003870036900000035
Figure FDA0003870036900000036
in the formula (I), the compound is shown in the specification,
Figure FDA0003870036900000037
a collection of micro-grids representing energy vendors,
Figure FDA0003870036900000038
a collection of micro-grids representing energy buyers,
Figure FDA0003870036900000039
representing the revenue function of all energy vendors,
Figure FDA00038700369000000310
representing the revenue function of all energy buyers,
Figure FDA00038700369000000311
for the total generated output of the energy seller s,
Figure FDA00038700369000000312
for the amount of power of the transaction from the energy seller s to the buyer b,
Figure FDA00038700369000000313
is the total power load of the energy buyer b,
Figure FDA00038700369000000314
the power of the microgrid tie line of the power generation end and the power utilization end respectively, and the tie line power meets the network power flow constraint
Figure FDA00038700369000000315
Figure FDA00038700369000000316
For the total electricity usage efficiency of the energy buyer b,
Figure FDA00038700369000000317
for clearing prices for two-sided continuous auctions,
Figure FDA00038700369000000318
the total power generation cost of the energy seller s;
after the energy producers and consumers in each micro-grid complete the transaction in pairs, each micro-grid needs to bid for market operators according to the power excess and shortage of the micro-grid, and the transaction is established when the lowest price received by the energy seller is lower than the highest price output by the energy buyer.
5. The method of claim 4, wherein the total load of the user is a total load of the user
Figure FDA00038700369000000319
Base value of time-of-use electricity price
Figure FDA00038700369000000320
Is in direct proportion.
6. The non-iterative decentralized clearing method for the P2P energy market according to claim 1, wherein the two-tier optimized transaction is constructed by using a Stackelberg game model, and the process of solving the piecewise constant relationship of the upper and lower tier optimized variables based on the optimal response of the energy buyer includes the following steps:
reconstructing the model of the original energy buyer into a matrix form as follows:
min p (λ+θ) T p
Ap≤b:γ
p≥0
in the formula, λ and θ are respectively corresponding price and constant coefficient parameters, p is the electric quantity of the transaction, a and b are respectively constant matrixes constrained by inequalities, and γ is a corresponding lagrange dual variable;
the reconstructed matrix form is dualized as follows:
max γ b T γ
A T γ≤λ+θ:p
γ≤0
writing the dual form into a uniform form as follows:
Figure FDA0003870036900000041
Figure FDA0003870036900000042
Figure FDA0003870036900000043
the optimal solution can be obtained only at the extreme point of the feasible region, the invention takes the constraint forming the extreme point as an equality, and the residual constraint as an inequality, when
Figure FDA0003870036900000044
The optimal solution is unchanged.
7. The non-iterative P2P marketable decentralized closeout method according to claim 1, wherein said process of screening security constraints on corresponding piecewise constant relationships comprises the steps of:
linearizing the operational constraints of the entire power network:
Figure FDA0003870036900000051
Figure FDA0003870036900000052
Figure FDA0003870036900000053
Figure FDA0003870036900000054
Figure FDA0003870036900000055
Figure FDA0003870036900000056
Figure FDA0003870036900000057
Figure FDA0003870036900000058
in the formula (I), the compound is shown in the specification,
Figure FDA0003870036900000059
indicating that the transaction is on a marginal segment of play,
Figure FDA00038700369000000510
for the upper limit of the transaction amount of the transaction section s,
Figure FDA00038700369000000511
is the upper limit of the reactive power corresponding to the transaction section s,
Figure FDA00038700369000000512
is the generated power of the node k and,
Figure FDA00038700369000000513
is the load power of the node k and,
Figure FDA00038700369000000514
in order for the amount of transaction power to be at the upper limit,
Figure FDA00038700369000000515
is the reactive power generated at node k,
Figure FDA00038700369000000516
is the reactive load power of the node k,
Figure FDA00038700369000000517
is the reactive transaction electric quantity at the upper limit, N is the linear segment of the corresponding transaction electric quantity, N is the paragraph number corresponding to the linear segment,
Figure FDA00038700369000000518
in order to be the upper limit of the amount of electricity traded,
Figure FDA00038700369000000519
for conductance between buyer and seller k and l,
Figure FDA00038700369000000520
respectively representing node voltage magnitudes for buyer and seller k and l,
Figure FDA00038700369000000521
for the susceptance between the buyer and seller k and l,
Figure FDA00038700369000000522
respectively representing the node voltage phase angles of the buyer and the seller k and l,
Figure FDA00038700369000000523
a lower limit and an upper limit representing the square of the voltage amplitude of the node k, respectively;
the operating constraints are modeled using the large M method as follows:
Figure FDA00038700369000000524
Figure FDA00038700369000000525
Figure FDA0003870036900000061
Figure FDA0003870036900000062
Figure FDA0003870036900000063
Figure FDA0003870036900000064
Figure FDA0003870036900000065
Figure FDA0003870036900000066
Figure FDA0003870036900000067
0≤α n ≤M(1-t n ),n∈[1,N]
0≤β k,- ≤M(1-t k,- ),0≤β k,+ ≤M(1-t k,+ )
0≤γ p,- ≤M(1-s p,- ),0≤γ p,+ ≤M(1-s p,+ )
0≤η q,- ≤M(1-s q,- ),0≤η q,+ ≤M(1-s q,+ )
Figure FDA0003870036900000068
Figure FDA0003870036900000069
Figure FDA00038700369000000610
Figure FDA00038700369000000611
t n 、t k,- and t k,+ Respectively, an introduced auxiliary Boolean-type variable, s p,- 、s p,+ 、s q,- And s q,+ Respectively, the introduced auxiliary Boolean variables, wherein M is a positive number and is used for linearizing the original constraint;
based on the above-introduced auxiliary boolean variables and auxiliary boolean variables, marginal transaction segments and corresponding security constraints are modeled as follows:
Figure FDA00038700369000000612
in the formula, the flag bit corresponding to each transaction segment is maximized when the target function psi m When =0, marginal transaction segments and active safety constraints of the entire market optimization model are identified untilWhen the calculated objective function value is 0, the marginal transaction section and the safety constraint in the whole clearing model are identified.
8. The method of claim 7, wherein the auxiliary boolean variables are used to indicate whether an nth complex power constraint, an upper bound of a kth node voltage amplitude, and a lower bound of a kth node voltage amplitude are active, and the auxiliary boolean variables are used to indicate a lower bound and an upper bound of a working trading power segment and a lower bound and an upper bound of a non-working trading power segment.
9. An apparatus, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a non-iterative P2P marketable decentralized closeout method according to any one of claims 1-8.
10. A storage medium containing computer-executable instructions which, when executed by a computer processor, are configured to perform a non-iterative P2P energy marketplace decentralized closeout method according to any one of claims 1-8.
CN202211193797.2A 2022-09-28 2022-09-28 Non-iterative P2P energy-consumption market decentralized clearing method Pending CN115659603A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116562864A (en) * 2023-07-11 2023-08-08 国网湖北省电力有限公司经济技术研究院 Electric power point-to-point transaction method and system of direct-current micro-grid interconnection system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116562864A (en) * 2023-07-11 2023-08-08 国网湖北省电力有限公司经济技术研究院 Electric power point-to-point transaction method and system of direct-current micro-grid interconnection system
CN116562864B (en) * 2023-07-11 2023-09-15 国网湖北省电力有限公司经济技术研究院 Electric power point-to-point transaction method and system of direct-current micro-grid interconnection system

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