Electric power parameter auxiliary decision-making method and device based on intelligent bionic optimization model

Abstract

The invention relates to an electric power parameter auxiliary decision-making method based on an intelligent bionic optimization model, and the method comprises the steps: carrying out the collection and arrangement of market supply and demand data, transmittable electric quantity margin and market main data of each region, and obtaining the sample data of different collection periods of each day; establishing an intelligent bionic optimization model according to the sample data and cross-regional power transaction constraint conditions, wherein an objective function of the intelligent bionic optimization model takes cross-regional transaction income maximization as an optimization objective; and solving the intelligent bionic optimization model by adopting an artificial bee colony algorithm to obtain an optimal strategy of cross-regional transaction. According to the method, intelligent analysis and optimization are carried out by constructing an intelligent bionic optimization model, so that efficient trans-provincial transaction is realized, the transaction region is expanded, the new energy consumption space is increased, the investment and environmental protection benefits of a new energy unit and an ultra-high voltage power grid are fully exerted, the energy fuel cost is reduced, and the trans-provincial transaction effect and the resource utilization rate are improved.

Description

Technical Field

[0001] The present application relates to the technical field of application of cross-regional and cross-provincial power market transaction decision-making models, and specifically to a power parameter auxiliary decision-making method and device based on an intelligent bionic optimization model. Background Art

[0002] Affected by resource endowment, the current distribution of wind power and solar power generation capacity is uneven. For resource-rich regions, there are serious problems of wind and solar power abandonment due to factors such as limited local consumption space, insufficient system peak-shaving capacity, and imperfect cross-provincial consumption mechanisms. However, the power generation of new energy such as wind power and photovoltaic power has random fluctuations, and it is impossible to achieve accurate medium- and long-term predictions, and it is difficult to participate in cross-provincial transactions, which affects the consumption of new energy electricity across provinces.

[0003] Traditional electricity trading methods are difficult to adapt to the random fluctuations in the output of new energy, resulting in the inability to fully utilize the power generation of new energy. The contradiction between traditional electricity trading and the random fluctuations in the output of new energy makes it impossible to expand the space for new energy consumption in a wider area, to give full play to the investment and environmental benefits of new energy units and ultra-high voltage power grids, and to reduce the fuel cost of the whole society. In addition, cross-provincial spot trading of new energy also faces some challenges, such as the trade-off between the cost of purchasing electricity from new energy outside the region and the possible peak-shaving cost and the average cost of purchasing electricity from local units, as well as the influence of factors such as the regulation performance of conventional units in the receiving area, the installed capacity and output of new energy in the region, the interconnection of power grids, the scale of regional loads and the peak-valley difference. These factors seriously restrict the efficiency of cross-regional and cross-provincial trading of new energy, which is not conducive to the efficient use of resources. Summary of the invention

[0004] In order to overcome the deficiencies of the above-mentioned prior art, the present application provides a method and device for auxiliary decision-making of power parameters based on an intelligent bionic optimization model, which specifically adopts the following technical solutions:

[0005] An auxiliary decision-making method for power parameters based on an intelligent bionic optimization model comprises the following steps:

[0006] Step 1: Collect and sort out the market supply and demand data, transmittable power margin and market entity data of each region to obtain sample data at different collection periods every day;

[0007] Step 2: Establishing an intelligent bionic optimization model based on sample data and inter-regional power trading constraints, wherein the objective function of the intelligent bionic optimization model takes maximizing inter-regional trading revenue as the optimization goal;

[0008] Step 3: Use the artificial bee colony algorithm to solve the intelligent bionic optimization model and obtain the optimal strategy for cross-regional transactions.

[0009] Optionally: The objective function of the intelligent bionic optimization model is:

[0010]

[0011] in is the transaction volume between market member i and market member j in declaration period t; Price_sell i,t is the electricity price reported by market member i in reporting period t; Price_buy j,t Price_cost is the electricity purchase price declared by market member j in declaration period t; i,j is the comprehensive transmission cost unit price between market member i and market member j; β i,j is the comprehensive network loss coefficient between market member i and market member j; N is the number of market members on the electricity purchasing side; M is the number of market members on the electricity selling side; T is the total number of reporting periods.

[0012] Optional: the inter-regional power transaction constraints in step 2 include single subject role constraints and transmission path constraints;

[0013] The single subject role constraint is that the same market member cannot simultaneously act as a power buyer and a power seller to conduct power transactions;

[0014] The transmission path constraint requires that power transactions between market members must be conducted through existing transmission paths, and power transactions between market members that do not have physically connected transmission paths are not allowed.

[0015] Optional: the inter-regional power transaction constraint in step 2 includes a transaction quantity constraint;

[0016] The transaction volume constraint is that the transaction volume between the electricity seller and the electricity buyer cannot exceed the volume declared by both parties, and the transaction volume between the electricity seller and the electricity buyer cannot be a negative value.

[0017] Optionally: the inter-regional power transaction constraint in step 2 includes a transaction type constraint;

[0018] The transaction type constraint is that the transaction volume between market members must be divided into power purchase transactions and power sales transactions.

[0019] Optional: the inter-regional power transaction constraints in step 2 include power purchase limit constraints and power sales limit constraints;

[0020] The power purchase limit constraint is that the total power purchase amount of a single market member of the power purchaser and all the market members of the power sellers with whom it trades in the declaration period t does not exceed its own declared power amount;

[0021] The power sales limit constraint is that the total power sales of a single market member of the power seller and all the market members of the power buyers with whom it trades during the reporting period t does not exceed its own reported power.

[0022] Optionally: the inter-regional power transaction constraint in step 2 includes a power grid security constraint;

[0023] The grid safety constraint is that the planned power transmission of the interconnection lines between regions cannot exceed the upper limit of the interconnection line capacity.

[0024] Optionally: the inter-regional power trading constraint in step 2 includes channel capacity constraint;

[0025] The channel capacity constraint is that the minimum power demand in the power system is the minimum value of the difference between the load demand power and the total power generation in the power system and the power reserve margin.

[0026] Optional: The inter-regional power trading constraints in step 2 include power balance constraints:

[0027] The power balance constraint is that the total power transaction of the power buyer or seller must meet the corresponding power grid power reserve margin requirement, that is, when the power delivered is greater than the power grid reserve demand, the total power transaction of the power buyer or seller does not exceed the saleable power.

[0028] Furthermore, the present application also discloses an auxiliary decision-making device for power parameters based on an intelligent bionic optimization model, the device comprising:

[0029] The sample data collection module is used to collect and sort out the market supply and demand data, the margin of power that can be transmitted, and the data of market entities in each region, and obtain sample data at different collection periods every day;

[0030] An optimization model building module is used to establish an intelligent bionic optimization model based on sample data and inter-regional power transaction constraints, wherein the objective function of the intelligent bionic optimization model takes maximizing inter-regional transaction revenue as an optimization goal;

[0031] The optimization model solving module is used to solve the intelligent bionic optimization model using the artificial bee colony algorithm to obtain the optimal strategy for cross-regional transactions.

[0032] Beneficial Effects

[0033] The technical solution of this application has the following beneficial effects:

[0034] The power parameter decision-making assistance method of the present application constructs an intelligent bionic optimization model by comprehensively considering cross-regional power market rules and conditions, power grid security constraints, power balance, channel capacity, price fluctuations and other constraints. Through intelligent analysis and optimization of the model, efficient cross-provincial transactions can be achieved, effectively resolving the contradiction between traditional power transactions and random fluctuations in new energy output. By expanding the trading area to increase the space for new energy consumption, the investment and environmental benefits of new energy units and ultra-high voltage power grids are fully utilized, the energy and fuel costs are reduced, and the cross-regional transaction effects and resource utilization are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Flow chart of the power parameter auxiliary decision-making method in an embodiment of the present application.

[0036] Figure 2 Schematic diagram of the principle of the artificial bee colony algorithm used in the embodiments of the present application.

[0037] Figure 3 It is a structural schematic diagram of the power parameter auxiliary decision-making method device in an embodiment of the present application.

[0038] Figure 4 This is a structural diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The present application is further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and cannot be used to limit the scope of protection of the present application. It should be noted that the following detailed descriptions are all exemplary and are intended to provide further explanation of the present application.

[0040] Combination Figure 1 As shown, the embodiment of the present application specifically discloses a power parameter auxiliary decision-making method based on an intelligent bionic optimization model, which includes the following steps:

[0041] Step 1: Collect and collate the market supply and demand data, transmittable power margin and market subject data of each region to obtain sample data at different collection periods every day; for example, the market supply and demand data in step 1 include data on the load of the entire network, the total power generation output of the entire network and other data, while the market subject data include contract data and declared limit data, etc. In the process of data collection and collation in step 1 of this application, the data is generally collected and collated in units of days and every 15 minutes, so that T (96) types of disclosure data can be obtained every day.

[0042] Step 2: Establishing an intelligent bionic optimization model based on sample data and inter-regional power trading constraints, wherein the objective function of the intelligent bionic optimization model takes maximizing inter-regional trading revenue as the optimization goal;

[0043] Step 3: Use the artificial bee colony algorithm to solve the intelligent bionic optimization model and obtain the optimal strategy for cross-regional transactions.

[0044] Specifically, the objective function of the intelligent bionic optimization model in step 2 of the present application is:

[0045]

[0046] in is the transaction volume between market member i and market member j in declaration period t; Price_sell i,t is the electricity price reported by market member i in reporting period t; Price_buy j,t Price_cost is the electricity purchase price declared by market member j in declaration period t; i,j is the comprehensive transmission cost unit price between market member i and market member j; β i,j is the comprehensive network loss coefficient between market member i and market member j; N is the number of market members on the electricity purchasing side; M is the number of market members on the electricity selling side; T is the total number of reporting periods.

[0047] Furthermore, the cross-regional electricity trading constraints in step 2 described in this application include single subject role constraints, transmission path constraints, transaction quantity constraints, transaction type constraints, power purchase quantity restriction constraints, power sales quantity restriction constraints, grid security constraints, channel capacity constraints and power balance constraints.

[0048] (1) The single subject role constraint is that the same market member cannot simultaneously act as a power buyer and a power seller to conduct power transactions; and the transmission path constraint is that market members must conduct power transactions through existing transmission paths, and power transactions are not allowed between market members that do not have physically connected transmission paths, that is:

[0049]

[0050] Where δ_user i,j ∈{0.1} indicates whether there is a transaction relationship between the electricity seller market member i and the electricity buyer market member j, 1 indicates that there is a transaction relationship, and 0 indicates that there is no transaction relationship; Path_trans i,j ∈{0.1} indicates whether there is an available transmission path between the electricity seller market member i and the electricity buyer market member j, 1 indicates that there is an available transmission path, and 0 indicates that there is no available transmission path.

[0051] (2) The transaction power constraint is that the transaction power between the electricity seller and the electricity buyer cannot exceed the power declared by both parties, and the transaction power between the electricity seller and the electricity buyer cannot be a negative value, that is:

[0052]

[0053] Qbuy j,t Qsell is the electricity purchase amount declared by market member j in period t; i,t The electricity sales reported by electricity seller market member i in period t; is the transaction volume between market member i and market member j in declaration period t; δ_user i,j Represents the transaction relationship between electricity seller market member i and electricity buyer market member j.

[0054] (3) The transaction type constraint is that the transaction volume between market members must be divided into power purchase transactions and power sales transactions, that is:

[0055]

[0056] in When it is 1, it means that the electricity seller market member i sells electricity to the electricity buyer market member j; When it is 0, it means that the electricity purchaser market member j purchases electricity from the electricity seller market member i; It is the transaction volume between market members i and j in the declaration period t.

[0057] (4) The power purchase limit constraint is that the total power purchase of a single market member of the power purchaser and all the market members of the power seller with whom it trades in the declaration period t does not exceed its own declared power; the power sales limit constraint is that the total power sales of a single market member of the power seller and all the market members of the power purchaser with whom it trades in the declaration period t does not exceed its own declared power, that is:

[0058]

[0059] in is the transaction volume between market member i and market member j in declaration period t; Qbuy j,t Qsell is the electricity purchase amount declared by market member j in period t; i,t is the electricity purchase amount declared by market member i of the electricity seller in period t; N is the number of market members of the electricity buyer; M is the number of market members of the electricity seller.

[0060] (5) The grid security constraint is that the planned power transmission of the interconnection lines between regions cannot exceed the upper limit of the interconnection line capacity, that is:

[0061]

[0062] in is the transaction volume between market member i and market member j in declaration period t; α l is the transmission sensitivity coefficient on the tie line l; Rlossl is the transmission network loss on the tie line l; Capacity_pos is the planned transmission capacity that market members i and j have arranged through tie line l; l and Capacity_neg l They are respectively the upper and lower limits of the forward and reverse transmission capacity of the tie line l.

[0063] (6) The channel capacity constraint is that the minimum power demand in the power system is the minimum value of the difference between the load demand power and the total power generation in the power system and the power reserve margin, that is:

[0064]

[0065] Where Power_min t Indicates the minimum power demand in the power system during period t; Q_load i Indicates the load power demand of the power grid system where market member i is located; Q_power i Represents the total power generation of the power grid system where market member i is located; R_margin i It represents the reserve margin requirement of the power grid where market member i is located.

[0066] (7) The power balance constraint is that the total power transaction of the power purchaser or the power seller must meet the corresponding power reserve margin requirement of the power grid, that is, when the power delivered is greater than the power reserve demand of the power grid, the total power transaction of the power purchaser or the power seller shall not exceed the power available for sale, that is:

[0067]

[0068] in Indicates the type of electricity traded between electricity seller market member i and electricity buyer market member j;

[0069] Power_min is the transaction volume between market member i and market member j in declaration period t; t represents the minimum power demand in the power system during period t; R_buy i Q_load is the maximum allowable external power purchase ratio of the electricity seller market member i; i Represents the load demand power required by the power grid system where market member i is located; R_margin i represents the power reserve margin requirement of the power grid where market member i is located; Q_adjust i It represents the coordinated power generation capacity of the power grid where market member i is located.

[0070] Due to the complex variables and high degree of nonlinearity of the cross-regional electricity trading constraint conditions of the intelligent bionic optimization model, there are difficulties such as slow solution speed when using traditional operations research optimization methods to solve the model. Therefore, this application uses an artificial bee colony algorithm to solve the intelligent bionic optimization model.

[0071] Among them, the Artificial Bee Colony (ABC) algorithm is a new swarm intelligence optimization technology inspired by the foraging behavior of bees. The advantages of this algorithm are good adaptability to complex programs of problems, easy implementation, and certain stability, robustness and scalability. At the same time, the artificial bee colony algorithm can use its group behavior to conduct a comprehensive search during the optimization process. Therefore, when solving optimization problems, it has both good global search capabilities and strong parallelism and fast solution speed.

[0072] Specific, combined Figure 2 As shown, the specific steps of the artificial bee colony algorithm in step three are as follows:

[0073] (1) First, determine the population size (including the number of employed bees, observer bees, and scout bees), the maximum number of iterations, and other parameters. Then randomly generate initial nectar source locations, which correspond to the initial solutions to the optimization problem. For example, in a two-dimensional function optimization problem, assuming that the population size is n, then randomly generate n two-dimensional vectors as the initial nectar source locations, and the two components of each vector represent the horizontal and vertical coordinates in the two-dimensional space.

[0074] (2) The employed bees generate a new nectar source location based on the current nectar source location. Assume that the current nectar source location is x u , new nectar source location The calculation formula is usually where x v is another nectar source location randomly selected from the population, is a random number in the interval [-1.1].

[0075] Calculate the fitness value of the new nectar source location (for example, for function optimization problems, substitute the new location into the value obtained by the objective function) and compare it with the fitness value of the original nectar source location. If the fitness value of the new nectar source location is better, update the nectar source location; otherwise, keep the original nectar source location unchanged.

[0076] (3) The observer bees select the nectar source according to the probability based on the nectar source quality information (fitness value) transmitted by the employed bees. The probability calculation formula is usually:

[0077]

[0078] where p u is the fitness value of the u-th nectar source, and n is the number of nectar sources.

[0079] After selecting the nectar source, the observer bees, like the employed bees, conduct a local search near the nectar source, and the process of updating the nectar source location is the same as that of the employed bee stage.

[0080] (4) When the fitness value of a nectar source does not improve after a certain number of iterations, the employed bees will abandon this nectar source, and the scout bees will start looking for a new nectar source. The scout bees randomly generate a new nectar source location, which is randomly generated in the entire search space.

[0081] The method of generating the new nectar source location is similar to that in the initialization phase, which is to randomly generate a position vector that meets the requirements of the problem.

[0082] (5) When the maximum number of iterations is reached or other predetermined termination conditions are met (such as the optimal solution found reaches a certain accuracy requirement), the algorithm terminates and outputs the optimal solution.

[0083] Further, such as Figure 3 As shown, the present application also discloses an auxiliary decision-making device for power parameters based on an intelligent bionic optimization model, the device comprising:

[0084] The sample data collection module is used to collect and sort out the market supply and demand data, the margin of power that can be transmitted, and the data of market entities in each region, and obtain sample data at different collection periods every day;

[0085] An optimization model building module is used to establish an intelligent bionic optimization model based on sample data and inter-regional power transaction constraints, wherein the objective function of the intelligent bionic optimization model takes maximizing inter-regional transaction revenue as an optimization goal;

[0086] The optimization model solving module is used to solve the intelligent bionic optimization model using the artificial bee colony algorithm to obtain the optimal strategy for cross-regional transactions.

[0087] The device provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented by the method embodiment will not be described again here.

[0088] like Figure 4 As shown, the embodiment of the present application also provides an electronic device, including a processor and a memory, a program or instruction stored in the memory and executable on the processor, and when the program or instruction is executed by the processor, the following is achieved: Figure 1 The various processes of the method embodiment shown in the figure can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0089] The embodiment of the present application also provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by a processor, the above Figure 1 The various processes of the method embodiments described above can achieve the same technical effect, and will not be described again here to avoid repetition.

[0090] The present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the method embodiments described above can achieve the same technical effect, and will not be described again here to avoid repetition.

[0091] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0092] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0093] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another device, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0094] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; 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 present embodiment.

[0095] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0096] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, etc., various media that can store program codes.

[0097] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a device (which can be a terminal or a platform, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0098] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A power parameter auxiliary decision-making method based on an intelligent bionic optimization model, characterized in that: The steps include: Step 1: Collect and sort out the market supply and demand data, transmittable power margin and market entity data of each region to obtain sample data at different collection periods every day; Step 2: Establishing an intelligent bionic optimization model based on sample data and inter-regional power trading constraints, wherein the objective function of the intelligent bionic optimization model takes maximizing inter-regional trading revenue as the optimization goal; Step 3: Use the artificial bee colony algorithm to solve the intelligent bionic optimization model and obtain the optimal strategy for cross-regional transactions.

2. The power parameter decision-making auxiliary method according to claim 2, characterized in that: The objective function of the intelligent bionic optimization model is: in is the transaction volume of market member i and market member j in the declaration period t; Price_sell i,t is the electricity price reported by market member i in reporting period t; Price_buy j,t Price_cost is the electricity purchase price declared by market member j in declaration period t; i,j is the comprehensive transmission cost unit price between market member i and market member j; β i,j is the comprehensive network loss coefficient between market member i and market member j; N is the number of market members on the electricity purchasing side; M is the number of market members on the electricity selling side; T is the total number of reporting periods.

3. The power parameter decision-making auxiliary method according to claim 1, characterized in that: The inter-regional power trading constraints in step 2 include single subject role constraints and transmission path constraints; The single subject role constraint is that the same market member cannot simultaneously act as a power buyer and a power seller to conduct power transactions; The transmission path constraint requires that power transactions between market members must be conducted through existing transmission paths, and power transactions between market members that do not have physically connected transmission paths are not allowed.

4. The power parameter decision-making auxiliary method according to claim 1, characterized in that: The inter-regional power transaction constraint conditions in step 2 include transaction quantity constraint; The transaction volume constraint is that the transaction volume between the electricity seller and the electricity buyer cannot exceed the volume declared by both parties, and the transaction volume between the electricity seller and the electricity buyer cannot be a negative value.

5. The power parameter decision-making auxiliary method according to claim 1, characterized in that: The inter-regional power transaction constraint conditions in step 2 include transaction type constraints; The transaction type constraint is that the transaction volume between market members must be divided into power purchase transactions and power sales transactions.

6. The power parameter decision-making auxiliary method according to claim 5, characterized in that: The inter-regional power transaction constraints in step 2 include power purchase limit constraints and power sales limit constraints; The power purchase limit constraint is that the total power purchase amount of a single market member of the power purchaser and all the market members of the power sellers with whom it trades in the declaration period t does not exceed its own declared power amount; The power sales limit constraint is that the total power sales of a single market member of the power seller and all the market members of the power buyers with whom it trades during the reporting period t does not exceed its own reported power.

7. The power parameter decision-making auxiliary method according to claim 1, characterized in that: The inter-regional power transaction constraints in step 2 include power grid security constraints; The grid safety constraint is that the planned power transmission of the interconnection lines between regions cannot exceed the upper limit of the interconnection line capacity.

8. The power parameter decision-making auxiliary method according to claim 1, characterized in that: The inter-regional power trading constraints in step 2 include channel capacity constraints; The channel capacity constraint is that the minimum power demand in the power system is the minimum value of the difference between the load demand power and the total power generation in the power system and the power reserve margin.

9. The power parameter decision-making auxiliary method according to claim 1, characterized in that: The inter-regional power trading constraints in step 2 include power balance constraints: The power balance constraint is that the total power transaction of the power buyer or seller must meet the corresponding power grid power reserve margin requirement, that is, when the power delivered is greater than the power grid reserve demand, the total power transaction of the power buyer or seller does not exceed the saleable power.

10. An auxiliary decision-making device for power parameters based on an intelligent bionic optimization model, characterized in that: The device comprises: The sample data collection module is used to collect and sort out the market supply and demand data, the margin of power that can be transmitted, and the data of market entities in each region, and obtain sample data at different collection periods every day; An optimization model building module is used to establish an intelligent bionic optimization model based on sample data and inter-regional power transaction constraints, wherein the objective function of the intelligent bionic optimization model takes maximizing inter-regional transaction revenue as an optimization goal; The optimization model solving module is used to solve the intelligent bionic optimization model using the artificial bee colony algorithm to obtain the optimal strategy for cross-regional transactions.

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