Inter-regional power transaction clearing optimization method, system, equipment and medium

Abstract

The invention relates to the technical field of power transaction clearing, and discloses an inter-regional power transaction clearing optimization method, system and device and a medium, and the method gives consideration to the transaction rights and interests of all parties, and achieves the optimization of the power transaction clearing in the region with the maximization of the power transaction income in the region, the maximization of the new energy consumption level between the regions and the maximization of the power transaction volume between the regions. The method comprises the following steps: constructing a multi-target power transaction clearing optimization model by taking power transaction frequency minimization between regions as a multi-optimization target and taking inter-region power transaction frequency minimization as a multi-optimization target, performing sequential boundaries on the multi-target power transaction clearing optimization model, and performing optimization solution on the multi-target power transaction clearing optimization model to obtain a power transaction clearing optimization model. According to the method, the clearing optimization problem among the regions can be quickly solved under the condition of large-scale regional power data, and the calculation efficiency and precision of power transaction clearing among the regions are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power trading, and in particular to an inter-regional power trading clearing optimization method, system, equipment and medium. Background Art

[0002] Inter-regional power trading clearing is a key link in improving regional energy allocation and solving the problem of reverse distribution of regional energy supply and demand. Since inter-provincial medium- and long-term power trading involves a large number of participants, including both parties of power supply and demand and professional institutions (such as power traders and VPPs), and the clearing results have a significant coupling relationship with the control of power grid nodes, the power market clearing needs to take into account the rights and interests of all market participants and dispatchers, which may involve the situation of mutual exclusion of multiple variables and targets, and involves the calculation process of multivariate variables and huge data capacity. However, the current inter-regional power trading clearing optimization problem is difficult to take into account the rights and interests of multiple parties, and it is difficult to solve the situation of mutual exclusion of multiple variables and targets, which will lead to poor calculation efficiency and accuracy of inter-regional power trading clearing. Summary of the invention

[0003] In view of this, the present invention provides an inter-regional power transaction clearing optimization method, system, device and medium, which solves the current inter-regional power transaction clearing optimization problem that it is difficult to take into account the rights and interests of multiple parties and it is difficult to solve the situation where multiple variables and targets are mutually exclusive, which will lead to the technical problem of poor computational efficiency and accuracy of inter-regional power transaction clearing.

[0004] A first aspect of the present invention provides an inter-regional power transaction clearing optimization method, comprising:

[0005] Establish multiple optimization objectives, including maximizing the revenue from power trading within the region, maximizing the level of new energy consumption between regions, maximizing the volume of power trading between regions, and minimizing the frequency of power trading between regions, determine the constraints of each optimization objective, and construct a multi-objective power trading clearing optimization model;

[0006] Determining sequential boundaries for multiple optimization objectives in the multi-objective power trading clearing optimization model, and using the sequential boundaries to transform the multi-objective power trading clearing optimization model;

[0007] The transformed multi-objective power trading clearing optimization model is solved by multi-objective optimization and the power trading clearing optimization strategy is obtained.

[0008] Preferably, the multi-objective power trading clearing optimization model includes a first objective function with maximizing the power transaction revenue in the region as the optimization objective and constraints of the first objective function; the first objective function is:

[0009]

[0010] In the formula, is the electricity transaction revenue in the region, is a variable set of transaction records, Index for buyers, For the buyer collection, Index for sellers, For sellers, The line node index involved in the power supply of the winning bid. is the set of line nodes between regions, is the transaction period index, It is a collection of transaction periods. The bid price of the seller of the transaction, The buyer's offer for the transaction. is the transaction fee of the transaction;

[0011]

[0012]

[0013] In the formula, The amount of electricity declared by the buyer, The electricity quantity declared by the seller. is the transmission unit price from node r to the receiving end, is the network loss rate of the line from node r to the receiving end, is the transmission price conversion function related to the network loss rate; After considering the network loss, it is the discount function related to the line loss rate borne by the receiving end;

[0014] The constraints of the first objective function include reported power limit constraints, power line flow constraints and transmission capacity constraints.

[0015] Preferably, the multi-objective power trading clearing optimization model includes a second objective function with the maximization of the inter-regional new energy consumption level as the optimization goal; the second objective function is:

[0016]

[0017] In the formula, is the level of new energy consumption, n is the index of the new energy unit supplier, It is the collection of all new energy unit suppliers. A variable set that provides transaction records for new energy units; among them,

[0018]

[0019] In the formula, The amount of electricity reported by the buyer to the supplier of new energy units. The electricity amount reported by the supplier of new energy units.

[0020] Preferably, the multi-objective power trading clearing optimization model includes a third objective function with maximizing the inter-regional power trading volume as the optimization objective; the third objective function is:

[0021]

[0022] In the formula, It is the volume of inter-regional electricity trading.

[0023] Preferably, the multi-objective power trading clearing optimization model includes a fourth objective function with minimization of the frequency of inter-regional power trading as the optimization objective; the fourth objective function is:

[0024]

[0025] In the formula, is the frequency of inter-regional electricity trading, For the kth transaction between the same buyer and seller in time period t, is the set of all parties that make transactions during period t, is the total number of transactions that are matched. To match the set of transmission lines involved in the transaction pair k, A set of buyers that match transaction pairs. A collection of sellers that match transaction pairs.

[0026] Preferably, determining sequential boundaries for multiple optimization objectives in the multi-objective power trading clearing optimization model, and using the sequential boundaries to transform the multi-objective power trading clearing optimization model, comprises:

[0027] Based on the Pareto principle, according to the objective function of the multi-objective power trading clearing optimization model with maximizing the power trading revenue within the region, maximizing the level of new energy consumption between regions and maximizing the power trading volume between regions as the optimization objectives, the sequential boundary is determined; the sequential boundary is:

[0028]

[0029] In the formula, is the global optimal solution of the objective function with the optimization goal of maximizing the power transaction revenue in the region. is the global optimal solution of the objective function with the optimization goal of maximizing the level of new energy consumption among regions. The global optimal solution of the objective function with the optimization goal of maximizing the volume of inter-regional power trading;

[0030] The multi-objective power trading clearing optimization model is deformed and simplified by using the sequential boundary, and the deformed multi-objective power trading clearing optimization model is obtained as follows:

[0031]

[0032] In the formula, is the decision variable, h is the coefficient term of the decision variable, is the coefficient term of the decision variable in the constraint equation, c is the constant term on the right side of the constraint equation, is the matrix composed of the coefficients of the decision variables on the left side of the inequality constraints, is the vector of constant terms on the right side of the inequality constraint, i is the row number in matrix J, is the set of constraints corresponding to all solutions that satisfy the sequential boundaries.

[0033] Preferably, performing multi-objective optimization on the deformed multi-objective power trading clearing optimization model to obtain the power trading clearing optimization strategy comprises: performing multi-objective optimization on the deformed multi-objective power trading clearing optimization model using a differential evolution algorithm to obtain the power trading clearing optimization strategy;

[0034] The differential evolution algorithm is used to perform multi-objective optimization on the deformed multi-objective power trading clearing optimization model to obtain the power trading clearing optimization strategy, including:

[0035] Using uniform distribution to randomly generate individuals in the solution space of the multi-objective power trading clearing optimization model;

[0036] Perform mutation and crossover operations on the individuals in the solution space to generate a new solution space;

[0037] Calculating the fitness value of the new solution space using a preset fitness function;

[0038] Determine whether the fitness value has reached the optimum or whether the current number of iterations has reached a preset maximum number of iterations;

[0039] When it is determined that the fitness value has not reached the optimum or the current number of iterations has not reached the preset maximum number of iterations, the process proceeds to the step of performing mutation and crossover operations on the individuals in the solution space to generate a new solution space;

[0040] When it is determined that the fitness value reaches the optimum or the current number of iterations reaches a preset maximum number of iterations, the iteration is stopped, and the solution space obtained after the iteration is stopped is output as an optimization strategy for clearing electricity transactions.

[0041] In a second aspect, the present invention provides an inter-regional power transaction clearing optimization system, comprising:

[0042] The model building module is used to establish multiple optimization objectives, including maximizing the power transaction revenue within the region, maximizing the level of new energy consumption between regions, maximizing the power transaction volume between regions, and minimizing the power transaction frequency between regions, determine the constraints of each optimization objective, and construct a multi-objective power transaction clearing optimization model;

[0043] A model deformation module, used for determining sequential boundaries for multiple optimization objectives in the multi-objective power trading clearing optimization model, and using the sequential boundaries to deform the multi-objective power trading clearing optimization model;

[0044] The clearing optimization module is used to perform multi-objective optimization on the transformed multi-objective power trading clearing optimization model to obtain the power trading clearing optimization strategy.

[0045] In a third aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the inter-regional power transaction clearing optimization method as described in the first aspect.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the inter-regional power transaction clearing optimization method as described in the first aspect.

[0047] It can be seen from the above technical solutions that the present invention takes into account the trading rights and interests of all parties, establishes multiple optimization objectives such as maximizing the power transaction revenue within the region, maximizing the level of new energy consumption between regions, maximizing the power transaction volume between regions, and minimizing the power transaction frequency between regions, and constructs a multi-objective power transaction clearing optimization model. By sequentially bounding the multi-objective power transaction clearing optimization model and then optimizing and solving the multi-objective power transaction clearing optimization model, it is possible to quickly solve the inter-regional clearing optimization problem under the condition of large-scale regional power data, thereby improving the computational efficiency and accuracy of inter-regional power transaction clearing. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1An application environment of an inter-regional power transaction clearing optimization method provided by an embodiment of the present invention;

[0050] Figure 2 A flow chart of an inter-regional power transaction clearing optimization method provided by an embodiment of the present invention;

[0051] Figure 3 A schematic diagram of the structure of an inter-regional power transaction clearing optimization system provided by an embodiment of the present invention;

[0052] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] At present, for the multi-objective optimization problems in the power market, the weighted function method and the Pareto optimization method are generally used. For the weighted function method, the main problem is that there is a lack of fair and objective standards for determining the weights of each objective function, and it is difficult to take into account the rights and interests of all market participants and dispatchers; the Pareto optimization method attempts to find improvements in the solution space that will increase the utility of at least one participant without reducing the utility level of all participants. Although it can take into account the fairness of all participants, the solution it obtains conforms to the welfare economics principle of Pareto optimality, but the solution efficiency is low and the difficulty of obtaining the Pareto optimal solution is high. In addition, the global solution is a calculation process involving multiple variables and huge data capacity. The traditional LP solution method is difficult to meet the requirements of solution efficiency and intelligence.

[0055] The inter-regional power transaction clearing optimization method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the power system of each region communicates with the server 102 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The server 102 establishes multiple optimization objectives, including maximizing the power transaction revenue within the region, maximizing the level of new energy consumption between regions, maximizing the volume of power transactions between regions, and minimizing the frequency of power transactions between regions, determines the constraints of each optimization objective, and constructs a multi-objective power transaction clearing optimization model; determines the sequential boundaries of the multiple optimization objectives in the multi-objective power transaction clearing optimization model, and uses the sequential boundaries to deform the multi-objective power transaction clearing optimization model; performs multi-objective optimization on the deformed multi-objective power transaction clearing optimization model to obtain a power transaction clearing optimization strategy. The server 102 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0056] like Figure 2 As shown, the embodiment of the present application provides an optimization method for clearing inter-regional power transactions, and the method is applied to Figure 1 The server 102 in the example is used as an example to illustrate the method, which includes the following steps S1 to S3. Among them:

[0057] Step S1, establish multiple optimization objectives, including maximizing the power transaction revenue within the region, maximizing the level of new energy consumption between regions, maximizing the power transaction volume between regions, and minimizing the power transaction frequency between regions, determine the constraints of each optimization objective, and construct a multi-objective power transaction clearing optimization model.

[0058] Among them, the regions in the embodiments of the present application can be divided according to needs or jurisdictional areas, such as provinces.

[0059] Among them, the process of inter-regional power trading is characterized by a multi-objective power trading clearing optimization model. The embodiment of this application takes into account both the power supply and demand sides and professional institutions (such as power traders and VPPs), and the clearing results have a significant coupling relationship with the grid node control, and the rights and interests of all market participants and dispatchers need to be taken into account. Therefore, the embodiment of this application takes the maximization of power transaction revenue within the region, the maximization of the level of new energy consumption between regions, the maximization of inter-regional power transaction volume, and the minimization of inter-regional power transaction frequency as multiple optimization goals.

[0060] Among them, considering the transaction costs, from the perspective of buyers and sellers, the multi-objective power trading clearing optimization model includes the first objective function with the maximization of power transaction revenue in the region as the optimization goal and the constraints of the first objective function; the first objective function is:

[0061]

[0062] In the formula, is the electricity transaction revenue in the region, is a variable set of transaction records, Index for buyers, For the buyer collection, Index for sellers, For sellers, The line node index involved in the power supply of the winning bid. is the set of line nodes between regions, is the transaction period index, It is a collection of transaction periods. The bid price of the seller of the transaction, The buyer's offer for the transaction. is the transaction cost of the transaction; among them, since the medium- and long-term electricity transactions between regions are considered, the transaction information stamp is defined first, taking the transaction elements as the starting point. It is a variable set that records the transaction records, that is, the variable set of the transaction records for:

[0063]

[0064] In practice, the transaction costs include transmission costs and network losses. for:

[0065]

[0066] In the formula, The amount of electricity declared by the buyer, The electricity quantity declared by the seller. is the transmission unit price from node r to the receiving end, is the network loss rate of the line from node r to the receiving end, is the transmission price conversion function related to the network loss rate; After considering network loss, it is a discount function related to the line loss rate borne by the receiving end. The first item in represents the transmission cost considering line loss, and the second item represents the network loss borne by the receiving end.

[0067] The constraints of the first objective function include reported power limit constraints, power line flow constraints and transmission capacity constraints.

[0068] Specifically, the declared power limit constraints are:

[0069]

[0070] In the formula, is the total amount of electricity that the seller can supply during period t; is the total amount of electricity demanded by the buyer during period t.

[0071] Considering line l, the power flow and transaction information stamp of line l in period t have the following constraint relationship:

[0072]

[0073] In the formula, Indicates the power flow distribution factor of the node r involving the tie line; It represents the power flow conversion factor related to the network loss rate; Indicates the direction of the tide, 1 is positive and -1 is negative; It represents the net power flow measured on line l during period t.

[0074] For the transmission line l, the power flow in period t should not exceed the maximum transmission capacity of the line, so the ATC (available transmission capacity) constraint is:

[0075]

[0076] In the formula, It represents the maximum transmittable capacity of transmission line l.

[0077] In addition, the constraints on the power system also include power balance, unit climbing constraints, etc., which will not be elaborated here.

[0078] In some embodiments, considering the dual carbon problem in the clearing conditions, the multi-objective power trading clearing optimization model includes a second objective function with the maximization of the inter-regional new energy consumption level as the optimization goal; the second objective function is:

[0079]

[0080] In the formula, is the level of new energy consumption, n is the index of the new energy unit supplier, It is the collection of all new energy unit suppliers. A variable set that provides transaction records for new energy units; among them,

[0081]

[0082] In the formula, The amount of electricity reported by the buyer to the supplier of new energy units. The electricity amount reported by the supplier of new energy units.

[0083] In some embodiments, considering that market activity is an important manifestation of market vitality, and transaction volume is the main indicator for measuring market activity, the multi-objective power transaction clearing optimization model includes a third objective function with maximizing the transaction volume of inter-regional power transactions as the optimization objective; the third objective function is:

[0084]

[0085] In the formula, It is the volume of inter-regional electricity trading.

[0086] In some embodiments, in order to avoid market manipulation and promote market competition, at the transaction level, it is necessary to avoid the transaction volume being concentrated in certain time periods and between certain buyers and sellers. To this end, the multi-objective power transaction clearing optimization model includes a fourth objective function with minimizing the frequency of inter-regional power transactions as the optimization objective; the fourth objective function is:

[0087]

[0088] In the formula, is the frequency of inter-regional electricity trading, For the kth transaction between the same buyer and seller in time period t, is the set of all parties that make transactions during period t, is the total number of transactions that are matched. To match the set of transmission lines involved in the transaction pair k, A set of buyers that match transaction pairs. A collection of sellers that match transaction pairs.

[0089] Step S2: determining sequential boundaries for multiple optimization objectives in the multi-objective power trading clearing optimization model, and using the sequential boundaries to transform the multi-objective power trading clearing optimization model.

[0090] It is understandable that, in actual operation, the aforementioned multiple objectives cannot be guaranteed to be met at the same time, and market clearing does not necessarily require the above multi-objective optimization conditions to be met at the same time. In order to solve the situation where multiple variables and objectives are mutually exclusive, in the embodiment of the present application, the multi-objective power trading clearing optimization model is simplified by determining the sequential boundaries of the multiple optimization objectives in the multi-objective power trading clearing optimization model, and the multi-objective power trading clearing optimization model is deformed using the sequential boundaries, so that the model satisfies the Pareto improvement, that is, the sequential solution process of the model is better than the single-layer objective.

[0091] Specifically, step S2 includes:

[0092] Step S201: Based on the Pareto principle, according to the objective function of the multi-objective power trading clearing optimization model, which takes maximizing the power trading revenue within the region, maximizing the level of new energy consumption between regions and maximizing the power trading volume between regions as the optimization objectives, the sequential boundary is determined; the sequential boundary is:

[0093]

[0094] In the formula, is the global optimal solution of the objective function with the optimization goal of maximizing the power transaction revenue in the region. is the global optimal solution of the objective function with the optimization goal of maximizing the level of new energy consumption among regions. It is the global optimal solution of the objective function with maximizing the inter-regional electricity trading volume as the optimization goal.

[0095] Among them, when considering the second objective function, the global solution should ensure that the social welfare is greater than or equal to the value when only considering the first objective function; the second formula indicates that when considering the third objective function, the global solution should also make the value of the second objective function greater than the optimal value of the second objective function when only considering the first two objective functions. ; The third formula is similar.

[0096] Step S202: The multi-objective power trading clearing optimization model is deformed and simplified using the sequential boundary, and the deformed multi-objective power trading clearing optimization model is obtained as follows:

[0097]

[0098] In the formula, is the decision variable, h is the coefficient term of the decision variable, is the coefficient term of the decision variable in the constraint equation, c is the constant term on the right side of the constraint equation, is the matrix composed of the coefficients of the decision variables on the left side of the inequality constraints, is the vector of constant terms on the right side of the inequality constraint, i is the row number in matrix J, is the set of constraints corresponding to all solutions that satisfy the sequential boundaries.

[0099] Step S3: performing multi-objective optimization search on the transformed multi-objective power trading clearing optimization model to obtain a power trading clearing optimization strategy.

[0100] It can be understood that since the multi-objective electricity trading clearing optimization model is an LP problem containing multiple objectives, and the objective function includes both a function for finding the maximum value and a function for finding the minimum value, the solution process involves a huge amount of variable data, and the traditional method is inefficient. Therefore, the embodiment of the present application introduces a differential evolution algorithm to solve the model.

[0101] In the embodiment of the present application, the differential evolution algorithm is used to perform multi-objective optimization on the deformed multi-objective power trading clearing optimization model to obtain the power trading clearing optimization strategy. By introducing the improved differential evolution algorithm, it is possible to quickly solve the optimization problem of medium- and long-term transactions between regions under the condition of large-scale regional power data.

[0102] Specifically, the differential evolution algorithm is used to perform multi-objective optimization on the transformed multi-objective power trading clearing optimization model, and the power trading clearing optimization strategy is obtained, including:

[0103] Step S301: randomly generate individuals in the solution space of a multi-objective power trading clearing optimization model using uniform distribution.

[0104] Among them, the embodiment of the present application takes the declared quantity, declared price, and declared quantity of the new energy units of the two parties to the transaction as the independent variables to be determined, and takes the objective function and constraints as the conditions for generating the solution space.

[0105] Among them, the individuals in the solution space are defined as:

[0106]

[0107] In the formula, t represents the number of iteration steps; represents the i-th solution in the solution space at step t, where Represents the j-th dimension element of the variable, that is, the j-th independent variable; n is the total number of independent variables.

[0108] The initial values ​​of individuals are randomly generated using uniform distribution, specifically:

[0109]

[0110] In the formula, It represents the lower bound number generated by the algorithm for the j-th dimension component of the solution space; It represents the upper bound number generated by the algorithm for the j-th dimension component of the solution space individual; represents a rational number randomly generated by a machine in the interval [0,1]; the interval [ It can be used as an optimization interval to limit the solution search.

[0111] Step S302: Perform mutation and crossover operations on individuals in the solution space to generate a new solution space.

[0112] At the t-th iteration, three different individuals are extracted from the population (i.e., the set of individuals in the solution space) to form the following variant individuals:

[0113]

[0114] In the formula, , , They represent the numbers of individuals drawn from the population, and there are:

[0115]

[0116] In the formula, represents the mutation operator, and its expression is:

[0117]

[0118] In the formula, Represents the initial value of the mutation operator; represents the adaptive coefficient; The maximum number of steps that indicates the end of iteration or mutation.

[0119] For variant individuals, in order to judge the quality of the extracted individuals, we set the following fitness function:

[0120]

[0121] Substituting the three individuals drawn in step t into the above formula, we can judge the quality of the individuals. For all the three individuals drawn in this way, we sort them from high to low according to their fitness, recorded as , rewrite the mutant individual as:

[0122]

[0123] Performing crossover operation on the mutant individuals, we have:

[0124]

[0125] In the formula, According to formula (18), after considering the iteration step The jth component at iteration step t; is the crossover probability, given by:

[0126]

[0127] In the formula, represents the crossover probability of the i-th individual in the solution space; and Respectively represent the lower and upper bounds of the crossover probability of the population; represents the fitness of the i-th individual; and Respectively represent the maximum and minimum values ​​of population fitness; It represents the average fitness of the population.

[0128] Step S303: Calculate the fitness value of the new solution space using a preset fitness function.

[0129] Among them, the fitness function is:

[0130] .

[0131] Step S304: determine whether the fitness value has reached the optimum or whether the current number of iterations has reached the preset maximum number of iterations.

[0132] Step S305: When it is determined that the fitness value has not reached the optimum or the current number of iterations has not reached the preset maximum number of iterations, the process proceeds to the step of performing mutation and crossover operations on the individuals in the solution space to generate a new solution space.

[0133] Step S306: When it is determined that the fitness value reaches the optimum or the current number of iterations reaches the preset maximum number of iterations, the iteration is stopped, and the solution space obtained after the iteration is stopped is output as the power trading clearing optimization strategy.

[0134] Among them, the idea of ​​the differential evolution algorithm is that through iteration, the optimized solution we get should be better than or at least equal to the solution before optimization. This is a greedy algorithm idea. Under this idea, the iterative process of individuals in the solution space can be expressed as:

[0135]

[0136] Based on the above iterative process, when the mutation terminates or the iteration conditions are met, the algorithm gives the global optimal solution, that is, the solution space is obtained as the optimization strategy for clearing electricity transactions, where the optimization strategy for clearing electricity transactions includes the declared quantities, declared prices and declared quantities of new energy units by both parties to the transaction.

[0137] It should be noted that the embodiment of the present application takes into account the trading rights and interests of all parties, establishes multiple optimization objectives such as maximizing the power transaction revenue within the region, maximizing the level of new energy consumption between regions, maximizing the power transaction volume between regions, and minimizing the power transaction frequency between regions, and constructs a multi-objective power transaction clearing optimization model. By sequentially bounding the multi-objective power transaction clearing optimization model and then optimizing and solving the multi-objective power transaction clearing optimization model, it is possible to quickly solve the inter-regional clearing optimization problem under the condition of large-scale regional power data, thereby improving the computational efficiency and accuracy of inter-regional power transaction clearing.

[0138] Based on the same inventive concept, an embodiment of the present application also provides an inter-regional power transaction clearing optimization system for implementing the above-mentioned inter-regional power transaction clearing optimization method.

[0139] The implementation solution for solving the problem provided by the system is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more inter-regional power transaction clearing optimization system embodiments provided below can be referred to the limitations on the inter-regional power transaction clearing optimization method above and will not be repeated here.

[0140] like Figure 3 As shown, the embodiment of the present application also provides an inter-regional power transaction clearing optimization system, including:

[0141] The model building module 100 is used to establish multiple optimization objectives, including maximizing the power transaction revenue within the region, maximizing the level of new energy consumption between regions, maximizing the power transaction volume between regions, and minimizing the power transaction frequency between regions, determine the constraints of each optimization objective, and construct a multi-objective power transaction clearing optimization model;

[0142] A model deformation module 200, for determining sequential boundaries for multiple optimization objectives in a multi-objective power trading clearing optimization model, and using the sequential boundaries to deform the multi-objective power trading clearing optimization model;

[0143] The clearing optimization module 300 is used to perform multi-objective optimization on the transformed multi-objective power transaction clearing optimization model to obtain a power transaction clearing optimization strategy.

[0144] In some embodiments, the multi-objective power trading clearing optimization model includes a first objective function with maximizing the power transaction revenue in the region as the optimization goal and constraints of the first objective function; the first objective function is:

[0145]

[0146] In the formula, is the electricity transaction revenue in the region, is a variable set of transaction records, Index for buyers, For the buyer collection, Index for sellers, For sellers, The line node index involved in the power supply of the winning bid. is the set of line nodes between regions, is the transaction period index, It is a collection of transaction periods. The bid price of the seller of the transaction, The buyer's offer for the transaction. is the transaction fee of the transaction;

[0147]

[0148]

[0149] In the formula, The amount of electricity declared by the buyer, The electricity quantity declared by the seller. is the transmission unit price from node r to the receiving end, is the network loss rate of the line from node r to the receiving end, is the transmission price conversion function related to the network loss rate; After considering the network loss, it is the discount function related to the line loss rate borne by the receiving end;

[0150] The constraints of the first objective function include reported power limit constraints, power line flow constraints and transmission capacity constraints.

[0151] In some embodiments, the multi-objective power trading clearing optimization model includes a second objective function with the maximization of the inter-regional new energy consumption level as the optimization goal; the second objective function is:

[0152]

[0153] In the formula, is the level of new energy consumption, n is the index of the new energy unit supplier, It is the collection of all new energy unit suppliers. A variable set that provides transaction records for new energy units; among them,

[0154]

[0155] In the formula, The amount of electricity reported by the buyer to the supplier of new energy units. The electricity amount reported by the supplier of new energy units.

[0156] In some embodiments, the multi-objective power trading clearing optimization model includes a third objective function with maximizing the inter-regional power trading volume as the optimization objective; the third objective function is:

[0157]

[0158] In the formula, It is the volume of inter-regional electricity trading.

[0159] In some embodiments, the multi-objective power trading clearing optimization model includes a fourth objective function with minimizing the frequency of inter-regional power trading as the optimization objective; the fourth objective function is:

[0160]

[0161] In the formula, is the frequency of inter-regional electricity trading, For the kth transaction between the same buyer and seller in time period t, is the set of all parties that make transactions during period t, is the total number of transactions that are matched. To match the set of transmission lines involved in the transaction pair k, A set of buyers that match transaction pairs. A collection of sellers that match transaction pairs.

[0162] In some embodiments, the model deformation module 200 is used to determine the sequential boundary based on the Pareto principle according to the objective function of the multi-objective power transaction clearing optimization model with maximizing the power transaction revenue within the region, maximizing the level of new energy consumption between regions, and maximizing the power transaction volume between regions as the optimization objectives; the sequential boundary is:

[0163]

[0164] In the formula, is the global optimal solution of the objective function with the optimization goal of maximizing the power transaction revenue in the region. is the global optimal solution of the objective function with the optimization goal of maximizing the level of new energy consumption among regions. The global optimal solution of the objective function with the optimization goal of maximizing the volume of inter-regional power trading;

[0165] The multi-objective power trading clearing optimization model is transformed and simplified using the sequential boundary, and the transformed multi-objective power trading clearing optimization model is obtained as follows:

[0166]

[0167] In the formula, is the decision variable, h is the coefficient term of the decision variable, is the coefficient term of the decision variable in the constraint equation, c is the constant term on the right side of the constraint equation, is the matrix composed of the coefficients of the decision variables on the left side of the inequality constraints, is the vector of constant terms on the right side of the inequality constraint, i is the row number in matrix J, is the set of constraints corresponding to all solutions that satisfy the sequential boundaries.

[0168] In some embodiments, the clearing optimization module 300 is used to perform multi-objective optimization on the transformed multi-objective power trading clearing optimization model using a differential evolution algorithm to obtain a power trading clearing optimization strategy;

[0169] The differential evolution algorithm is used to perform multi-objective optimization on the transformed multi-objective power trading clearing optimization model, and the power trading clearing optimization strategy is obtained, including:

[0170] The individuals in the solution space of the multi-objective power trading clearing optimization model are randomly generated using uniform distribution;

[0171] Perform mutation and crossover operations on individuals in the solution space to generate a new solution space;

[0172] Calculate the fitness value of the new solution space using the preset fitness function;

[0173] Determine whether the fitness value has reached the optimal value or whether the current number of iterations has reached the preset maximum number of iterations;

[0174] When it is judged that the fitness value has not reached the optimal value or the current number of iterations has not reached the preset maximum number of iterations, the individuals in the solution space are mutated and crossover operated to generate a new solution space;

[0175] When it is judged that the fitness value reaches the optimal value or the current number of iterations reaches the preset maximum number of iterations, the iteration is stopped, and the solution space obtained after the iteration is stopped is output as the optimization strategy for clearing electricity transactions.

[0176] like Figure 4 As shown, an embodiment of the present application also provides an electronic device, the electronic device 10 includes a memory 20 and a processor 30, the memory 20 stores a computer program, and when the computer program is executed by the processor 30, the processor 30 executes the steps of the inter-regional power transaction clearing optimization method in any of the above embodiments.

[0177] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the inter-regional power transaction clearing optimization method as in any of the above embodiments.

[0178] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, electronic device and computer storage medium can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0179] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0180] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and a part of a module, a program segment or a code includes one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0181] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0182] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0183] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0184] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the various embodiments of the method of the present invention through a computer device (which can be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk, etc. Various media that can store program codes.

[0185] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An inter-regional power transaction clearing optimization method, characterized in that: include: Establish multiple optimization objectives, including maximizing the revenue from power trading within the region, maximizing the level of new energy consumption between regions, maximizing the volume of power trading between regions, and minimizing the frequency of power trading between regions, determine the constraints of each optimization objective, and construct a multi-objective power trading clearing optimization model; Determining sequential boundaries for multiple optimization objectives in the multi-objective power trading clearing optimization model, and using the sequential boundaries to transform the multi-objective power trading clearing optimization model; The transformed multi-objective power trading clearing optimization model is solved by multi-objective optimization and the power trading clearing optimization strategy is obtained.

2. The inter-regional power transaction clearing optimization method according to claim 1, characterized in that: The multi-objective power trading clearing optimization model includes a first objective function with maximizing the power transaction revenue in the region as the optimization objective and constraints of the first objective function; the first objective function is: In the formula, is the electricity transaction revenue in the region, is a variable set of transaction records, Index for buyers, For the buyer collection, Index for sellers, For sellers, The line node index involved in the power supply of the winning bid. is the set of line nodes between regions, is the transaction period index, It is a collection of transaction periods. The bid price of the seller of the transaction, The buyer's offer for the transaction. is the transaction fee of the transaction; In the formula, The amount of electricity declared by the buyer, The electricity quantity declared by the seller, is the transmission unit price from node r to the receiving end, is the network loss rate of the line from node r to the receiving end, is the transmission price conversion function related to the network loss rate; After considering the network loss, it is the discount function related to the line loss rate borne by the receiving end; The constraints of the first objective function include reported power limit constraints, power line flow constraints and transmission capacity constraints.

3. The inter-regional power transaction clearing optimization method according to claim 1, characterized in that: The multi-objective power trading clearing optimization model includes a second objective function with the maximization of the inter-regional new energy consumption level as the optimization goal; the second objective function is: In the formula, is the level of new energy consumption, n is the index of the new energy unit supplier, It is the collection of all new energy unit suppliers. A variable set that provides transaction records for new energy units; among them, In the formula, The amount of electricity reported by the buyer to the supplier of new energy units. The electricity amount reported by the supplier of new energy units.

4. The inter-regional power transaction clearing optimization method according to claim 1, characterized in that: The multi-objective power trading clearing optimization model includes a third objective function with maximizing the inter-regional power trading volume as the optimization objective; the third objective function is: In the formula, It is the volume of inter-regional electricity trading.

5. The inter-regional power transaction clearing optimization method according to claim 1, characterized in that: The multi-objective power trading clearing optimization model includes a fourth objective function with minimizing the frequency of inter-regional power trading as the optimization objective; the fourth objective function is: In the formula, is the frequency of inter-regional electricity trading, For the kth transaction between the same buyer and seller in period t, is the set of all parties that make transactions during period t, is the total number of transactions that are matched. To match the set of transmission lines involved in the transaction pair k, A set of buyers that match transaction pairs. A collection of sellers that match transaction pairs.

6. The inter-regional power transaction clearing optimization method according to claim 1, characterized in that: The determining of sequential boundaries for multiple optimization objectives in the multi-objective power trading clearing optimization model, and using the sequential boundaries to transform the multi-objective power trading clearing optimization model, includes: Based on the Pareto principle, according to the objective function of the multi-objective power trading clearing optimization model with maximizing the power trading revenue within the region, maximizing the level of new energy consumption between regions and maximizing the power trading volume between regions as the optimization objectives, the sequential boundary is determined; the sequential boundary is: In the formula, is the global optimal solution of the objective function with the optimization goal of maximizing the power transaction revenue in the region. is the global optimal solution of the objective function with the optimization goal of maximizing the level of new energy consumption among regions. The global optimal solution of the objective function with the optimization goal of maximizing the volume of inter-regional power trading; The multi-objective power trading clearing optimization model is deformed and simplified by using the sequential boundary, and the deformed multi-objective power trading clearing optimization model is obtained as follows: In the formula, is the decision variable, h is the coefficient term of the decision variable, is the coefficient term of the decision variable in the constraint equation, c is the constant term on the right side of the constraint equation, is the matrix composed of the coefficients of the decision variables on the left side of the inequality constraints, is the vector of constant terms on the right side of the inequality constraint, i is the row number in matrix J, is the set of constraints corresponding to all solutions that satisfy the sequential boundaries.

7. The inter-regional power transaction clearing optimization method according to claim 1, characterized in that: The method of performing multi-objective optimization on the deformed multi-objective power trading clearing optimization model to obtain the power trading clearing optimization strategy includes: performing multi-objective optimization on the deformed multi-objective power trading clearing optimization model using a differential evolution algorithm to obtain the power trading clearing optimization strategy; The differential evolution algorithm is used to perform multi-objective optimization on the deformed multi-objective power trading clearing optimization model to obtain the power trading clearing optimization strategy, including: Using uniform distribution to randomly generate individuals in the solution space of the multi-objective power trading clearing optimization model; Perform mutation and crossover operations on the individuals in the solution space to generate a new solution space; Calculating the fitness value of the new solution space using a preset fitness function; Determine whether the fitness value has reached the optimum or whether the current number of iterations has reached a preset maximum number of iterations; When it is determined that the fitness value has not reached the optimum or the current number of iterations has not reached the preset maximum number of iterations, the step of performing mutation and crossover operations on the individuals in the solution space to generate a new solution space is performed; When it is determined that the fitness value reaches the optimum or the current number of iterations reaches a preset maximum number of iterations, the iteration is stopped, and the solution space obtained after the iteration is stopped is output as an optimization strategy for clearing electricity transactions.

8. An inter-regional power transaction clearing optimization system, characterized in that: include: The model building module is used to establish multiple optimization objectives, including maximizing the power transaction revenue within the region, maximizing the level of new energy consumption between regions, maximizing the power transaction volume between regions, and minimizing the power transaction frequency between regions, determine the constraints of each optimization objective, and construct a multi-objective power transaction clearing optimization model; A model deformation module, used for determining sequential boundaries of multiple optimization objectives in the multi-objective power trading clearing optimization model, and using the sequential boundaries to deform the multi-objective power trading clearing optimization model; The clearing optimization module is used to perform multi-objective optimization on the transformed multi-objective power trading clearing optimization model to obtain the power trading clearing optimization strategy.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the inter-regional power transaction clearing optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the inter-regional power transaction clearing optimization method as described in any one of claims 1-7 are implemented.

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