Power transaction method, system and equipment based on multi-objective planning model, and medium

Abstract

The invention relates to the technical field of electricity transaction, and particularly provides an electricity transaction method, system and device based on a multi-objective planning model, and a medium. Comprising the following steps: S1, constructing a power transmission cost function of a provincial power grid in a regional power grid range; s2, a double-layer multi-target planning model for promoting power transaction by the regional power grid power transmission price mechanism is constructed, the model comprises an upper-layer planning model and a lower-layer planning model, the upper-layer planning model is used for representing regional power transaction earnings and meeting demand conditions, and the lower-layer planning model is used for representing regional power transaction earnings and meeting demand conditions; the lower-layer planning model is used for representing that the power transmission cost is minimized under the assumption that collusion does not exist among the provincial power grids; and S3, solving a double-layer multi-target planning model for promoting power transaction by a regional power grid power transmission price mechanism based on an improved particle swarm algorithm, and obtaining model configuration parameters. According to the method, the problem that cost control is mainly emphasized and cost structure optimization is neglected in an existing electricity transaction system is solved, and electricity market transaction is effectively promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power trading, and in particular to a power trading method, system, equipment and medium based on a multi-objective programming model. Background Art

[0002] With the advancement of the market-oriented reform of electricity and the construction of a unified national electricity market system, it is particularly important to formulate reasonable transmission prices and cross-provincial and cross-regional transmission pricing mechanisms. This not only helps to accurately reflect the actual cost and reflect the fair competition of the electricity market, but also can reasonably share costs, improve grid efficiency, and greatly promote the smooth conduct of electricity transactions. In fact, the reform of my country's transmission price mechanism overemphasizes the cost compensation function of the reform of transmission and distribution prices, but ignores the market function of transmission prices in promoting grid opening, electricity trading and optimizing resource allocation, which is difficult to adapt to the construction and development of a unified national electricity market system. The recovery of fixed costs including depreciation and labor costs through capacity electricity charges is conducive to the generation of capacity cost sharing mechanisms and sunk cost exclusion decision-making mechanisms that promote electricity transactions. However, the inclusion of all operation and maintenance costs except labor costs in the recovery of electricity charges does not fully conform to the functional positioning and cost characteristics of regional power grids. Therefore, the transmission price promotion mechanism for electricity transactions is still insufficient. From the perspective of management accounting, after the fixed costs are paid once, they become sunk costs in consumers' consumption or transaction decisions, and no longer affect consumers' consumption or transaction decisions. Consumers can and should make decisions based only on variable costs. Since the variable cost of two-part pricing is significantly lower than the average cost pricing, according to the law of consumer demand curve, lower prices will lead to greater consumption or transaction volume. At the same time, when the consumer consumption or transaction volume is larger, the fixed cost per unit consumption or transaction volume is lower, and the actual average price per unit consumption or transaction volume after adding the constant output price (assuming that the variable cost does not change with output) is also lower. According to the law of demand curve, the lower the price, the more consumers consume. In this case, the operation and maintenance fees that have little to do with the actual amount of electricity transmitted and are fixed costs according to cost characteristics should be included in the capacity electricity fee recovery, so as to produce a greater effect of promoting electricity market transactions.

[0003] Therefore, according to the task of building a national unified electricity market system and assuming different regional electricity market scenarios, it is necessary to establish a multi-objective planning model for regional power grid transmission price mechanism to promote electricity trading, effectively promote electricity market transactions, optimize resource allocation, and promote the healthy development of the electricity market. Summary of the invention

[0004] In view of the promotion of the deepening reform of my country's power marketization and the construction of a unified national power market system, as well as the increasingly severe challenges faced by regional power grids in achieving cross-regional optimal allocation of energy resources, the present invention promotes a multi-objective planning model for power trading by constructing a transmission cost function of the provincial power grid within the scope of the regional power grid and a regional power grid transmission price mechanism; based on the two-layer multi-objective planning model to determine the objective function and its constraints, an improved particle swarm algorithm is introduced to obtain the configuration parameters of the two-layer multi-objective planning model. This model will provide a theoretical basis and practical operation guidance for the regional power grid transmission price mechanism to promote power trading, and is used to solve the problem of unreasonable resource allocation caused by imperfect power trading schemes in the prior art.

[0005] To achieve the above objectives, the present invention provides an electricity trading method, system, device and medium based on a multi-objective programming model.

[0006] In a first aspect, the present invention provides a method for power trading based on a multi-objective programming model, the method comprising:

[0007] Step S1: constructing a transmission cost function of the provincial power grid within the regional power grid;

[0008] Step S2: construct a two-level multi-objective planning model for regional power grid transmission price mechanism to promote power trading, the model includes an upper-level planning model and a lower-level planning model, the upper-level planning model is used to characterize the regional power trading benefits and the satisfaction of demand, and the lower-level planning model is used to characterize the minimization of transmission costs assuming that there is no collusion between provincial power grids;

[0009] Step S3: Solve the two-layer multi-objective programming model of regional power grid transmission price mechanism to promote power trading based on the improved particle swarm algorithm to obtain the model configuration parameters.

[0010] Among them, the transmission cost function of the provincial power grid within the regional power grid is constructed by proposing three basic assumptions and introducing a primary cost function to analyze the cost behavior of the provincial power grid operation and maintenance costs (excluding labor costs) and electricity sales;

[0011] The basic assumption 1:

[0012] There are M electricity-selling provinces (M = 1, 2, 3…i) and N electricity-purchasing provinces (N = 1, 2, 3…j) within a regional power grid. ij It represents the amount of electricity sold from the i-th province to the j-th province, i≠j. Since there are many types (variety) of cross-regional and cross-provincial electricity transactions, in order to illustrate the impact of the transmission price mechanism on electricity transactions, only the centralized mode of electricity purchase and sale transactions between provincial power grids is discussed, without involving other market players such as electricity users and power generation enterprises.

[0013] The basic assumption 2:

[0014] For the sake of calculation simplicity, it is assumed that the power supply and demand in the region are balanced, the load between provinces is transmitted in a straight line, and the network loss is not considered. It is assumed that the on-grid electricity price (power grid purchase cost) of each province within the regional power grid is the same price. The inter-provincial power transmission service is a service for transmitting power between different provincial power grids. It is necessary to consider the allocation of inter-provincial power transmission costs among provinces. The regional power grid capacity electricity fee is allocated to each province according to a certain proportion.

[0015] The basic assumption 3:

[0016] In the problem of power system optimization and dispatch, the transmission cost function is represented by a linear function. Therefore, a linear cost function is introduced to analyze the cost characteristics of the provincial power grid operation and maintenance fees (excluding labor costs) and electricity sales. Then, the transmission cost function of the provincial power grid within the regional power grid is:

[0017]

[0018] Among them, a i and b i They are all constant terms obtained by fitting the transmission cost function, including depreciation, labor costs and a certain proportion of operation and maintenance costs (excluding labor costs).

[0019] Furthermore, the upper-level planning model objective function includes two objective functions and three constraints; the lower-level planning model includes one objective function and two constraints;

[0020] Furthermore, the objective function and constraints of the upper-level planning model are specifically as follows:

[0021] Considering the constraints of regional transmission volume, the objective function of maximizing the revenue of regional power grid power trading is:

[0022]

[0023] Among them, P j is the transmission price of the jth province, Pi is the transmission price of the ith province, (P j -P i )Q ij represents the revenue from the i-th province selling electricity to the j-th province. Usually, the line loss rate coefficient δ is introduced ij , the price P in the province where electricity is sold i must be smaller than the province purchasing electricity P j The price, then,

[0024] The maximum satisfaction demand objective function is:

[0025]

[0026] Among them, Q bj is the power demand of the jth power buyer, R ij is the number of power transaction paths between the i-th power seller and the j-th power buyer, δ ij,w represents the line loss rate corresponding to the wth path, Q ij,w It indicates the amount of electricity traded between the i-th electricity seller and the j-th electricity buyer through the w-th path;

[0027] The non-negative constraint condition of transaction power is:

[0028] Q B i ,Q P ij ,Q P ij(u) ,Q S il ,Q S il(v) ≥0

[0029] Among them, the variables involved are i=1,2,...,N,j=1,2,...,M1,l=1,2,...,M2,u=1,2,...,S1,v=1,2,...,S2,Q P ij(u) and Q S il(v) They represent the transaction electricity of the uth and vth items respectively, calculated by their average transmission network loss rates on the path with the minimum network loss rate;

[0030] The capacity constraints of the transmission line are:

[0031]

[0032] Where N is the total number of all power trading paths expected to pass through a specific arc segment, P l The amount of electricity flowing in a specified direction on a line for a specific transaction route;

[0033] The transmission capacity limit of the line is divided into positive and negative parts, namely P l -max and P l +max, when the line is undergoing forward maintenance, its forward transmission capacity limit P l +max=P l -max = 0, which means that power is not allowed to flow in this direction;

[0034] The balance constraint of electricity purchase and sale within the region is:

[0035]

[0036] in, Represents the total electricity sales of each provincial power grid, Equal to the total purchase amount.

[0037] Furthermore, the objective function and constraint conditions of the lower-level planning model are specifically as follows:

[0038] The objective function for minimizing the transmission cost of the provincial power grid is:

[0039]

[0040] Among them, (LL)maxS i is the objective function of the lower-level planning model. Assuming that there is no collusion among provincial power grids, the objective function of provincial power grid i participating in inter-provincial power trading within the regional power grid is to minimize the transmission cost.

[0041] The non-negative constraint condition for the power transaction volume of the provincial power grid is:

[0042] Q ij ≥0

[0043] The maximum power supply capability constraint is:

[0044]

[0045] in, Q i Indicates the basic power supply demand, Indicates the actual electricity sales. Indicates the amount of electricity available for sale.

[0046] Furthermore, the step S3 is specifically as follows:

[0047] Step S31: Initialize particles; assuming the particle size is N, randomly initialize the particle position X in the particle group i and speed V i , randomly generate initial solutions that meet the constraints of the lower-level planning model within the allowed activity range;

[0048] Step S32: Setting the particle extreme value; Setting the individual extreme value P of each particle in the particle group best and the global extreme value G of the entire particle swarm best ;

[0049] Step S33: iterative solution;

[0050] Step S33-1: Update the position x of a particle in the particle swarm i and speed v i The formula is:

[0051]

[0052] Among them, the current iteration number of the improved particle swarm algorithm is t, and the learning factor C 1 , C 2 Usually the value is 2; R 1 , R 2 is a random number uniformly distributed between [0,1], ω represents the adaptive nonlinear decreasing inertia weight;

[0053] Step S33-2: Determine the location X of the decision variable network loss coefficient and fairness weight coefficient that meet the constraints of the upper-level planning model i , substitute it into the lower-level planning model, and use the traditional particle swarm algorithm to solve the optimal solution Y of the lower-level planning model i ;

[0054] Step S33-3: i , Y i ) is substituted into the objective function of the upper-level planning model to obtain the upper-level fitness value H(X i , Y i ), i∈[1,N].

[0055] Step S33-4: If the adaptation value H(X i , Y i ) is better than the fitness value of the current individual extreme value, then position X i Update to P best , the optimal solution YP corresponding to the lower-level planning best Update to Y accordingly i ; If the fitness value H(X i , Y i ) is better than the fitness value of the current global extreme value, then position X i Updated to G best , the optimal solution YG corresponding to the lower-level planning best Update to Y accordingly i ;

[0056] Step S34: Determine convergence; check whether the algorithm meets the convergence conditions, such as reaching the maximum number of iterations or the fitness change is less than a certain threshold; if the convergence conditions are met, proceed to step S35, otherwise return to step S33 to continue the calculation;

[0057] Step 35: Output the optimal solution G of the two-level programming model best and YG best , and find the corresponding upper layer objective function value.

[0058] In a second aspect, the present invention further provides an electric power trading system based on a multi-objective programming model, the system comprising:

[0059] A construction module is used to construct a transmission cost function, wherein the transmission cost function is used to characterize the cost behavior analysis of the operation and maintenance fees and electricity sales of each provincial power grid;

[0060] A two-layer multi-objective planning model building module is used to build a two-layer multi-objective planning model, and the two-layer multi-objective planning model is used to characterize the impact mechanism of the regional power grid transmission price mechanism on power trading;

[0061] The solution module, based on the improved particle swarm algorithm, obtains the parameters of the multi-objective programming model construction device of the regional power grid transmission price mechanism to promote power trading through the objective function and constraint conditions of the double-layer multi-objective programming model.

[0062] Furthermore, the two-level multi-objective programming model is:

[0063]

[0064] In a third aspect, an embodiment of the present invention further provides a computer device, including a processor and a memory, wherein the memory is used to store a computer program, and the computer program is executed by the processor to implement the above-mentioned power trading method based on the multi-objective programming model.

[0065] In a fourth aspect, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned power trading method based on a multi-objective programming model.

[0066] Beneficial effects of the present invention:

[0067] 1. The improved particle swarm algorithm based on the nonlinear representative algorithm in the embodiment of the present invention solves the double-layer multi-objective programming model. Therefore, the model has higher accuracy and the output power trading results are more reasonable.

[0068] 2. The present invention solves the problem that the existing power trading system mainly focuses on cost control while ignoring the optimization of cost structure. It promotes fair market competition and rational allocation of resources, cross-provincial and cross-regional power market-based transactions, and optimized allocation of clean energy on a larger scale, while ensuring safe and stable power supply and trading within the region. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0070] Figure 1It is a flowchart of a method for constructing a multi-objective programming model for promoting power trading through a regional power grid transmission price mechanism provided by an embodiment of the present invention;

[0071] Figure 2 It is a relationship diagram of two types of subjects in a double-layer multi-objective programming model provided by an embodiment of the present invention;

[0072] Figure 3 The specific implementation process of the improved particle swarm algorithm in the power trading method based on the multi-objective programming model provided in the embodiment of the present invention;

[0073] Figure 4 This is the cross-regional electricity trading model provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0075] like Figure 1 As shown, an embodiment of the present invention provides a power trading method based on a multi-objective programming model, which specifically includes the following steps:

[0076] Step S1: By proposing three basic assumptions, a primary cost function is introduced to analyze the cost characteristics of the provincial power grid operation and maintenance fees (excluding labor costs) and electricity sales, and the transmission cost function of the provincial power grid within the regional power grid is constructed;

[0077] It should be noted that the three basic assumptions are:

[0078] Basic Assumption 1:

[0079] There are M electricity-selling provinces (M = 1, 2, 3…i) and N electricity-purchasing provinces (N = 1, 2, 3…j) within a regional power grid. ij It represents the amount of electricity sold from the i-th province to the j-th province, i≠j. Since there are many types (variety) of cross-regional and cross-provincial electricity transactions, in order to illustrate the impact of the transmission price mechanism on electricity transactions, only the centralized mode of electricity purchase and sale transactions between provincial power grids is discussed, without involving other market players such as electricity users and power generation enterprises.

[0080] Basic Assumption 2:

[0081] For the sake of calculation simplicity, it is assumed that the power supply and demand in the region are balanced, the load between provinces is transmitted in a straight line, and the network loss is not considered. It is assumed that the on-grid electricity price (power grid purchase cost) of each province within the regional power grid is the same price. The inter-provincial power transmission service is a service for transmitting power between different provincial power grids. It is necessary to consider the allocation of inter-provincial power transmission costs among provinces. The regional power grid capacity electricity fee is allocated to each province according to a certain proportion.

[0082] Basic Assumption 3:

[0083] In the problem of power system optimization and dispatch, the transmission cost function is represented by a linear function. Therefore, a linear cost function is introduced to analyze the cost characteristics of the provincial power grid operation and maintenance fees (excluding labor costs) and electricity sales. Then, the transmission cost function of the provincial power grid within the regional power grid is:

[0084]

[0085] Among them, a i and b i They are all constant terms obtained by fitting the transmission cost function, including depreciation, labor costs and a certain proportion of operation and maintenance costs (excluding labor costs).

[0086] Step S2: According to the task of building a unified national power market system, different regional power market scenarios are assumed to build a two-level multi-objective planning model for regional power grid transmission price mechanism to promote power trading, including an upper-level planning model and a lower-level planning model. The upper-level planning model is used to characterize the regional power trading income and demand satisfaction, and the lower-level planning model is used to characterize the minimization of transmission costs assuming that there is no collusion between provincial power grids.

[0087] See also Figure 2 , a two-class subject relationship diagram of the double-layer multi-objective programming model provided by the embodiment of the present invention. In a non-cooperative market environment, each provincial power grid formulates its own power trading strategy based on the upper-level decision-making information to maximize its own benefits. Since the power trading decisions of the provincial power grid are directly related to the power sales volume and transmission costs, the upper-level decision variables of the double-layer multi-objective programming are the network loss coefficient and the fairness weight coefficient, and the lower-level decision variables are the cost function coefficient.

[0088] The upper-level planning model includes two objective functions and three constraints; the lower-level planning model includes one objective function and two constraints;

[0089] It should be noted that the objective function and constraint conditions of the upper-level planning model are specifically as follows:

[0090] Considering the constraints of regional transmission volume, the objective function of maximizing the revenue of regional power grid power trading is:

[0091]

[0092] Among them, P j is the transmission price of the jth province, P i is the transmission price of the ith province, (P j -P i )Q ij represents the revenue from the i-th province selling electricity to the j-th province. Usually, the line loss rate coefficient δ is introduced ij , the price P in the province where electricity is sold i must be smaller than the province purchasing electricity P j The price, then, Here we need to explain the transmission price in particular. If the regional transmission price is similar to the single capacity price implemented in the US ISO-NE and Australian Transgrid power markets, it is equivalent to the zero-power transaction price, that is, P j =P i = 0, which will help stimulate users to use more electricity and have a greater effect on promoting electricity market transactions; if most ISOs such as PJM, ERCOT, CAISO in the United States and my country implement a two-part electricity price, then the formula P j The calculation formula is the same as that of .

[0093] The maximum satisfaction demand objective function is:

[0094]

[0095] Among them, Q bj Corresponding to the jth electricity buyer’s demand, R ij is the number of power transaction paths between the i-th power seller and the j-th power buyer, δ ij,w represents the line loss rate corresponding to the wth path, Q ij,w It indicates the amount of electricity traded between the i-th electricity seller and the j-th electricity buyer through the w-th path.

[0096] The non-negative constraint condition of transaction power is:

[0097] Q B i ,Q P ij ,Q P ij(u) ,Q S il ,Q S il(v) ≥0

[0098] Among them, the variables involved are i=1,2,...,N,j=1,2,...,M1,l=1,2,...,M2,u=1,2,...,S1,v=1,2,...,S2,Q P ij(u) and Q S il(v) They respectively represent the transaction electricity of the u-th and v-th items calculated by their average transmission network loss rates on the path with the minimum network loss rate.

[0099] The capacity constraints of the transmission line are:

[0100]

[0101] Where N represents the total number of all power trading paths expected to pass through a specific arc segment, P l It represents the amount of electricity flowing in a specified direction on a specific transaction path. The transmission capacity limit of the line is divided into positive and negative parts, namely P l -max and P l +max, when the line is undergoing forward maintenance, its forward transmission capacity limit P l +max=P l -max=0, that is, power is not allowed to flow in this direction.

[0102] The balance constraint of electricity purchase and sale is:

[0103]

[0104] in, Represents the total electricity sales of each provincial power grid, Equal to the total purchase amount.

[0105] The lower-level planning model and constraints are specifically as follows:

[0106] The objective function for minimizing the transmission cost of the provincial power grid is:

[0107]

[0108] Among them, (LL)maxS i is the lower-level planning objective function. Assuming that there is no collusion among provincial power grids, the objective function of provincial power grid i participating in inter-provincial power trading within the regional power grid is to minimize the transmission cost.

[0109] The non-negative constraint condition for the power transaction volume of the provincial power grid is:

[0110] Q ij ≥0

[0111] Among them, Q ijRepresents the power trading volume of each provincial power grid within the region.

[0112] The maximum power supply capability constraint is:

[0113]

[0114] in, Q i Indicates the basic power supply demand, ∑ j,i≠j Q ij Indicates the actual electricity sales. Indicates the amount of electricity available for sale.

[0115] Step S3, using the objective function and constraints of the two-layer multi-objective programming model, and based on the improved particle swarm algorithm, solve the multi-objective programming model of regional power grid transmission price mechanism to promote power trading.

[0116] It should be noted that the step S3 is specifically as follows:

[0117] Step S31: Initialize particles; assuming the particle size is N, randomly initialize the particle position X in the particle group i and speed V i , randomly generate initial solutions that meet the constraints of the lower-level planning model within the allowed activity range;

[0118] Step S32: Setting the particle extreme value; Setting the individual extreme value P of each particle in the particle group best and the global extreme value G of the entire particle swarm best ;

[0119] Step S33: iterative solution;

[0120] Step S33-1: Update the position x of a particle in the particle swarm i and speed v i The formula is:

[0121]

[0122] Among them, the current iteration number of the improved particle swarm algorithm is t, and the learning factor C 1 , C 2 Usually the value is 2; R 1 , R 2 is a random number uniformly distributed between [0, 1], and ω represents an adaptive nonlinear decreasing inertia weight; it effectively improves the convergence performance of the algorithm. In each step of the iteration, the particle's speed, position, individual optimal value, and global optimal value will be continuously updated until the algorithm obtains the optimal solution G best . Reach the preset maximum number of iterations T max Or the optimal solution reaches a specified accuracy. The iteration process usually stops when one of the two conditions is met.

[0123] Step S33-2: Determine the location X of the decision variable network loss coefficient and fairness weight coefficient that meet the constraints of the upper-level planning model i , substitute it into the lower-level planning model, and use the traditional particle swarm algorithm to solve the optimal solution Y of the lower-level planning model i ;

[0124] Step S33-3: i , Y i ) is substituted into the objective function of the upper-level planning model to obtain the upper-level fitness value H(X i , Y i ), i∈[1,N].

[0125] Step S33-4: If the adaptation value H(X i , Y i ) is better than the fitness value of the current individual extreme value, then position X i Update to P best , the optimal solution YP corresponding to the lower-level planning best Update to Y accordingly i ; If the fitness value H(X i , Y i ) is better than the fitness value of the current global extreme value, then position X i Updated to G best , the optimal solution YG corresponding to the lower-level planning best Update to Y accordingly i ;

[0126] Step S34: Determine convergence; check whether the algorithm meets the convergence conditions, such as reaching the maximum number of iterations or the fitness change is less than a certain threshold; if the convergence conditions are met, proceed to step S35, otherwise return to step S33 to continue the calculation;

[0127] Step 35: Output the optimal solution G of the two-level programming model best and YG best , and find the corresponding upper layer objective function value.

[0128] See also Figure 3 , the specific implementation process of the improved particle swarm algorithm in the power trading method based on the multi-objective programming model provided by the embodiment of the present invention. The five key steps followed by the improved particle swarm algorithm: one is to initialize the particles; the second is to set the particle extreme value; the third is to perform iterative calculations, calculate the fitness value of each particle (that is, the objective function value), and compare it with Pbesti and Gbest. If it is better, update it and adjust the position and speed of the particle. The fourth is to judge the convergence and check whether the algorithm meets the convergence conditions, such as reaching the maximum number of iterations or the fitness change is less than a certain threshold; if the convergence conditions are met, continue with step five, otherwise return to step three to continue the calculation; the fifth is to output the optimal solution G of the two-level programming model.best and YG best , and find the corresponding upper layer objective function value.

[0129] See also Figure 4 , the cross-regional power trading model provided by the embodiment of the present invention. In order to achieve full trading among the four provincial power grids in the region, to ensure the maximization of their own power trading profits, and to achieve the goal of maximizing regional trading revenue and minimizing network losses, minimizing deviation is taken as an important consideration. By optimizing power dispatching and trading strategies, we strive to promote the efficient and fair operation of the entire regional power grid on the basis of ensuring that the sum of the squares of the deviations between the purchase (sale) power of each party and the demand (external sales capacity) is as small as possible.

[0130] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. The power trading method based on the multi-objective programming model is characterized by: The following steps are involved: Step S1: constructing a transmission cost function of the provincial power grid within the regional power grid; Step S2: construct a two-level multi-objective planning model for regional power grid transmission price mechanism to promote power trading, the model includes an upper-level planning model and a lower-level planning model, the upper-level planning model is used to characterize the regional power trading benefits and the satisfaction of demand, and the lower-level planning model is used to characterize the minimization of transmission costs assuming that there is no collusion between provincial power grids; Step S3: Solve the two-layer multi-objective programming model of regional power grid transmission price mechanism to promote power trading based on the improved particle swarm algorithm to obtain the model configuration parameters.

2. The power trading method based on the multi-objective programming model according to claim 1, characterized in that: The transmission cost function of the provincial power grid within the regional power grid is: Among them, a i and b i are constant terms obtained by fitting the transmission cost function, including depreciation, labor costs and a certain proportion of operation and maintenance costs, excluding labor costs; Q ij Represents the amount of electricity sold from the i-th province to the j-th province, i≠j.

3. The power trading method based on the multi-objective programming model according to claim 1, characterized in that: The upper-level planning model includes two objective functions and three constraints; the lower-level planning model includes one objective function and two constraints.

4. The power trading method based on the multi-objective programming model according to claim 3 is characterized in that: The objective function and constraints of the upper-level planning model are specifically as follows: Considering the constraints of regional transmission volume, the objective function of maximizing the revenue of regional power grid power trading is: Among them, P j is the transmission price of the jth province, P i is the transmission price of the ith province, (P j -P i )Q ij represents the revenue from the i-th province selling electricity to the j-th province, δ ij is the line loss coefficient, The maximum satisfaction demand objective function is: Among them, Q bj is the power demand of the jth power buyer, R ij is the number of power transaction paths between the i-th power seller and the j-th power buyer, δ ij,w represents the line loss rate corresponding to the wth path, Q ij,w It indicates the amount of electricity traded between the i-th electricity seller and the j-th electricity buyer through the w-th path; The non-negative constraint condition of transaction power is: Q B i ,Q P ij ,Q P ij(u) ,Q S il ,Q S il(v) ≥0 Among them, the variables involved are i=1,2,...,N,j=1,2,...,M1,l=1,2,...,M2,u=1,2,...,S1,v=1,2,...,S2,Q P ij(u) and Q S il(v) They represent the transaction electricity of the uth and vth items respectively, calculated by their average transmission network loss rates on the path with the minimum network loss rate; The capacity constraints of the transmission line are: Where N is the total number of all power trading paths expected to pass through a specific arc segment, P l The amount of electricity flowing in a specified direction on a line for a specific transaction route; The balance constraint of electricity purchase and sale within the region is: in, Represents the total electricity sales of each provincial power grid, Equal to the total purchase amount.

5. The power trading method based on the multi-objective programming model according to claim 3, characterized in that: The objective function and constraints of the lower-level planning model are specifically as follows: The objective function for minimizing the transmission cost of the provincial power grid is: The non-negative constraint condition for the power transaction volume of the provincial power grid is: Q ij ≥0 The maximum power supply capability constraint is: in, Q i Indicates the basic power supply demand, ∑ j,i≠j Q ij Indicates the actual electricity sales. Indicates the amount of electricity available for sale.

6. The power trading method based on the multi-objective programming model according to claim 1, characterized in that: The step S3 is specifically as follows: Step S31: Initialize particles; assuming the particle size is N, randomly initialize the particle position X in the particle group i and speed V i , randomly generate initial solutions that meet the constraints of the lower-level planning model within the allowed activity range; Step S32: setting particle extreme values; Set the individual extreme value P of each particle in the particle swarm best and the global extreme value G of the entire particle swarm best ; Step S33: iterative solution; Step S33-1: Update the position x of a particle in the particle swarm i and speed v i The formula is: Among them, the current number of iterations of the improved particle swarm algorithm is t, the learning factors C1 and C2 are usually 2; R1 and R2 are random numbers uniformly distributed between [0,1], and ω represents the adaptive nonlinear decreasing inertia weight; Step S33-2: Determine the location X of the decision variable network loss coefficient and fairness weight coefficient that meet the constraints of the upper-level planning model i , substitute it into the lower-level planning model, and use the traditional particle swarm algorithm to solve the optimal solution Y of the lower-level planning model i ; Step S33-3: i , Y i ) is substituted into the objective function of the upper-level planning model to obtain the upper-level fitness value H(X i , Y i ), i∈[1,N]. Step S33-4: If the adaptation value H(X i , Y i ) is better than the fitness value of the current individual extreme value, then position X i Update to P best , the optimal solution YP corresponding to the lower-level planning best Update to Y accordingly i ; If the fitness value H(X i , Y i ) is better than the fitness value of the current global extreme value, then position X i Updated to G best , the optimal solution YG corresponding to the lower-level planning best Update to Y accordingly i ; Step S34: Determine convergence; check whether the algorithm meets the convergence conditions, such as reaching the maximum number of iterations or the fitness change is less than a certain threshold; if the convergence conditions are met, proceed to step S35, otherwise return to step S33 to continue the calculation; Step 35: Output the optimal solution G of the two-level programming model best and YG best , and find the corresponding upper layer objective function value.

7. An electric power trading system based on a multi-objective programming model, characterized in that: The system comprises: A construction module is used to construct a transmission cost function, wherein the transmission cost function is used to characterize the cost behavior analysis of the operation and maintenance fees and electricity sales of each provincial power grid; A two-layer multi-objective planning model building module is used to build a two-layer multi-objective planning model, and the two-layer multi-objective planning model is used to characterize the impact mechanism of the regional power grid transmission price mechanism on power trading; The solution module, based on the improved particle swarm algorithm, obtains the parameters of the device for constructing the multi-objective programming model of regional power grid transmission price mechanism to promote power trading through the objective function and constraint conditions of the double-layer multi-objective programming model.

8. The power trading system based on the multi-objective programming model according to claim 7, characterized in that: The two-level multi-objective programming model is: 。 9. A computer device, characterized in that: It comprises a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the power trading method based on the multi-objective programming model as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the power trading method based on the multi-objective programming model as described in any one of claims 1 to 6.

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