CN116505596A - A multi-regional power system cross-regional support time-series production simulation method and system - Google Patents

Abstract

The invention provides a multi-region power system cross-region support time sequence production simulation method and system, wherein the method comprises the following steps: based on the established conventional unit output model and new energy output sequential model, randomly generating simulation output scenes of the multi-region power system in any time sequence according to conventional unit information and new energy unit information to serve as sampling scenes for cross-region support time sequence production simulation; and based on each sampling scene, carrying out time sequence production simulation on the multi-region power system according to the established tie fault probability model and the tie transregional support model, and determining a tie transregional power transmission result and a regional power balance result. According to the method and the system, the randomness of the new energy, the conventional unit and the connecting lines is considered, the time sequence production simulation is carried out on the multi-region power system, and the simulation of the reliability of the contribution output of the new energy under the future running condition of the multi-region power system is realized.

Description

A method and system for simulating cross-region support sequential production of multi-regional power systems

Technical Field

The present invention relates to the field of random production simulation of multi-regional power systems, and more specifically, to a method and system for simulating cross-regional support sequential production of multi-regional power systems.

Background Art

Power system random production simulation can take into account random fluctuations in load and random outages of units, simulate the power generation dispatching process in actual operation of the power system, and calculate load loss indicators. It is an important tool for reliability assessment. At present, there are many mature and commonly used power system random production simulation methods, including semi-invariant method, frequency-duration method and equivalent power function method, which have been widely used in traditional power systems.

When conducting production simulation in the framework of power generation taking into account renewable energy, the unit dispatching process must not only meet the fluctuation of load, but also consider the impact of renewable energy. When only considering the randomness of renewable energy output, the uncertainty of forced shutdown of conventional units is still handled according to the model that retains sufficient accident backup. The system power balance under the influence of renewable energy can be analyzed in a targeted manner. Through the cross-regional support model of multi-regional power system, the transmission capacity of power between regions can be stochastically simulated, the networking benefits of the interconnected system can be fully utilized, and the new energy consumption indicators can be evaluated more comprehensively. Compared with the current deterministic principle of ensuring output to participate in power balance, this random method can provide more information about the reliability of renewable energy contribution to power, and the time consumption is acceptable. It has certain reference significance for the guidance and planning of new power systems with a gradually increasing proportion of renewable energy output under the "dual carbon" goal.

Summary of the invention

In order to solve the technical problems in the prior art that the output of new energy in the power system is not fully considered according to the deterministic principle of ensuring output to participate in power balance, and the evaluation flexibility is insufficient, the present invention provides a multi-regional power system cross-regional support timing production simulation method and system.

According to one aspect of the present invention, the present invention provides a method for simulating cross-region support sequential production of a multi-region power system, the method comprising:

Obtain the partition data and tie line data of the multi-regional power system, as well as the conventional unit information and new energy unit information in each partition;

Based on the established conventional unit output model and the new energy output sequential model, a plurality of simulated output scenarios of the multi-regional power system in any time series are randomly generated according to the conventional unit information and the new energy unit information, and the simulated output scenarios are used as sampling scenarios for cross-regional support sequential production simulation;

Based on the sampling scenario of each cross-region support sequential production simulation, a sequential production simulation is performed on the multi-region power system according to the established tie line fault probability model and tie line cross-region support model to determine the tie line cross-region transmission result and the sub-region power balance result;

Based on the inter-regional transmission results of the interconnection lines and the sub-regional electricity balance results of each sampling scenario of the inter-regional support sequential production simulation, the probability evaluation index considering the supply guarantee and new energy consumption level of the multi-regional power system is calculated and output.

According to another aspect of the present invention, the present invention provides a multi-regional power system cross-regional support time sequence production simulation system, the system comprising:

A data acquisition module is used to acquire the partition data and tie line data of the multi-region power system, as well as the conventional unit information and new energy unit information in each partition;

A sampling scenario module, for randomly generating a plurality of simulated output scenarios of the multi-regional power system in any time series based on the established conventional unit output model and the renewable energy output sequential model according to the conventional unit information and the renewable energy unit information, and using the simulated output scenarios as sampling scenarios for cross-region support sequential production simulation;

The inter-regional support module is used to perform a time-series production simulation on the multi-regional power system based on the sampling scenario of each inter-regional support time-series production simulation according to the established tie line failure probability model and tie line inter-regional support model, and determine the tie line inter-regional transmission result and the sub-regional power balance result;

The output result module is used to calculate and output the probability evaluation index considering the supply guarantee and new energy consumption level of the multi-regional power system based on the inter-regional transmission results of the interconnection lines and the sub-regional power balance results of the sampling scenario of each inter-regional support timing production simulation.

The present invention provides a method and system for simulating cross-regional support sequential production of a multi-regional power system, wherein the method comprises: obtaining partition data and interconnection line data of the multi-regional power system, as well as conventional unit information and new energy unit information in each partition; based on the established conventional unit output model and new energy output sequential model, randomly generating a plurality of simulated output scenarios of the multi-regional power system in any time series according to the conventional unit information and new energy unit information, and using the simulated output scenarios as sampling scenarios for cross-regional support sequential production simulation; based on each sampling scenario of cross-regional support sequential production simulation, performing sequential production simulation on the multi-regional power system according to the established interconnection line fault probability model and interconnection line cross-regional support model, and determining interconnection line cross-regional transmission results and partition electricity balance results; according to the interconnection line cross-regional transmission results and partition electricity balance results of each sampling scenario of cross-regional support sequential production simulation, calculating and outputting probability evaluation indicators considering the supply guarantee and new energy consumption level of the multi-regional power system. The method and system simultaneously consider the randomness of new energy, conventional units and interconnection lines, and perform time-series production simulation on a multi-regional power system through an interconnection line failure probability model and an interconnection line cross-regional support model, thereby achieving a simulation of the reliability of the output contribution of new energy under the future operation of the multi-regional power system, which has important reference significance for the guiding planning of new power systems with a gradually increasing proportion of new energy output.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

FIG1 is a flow chart of a method for simulating cross-regional support sequential production of a multi-regional power system according to a preferred embodiment of the present invention;

2 is a flow chart of performing sequential production simulation on a multi-regional power system based on a plurality of sampling scenarios of cross-regional support sequential production simulation according to a preferred embodiment of the present invention;

3 is a flow chart of a multi-regional power system cross-region support sequential production simulation method according to another preferred embodiment of the present invention;

FIG4 is a schematic diagram of a topological structure of a multi-regional power system according to another preferred embodiment of the present invention;

FIG5 is a schematic diagram of a cross-regional power transmission result of a tie line according to another preferred embodiment of the present invention;

FIG6 is a schematic diagram of another cross-regional power transmission result of a tie line according to another preferred embodiment of the present invention;

7 is a schematic diagram of an evaluation index sampling result of a multi-regional power system cross-regional support sequential production simulation according to another preferred embodiment of the present invention;

FIG8 is a schematic structural diagram of a multi-regional power system cross-region support sequential production simulation system according to a preferred embodiment of the present invention.

DETAILED DESCRIPTION

Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units/elements are marked with the same reference numerals.

Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

Exemplary Method 1

Fig. 1 is a flow chart of a multi-regional power system cross-region support sequential production simulation method according to a preferred embodiment of the present invention. As shown in Fig. 1 , the multi-regional power system cross-region support sequential production simulation method described in this preferred embodiment starts from step 101 .

In step 101, the partition data and tie line data of the multi-region power system, as well as the conventional unit information and the new energy unit information in each partition are obtained.

In step 102, based on the established conventional unit output model and the new energy output sequential model, a plurality of simulated output scenarios of the multi-regional power system in any time series are randomly generated according to the conventional unit information and the new energy unit information, and the simulated output scenarios are used as sampling scenarios for cross-region support sequential production simulation.

In this preferred embodiment, the conventional unit output model is obtained after training and verifying the established conventional unit output model based on the historical output data of the conventional unit, and the model parameters are determined. The number of failures of the established conventional unit output model in the set time period follows the Poisson distribution, and the duration of the unit failure follows the exponential distribution. The new energy output sequential model is also generated based on the same principle, which will not be repeated here.

In step 103, based on the sampling scenario of each inter-regional support sequential production simulation, a sequential production simulation is performed on the multi-regional power system according to the established interconnection line failure probability model and interconnection line inter-regional support model to determine the interconnection line inter-regional transmission results and the partition power balance results.

Preferably, based on the sampling scenario of each cross-region support sequential production simulation, the multi-region power system is subjected to sequential production simulation according to the established interconnection line failure probability model and interconnection line cross-region support model to determine the interconnection line cross-region transmission results and the partition power balance results.

Fig. 2 is a flow chart of performing sequential production simulation on a multi-regional power system based on a plurality of sampling scenarios of cross-regional support sequential production simulation according to a preferred embodiment of the present invention. As shown in Fig. 2, performing sequential production simulation on a multi-regional power system based on a plurality of sampling scenarios of cross-regional support sequential production simulation according to a preferred embodiment of the present invention starts from step 201.

In step 201, the nth sampling scenario of cross-region support sequential production simulation is selected, where 1≤n≤N. In this preferred embodiment, the total number of sampling scenarios generated by random simulation based on conventional unit output model and new energy output sequential model is N.

In step 202, the effect of the inter-regional interconnection line is ignored, and the independent power balance of the new energy in each sub-region is performed to determine the first power surplus/gap of each sub-region.

In step 203, according to the principle of balancing the utilization hours of conventional units in the multi-regional power system, the average utilization hours of conventional units in each zone are determined, and the second power surplus/gap of each zone is calculated in combination with the first power surplus/first power gap.

Since there are many mature methods in the prior art for independently balancing the power of a region and determining the power surplus/shortage, they will not be described here.

In step 204, based on the established tie line fault probability model, tie line simulated fault results of the multi-regional power system are randomly generated.

Preferably, the randomly generating the tie line fault simulation result of the multi-regional power system based on the established tie line fault probability model includes:

For each tie line of the multi-regional power system, according to the time series of the sampling scenario of the n-th cross-regional support sequential production simulation, tie line simulation fault data of the tie line is randomly generated based on the established tie line fault probability model, wherein the tie line fault probability model includes a tie line fault number probability model and a tie line fault state maintenance time probability model, specifically:

The number of tie line faults follows Poisson distribution, and the expression of the tie line fault probability model is:

Where k represents the number of failures in the corresponding time series determined based on the historical failure data of the corresponding time series, λ represents the average number of failures in the corresponding time series determined based on the historical failure data of the corresponding time series, and P(X=k) represents the probability when the number of failures in the corresponding time series determined based on the historical failure data of the corresponding time series is k;

The tie line fault state maintenance time follows an exponential distribution, and the expressions of the tie line fault state maintenance time probability models f(x) and F(x) are:

Wherein, x represents the fault state maintenance time of the corresponding time series determined based on the historical fault data of the corresponding time series, f(x) represents the density function of the fault state maintenance time distribution, F(x) represents the probability distribution function of the fault state maintenance time distribution, θ represents the expected value of the distribution, h represents the average fault duration of the corresponding time series determined based on the historical fault data of the corresponding time series, SF represents the operation coefficient of the tie line in the multi-regional power system, EFOR represents the equivalent forced outage rate, and T represents the duration of the corresponding time series.

In step 205, the role of inter-regional interconnection lines is taken into consideration, and based on the transmission constraints in the interconnection line data and the simulated fault results of the interconnection lines, as well as the second power surplus/second power gap of each partition, the established interconnection line inter-regional support model is used to determine the interconnection line inter-regional transmission results, and the final power surplus/gap of each partition is obtained.

Preferably, the cross-region tie line effect is considered, and according to the transmission constraints in the tie line data and the tie line simulated fault results, as well as the second power surplus/second power gap of each partition, the established tie line cross-region support model is used to determine the tie line cross-region transmission result, wherein the objective function of the tie line cross-region support model is obtained by minimizing the tie line active loss and superimposing the square of the power deviation of the partition with power gap, and the expression of the objective function is:

Where, P _EX —— tie line power arrangement vector;

P _ZN ——the incoming external power arrangement vector of the zone;

N _EX ——Number of contact lines;

N _ZN – number of partitions;

P _EX.i ——Power arrangement value of the ith tie line, which defines the flow from the sending end to the receiving end as the positive direction;

P _ZN.i ——the value of the external power received by the ith zone;

D _ZN.i ——The power supply gap of the ith zone. A value greater than zero indicates a gap, and a value less than zero indicates the ability to provide external support;

k _i ——The coefficient of the power term of the ith tie line, taken as , when the resistance and tie line voltage are known, R/U ² can be taken;

The objective function needs to satisfy the equality constraint that the power received by each partition is the sum of the powers arranged by the tie lines related to it. The equality expression is as follows:

Wherein, j represents the jth tie line connected to the i-th partition in the multi-region power system;

The objective function also needs to satisfy the inequality constraint, that is, the transmission power of each tie line is subject to the forward and reverse limit red beans, and the input power of each partition is subject to the partition power gap constraint. The expression of the inequality is as follows:

Where, P _EXi.pos is the upper limit of the forward transmission power of the ith tie line;

P _EXi.neg ——the upper limit of the reverse transmission power of the ith tie line;

D _i ——power gap of the ith partition. When D _i is greater than 0, it means there is a gap. When D _i is less than 0, it means there is power redundancy.

For the objective function with equality constraints and inequality constraints, by introducing Lagrange multipliers, an objective function expression for eliminating the equality constraints is obtained;

According to the Lagrange extreme value condition, a linear equation of the objective function expression that eliminates the equality constraint is generated, and the linear equation is solved by using the elimination method of the selected principal element;

When all solutions of the linear equations are within a preset constraint range and satisfy the inequality constraint, the solutions of the linear equations are determined as the inter-regional power transmission results of the interconnection lines of the multi-regional power system.

In this preferred embodiment, the tie line cross-region support model is a quadratic optimization problem with linear equality constraints. Therefore, the equality constraints can be superimposed on the objective function by introducing Lagrange multipliers. However, unlike the general quadratic optimization problem with linear equality constraints, for the tie line cross-region support model, when the incoming power cannot meet the gap, the inequality constraints of the incoming power variable will not be satisfied. Therefore, strictly speaking, there are differences in the solution of the general problem and the tie line cross-region support model in terms of inequality constraints. Therefore, when solving the general problem, the inequality constraints of the tie line cross-region support model should be fully considered.

The expression of the first mathematical model with equality constraints is as follows:

Where, X is an n-dimensional real variable;

a _i , b _i , c _i ——coefficients of the quadratic term of variable x _i . The three coefficients may be 0 at the same time.

A _m×n ——coefficient matrix of linear equality constraints, m×n dimensions;

B——constant vector on the right side of the equality constraint, m-dimensional;

X _min — is the lower limit of variable X;

X _max ——is the upper limit of variable X;

Introducing m Lagrange multipliers and eliminating the equality constraints, the second mathematical model expression is as follows:

According to the Lagrange extreme value condition, the following first linear equation can be obtained:

Arranging the above linear equations gives the following second linear equation:

The above coefficient matrix is solved by using the elimination method of the selected pivot element. When the coefficient matrix is singular, the corresponding columns and rows are ignored, and the corresponding variables are set to 0 to continue the elimination operation;

After solving the variable X, determine whether it is within the set constraint range [ _Xmin , _Xmax ]. If _xi exceeds the range, _xi takes the upper or lower limit value and substitutes it as a known quantity into the first mathematical model expression. The coefficient matrix of the linear equality constraint and the second linear equation are reduced in dimension and solved again. Repeat this process until all the solved variables X are within the constraint range or are all determined.

In this preferred embodiment, the inter-regional power transmission of the interconnection lines is equivalent to the overall dispatch of power in the multi-regional power system. Therefore, it is obvious to determine the final power surplus/shortage of each partition by combining the second power surplus/shortage of each partition and the inter-regional power transmission results of the interconnection lines, which will not be repeated here.

In step 206, according to the pre-established internal output rules of the sub-zones and the inter-zone power transmission results of the interconnection lines, the unit output combination of each sub-zone is determined, and the unit output combination is the sub-zone power balance result, wherein the unit output combination includes at least one of conventional units and new energy units.

Preferably, according to the pre-established internal output rules of the sub-areas, the output combination of the units in each sub-area is determined according to the inter-area power transmission result of the tie line, wherein the internal output rules of the sub-areas include:

According to the inter-regional power transmission results of the tie line, for conventional units and new energy units in the sub-regions that need to output power across regions, zero-carbon units are given priority;

When pumped storage is present, the output of zero-carbon units and pumped storage is arranged comprehensively;

When the above output cannot meet the cross-regional power output requirements, thermal power output will be arranged. Among them, the gas-fired units and coal-fired units in the thermal power output are in a competitive relationship and are arranged according to the principle of equal utilization hours.

In this preferred embodiment, when the renewable energy generation is large, the curve formed by the renewable energy superposition load will have a deep valley, or even less than zero. If the peak load regulation effect of various power sources in the multi-regional power system is taken into consideration, the negative balance deviation at this time will constitute the abandoned power. When the peak load cannot be balanced, it will constitute insufficient power.

In step 207, the carbon emission of each partition is calculated based on the output of the conventional units in the unit output combination of each partition, the set energy consumption data of the conventional units per unit power generation, and the average carbon emission of the energy used by the conventional units.

In step 208, let n=n+1. When n≤N, go to step 1, where N is the total number of sample scenarios generated for cross-region support sequential production simulation.

In step 104, based on the interconnection line inter-regional transmission results and the sub-regional electricity balance results of each sampling scenario of the inter-regional support sequential production simulation, the probability evaluation index considering the supply guarantee and new energy consumption level of the multi-regional power system is calculated and output.

Preferably, based on the inter-regional transmission results of the interconnection lines and the sub-regional electricity balance results of the sampling scenarios of each inter-regional support sequential production simulation, the probability evaluation index considering the power supply guarantee and new energy consumption level of the multi-regional power system is calculated and output, wherein the probability evaluation index includes the power shortage probability of the time series corresponding to the sampling scenarios of each inter-regional support sequential production simulation, the expected value of power shortage, the maximum power gap value and the expected value of new energy utilization rate.

In this preferred embodiment, the power shortage probability LOLP (Loss of load probability), the power shortage expected value EENS (expected energy not supplied), the maximum power gap value MLNS (Maximum Load Not Supplied) and the expected value of new energy utilization rate ENEUR (Expected value of new energy utilization rate) are selected as evaluation indicators for measuring the effect of multi-regional power system cross-regional support timing production simulation, which comprehensively considers the power system supply guarantee and the new energy consumption level in the system, so that the interconnection line cross-regional support results under the optimal sampling scenario can be selected. Since the calculation of the above four evaluation indicators can be calculated using conventional methods in the prior art after determining the conventional unit output and new energy output of the multi-regional power system, as well as the cross-regional transmission results and the regional power balance results, they will not be repeated here.

Exemplary Method 2

Fig. 3 is a flow chart of a multi-regional power system cross-region support sequential production simulation method according to another preferred embodiment of the present invention. As shown in Fig. 3, the multi-regional power system cross-region support sequential production simulation method of the preferred embodiment starts from step 301.

In step 301, the partition data and tie line data of the multi-region power system, as well as the conventional unit information and the new energy unit information in each partition are obtained;

In step 302, based on the established conventional unit output model and the new energy output sequential model, N simulated output scenarios of the multi-regional power system in any time sequence are randomly generated according to the conventional unit information and the new energy unit information, and the simulated output scenarios are used as sampling scenarios for cross-region support sequential production simulation;

In step 303, the nth sampling scenario of cross-region support sequential production simulation is selected, where 1≤n≤N;

In step 304, the effect of the inter-regional tie line is ignored, and the independent power balance of the new energy in each sub-region is performed to determine the first power surplus/gap of each sub-region;

In step 305, according to the principle of balancing the utilization hours of conventional units in the multi-regional power system, the average utilization hours of conventional units in each zone are determined, and the second power surplus/gap of each zone is calculated in combination with the first power surplus/first power gap;

In step 306, based on the established tie line fault probability model, a tie line simulation fault result of the multi-regional power system is randomly generated;

In step 307, considering the role of the inter-regional tie line, according to the transmission constraints in the tie line data and the tie line simulation fault results, as well as the second power surplus/second power gap of each sub-area, the established tie line inter-regional support model is used to determine the tie line inter-regional transmission results, and the final power surplus/gap of each sub-area is obtained;

In step 308, according to the pre-established internal output rules of the sub-area and the inter-area power transmission result of the tie line, the unit output combination of each sub-area is determined, and the unit output combination is the sub-area power balance result, wherein the unit output combination includes at least one of a conventional unit and a new energy unit;

In step 309, the carbon emissions of each partition are calculated according to the conventional unit output in the unit output combination of each partition, the set energy consumption data of the conventional unit per unit power generation, and the average carbon emissions of the energy used by the conventional unit;

In step 310, let n=n+1, when n≤N, go to step 1, where N is the total number of sample scenarios generated for cross-region support sequential production simulation;

In step 311, based on the interconnection line inter-regional transmission results and the sub-regional electricity balance results of each sampling scenario of the inter-regional support sequential production simulation, the probability evaluation index considering the supply guarantee and new energy consumption level of the multi-regional power system is calculated and output.

In this preferred embodiment, the multi-regional power system is a power system comprising 4 partitions and 5 interconnection lines. Figure 4 is a schematic diagram of the topological structure of a multi-regional power system according to another preferred embodiment of the present invention. As shown in Figure 4, the 4 partitions of the multi-regional power system are regions 1 to 4, and the 5 DC interconnection lines are interconnection lines 1 to 5, wherein interconnection line 1 connects region 1 and region 2, interconnection line 2 connects region 1 and region 3, interconnection line 3 connects region 2 and region 3, interconnection line 4 connects region 2 and region 4, and interconnection line 5 connects region 3 and region 4. When the 4 partitions perform independent power balance, region 1 and region 2 have a redundancy of 20GW, and region 3 and region 4 have a gap of 30GW and 10GW, respectively.

FIG5 is a schematic diagram of a tie line inter-regional transmission result according to another preferred embodiment of the present invention. As shown in FIG5, the tie line fault probability model and tie line inter-regional support model identical to those in the exemplary method 1 are used to simulate the tie line inter-regional support timing production in 2025 for the multi-regional power system shown in FIG4. When the transmission constraint of each tie line is 10GW in both directions, the tie line inter-regional transmission result obtained is that tie lines 2, 3 and 5 transmit 10GW to region 3 respectively, tie line 4 transmits 10GW to region 4, and tie line 1 transmits 0 to region 1. At this time, the gap of 30GW in region 3 is fully supported, while there is still a gap of 10GW in region 4.

Figure 6 is a schematic diagram of another interconnection line cross-regional transmission result according to another preferred embodiment of the present invention. As shown in Figure 6, for the same multi-regional power system, based on the interconnection line cross-regional transmission result described in Figure 5, the capacity of interconnection line 4 is expanded to 20GW. For the same sampling scenario, the interconnection line cross-regional transmission results are calculated again, and it is obtained that interconnection lines 2, 3 and 5 transmit 10GW to region 3 respectively, interconnection line 4 transmits 20GW to region 4, and interconnection line 1 transmits 10GW to region 1. At this time, the gaps in regions 3 and 4 are both supported. It can be seen that by adopting the method described in this embodiment, a reasonable production simulation can be performed on the power supply and new energy consumption of future multi-regional power systems, so that the power system containing new energy can be effectively guided and planned.

Table 1 shows the results of the main evaluation indicators obtained by random production simulation of the above four areas in 2025 under a sampling scenario.

Table 1

As shown in Table 1, the sample mean of LOLP in Region 1 is 0.02136, indicating that there is an electricity gap for an average of about 2.14% of the time. The expected value of the annual power shortage duration is 184.8 hours. By comparing the expected annual power shortage duration with the reserve rate duration, it can be determined whether there will be power shortage in the region. The EENS index of Region 1 is 60.17 million kWh, indicating that the power gap in Region 1 is 60.17 million kWh; the MLNS index gives the power value of the maximum power gap throughout the year, for example, the sample mean of the maximum power gap in Region 1 is 47.711 million kW; the ENEUR index gives the percentage of new energy consumption, and the new energy consumption rate in Region 1 is 100%. Through the above four evaluation indicators, the power supply and new energy consumption level of the region under each sampling scenario can be accurately evaluated.

Figure 7 is a schematic diagram of an evaluation index sampling result of a multi-regional power system cross-regional support sequential production simulation according to another preferred embodiment of the present invention. As shown in Figure 7, Figure 7 is a schematic diagram of the LOLP sampling result generated by the LOLP index obtained by performing cross-regional support production simulation in region 1 under multiple sampling scenarios. According to the histogram, the LOLP value is most concentrated in the interval where the number of samples is between 120 and 140. Therefore, in the actual power planning in the future, setting the reserve rate duration of the power system based on the LOLP value of this interval can effectively ensure the power supply in the area.

It can be seen from the above two embodiments that the method of the present invention simultaneously takes into account the randomness of new energy, conventional units and interconnection lines, and performs time-series production simulation on multi-regional power systems through interconnection line failure probability models and interconnection line cross-regional support models, thereby realizing simulation of the reliability of new energy contribution to power output under future operation of multi-regional power systems, which has important reference significance for guiding the planning of new power systems with a gradually increasing proportion of new energy output.

Exemplary Systems

FIG8 is a schematic diagram of the structure of a multi-regional power system cross-region support sequential production simulation system according to a preferred embodiment of the present invention. As shown in FIG8 , the multi-regional power system cross-region support sequential production simulation system 800 described in this preferred embodiment includes:

The data acquisition module 801 is used to acquire the partition data and tie line data of the multi-region power system, as well as the conventional unit information and the new energy unit information in each partition;

The sampling scenario module 802 is used to randomly generate a plurality of simulated output scenarios of the multi-regional power system in any time sequence based on the established conventional unit output model and the new energy output sequential model according to the conventional unit information and the new energy unit information, and use the simulated output scenarios as sampling scenarios for cross-region support sequential production simulation;

The inter-regional support module 803 is used to perform a time-series production simulation on the multi-regional power system according to the established tie line failure probability model and tie line inter-regional support model based on the sampling scenario of each inter-regional support time-series production simulation, and determine the tie line inter-regional transmission result and the sub-regional power balance result;

The output result module 804 is used to calculate and output the probability evaluation index considering the supply guarantee and new energy consumption level of the multi-regional power system based on the inter-regional transmission results of the interconnection lines and the sub-regional power balance results of the sampling scenario of each inter-regional support timing production simulation.

Preferably, the inter-regional support module 803 performs a sequential production simulation on the multi-regional power system according to the established tie line failure probability model and tie line inter-regional support model based on the sampling scenario of each inter-regional support sequential production simulation, and determines the tie line inter-regional transmission result and the partition power balance result, including:

Step 1: Select the nth sampling scenario of cross-region support sequential production simulation, where 1≤n≤N;

Step 2: Ignoring the role of inter-district tie lines, balance the power of each sub-district's renewable energy independently to determine the first power surplus/gap of each sub-district;

Step 3: according to the principle of balancing the utilization hours of conventional units in the multi-regional power system, determine the average utilization hours of conventional units in each zone, and calculate the second power surplus/gap of each zone in combination with the first power surplus/first power gap;

Step 4: randomly generating tie line fault simulation results of the multi-regional power system based on the established tie line fault probability model;

Step 5: Considering the role of inter-regional tie lines, according to the transmission constraints in the tie line data and the simulated fault results of the tie lines, as well as the second power surplus/second power gap of each sub-area, the established tie line inter-regional support model is used to determine the tie line inter-regional transmission results, and the final power surplus/gap of each sub-area is obtained;

Step 6: According to the pre-established internal output rules of the sub-areas and the inter-area power transmission results of the tie line, determine the unit output combination of each sub-area, the unit output combination is the sub-area power balance result, wherein the unit output combination includes at least one of a conventional unit and a new energy unit;

Step 7: Calculate the carbon emissions of each partition based on the output of the conventional units in the unit output combination of each partition, the set energy consumption data of the conventional units per unit of power generation, and the average carbon emissions of the energy used by the conventional units;

Step 8, let n=n+1, when n≤N, go to step 1, where N is the total number of sampling scenarios generated for cross-region support sequential production simulation.

Preferably, the cross-region support module 803 randomly generates tie line simulation failure results of the multi-region power system based on the established tie line failure probability model, including:

For each tie line of the multi-regional power system, according to the time series of the sampling scenario of the n-th cross-regional support sequential production simulation, tie line simulation fault data of the tie line is randomly generated based on the established tie line fault probability model, wherein the tie line fault probability model includes a tie line fault number probability model and a tie line fault state maintenance time probability model, specifically:

The number of tie line faults follows Poisson distribution, and the expression of the tie line fault probability model is:

Where k represents the number of failures in the corresponding time series determined based on the historical failure data of the corresponding time series, λ represents the average number of failures in the corresponding time series determined based on the historical failure data of the corresponding time series, and P(X=k) represents the probability when the number of failures in the corresponding time series determined based on the historical failure data of the corresponding time series is k;

The tie line fault state maintenance time follows an exponential distribution, and the expressions of the tie line fault state maintenance time probability models f(x) and F(x) are:

Wherein, x represents the fault state maintenance time of the corresponding time series determined based on the historical fault data of the corresponding time series, f(x) represents the density function of the fault state maintenance time distribution, F(x) represents the probability distribution function of the fault state maintenance time distribution, θ represents the expected value of the distribution, h represents the average fault duration of the corresponding time series determined based on the historical fault data of the corresponding time series, SF represents the operation coefficient of the tie line in the multi-regional power system, EFOR represents the equivalent forced outage rate, and T represents the duration of the corresponding time series.

Preferably, the inter-regional support module 803 considers the role of inter-regional tie lines, and determines the tie line inter-regional transmission results using the established tie line inter-regional support model according to the transmission constraints in the tie line data and the tie line simulated fault results, as well as the second power surplus/second power gap of each partition, wherein the objective function of the tie line inter-regional support model is obtained by minimizing the tie line active loss and superimposing the square of the power deviation of the partition with power gap, and the expression of the objective function is:

Where, P _EX —— tie line power arrangement vector;

P _ZN ——the incoming external power arrangement vector of the zone;

N _EX ——Number of contact lines;

N _ZN – number of partitions;

P _EX.i ——Power arrangement value of the ith tie line, which defines the flow from the sending end to the receiving end as the positive direction;

P _ZN.i ——the value of the external power received by the ith zone;

D _ZN.i ——The power supply gap of the ith zone. A value greater than zero indicates a gap, and a value less than zero indicates the ability to provide external support;

k _i ——coefficient of the power term of the ith tie line, taken as 1.0. When the resistance and tie line voltage are known, R/U ² can be taken;

The objective function needs to satisfy the equality constraint that the power received by each partition is the sum of the powers arranged by the tie lines related to it. The equality expression is as follows:

Wherein, j represents the jth tie line connected to the i-th partition in the multi-region power system;

The objective function also needs to satisfy the inequality constraint, that is, the transmission power of each tie line is subject to the forward and reverse limit red beans, and the input power of each partition is subject to the partition power gap constraint. The expression of the inequality is as follows:

Where, P _EXi.pos is the upper limit of the forward transmission power of the ith tie line;

P _EXi.neg ——the upper limit of the reverse transmission power of the ith tie line;

D _i ——power gap of the ith partition. When D _i is greater than 0, it means there is a gap. When D _i is less than 0, it means there is power redundancy.

For the objective function with equality constraints and inequality constraints, by introducing Lagrange multipliers, an objective function expression for eliminating the equality constraints is obtained;

According to the Lagrange extreme value condition, a linear equation of the objective function expression that eliminates the equality constraint is generated, and the linear equation is solved by using the elimination method of the selected principal element;

When all solutions of the linear equations are within a preset constraint range and satisfy the inequality constraint, the solutions of the linear equations are determined as the inter-regional power transmission results of the interconnection lines of the multi-regional power system.

Preferably, the inter-region support module 803 determines the unit output combination of each sub-region according to the pre-established sub-region internal output rules and the inter-region power transmission results of the tie line, wherein the sub-region internal output rules include:

According to the inter-regional power transmission results of the tie line, for conventional units and new energy units in the sub-regions that need to output power across regions, zero-carbon units are given priority;

When pumped storage is present, the output of zero-carbon units and pumped storage is arranged comprehensively;

When the above output cannot meet the cross-regional power output requirements, thermal power output will be arranged. Among them, the gas-fired units and coal-fired units in the thermal power output are in a competitive relationship and are arranged according to the principle of equal utilization hours.

Preferably, the output result module 804 calculates and outputs the probability evaluation index considering the power supply guarantee and new energy consumption level of the multi-regional power system according to the inter-regional transmission results of the interconnection lines and the sub-regional power balance results of the sampling scenarios of each inter-regional support sequential production simulation, wherein the probability evaluation index includes the power shortage probability of the time series corresponding to the sampling scenarios of each inter-regional support sequential production simulation, the expected value of power shortage, the maximum power gap value and the expected value of new energy utilization rate.

The multi-regional power system inter-regional support sequential production simulation system described in this preferred embodiment is based on the established conventional unit output model and new energy output sequential model, and generates multiple sampling scenarios of inter-regional support sequential production simulation according to the parameters of conventional units and new energy units in the multi-regional power system. The steps of performing inter-regional support sequential production simulation for each sampling scenario are based on the established interconnection line failure probability model and interconnection line inter-regional support model. The steps are the same as the steps adopted by the multi-regional power system inter-regional support sequential production simulation method, and the technical effects achieved are also the same, which will not be repeated here.

The invention has been described above with reference to a few embodiments. However, it is readily apparent to a person skilled in the art that other embodiments than the ones disclosed above are equally within the scope of the invention, as defined by the appended patent claims.

Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/said/the [means, components, etc.]" are to be openly interpreted as at least one instance of said means, components, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not necessarily have to be performed in the exact order disclosed, unless explicitly stated otherwise.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may 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 codes.

The present application is described with reference to the flowcharts and/or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the processes and/or boxes in the flowchart and/or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the functions specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims (10)

1. A multi-zone power system cross-zone support timing production simulation method, the method comprising:

the method comprises the steps of obtaining partition data and tie line data of a multi-region power system, and conventional unit information and new energy unit information in each partition;

based on the established conventional unit output model and new energy output sequential model, randomly generating simulated output scenes of a plurality of multi-region power systems in any time sequence according to the conventional unit information and the new energy unit information, and taking the simulated output scenes as sampling scenes of cross-region support time sequence production simulation;

based on a sampling scene of each transregional support time sequence production simulation, performing time sequence production simulation on the multi-region power system according to the established tie line fault probability model and the tie line transregional support model, and determining a tie line transregional power transmission result and a regional power balance result;

and calculating and considering probability evaluation indexes of the multi-region power system conservation and new energy consumption level according to the cross-region power transmission result and the regional power balance result of the connecting line of the sampling scene of each cross-region support time sequence production simulation, and outputting the probability evaluation indexes.

2. The method of claim 1, wherein the determining the link transregional power transmission result and the partition power balance result based on the sampling scenario of each transregional support time-series production simulation by performing time-series production simulation on the multi-region power system according to the established link fault probability model and the link transregional support model comprises:

Step 1, selecting an nth cross-region supporting time sequence production simulation sampling scene, wherein N is more than or equal to 1 and less than or equal to N;

step 2, ignoring the action of a cross-region interconnecting line, carrying out independent power balance of new energy sources of each region, and determining a first power surplus/gap of each region;

step 3, determining average utilization hours of the conventional units of each partition according to the utilization hours balancing principle of the conventional units of the multi-region power system, and calculating second power surplus/gaps of each partition by combining the first power surplus/first power gaps;

step 4, randomly generating a tie line simulation fault result of the multi-region power system based on the established tie line fault probability model;

step 5, considering the action of a cross-region tie, determining a cross-region power transmission result of the tie by adopting an established tie cross-region support model according to the power transmission constraint in the tie data, the tie simulation fault result and the second power surplus/second power gap of each partition, and obtaining the final power surplus/gap of each partition;

step 6, determining a unit output combination of each subarea according to a preset subarea internal output rule and the cross-regional transmission result of the tie line, wherein the unit output combination is a subarea electric quantity balance result, and comprises at least one of a conventional unit and a new energy unit;

Step 7, calculating the carbon emission of each partition according to the conventional unit output in the unit output combination of each partition, the set energy consumption data of the conventional unit power generation and the carbon emission average value of the conventional unit using energy;

and 8, let n=n+1, and when N is less than or equal to N, go to step 1, wherein N is the total number of generated sampling scenes simulated by the cross-region support time sequence production.

3. The method of claim 1, wherein the randomly generating a tie-line simulated fault result for the multi-zone power system based on the established tie-line fault probability model comprises:

for each tie line of the multi-region power system, randomly generating tie line simulated fault data of the tie line based on an established tie line fault probability model according to a time sequence of a sampling scene of the nth trans-regional support time sequence production simulation, wherein the tie line fault probability model comprises a tie line fault frequency probability model and a tie line fault state maintenance time probability model, and specifically:

the number of times of occurrence of the tie line faults obeys poisson distribution, and the expression of the tie line fault number probability model is as follows:

where k represents the number of failures occurring in the corresponding time series determined based on the historical failure data of the corresponding time series, λ represents the average number of failures of the corresponding time series determined based on the historical failure data of the corresponding time series, and P (x=k) represents the probability when the number of failures occurring in the corresponding time series determined based on the historical failure data of the corresponding time series is k;

The tie line fault state maintaining time obeys an exponential distribution, and the expressions of the tie line fault state maintaining time probability models F (x) and F (x) are as follows:

wherein x represents a fault state maintenance time of the corresponding time series determined based on the historical fault data of the corresponding time series, F (x) represents a density function of the fault state maintenance time distribution, F (x) represents a probability distribution function of the fault state maintenance time distribution, θ represents a desired value of the distribution, h represents an average fault duration of the corresponding time series determined based on the historical fault data of the corresponding time series, SF represents an operation coefficient of the link line in the multi-region power system, EFOR represents an equivalent forced outage rate, and T represents a duration of the corresponding time series.

4. The method of claim 2, wherein the cross-zone tie action is considered, and a tie cross-zone transmission result is determined by using an established tie cross-zone support model according to transmission constraint in the tie data and the tie simulation fault result and a second power surplus/second power gap of each zone, wherein an objective function of the tie cross-zone support model is obtained by superposing squares of power deviations of zones with the power gaps, and an objective function is expressed as:

Wherein P is _EX -tie power scheduling vector;

P _ZN -an incoming foreign power schedule vector for the partition;

N _EX -number of tie lines;

N _ZN -number of partitions;

P _EX.i -defining the forward direction of the flow of the transmitting end to the receiving end as the positive direction by the power arrangement value of the ith interconnecting line;

P _ZN.i -the ith partition is subject to the value of the extraneous power;

D _ZN.i -a power supply gap of the ith partition, the value being greater than zero indicating the presence of a gap and less than zero indicating the ability to support externally;

k _i the coefficient of the ith link power term, 1.0, R/U when the resistance and link voltage are known ² ；

The objective function is constrained by an equation to be satisfied, namely, the power received by each partition is the sum of the power of each interconnection line arrangement related to the partition, and the equation expression is as follows:

wherein j represents a j-th connecting wire connected with an i-th subarea in the multi-area power system;

the objective function also needs to satisfy the inequality constraint that the transmission power of each tie line is limited by forward and reverse limit red beans, the input power of each partition is limited by a partition power gap, and the inequality expression is as follows:

wherein P is _EXi.pos -upper forward power limit of the ith tie line;

P _EXi.neg -upper limit of reverse transmission power of the ith link;

D _i -power gap of ith partition, when D _i Above 0, a gap is indicated, when D _i Less than 0, indicating that there is power redundancy;

for the objective function with equality constraint and inequality constraint, obtaining an objective function expression eliminating the equality constraint by introducing a Bragg day multiplier;

generating a linear equation for eliminating the objective function expression of the equation constraint according to the Lagrange extremum condition, and solving the linear equation by adopting a principal component elimination method;

and when the solutions of the linear equations are all in a preset constraint range and the inequality constraint is met, determining the solutions of the linear equations as a cross-region transmission result of the connecting line of the multi-region power system.

5. The method of claim 2, wherein the unit output combination of each partition is determined according to the cross-partition transmission result of the tie according to a pre-established partition internal output rule, wherein the partition internal output rule comprises:

according to the cross-region transmission result of the connecting line, the output of the zero-carbon unit is preferentially arranged for the conventional unit and the new energy unit in the region needing cross-region output power;

When pumped storage exists, comprehensively arranging the zero-carbon unit and the pumped storage output;

and when the output power can not meet the power output requirement of the cross region, arranging the thermal power output, wherein a gas unit and a coal-fired unit in the thermal power output are in a competitive relationship, and are arranged according to the principle of equal utilization hours.

6. The method according to claim 2, wherein the probability evaluation index considering the multi-region power system conservation and new energy consumption level is calculated and output according to the link line cross-region power transmission result and the regional power balance result of the sampling scenario simulated by each cross-region support time sequence production, wherein the probability evaluation index comprises a power shortage probability, a power shortage expected value, a maximum power gap value and a new energy utilization expected value of a time sequence corresponding to the sampling scenario simulated by each cross-region support time sequence production.

7. A multi-zone power system cross-zone support timing production simulation system, the system comprising:

the data acquisition module is used for acquiring partition data and tie line data of the multi-region power system, and conventional unit information and new energy unit information in each partition;

The sampling scene module is used for randomly generating simulated output scenes of a plurality of multi-region power systems in any time sequence according to the conventional unit information and the new energy unit information based on the established conventional unit output model and the new energy output sequential model, and taking the simulated output scenes as sampling scenes of cross-region support time sequence production simulation;

the cross-region supporting module is used for carrying out time sequence production simulation on the multi-region power system according to the established tie line fault probability model and the tie line cross-region supporting model based on the sampling scene of each cross-region supporting time sequence production simulation, and determining a tie line cross-region power transmission result and a partition electric quantity balance result;

and the output result module is used for calculating and considering probability evaluation indexes of the multi-region power system conservation level and the new energy consumption level according to the cross-region power transmission result and the regional power balance result of the connecting line of the sampling scene simulated by each cross-region support time sequence production and outputting the probability evaluation indexes.

8. The system of claim 7, wherein the cross-zone support module performs time-series production simulation on the multi-zone power system according to the established tie-line fault probability model and the tie-line cross-zone support model based on the sampling scenario of each cross-zone support time-series production simulation, and determines a tie-line cross-zone power transmission result and a zone power balance result, comprising:

Step 1, selecting an nth cross-region supporting time sequence production simulation sampling scene, wherein N is more than or equal to 1 and less than or equal to N;

step 2, ignoring the action of a cross-region interconnecting line, carrying out independent power balance of new energy sources of each region, and determining a first power surplus/gap of each region;

step 3, determining average utilization hours of the conventional units of each partition according to the utilization hours balancing principle of the conventional units of the multi-region power system, and calculating second power surplus/gaps of each partition by combining the first power surplus/first power gaps;

step 4, randomly generating a tie line simulation fault result of the multi-region power system based on the established tie line fault probability model;

step 5, considering the action of a cross-region tie, determining a cross-region power transmission result of the tie by adopting an established tie cross-region support model according to the power transmission constraint in the tie data, the tie simulation fault result and the second power surplus/second power gap of each partition, and obtaining the final power surplus/gap of each partition;

step 6, determining a unit output combination of each subarea according to a preset subarea internal output rule and the cross-regional transmission result of the tie line, wherein the unit output combination is a subarea electric quantity balance result, and comprises at least one of a conventional unit and a new energy unit;

Step 7, calculating the carbon emission of each partition according to the conventional unit output in the unit output combination of each partition, the set energy consumption data of the conventional unit power generation and the carbon emission average value of the conventional unit using energy;

and 8, let n=n+1, and when N is less than or equal to N, go to step 1, wherein N is the total number of generated sampling scenes simulated by the cross-region support time sequence production.

9. The system of claim 7, wherein the trans-regional support module randomly generates a tie-line simulated fault result for the multi-regional power system based on the established tie-line fault probability model, comprising:

for each tie line of the multi-region power system, randomly generating tie line simulated fault data of the tie line based on an established tie line fault probability model according to a time sequence of a sampling scene of the nth trans-regional support time sequence production simulation, wherein the tie line fault probability model comprises a tie line fault frequency probability model and a tie line fault state maintenance time probability model, and specifically:

the number of times of occurrence of the tie line faults obeys poisson distribution, and the expression of the tie line fault number probability model is as follows:

Where k represents the number of failures occurring in the corresponding time series determined based on the historical failure data of the corresponding time series, λ represents the average number of failures of the corresponding time series determined based on the historical failure data of the corresponding time series, and P (x=k) represents the probability when the number of failures occurring in the corresponding time series determined based on the historical failure data of the corresponding time series is k;

the tie line fault state maintaining time obeys an exponential distribution, and the expressions of the tie line fault state maintaining time probability models F (x) and F (x) are as follows:

wherein x represents a fault state maintenance time of the corresponding time series determined based on the historical fault data of the corresponding time series, F (x) represents a density function of the fault state maintenance time distribution, F (x) represents a probability distribution function of the fault state maintenance time distribution, θ represents a desired value of the distribution, h represents an average fault duration of the corresponding time series determined based on the historical fault data of the corresponding time series, SF represents an operation coefficient of the link line in the multi-region power system, EFOR represents an equivalent forced outage rate, and T represents a duration of the corresponding time series.

10. The system of claim 8, wherein the cross-zone support module considers cross-zone tie actions, determines tie cross-zone transmission results using an established tie cross-zone support model based on transmission constraints in the tie data and the tie simulation fault results, and the second power surplus/second power gap for each zone, wherein an objective function of the tie cross-zone support model is obtained by summing squares of power deviations of zones where power gaps exist, and wherein the objective function is expressed as:

Wherein P is _EX -tie power scheduling vector;

P _ZN -an incoming foreign power schedule vector for the partition;

N _EX -number of tie lines;

N _ZN -number of partitions;

P _EX.i -defining the forward direction of the flow of the transmitting end to the receiving end as the positive direction by the power arrangement value of the ith interconnecting line;

P _ZN.i -the ith partition is subject to the value of the extraneous power;

D _ZN.i -a power supply gap of the ith partition, the value being greater than zero indicating the presence of a gap and less than zero indicating the ability to support externally;

k _i the coefficient of the ith link power term, 1.0, R/U when the resistance and link voltage are known ² ；

The objective function is constrained by an equation to be satisfied, namely, the power received by each partition is the sum of the power of each interconnection line arrangement related to the partition, and the equation expression is as follows:

wherein j represents a j-th connecting wire connected with an i-th subarea in the multi-area power system;

the objective function also needs to satisfy the inequality constraint that the transmission power of each tie line is limited by forward and reverse limit red beans, the input power of each partition is limited by a partition power gap, and the inequality expression is as follows:

wherein P is _EXi.pos -upper forward power limit of the ith tie line;

P _EXi.neg -upper limit of reverse transmission power of the ith link;

D _i -power gap of ith partition, when D _i Above 0, a gap is indicated, when D _i Less than 0, indicating that there is power redundancy;

for the objective function with equality constraint and inequality constraint, obtaining an objective function expression eliminating the equality constraint by introducing a Bragg day multiplier;

generating a linear equation for eliminating the objective function expression of the equation constraint according to the Lagrange extremum condition, and solving the linear equation by adopting a principal component elimination method;

and when the solutions of the linear equations are all in a preset constraint range and the inequality constraint is met, determining the solutions of the linear equations as a cross-region transmission result of the connecting line of the multi-region power system.