An optimization method and system for calculating electricity quantity in the spot trading market

Abstract

The present invention discloses an optimization method and system for calculating electricity quantity in the spot trading market. The method includes: inputting the bidding electricity quantity and electricity price of all bidding power plants in each province into a pre-constructed intra-provincial bidding model, and using an improved simplex method to calculate the intra-provincial bidding model to obtain the surplus and deficit electricity quantities in each province; inputting the surplus and deficit electricity quantities in each province and the bidding curves of each province into a pre-constructed inter-provincial bidding model, and using an improved simplex method to calculate the inter-provincial bidding model to obtain the trading electricity quantities between provinces; the improved simplex method is when calculating the basic credible solution using pivot calculation, making the column where the entering variable is located the pivot column, the row where the leaving variable is located the pivot row, and using Gaussian elimination method to perform pivot elimination operation. The present invention simplifies the calculation process, ensures the calculation accuracy, and avoids the problem of reducing economic losses caused by precision errors.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching automation, and in particular to an optimization method and system for calculating electricity quantity in the spot trading market. Background Art

[0002] At present, the construction of the power sales side market in the power trading market has basically achieved the separation of power plants from the grid and competitive power generation. In the past two years, the trading volume in the power market has increased rapidly. In 2016, 20% of the total electricity consumption in the whole society was through market trading. However, there are still some problems in the current power market, such as the power trading mainly concentrated in the provincial market and the management mode being overly centralized. On the one hand, these problems make it difficult for the existing power market to meet the market demand. On the other hand, it is necessary to increase the cross-regional consumption of surplus renewable energy and promote the cleanization of energy. Therefore, at this stage, the calculation of electricity quantity in the spot market not only includes the calculation of the surplus and deficit electricity quantity within the province, but also includes the calculation of the trading electricity quantity between provinces. In order to promote the reasonable development of electricity quantity resources, the bidding in the trading electricity quantity spot market is introduced.

[0003] Due to the limited conditions of power plants, such as the constraints of unit output increase and maximum power generation, etc., which are all linear, the bidding curves reflecting electricity quantity trading given by power plants also conform to linear characteristics, and the objective function of the problem to be solved is a linear polynomial. Therefore, the optimal strategy of power plant bidding electricity quantity can be regarded as a linear programming problem and can be solved by the simplex method. By following the objective function of the optimal power plant bidding electricity quantity and solving the feasible solution of the linear programming problem, the feasible solution that makes the objective function value reach the maximum is the optimal solution. Usually, when writing a program to calculate the optimal solution of power plant bidding electricity quantity on a computer, it is very difficult to find the initial basic variables. Therefore, the "two-stage method" is often used, that is, first solve a basic feasible solution. However, the traditional method must calculate all columns of the coefficient matrix, the calculation process is cumbersome, and the accuracy will also decrease as the depth of pivot calculation increases. In the power market, a very small accuracy error will also cause huge economic losses, so it is necessary to improve the existing calculation method to improve the calculation accuracy. Summary of the Invention

[0004] In order to solve the above deficiencies in the prior art, the present invention provides an optimization method for calculating electricity quantity in the spot trading market, including:

[0005] Substitute the bidding electricity quantity and electricity price of all bidding power plants in each province into the pre-constructed provincial bidding model, and use the improved simplex method to calculate the provincial bidding model to obtain the surplus and deficit electricity quantity in each province;

[0006] Substitute the surplus and deficit electricity quantity in each province and the bidding curves of each province into the pre-constructed inter-provincial bidding model, and use the improved simplex method to calculate the inter-provincial bidding model to obtain the trading electricity quantity between provinces;

[0007] Among them, when using the improved simplex method to calculate the basic credible solution by pivot operation, the column where the entering variable is located is set as the pivot column, the row where the leaving variable is located is set as the pivot row, and the Gaussian elimination method is used to perform the pivot element operation.

[0008] Preferably, the construction of the intra-provincial bidding model includes:

[0009] Constructing an intra-provincial objective function with the minimum total intra-provincial bidding transaction amount obtained from the bidding electricity quantity and price of all bidding power plants within the province;

[0010] Constructing a bidding constraint equation system for the intra-provincial objective function based on power plant parameters;

[0011] Among them, the power plant parameters include: the maximum output limit, minimum output limit, and rising output speed limit of each bidding power plant.

[0012] Preferably, the bidding constraint equation system is shown as follows:

[0013]

[0014] In the formula, C i,j (t) represents the bid corresponding to power plant i in power segment j at time t, P i,max (t) represents the maximum power generation of the unit in power plant i at time t, down i represents the descending output speed of the unit in power plant i, Δt represents the time interval, n represents the total number of power segments, m represents the total number of time periods, raval day represents the total daily bidding electricity quantity.

[0015] Preferably, calculating the surplus and deficit electricity quantities in each province by using the improved simplex method for the intra-provincial bidding model includes:

[0016] Substituting the coefficient matrix of the bidding constraint equation system into the improved simplex method to obtain the bidding results of each power plant within the province;

[0017] Based on the bidding results of each power plant within the province and the obtained intra-provincial pre-demand electricity quantity and intra-provincial contract electricity quantity, obtaining the surplus and deficit electricity quantities in each province.

[0018] Preferably, the construction of the inter-provincial bidding model includes:

[0019] Constructing an inter-provincial objective function with the minimum total inter-provincial transaction amount as the goal;

[0020] Constructing constraint conditions for the inter-provincial objective function based on the surplus and deficit electricity quantities in each province and the bidding curves of each province.

[0021] Preferably, the constraint conditions are shown as follows:

[0022]

[0023] Where: M a,b represents the amount of electricity transmitted from Province a to Province b, represents the average transmitted electricity of the transmission channels between Province a and Province b within the set period, M max (a) represents the total average transmitted electricity of the transmission channels between Province a and external regions, K represents the number of provinces, raval day represents the daily competitive bidding electricity volume, and Δday represents the surplus or deficit of electricity within the province.

[0024] Preferably, the improved simplex method is used to calculate the inter-provincial bidding model to obtain the trading electricity volume between provinces, including:

[0025] Substitute the coefficient matrix of the constraint equation into the improved simplex method to obtain the trading electricity volume between provinces.

[0026] Preferably, the improved simplex method includes:

[0027] Judge whether the current basic feasible solution is the optimal solution. When the basic feasible solution is the optimal solution, obtain the current basic feasible solution; otherwise, select the entering variable and the leaving variable;

[0028] Let the column where the entering variable is located be the pivot column, and the row where the leaving variable is located be the pivot row. The first equation indicates that the element at the intersection of the pivot row and the pivot column is the pivot element, and the Gauss elimination method is used to perform the pivot element elimination operation to update the basic feasible solution;

[0029] Continue to judge whether the basic feasible solution is the optimal solution until the basic feasible solution is the optimal solution and the calculation ends.

[0030] Preferably, after obtaining the trading electricity volume between provinces, it further includes:

[0031] Based on the inter-provincial electricity trading algorithm in the smart contract, obtain the bidding strategy for the spot market.

[0032] Based on the same inventive concept, the present invention also provides an optimization system for calculating the electricity volume in the spot trading market, including:

[0033] An intra-provincial optimization module for substituting the bidding electricity volume and electricity price of all bidding power plants within each province into a pre-constructed intra-provincial bidding model, and using the improved simplex method to calculate the intra-provincial bidding model to obtain the surplus or deficit of electricity within each province;

[0034] An inter-provincial optimization module for substituting the surplus or deficit of electricity within each province and the bidding curves of each province into a pre-constructed inter-provincial bidding model, and using the improved simplex method to calculate the inter-provincial bidding model to obtain the trading electricity volume between provinces;

[0035] Among them, when using the improved simplex method to calculate the basic credible solution by pivot operation, the column where the entering variable is located is set as the pivot column, the row where the leaving variable is located is set as the pivot row, and the Gaussian elimination method is used to perform the pivot elimination operation.

[0036] Preferably, the in-province optimization module is specifically used for:

[0037] Determine whether the current basic feasible solution is the optimal solution. When the basic feasible solution is the optimal solution, obtain the current basic feasible solution; otherwise, select the entering variable and the leaving variable;

[0038] Set the column where the entering variable is located as the pivot column, the row where the leaving variable is located as the pivot row, and the first equation indicates that the element at the intersection of the pivot row and the pivot column is the pivot. Use the Gaussian elimination method to perform the pivot elimination operation to update the basic feasible solution;

[0039] Continue to determine whether the basic feasible solution is the optimal solution until the basic feasible solution is the optimal solution to end the calculation.

[0040] The technical solution provided by the present invention has the following beneficial effects:

[0041] The technical solution provided by the present invention brings the bidding electricity quantity and electricity price of all bidding power plants in each province into the pre-constructed in-province bidding model, and uses the improved simplex method to calculate the in-province bidding model to obtain the surplus and deficit electricity quantities in each province; brings the surplus and deficit electricity quantities in each province and the bidding curves of each province into the pre-constructed inter-province bidding model, and uses the improved simplex method to calculate the inter-province bidding model to obtain the trading electricity quantities between provinces; when using the improved simplex method to calculate the basic credible solution by pivot operation, the column where the entering variable is located is set as the pivot column, the row where the leaving variable is located is set as the pivot row, and the Gaussian elimination method is used to perform the pivot elimination operation. When performing the pivot operation in the present invention, the method of only using the column where the basic variable is located as the pivot column instead of using all columns as the pivot column is adopted, which greatly reduces the number of operations, reduces the loss of pivot operation accuracy, improves the calculation accuracy, and simplifies the calculation process while ensuring the calculation accuracy, avoiding the problem of economic losses caused by accuracy errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flowchart of an optimization method for calculating the electricity quantity in the spot trading market in the present invention;

[0043] Figure 2 It is a flowchart of the improved simplex method algorithm in the embodiment of the present invention. DETAILED DESCRIPTION

[0044] To better understand the present invention, the content of the present invention will be further described below in conjunction with the accompanying drawings of the specification and examples.

[0045] Embodiment 1: As Figure 1 shown, an optimization method for calculating the electricity quantity in the spot trading market provided by the present invention includes:

[0046] S1 Substitute the bidding electricity quantity and electricity price of all bidding power plants in each province into the pre-constructed provincial bidding model, and use the improved simplex method to calculate the provincial bidding model to obtain the surplus and deficit electricity quantities in each province;

[0047] S2 Substitute the surplus and deficit electricity quantities in each province and the bidding curves of each province into the pre-constructed inter-provincial bidding model, and use the improved simplex method to calculate the inter-provincial bidding model to obtain the trading electricity quantities between provinces;

[0048] The improved simplex method is that when calculating the basic credible solution using pivot rotation, the column where the entering variable is located is set as the pivot column, the row where the leaving variable is located is set as the pivot row, and the Gaussian elimination method is used to perform the pivot element operation.

[0049] The standard for dividing regions in the present invention is: Set the country as the first level, and set levels in sequence according to the power grid distribution form of the country;

[0050] Taking China as an example in this embodiment, set China as the first level; multiple provinces under China are the second level; each province corresponds to multiple cities, which are the third level; the counties corresponding to each city are the fourth level; taking the second level as an example, it includes Jiangsu, Anhui, Zhejiang, Fujian, etc. Although this embodiment takes China as an example, the present invention is not limited to China only. The mentioned provinces correspond to the second level in the power grid.

[0051] In the power plant bidding model, the limiting conditions of the power plant, such as the unit's rising output speed, maximum power generation, etc. are all linear; the bidding curve given by the power plant is a non-decreasing linear curve, which conforms to the linear characteristics; the objective function of the problem to be solved is also a linear polynomial. Therefore, finding the optimal strategy for the bidding electricity quantity of power plants within a province can be regarded as a linear programming problem, and it is considered to use the simplex method to solve this linear programming problem.

[0052] The basic idea of the simplex method is: First, find a basic feasible solution, identify it to see if it is the optimal solution; if not, then convert to a better basic feasible solution according to certain rules, and then identify it; if it is still not, then convert again, and repeat this step. Since the number of basic feasible solutions is limited, the optimal solution of the problem can be obtained after a finite number of conversions.

[0053] Objective function:

[0054] Constraint equation:

[0055] The coefficient of x j in the objective function is the value coefficient c j, control variables x1, x2, …, x n satisfy the constraint conditions. The column vector X = (x1 + x2 + … + x n ) T is a feasible solution to the linear programming problem, and the feasible solution that maximizes the objective function value is the optimal solution. Usually, when writing this program on a computer, it is difficult to find the initial basic variables. Therefore, the "two-phase method" is often used, that is, first solve a basic feasible solution, and then use the above method to find the optimal solution.

[0056] The improved simplex method provided by the present invention includes:

[0057] Step 1: Use the classical simplex method to find a feasible solution and input it into the system as the initial input as the initial value.

[0058] Step 2: Convert the linear programming problem into the standard form;

[0059] To make all constraint conditions become equalities, slack variables x1, x2, …, x n are added to the equalities of the constraint equations, and then it is converted into the standard form.

[0060] Step 3: Select an initial feasible solution; express the basic variables in terms of non-basic variables, and at the same time set the non-basic variables to 0, then a basic feasible solution is obtained.

[0061] Step 4: Determine whether it is the optimal solution, that is, determine whether there are still non-basic variables with positive coefficients in the objective function expression; if so, it means that the optimal solution has not been reached, and continue to run the following steps; if not, it means that the optimal solution has been obtained, end the calculation, and output the optimal strategy.

[0062] Step 5: Select the entering variable and the leaving variable; the method for selecting the entering variable is: take the basic variable corresponding to the largest number among the positive coefficients of the non-basic variables in the objective function. The method for selecting the leaving variable is: calculate the ratio of the right-hand side constant in each constraint equation to the coefficient of the entering variable in the constraint equation, and the leaving variable is the basic variable corresponding to the smallest non-negative ratio.

[0063] Step 6: Perform pivot calculation to obtain a basic feasible solution; let the column where the entering variable is located be the pivot column, the row where the leaving variable is located be the pivot row, and the first equation indicates that the element at the intersection of the pivot row and the pivot column is the pivot; then use the Gaussian elimination method to perform elimination operations to obtain a basic feasible solution, and return to Step 3 to determine whether this feasible solution is the optimal solution.

[0064] Step 7: Obtain the optimal solution for the power plant bidding through judgment.

[0065] Step 8: After obtaining the optimal solution, the calculation can be stopped and the result can be stored in the database.

[0066] The present invention proposes to use the solution of the classical simplex method as the initial feasible solution, and simplifies the system matrix in the simplex method. That is, during the pivot operation, only the column where the basic variable is located is used as the pivot column, rather than all columns, which simplifies the number of calculations step by step, greatly reduces the number of operations, and at the same time ensures that the calculation accuracy is not affected.

[0067] In step three of the technical solution provided by the present invention, when calculating the basic feasible solution, the improved simplex method is directly used as the module for finding the optimal solution and substituted into the algorithm for the competitive bidding of power plants within the province.

[0068] S1 Substitute the bidding electricity volume and electricity price of all competitive bidding power plants in each province into the pre-constructed intra-provincial bidding model, and use the improved simplex method to calculate the intra-provincial bidding model to obtain the surplus and deficit electricity volume in each province, including:

[0069] (1) Obtain the bidding constraint equation system according to the power plant parameters:

[0070]

[0071] In the formula, C i,j (t) represents the bid price of power plant i in power segment j at time t, P i,max (t) represents the maximum power generation of the unit in power plant i at time t, down i represents the descending output speed of the unit in power plant i, Δt represents the time interval, raval day represents the total daily bidding electricity volume.

[0072] (2) Calculate the objective function shown in the following formula:

[0073]

[0074] In the formula, P i,raval (t) represents the bidding electricity volume of power plants within the province, represents the average unit price of each power plant, and z represents the total intra-provincial bidding transaction amount.

[0075] (3) Substitute the coefficient matrix of the constraint equation into the improved simplex method to obtain the bidding result P i,raval (t) of each power plant, that is, the best strategy for intra-provincial bidding power transactions.

[0076] (4) Based on the bidding results of each power plant and combined with the input provincial pre-demand electricity volume and provincial contract electricity volume, obtain the intra-provincial surplus and deficit electricity volume Δday.

[0077] S2 Substitute the surplus and deficit electricity volume in each province and the bidding curve of each province into the pre-constructed inter-provincial bidding model, and use the improved simplex method to calculate the inter-provincial bidding model to obtain the transaction electricity volume between provinces, including:

[0078] (1) Based on the provincial quotation curves and inter-provincial congestion parameters, the following constraint equations are obtained:

[0079]

[0080] In the above equations, M a,b represents the electricity quantity transmitted from province a to province b, represents the average transmitted electricity quantity of the transmission channel between province a and province b within the set period, and M max (a) represents the total average transmitted electricity quantity of the power transmission channels between province a and other regions, K represents the number of provinces, and raval day represents the daily competitive bidding electricity quantity, and Δday represents the surplus or deficit of electricity within the province.

[0081] (2) Calculate the following objective function:

[0082]

[0083] In the formula, M a,b represents the electricity quantity transmitted from province a to province b, represents the average price of the transmitted electricity quantity, and z represents the total transaction amount.

[0084] (3) Substitute the coefficient matrix of the constraint equation into the improved simplex method to obtain the transaction electricity quantities of each province, and then find the optimal transaction strategies of each province.

[0085] Each province refers to the optimal transaction strategy, conducts transactions using smart contracts, and stores the transaction results in the database.

[0086] Based on the traditional simplex method, the present invention ensures the optimal goal of the power plant's competitive bidding electricity quantity. Each power plant gives its own bidding curve for competitive bidding, and uses smart contracts to generate the optimal bidding plan according to the improved simplex method. The present invention simplifies the calculation process, ensures the calculation accuracy, and avoids the economic loss problem caused by precision errors. When performing pivot operations, the method of only using the column where the basic variable is located as the pivot column instead of all columns is adopted, which greatly reduces the number of operations, reduces the precision loss of pivot operations, improves the calculation accuracy, and has positive guiding significance for renewable energy to enter the market.

[0087] Taking six provinces as an example in the embodiment of the present invention, the daily electricity demand of each province varies greatly, aiming to better reflect the actual individual differences of each province within the region. Table 1 shows the model parameters used in this example.

[0088] Table 1 Main parameters of the power plants participating in the bidding within the province

[0089]

[0090]

[0091] List the basic power generation parameters set in the test model for the provincial competitive power plants, including the maximum output limit, minimum output limit, and rising output speed limit of each competitive power plant. The second row of Table 1 indicates that in the test model, the maximum output limit of the first competitive power plant in Province 1 is 3000 MWh, the minimum output limit is 1500 MWh, and the rising output speed limit is 15 MWh / h.

[0092] Table 2 Provincial Competitive Electricity Transaction Results

[0093]

[0094]

[0095] Table 2 shows the provincial competitive electricity transaction results obtained by applying the present invention. As shown in the second row of Table 2, Province 1 purchases 2875 MWh of competitive electricity at a unit price of 2 yuan per kWh from Competitive Power Plant a, with a transaction amount of 57.5 million yuan. The total transaction amount for Province 1 to purchase competitive electricity from 3 provincial competitive power plants is 137.09 million yuan. From Table 3, it can be seen that each province purchases the corresponding electricity from 3 competitive electricity distribution power plants and achieves the optimal effect of the total transaction amount.

[0096] Table 3 Inter-provincial Power Transaction Results

[0097] Province Declared electricity volume / MWh Purchased (sold) electricity volume / MWh Purchased (sold) transaction amount / 10,000 yuan 1 2470 (profit) 322 (sold) 644 (sold) 2 1634 (profit) 328 (sold) 1640 (sold) 3 1252 (profit) 338 (sold) 2028 (sold) 4 467 (profit) 352 (sold) 2816 (sold) 5 668 (shortage) 698 (purchased) 3711 (purchased) 6 613 (shortage) 642 (purchased) 3417 (purchased)

[0098] Table 3 records the inter-provincial power transaction results. As shown in the second row of Table 3, Province 1 reports a production surplus electricity of 2470 MWh, sells 322 MWh of electricity, and the transaction amount of the sold electricity is 6.44 million yuan. From Table 3, it can be seen that through the operation of the smart contract, each province's power generation has its own surplus and deficit. Each province publishes the power generation surplus and deficit amount, and through the inter-provincial power transaction algorithm in the smart contract, conducts power trading and obtains the trading electricity volume and the corresponding transaction amount.

[0099] The present invention obtains the optimal bidding results of the power plants in 6 provinces by establishing a 3-power-plant system in six provinces, cooperating with the bidding parameters of the power plants and the corresponding constraint conditions, realizes the optimization of the interests of the power market transaction, and ensures the optimal allocation of the system computing resources.

[0100] The technical solution provided by the present invention can also be extended to the bidding strategies of inter-provincial power plants, which has guiding suggestions for the construction of the cross-provincial power market, and also gives the bidding results of inter-provincial power plants, which has a positive promoting effect on the cross-regional consumption of efficient renewable energy and effectively supports the bidding system of the power market.

[0101] Embodiment 2: Based on the same inventive concept, an embodiment of the present invention further provides an optimization system for calculating the electricity quantity in the spot trading market, including:

[0102] The intra-provincial optimization module is used to input the bidding electricity quantity and electricity price of all bidding power plants within each province into a pre-constructed intra-provincial bidding model, and use the improved simplex method to calculate the intra-provincial bidding model to obtain the surplus and deficit electricity quantities within each province;

[0103] The inter-provincial optimization module is used to input the surplus and deficit electricity quantities within each province and the quotation curves of each province into a pre-constructed inter-provincial bidding model, and use the improved simplex method to calculate the inter-provincial bidding model to obtain the trading electricity quantities between provinces;

[0104] The improved simplex method is that when calculating the basic credible solution using pivot calculation, the column where the entering variable is located is set as the pivot column, the row where the leaving variable is located is set as the pivot row, and the Gaussian elimination method is used to perform the pivot elimination operation.

[0105] In the embodiment, the intra-provincial optimization module is specifically used for:

[0106] Judge whether the current basic feasible solution is the optimal solution. When the basic feasible solution is the optimal solution, obtain the current basic feasible solution; otherwise, select the entering variable and the leaving variable;

[0107] Set the column where the entering variable is located as the pivot column, the row where the leaving variable is located as the pivot row. The first equation indicates that the element at the intersection of the pivot row and the pivot column is the pivot, and the Gaussian elimination method is used to perform the pivot elimination operation to update the basic feasible solution;

[0108] Continue to judge whether the basic feasible solution is the optimal solution until the basic feasible solution is the optimal solution and the calculation ends.

[0109] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the functions specified in one or more flows and / or blocks Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0111] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one or more flows and / or blocks Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or blocks Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0113] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval of the application.

Claims

1. An optimization method for calculating the electricity quantity in the spot trading market, characterized in that, Including: Input the bidding electricity quantity and price of all bidding power plants in each province into the pre - constructed intra - provincial bidding model, and use the improved simplex method to calculate the intra - provincial bidding model to obtain the surplus and deficit electricity quantities in each province; Input the surplus and deficit electricity quantities in each province and the bidding curves of each province into the pre - constructed inter - provincial bidding model, and use the improved simplex method to calculate the inter - provincial bidding model to obtain the trading electricity quantities between provinces; Among them, in the improved simplex method, when calculating the basic feasible solution using pivot operations, the column where the entering variable is located is set as the pivot column, the row where the leaving variable is located is set as the pivot row, and the Gauss elimination method is used to perform pivot elimination operations; The construction of the intra - provincial bidding model includes: Construct an intra - provincial objective function with the minimum total intra - provincial bidding transaction amount obtained from the bidding electricity quantity and price of all bidding power plants in the province; Construct a bidding constraint equation system for the intra - provincial objective function based on power plant parameters; Among them, the power plant parameters include: the maximum output limit, minimum output limit, and rising output speed limit of each bidding power plant; The bidding constraint equation system is shown as follows: Where C i,j (t) represents the quotation of power plant i in power segment j during period t, P i,max (t) represents the maximum power generation of the units in power plant i, down i represents the down-ramping rate of the units in power plant i, Δt represents the time interval, n represents the total number of power segments, m represents the total number of periods, raval day represents the total electricity quantity for daily competitive bidding; The construction of the inter - provincial bidding model includes: Construct an inter - provincial objective function with the minimum total inter - provincial transaction amount as the goal; Construct constraint conditions for the inter - provincial objective function based on the surplus and deficit electricity quantities in each province and the bidding curves of each province; The constraint conditions are shown as follows: Where: M a,b represents the amount of electricity transmitted from Province a to Province b, represents the average transmitted electricity of the transmission channel between Province a and Province b within a set period, M max (a) represents the total average transmitted electricity of the power transmission channels between Province a and other provinces, K represents the number of provinces, raval day represents the daily auction electricity, and Δday represents the power surplus or deficit within the province.

2. The method according to claim 1, wherein The calculation of the intra - provincial bidding model using the improved simplex method to obtain the surplus and deficit electricity quantities in each province includes: Substitute the coefficient matrix of the bidding constraint equation system into the improved simplex method to obtain the bidding results of each power plant in the province; Based on the bidding results of each power plant in the province and the obtained intra - provincial pre - demand electricity quantity and intra - provincial contract electricity quantity, obtain the surplus and deficit electricity quantities in each province.

3. The method according to claim 1, wherein The calculation of the inter - provincial bidding model using the improved simplex method to obtain the trading electricity quantities between provinces includes: Substitute the coefficient matrix of the constraint equation into the improved simplex method to obtain the trading electricity quantities between provinces.

4. The method according to any one of claims 1 or 3, characterized in that The improved simplex method includes: Judge whether the current basic feasible solution is the optimal solution. When the basic feasible solution is the optimal solution, obtain the current basic feasible solution; otherwise, select the entering variable and the leaving variable; Set the column where the entering variable is located as the pivot column, the row where the leaving variable is located as the pivot row. The first equation indicates that the element at the intersection of the pivot row and the pivot column is the pivot, and use the Gauss elimination method to perform pivot elimination operations to update the basic feasible solution; Continue to judge whether the basic feasible solution is the optimal solution until the basic feasible solution is the optimal solution to end the calculation.

5. The method according to claim 1, wherein After obtaining the trading electricity quantities between provinces, it also includes: Based on the inter - provincial power trading algorithm in the smart contract, obtain the bidding strategy in the spot market.

6. An optimized system for calculating the electricity quantity in the spot trading market, characterized in that, Including: An intra - provincial optimization module for inputting the bidding electricity quantity and price of all bidding power plants in each province into the pre - constructed intra - provincial bidding model, and using the improved simplex method to calculate the intra - provincial bidding model to obtain the surplus and deficit electricity quantities in each province; An inter - provincial optimization module for inputting the surplus and deficit electricity quantities in each province and the bidding curves of each province into the pre - constructed inter - provincial bidding model, and using the improved simplex method to calculate the inter - provincial bidding model to obtain the trading electricity quantities between provinces; Among them, when using the improved simplex method to calculate the basic credible solution by pivot operation, the column where the entering variable is located is taken as the pivot column, the row where the leaving variable is located is taken as the pivot row, and the Gaussian elimination method is used to perform the pivot elimination operation; The construction of the intra-provincial bidding model includes: Constructing an intra-provincial objective function with the minimum total intra-provincial bidding transaction amount obtained from the bidding electricity quantity and price of all bidding power plants within the province; Constructing a bidding constraint equation set for the intra-provincial objective function based on power plant parameters; Among them, the power plant parameters include: the maximum output limit, the minimum output limit, and the rising output speed limit of each bidding power plant; The bidding constraint equation set is shown as follows: Where C i,j (t) represents the quotation of power plant i in power segment j during time period t, and P i,max (t) represents the maximum power generation of the unit in power plant i, down i represents the down - ramp rate of the unit in power plant i, Δt represents the time interval, n represents the total number of power segments, m represents the total number of time periods, and raval day represents the total daily auction electricity volume; The construction of the inter-provincial bidding model includes: Constructing an inter-provincial objective function with the minimum total inter-provincial transaction amount as the goal; Constructing constraint conditions for the inter-provincial objective function based on the surplus and deficit electricity quantities within each province and the bidding curves of each province; The constraint conditions are shown as follows: Where: M a,b represents the amount of electricity transmitted from Province a to Province b, represents the average transmitted electricity of the transmission channel between Province a and Province b within the set period, M max (a) represents the total average transmitted electricity of the power transmission channels between Province a and other provinces, K represents the number of provinces, raval day represents the daily bid electricity, and Δday represents the surplus or deficit of electricity within the province.

7. The system according to claim 6, characterized in that, The intra-provincial optimization module is specifically used to determine whether the current basic feasible solution is the optimal solution. When the basic feasible solution is the optimal solution, obtain the current basic feasible solution; otherwise, select the entering variable and the leaving variable; take the column where the entering variable is located as the pivot column, the row where the leaving variable is located as the pivot row, and the first equation indicates that the element at the intersection of the pivot row and the pivot column is the pivot, and use the Gaussian elimination method to perform the pivot elimination operation to update the basic feasible solution; Continue to determine whether the basic feasible solution is the optimal solution until the basic feasible solution is the optimal solution and the calculation ends.

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