A new energy cross-regional consumption method and system based on scenario analysis

Abstract

The present invention provides a new energy cross-regional consumption method based on scenario analysis, which includes: respectively inputting the new energy historical prediction error data, the new energy predicted power generation curve, and the new energy load curve of the two regional power grids into a pre-constructed UHV DC cross-regional consumption model to obtain the start-stop of each regional unit and the optimized plan for DC line transmission that meets the maximum expected value of cross-regional new energy consumption electricity; implementing the start-stop of the two regional units and the optimized plan for DC line transmission to achieve the cross-regional consumption of new energy in the two regions. The technical solution provided by the present invention establishes a UHV DC cross-regional consumption model, takes the UHV DC line, the sending end region, and the receiving end region as the research objects, fully coordinates the new energy consumption space in the two regions, and promotes the cross-regional consumption of new energy in the two regions.

Description

Technical Field

[0001] The present invention relates to the field of optimizing UHVDC transmission plans, and particularly to a method for cross-regional consumption of new energy based on scenario analysis. Background Art

[0002] With the continuous grid connection of large-scale new energy, the penetration rate of new energy gradually increases. However, due to the limitation of local consumption capacity in some areas, it is impossible to fully consume new energy power generation, and the problem of new energy consumption is prominent. To promote the consumption of new energy in these areas, the strategy of Ultra-High Voltage Direct Current (UHVDC) transmission is adopted, and it is an important measure currently used to transport the surplus new energy power to the load center. However, how to utilize the complementary characteristics of the new energy consumption space in the sending and receiving end regional power grids to optimize the UHVDC line transmission plan to promote the consumption of new energy through transmission is the current research focus.

[0003] In this regard, some studies have proposed a wind-fire DC transmission dispatching model, which adjusts the DC transmission plan according to the load changes in the receiving end area to promote the peak shaving of DC transmission; some studies have constructed an integrated dispatching plan model for wind, light and fire, which coordinates the supporting thermal power generation plan and the DC transmission plan to promote the consumption of wind and light through transmission. Some studies have also proposed a virtual cost model for DC plan adjustment, which considers the cost of DC transmission plan adjustment to promote the coordinated optimization of transmitted new energy and conventional energy. There are also studies that construct an equivalent model for DC line operation to optimize the operation mode of UHVDC lines to promote the consumption of new energy through transmission.

[0004] The above studies have all achieved results in optimizing the DC line transmission plan to promote the consumption of new energy through transmission. However, since the models do not simultaneously model and study the UHVDC line and the two end regions, the power generation plans of one region or both regions have to be passively coordinated, and the complementary nature of the new energy consumption space in the two regions is not fully utilized, or the uncertainty of new energy prediction in the two regions is not processed to alleviate or solve the impact on the new energy consumption in the two regions and the safe and reliable operation of the power grid. Summary of the Invention

[0005] In view of the research gap in modeling the UHV DC line and the two regions simultaneously while considering the certainty of new energy prediction in the two regions, the present invention proposes a method for cross-regional consumption of new energy based on scenario analysis, and establishes a UHV DC cross-regional consumption model with the UHV DC line, the sending region and the receiving region as the research objects. First, the ARMA model and scenario reduction technology are used to describe the uncertainty of new energy prediction in the sending and receiving regions, and a new energy prediction scenario set for the two regions is constructed. Then, the new energy prediction scenario set for the two regions is used as the model input, and with the goal of maximizing the expected value of the cross-regional new energy consumption electricity, on the basis of considering the impact of new energy prediction uncertainty, the unit start-stop plan for the two regions and the DC line power transmission plan are optimized to cope with the impact of new energy prediction uncertainty on the cross-regional new energy consumption and the safe and reliable operation of the power grid.

[0006] The technical solution provided by the present invention is as follows:

[0007] A method for cross-regional consumption of new energy based on scenario analysis, comprising:

[0008] Respectively input the historical prediction error data of new energy, the new energy predicted power generation curve and the new energy load curve of the power grids in the two regions into the pre-constructed UHV DC cross-regional consumption model to obtain the optimized unit start-stop and DC line transmission plans for each region that meet the maximum expected value of cross-regional new energy consumption electricity;

[0009] Execute the optimized unit start-stop and DC line transmission plans for each region to achieve cross-regional new energy consumption;

[0010] Wherein, the two regions include: a sending region and a receiving region;

[0011] The pre-constructed UHV DC cross-regional consumption model includes: a new energy prediction scenario set, an objective function for maximizing the expected value of cross-regional system new energy consumption electricity, and an optimization constraint condition for the objective function.

[0012] Preferably, the construction of the UHV DC cross-regional consumption model includes:

[0013] Respectively based on the historical prediction error data of new energy in the sending region power grid and the receiving region power grid, obtain the expected sequence of new energy prediction errors for each region;

[0014] According to the new energy predicted power generation curve of each region and the expected sequence of new energy prediction errors for each region, obtain a new energy prediction scenario set based on the ARMA model;

[0015] According to the thermal power unit operation data and new energy prediction scenarios of the sending region power grid and the receiving region power grid, determine the objective function for maximizing the expected value of cross-regional system new energy consumption electricity;

[0016] Determine the optimization constraint conditions of the objective function according to the operating data of thermal power units, new energy prediction scenarios and new energy load curves in the sending-end regional power grid and the receiving-end regional power grid;

[0017] Substitute the data of the new energy prediction scenario set into the objective function with the maximum expected value of new energy consumption electricity in the cross-regional system, and combine the optimization constraint conditions of the objective function to obtain the start-stop optimization plan of each regional unit and the transmission optimization plan of the DC line that meet the constraint conditions and reach the requirements of the objective function;

[0018] The ARMA is: Autoregressive Moving Average Model.

[0019] Further, obtaining the expected sequence of new energy prediction errors for each region based on the historical prediction error data of new energy in the sending-end regional power grid and the receiving-end regional power grid respectively includes:

[0020] Respectively select the historical prediction error data of new energy under the same weather conditions in the historical time period with the same prediction time period as the sending-end regional power grid and the receiving-end regional power grid;

[0021] Based on the selected historical prediction error data of new energy, calculate the expected value of new energy prediction error for each region at each time point to obtain the expected sequence of new energy prediction errors for each region in the prediction time period.

[0022] Further, obtaining the new energy prediction scenario set based on the ARMA model according to the new energy prediction power generation curves of each region and the expected sequences of new energy prediction errors for each region includes:

[0023] Obtain the basic new energy prediction scenario sets for each region based on the ARMA model according to the new energy prediction power generation curves of each region and the expected sequences of new energy prediction errors for each region;

[0024] Use scenario reduction technology to reduce the scenarios of each regional basic new energy scenario set respectively to obtain the new energy prediction scenario sets for each region;

[0025] Pair the scenarios in the new energy prediction scenario sets of each region pairwise to obtain the new energy prediction scenario set describing the future output of new energy prediction in the two regions.

[0026] Further, obtaining the basic new energy prediction scenario sets for each region based on the ARMA model according to the new energy prediction power generation curves of each region and the expected sequences of new energy prediction errors for each region respectively includes:

[0027] Use the least squares method to solve the ARMA model parameters;

[0028] Select Gaussian white noise samples of the new energy prediction error expectation sequences with equal occurrence probabilities in each region respectively, and substitute the Gaussian white noise samples and the model parameters in each region into the ARMA model formula to obtain the new energy prediction error sequences in each region;

[0029] Correct the new energy predicted power generation curves in each region according to the new energy prediction error sequences in each region to obtain multiple corrected new energy predicted power generation curves in each region;

[0030] The multiple corrected new energy predicted power generation curves in each region and the occurrence probabilities of each predicted power generation curve constitute the new energy prediction basic scenario set in each region.

[0031] Further, use the scenario reduction technology to reduce the scenarios in the new energy basic scenario set in each region respectively to obtain the new energy prediction scenario set in each region, including:

[0032] S1: Calculate the number of scenarios in the new energy prediction basic scenario set in each region respectively, and reset the initial value of the occurrence probability of all scenarios in the new energy prediction basic scenario set in each region to the reciprocal of the number of scenarios in each region;

[0033] S2: Record the initial value of the remaining number of scenarios in each region as the number of scenarios in the new energy prediction basic scenario set in each region, and set the initial value of the number of scenarios to be deleted in each region to zero;

[0034] S3: Calculate the distance between every two new energy prediction scenarios in the new energy prediction basic scenario set in each region to form a scenario symmetric matrix in each region;

[0035] S4: Calculate the sum of the elements in each row of the scenario symmetric matrix in each region to obtain a column matrix representing the sum of the distances from all scenarios in the new energy basic scenario set in each region to other scenarios;

[0036] S5: Multiply each element of the column matrix in each region by the occurrence probability of each scenario in each region to obtain a scenario probability column matrix in each region;

[0037] S6: Select the scenario corresponding to the minimum value in the scenario probability column matrix in each region as the scenario to be deleted in each region, subtract one from the remaining number of scenarios in each region, and add one to the number of scenarios to be deleted in each region;

[0038] S7: In the scenario symmetric matrix in each region, determine the non-deleted scenario corresponding to the column where the non-zero minimum value is located in the row where the deleted scenario is located according to step S6 to obtain the non-deleted scenario closest to the deleted scenario;

[0039] S8: Add the occurrence probability of the deleted scenario to the non-deleted scenario closest to it;

[0040] S9: Determine whether the number of remaining scenarios in each region meets the set scenario number requirement. If not, return to step S3; otherwise, output the remaining scenarios in each region and the occurrence probabilities of the remaining scenarios to obtain the new energy prediction scenario set for each region.

[0041] Further, the calculation formula for the distance between every two new energy prediction scenarios is as follows:

[0042]

[0043] where C T (s m , s n ) is the distance between scenario s m and s n , is the value of scenario s m at time period t, is the value of scenario s n at time period t.

[0044] Further, the objective function for maximizing the expected value of new energy consumption electricity in the cross - regional system is shown as follows:

[0045]

[0046] where T1 is the model simulation time, Δt is the simulation time resolution, S is the number of new energy day - ahead prediction scenarios, p a is the probability of new energy prediction scenario a, is the total output of wind and photovoltaic power stations in two regions at time t in scenario a, is the actual output of wind farm wi in the sending - end region at time t in scenario a, is the actual output of photovoltaic power station pi in the sending - end region at time t in scenario a, is the actual output of wind farm wj in the receiving - end region at time t in scenario a, is the actual output of photovoltaic power station pj in the receiving - end region at time t in scenario a, WO is the number of wind farms in the sending - end region of the DC line, PO is the number of photovoltaic power stations in the sending - end region of the DC line, WT is the number of wind farms in the receiving - end region of the DC line, and PT is the number of photovoltaic power stations in the receiving - end region of the DC line.

[0047] Further, the optimization constraint conditions of the objective function include: constraints between scenarios, equality constraints and inequality constraints under each scenario;

[0048] where the equality constraints include: power balance constraint in the sending - end region, power balance constraint in the receiving - end region, and power transaction constraint of the DC channel;

[0049] The inequality constraints include: DC channel output constraint, power adjustment amplitude constraint, unidirectional power adjustment constraint, DC line transmission power adjustment time constraint, DC line transmission power adjustment frequency constraint, thermal power unit power constraint, thermal power unit power ramp constraint, unit start-stop time constraint, and new energy generation potential constraint.

[0050] Furthermore, the constraints between scenarios are as shown in the following formula:

[0051]

[0052] where K is the number of units in the cross-regional power grid, and S m 、S n are any two scenarios in the new energy prediction scenario set; is the start-stop status of unit k at time t in scenario S m , is the start-stop status of unit k at time t in scenario S n , is the power generation of unit k at time t in scenario S m , is the power generation of unit k at time t-1 in scenario S n , is the upward ramp rate of unit k, is the downward ramp rate of unit k.

[0053] Furthermore, the power balance constraint of the sending end area is as shown in the following formula:

[0054]

[0055] where, is the start-stop status of sending end area unit i at time t in scenario a, is the power generation of sending end area unit i at time t in scenario a, is the power generation of wind farm wi in the sending end area at time t in scenario a, is the power generation of PV power station pi in the sending end area at time t in scenario a, is the transmission power of DC channel l at time t, is the demand power of load node mi in the sending end area at time t. MO is the number of load nodes in the sending end area, L is the number of DC channels, NO is the number of units in the sending end area, WO is the number of wind farms in the sending end area, and PO is the number of PV power stations in the sending end area;

[0056] The power balance constraint of the receiving end area is as shown in the following formula:

[0057]

[0058] where, ​​​​​is the start-stop status of receiving-end area unit j at time t in scenario a, is the power generation of receiving-end area unit j at time t in scenario a, is the power generation of wind farm wj in receiving-end area at time t in scenario a, is the power generation of PV power station pj in receiving-end area at time t in scenario a, is the transmission power of DC channel l at time t, is the demand power of receiving-end area load node mj at time t. MT is the number of receiving-end area load nodes, L is the number of DC channels, NT is the number of receiving-end area units, WT is the number of receiving-end area wind farms, and PT is the number of receiving-end area PV power stations;

[0059] The electricity trading constraint of the DC channel is shown as the following formula:

[0060]

[0061] where T is the model simulation time, Q l is the trading electricity volume of DC channel l during the whole simulation time.

[0062] Furthermore, the output constraint of the DC channel is shown as the following formula:

[0063]

[0064] where, is the maximum output limit of the transmission power of DC channel l, is the minimum output limit of the transmission power of DC channel l;

[0065] The power adjustment amplitude constraint is shown as the following formula:

[0066]

[0067] where, is the maximum upward adjustment amplitude per unit time of the transmission power of DC line l at time t, is the maximum downward adjustment amplitude per unit time of the transmission power of DC line l at time t, is the state variable of the transmission power of DC line l upward at time t, is the state variable of the transmission power of DC line l downward adjustment at time t, where 1 represents adjustment and 0 represents no adjustment;

[0068] The power unidirectional adjustment constraint is shown as the following formula:

[0069]

[0070] The DC line transmission power adjustment time constraint is shown as the following formula:

[0071]

[0072] Among them, TL l is the minimum stable time after the transmission power of the DC line l is adjusted, is the state variable for the upward adjustment of the transmission power of the DC line l at the moment li, is the state variable for the downward adjustment of the transmission power of the DC line l at the moment li;

[0073] The constraint on the number of adjustments of the transmission power of the DC line is shown in the following formula:

[0074]

[0075] Among them, NL l is the maximum number of adjustments of the transmission power of the DC line l;

[0076] The power constraint of the thermal power unit is shown in the following formula:

[0077]

[0078]

[0079] Among them, is the maximum output limit of the unit i in the sending area, is the minimum output limit of the unit i in the sending area; is the maximum output limit of the unit j in the receiving area, is the minimum output limit of the unit j in the receiving area;

[0080] The power ramp-up constraint of the thermal power unit is shown in the following formula:

[0081]

[0082]

[0083] Among them, is the upward ramp limit of the unit i in the sending area at time t, is the downward ramp limit of the unit i in the sending area at time t, is the upward ramp limit of the unit j in the receiving area at time t, is the downward ramp limit of the unit j in the receiving area at time t;

[0084] The start-stop time constraint of the unit is shown in the following formula:

[0085]

[0086]

[0087]

[0088]

[0089] Among them, TSO i is the minimum shutdown time of the sending - end area unit i, and TOO i is the minimum startup time of the sending - end area unit i; TST j is the minimum shutdown time of the receiving - end area unit j, and TOT j is the minimum startup time of the receiving - end area unit j;

[0090] The new - energy power generation potential constraint is shown as the following formula:

[0091]

[0092] Among them, is the actual output of the wind farm wi in the sending - end area at time t in scenario a, is the actual output of the PV power station pi in the sending - end area at time t in scenario a, is the actual output of the wind farm wj in the receiving - end area at time t in scenario a, is the actual output of the PV power station pj in the receiving - end area at time t in scenario a, is the theoretical power generation of the wind farm wi in the sending - end area at time t in scenario a; is the theoretical power generation of the PV power station pi in the sending - end area at time t in scenario a; is the theoretical power generation of the wind farm wj in the receiving - end area at time t in scenario a; is the theoretical power generation of the PV power station pj in the receiving - end area at time t in scenario a.

[0093] A new - energy cross - regional consumption system based on scenario analysis, the system includes:

[0094] An optimization module, which is used to input the new - energy historical prediction error data, the new - energy predicted power generation curve and the new - energy load curve of the sending - end area and the receiving - end area into the UHVDC cross - regional consumption model, and obtain the start - stop optimization plan of the two - area units and the DC line power transmission that meets the goal of maximizing the expected value of the cross - regional new - energy consumption electricity;

[0095] A processing module, which is used to execute the start - stop optimization plan of the two - area units and the DC line power transmission, and realize the new - energy consumption of the two areas.

[0096] Furthermore, the UHVDC cross - regional consumption model includes: a new - energy prediction scenario set unit, an objective - function unit and a constraint - condition unit;

[0097] The new energy prediction scenario set unit is used to obtain a new energy prediction scenario set based on the historical prediction error data and new energy prediction curves of the sending-end area and the receiving-end area.

[0098] The objective function unit is used to determine an objective function with the maximum expected value of the new energy consumption electricity in the cross-regional system according to the operating data of the thermal power units in the sending-end area and the receiving-end area and the new energy prediction scenarios.

[0099] The constraint condition unit is used to determine the optimization constraint conditions of the objective function according to the operating data of the thermal power units in the sending-end area and the receiving-end area, the new energy prediction scenarios and the new energy load curves.

[0100] Furthermore, the optimization module includes: an input unit, an optimization unit, and an output unit;

[0101] The input unit is used to input the historical prediction error data of the new energy in the sending-end area and the receiving-end area, the new energy predicted power generation curve, and the new energy load curve into the new energy prediction scenario set unit of the UHVDC cross-regional consumption model to obtain a new energy prediction scenario set.

[0102] The optimization unit is used to input the new energy prediction scenario set data into the objective function unit and the constraint condition unit of the UHVDC cross-regional consumption model to obtain an optimization plan for the start-stop of the units in the two regions and the transmission of the DC line that meets the constraint conditions and reaches the requirements of the objective function.

[0103] The output unit is used to output the optimization plan for the start-stop of the units in the two regions and the transmission of the DC line obtained by the optimization unit to the processing module for execution.

[0104] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0105] The present invention provides a method for cross-regional consumption of new energy based on scenario analysis. The historical prediction error data of the new energy in the two regional power grids, the new energy predicted power generation curve, and the new energy load curve are respectively input into a pre-constructed UHVDC cross-regional consumption model to obtain an optimization plan for the start-stop of the units in each region and the transmission of the DC line that meets the maximum expected value of the cross-regional new energy consumption electricity; the optimization plan for the start-stop of the units in each region and the transmission of the DC line is executed to realize the cross-regional consumption of new energy in the two regions. The technical solution provided by the present invention establishes a UHVDC cross-regional consumption model, takes the UHVDC line, the sending-end area, and the receiving-end area as the research objects, fully coordinates the new energy consumption space in the two regions, and promotes the cross-regional consumption of new energy in the two regions.

[0106] The technical solution provided by the present invention is based on the new energy prediction curves in two regions and the historical new energy prediction error data. By using the ARMA model and scenario reduction technology, a new energy prediction scenario set is constructed to describe the scenarios of the future actual possible output of new energy. Taking the new energy prediction scenario set as the input data of the UHVDC cross-regional consumption model and combining the constraints between scenarios in the model, it can cope with the impact of the uncertainty of new energy prediction in two regions on the safe and reliable operation of the cross-regional power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 It is a flowchart of the implementation of the new energy cross-regional consumption method based on scenario analysis according to the present invention;

[0108] Figure 2 It is the UHVDC cross-regional consumption model based on scenario analysis in the embodiment of the present invention;

[0109] Figure 3 It is a flowchart of the construction process of the new energy prediction scenario set in the embodiment of the present invention;

[0110] Figure 4 It is a structure diagram of a new energy cross-regional consumption system based on scenario analysis according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0111] To better understand the present invention, the present invention will be further described in detail below in conjunction with the specification drawings and examples. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments.

[0112] Embodiment 1:

[0113] The new energy cross-regional consumption method based on scenario analysis provided by the embodiment of the present invention has the following specific implementation process as Figure 1 shown, including:

[0114] S101: Respectively input the new energy historical prediction error data, new energy predicted power generation curve, and new energy load curve of the two regional power grids into the pre-constructed UHVDC cross-regional consumption model to obtain the start-stop of each regional unit and the optimization plan for DC line transmission that maximize the expected value of cross-regional new energy consumption electricity;

[0115] S102: Execute the start-stop of each regional unit and the optimization plan for DC line transmission to achieve the cross-regional new energy consumption of the two regions.

[0116] Specifically, in step S101, respectively input the new energy historical prediction error data, new energy predicted power generation curve, and new energy load curve of the two regional power grids into the pre-constructed UHVDC cross-regional consumption model (the model structure is shown in the appendix Figure 2), an optimal unit start-stop and DC line transmission plan for each region that maximizes the expected value of cross-regional new energy consumption is obtained. Among them, the construction process of the UHV DC cross-regional consumption model includes:

[0117] S101-1. Construct a new energy prediction scenario set for two regions, specifically including:

[0118] S101-1-1. Combine the historical new energy prediction error data, select the historical error data sequence with the same prediction time period and similar weather as the sending region or the receiving region, calculate the expected value at each time point, and obtain the new energy prediction error expectation sequence for this time period;

[0119] S101-1-2. Combine the new energy prediction error expectation sequence, use the Autoregressive Moving Average Model (ARMA) to fit the data sequence, solve the ARMA model parameters by the least squares method, and then calculate the standard deviation of the white noise sequence to obtain the ARMA model describing the new energy prediction error expectation sequence;

[0120] The ARMA model formula for fitting the new energy prediction error is as follows:

[0121]

[0122] Among them, is the prediction error of the new energy power station g at time t; p and q are the orders of the autoregressive and moving average parts of the ARMA model respectively; α and β are the parameter sequences of the ARMA model; ε is σ 2 Gaussian white noise;

[0123] S101-1-3. Combine the ARMA model, perform multiple groups of sampling on the white noise, substitute it into the model to generate the basic new energy prediction scenario set for each region. To reduce the model calculation amount and speed up the model solution speed, use the synchronous back substitution reduction method to reduce the scenarios of the basic scenario set, and obtain the new energy prediction scenario set for each region. The specific execution process is as shown in the appendix Figure 3 as follows, including:

[0124] S101-1-3-1. According to the new energy prediction curve, perform multiple groups of sampling on the white noise, and substitute it into the ARMA model of the new energy prediction error expectation sequence obtained in step S101-2 to obtain the new energy prediction error sequence for each region;

[0125] S101-1-3-2. Correct the new energy prediction power generation curve for each region according to the new energy prediction error sequence for each region to obtain multiple corrected new energy prediction power generation curves for each region;

[0126] S101-1-3-3. The new energy predicted power generation curves of multiple corrected regions and the occurrence probabilities of each predicted power generation curve constitute the new energy prediction basic scenario set for each region;

[0127] S101-1-3-4. Calculate the number of scenarios in the new energy prediction basic scenario set for each region respectively, and reset the initial occurrence probability of all scenarios in the new energy prediction basic scenario set for each region to the reciprocal of the number of scenarios in each region;

[0128] S101-1-3-5. Record the initial value of the remaining number of scenarios in each region as the number of scenarios in the new energy prediction basic scenario set for each region, and set the initial value of the deleted scenarios in each region to zero;

[0129] S101-1-3-6. Calculate the distance between every two new energy prediction scenarios in the new energy prediction basic scenario set for each region according to the following formula (1). The distance of the self-scenario is zero, forming the scenario symmetric matrix for each region;

[0130]

[0131] In the formula, C T (s i , s j ) is the distance between scenario s i and s j . is the value of scenario s i at time period t, is the value of scenario s j at time period t;

[0132] S101-1-3-7. Calculate the sum of the elements in each row of the scenario symmetric matrix for each region, obtaining a column matrix representing the sum of the distances from all scenarios in the basic scenario set for each region to other scenarios;

[0133] S101-1-3-8. Multiply each element of the column matrix for each region by the probability of occurrence of each scenario in each region, obtaining the probability column matrix for each region;

[0134] S101-1-3-9. Select the scenario corresponding to the minimum value in the probability column matrix for each region according to the following formula (2), record it as the deleted scenario for each region, subtract one from the remaining number of scenarios in each region, and add one to the number of deleted scenarios in each region;

[0135]

[0136] In the formula, is the occurrence probability of scenario S i ; J is the set of deleted scenarios;

[0137] S101-1-3-10. In the symmetric matrix of each regional scenario, determine the non-deleted scenario corresponding to the column where the non-zero minimum value of the row where the deleted scenario is located according to the row where the deleted scenario is located in step S101-3-7, and obtain the non-deleted scenario closest to the deleted scenario.

[0138] S101-1-3-11. Add the probability of the deleted scenario to the non-deleted scenario closest to it.

[0139] S101-1-3-12. Determine whether the number of remaining scenarios in each region meets the set scenario number requirement. If not, return to step S101-1-3-6; otherwise, output the remaining scenarios in each region and the occurrence probabilities of each remaining scenario to obtain the new energy prediction scenario set for each region.

[0140] S101-1-4. Pair up the scenarios in the new energy prediction scenario set for each region, and multiply the occurrence probabilities of the paired scenarios to obtain a new energy prediction scenario set describing the future output of new energy in the two regions.

[0141] For example, there are two scenarios in the new energy prediction scenario set of the sending-end region, with occurrence probabilities of 40% for scenario a1 and 60% for scenario a2, and there are two scenarios in the new energy prediction scenario set of the receiving-end region, with occurrence probabilities of 30% for scenario b1 and 70% for scenario b2. After pairing, there are four scenarios in the scenario set describing the possible future output of new energy in the two regions: a1-b1 (occurrence probability 12%), a1-b2 (occurrence probability 28%), a2-b1 (occurrence probability 18%), and a2-b2 (occurrence probability 42%).

[0142] S101-2. Determine the objective function with the maximum expected value of new energy consumption electricity in the cross-regional system according to the operating data of thermal power units and new energy operating scenarios in the sending-end region and the receiving-end region, as shown in the following formula (3):

[0143]

[0144] where T1 is the model simulation time, Δt is the simulation time resolution, S is the number of new energy day-ahead prediction scenarios, p a is the probability of new energy prediction scenario a, is the total output of the wind and photovoltaic power plants in the two regions at time t in scenario a, is the actual output of the wind farm wi in the sending-end region at time t in scenario a, is the actual output of the photovoltaic power plant pi in the sending-end region at time t in scenario a, is the actual output of the wind farm wj in the receiving-end region at time t in scenario a, Let \(P_{PV}^{a,t}\) be the actual output of the PV power station \(P_i\) in the receiving - end area at time \(t\) in scenario \(a\), \(W_O\) be the number of wind farms in the sending - end area of the DC line, \(P_O\) be the number of PV power stations in the sending - end area of the DC line, \(W_T\) be the number of wind farms in the receiving - end area of the DC line, and \(P_T\) be the number of PV power stations in the receiving - end area of the DC line.

[0145] S101 - 3. The constraint conditions that the objective function in step S101 - 2 needs to satisfy include: inter - scenario constraints, equality constraints, and inequality constraints under each scenario, specifically including:

[0146] S101 - 3 - 1. The inter - scenario constraints are shown as follows:

[0147]

[0148] Among them, \(K\) is the number of units in the cross - regional power grid, \(S^{[m]}\) m 、\(S^{[n]}\) n are any two scenarios in the new - energy prediction scenario set; \(u_{k,t}^{[m]}\) is the start - stop state of unit \(k\) at time \(t\) in scenario \(S^{[m]}\), m \(u_{k,t}^{[n]}\) is the start - stop state of unit \(k\) at time \(t\) in scenario \(S^{[n]}\), \(P_{k,t}^{[m]}\) is the power generation of unit \(k\) at time \(t\) in scenario \(S^{[m]}\), n \(P_{k,t - 1}^{[m]}\) is the power generation of unit \(k\) at time \(t-1\) in scenario \(S^{[m]}\), \(P_{k,t}^{[n]}\) is the power generation of unit \(k\) at time \(t\) in scenario \(S^{[n]}\), m \(P_{k,t - 1}^{[n]}\) is the power generation of unit \(k\) at time \(t - 1\) in scenario \(S^{[n]}\), \(r_{k}^{up}\) is the upward ramp rate of unit \(k\), n \(r_{k}^{down}\) is the downward ramp rate of unit \(k\);

[0149]

[0150] S101 - 3 - 2. The equality constraints include: sending - end area power - balance constraint, receiving - end area power - balance constraint, and DC - channel power - trading constraint;

[0150] The sending - end area power - balance constraint is shown as follows:

[0151]

[0152] Among them, \(u_{i,t}^{a}\) is the start - stop state of unit \(i\) in the sending - end area at time \(t\) in scenario \(a\), \(P_{i,t}^{a}\) is the power generation of unit \(i\) in the sending - end area at time \(t\) in scenario \(a\), \(P_{w_i,t}^{a}\) is the power generation of wind farm \(w_i\) in the sending - end area at time \(t\) in scenario \(a\), \(P_{p_i,t}^{a}\) is the power generation of PV power station \(p_i\) in the sending - end area at time \(t\) in scenario \(a\), \(P_{l,t}\) is the transmission power of DC channel \(l\) at time \(t\), $P_{mi,t}$ is the demand power of the load node $m_i$ in the sending-end area at time $t$, $MO$ is the number of load nodes in the sending-end area, $L$ is the number of DC channels, $NO$ is the number of generating units in the sending-end area, $WO$ is the number of wind farms in the sending-end area, and $PO$ is the number of PV power plants in the sending-end area;

[0153] The power balance constraint of the receiving-end area is shown in the following formula:

[0154]

[0155] Among them, $u_{j,t}^a$ is the start-stop state of the generating unit $j$ in the receiving-end area at time $t$ in scenario $a$, $P_{j,t}^a$ is the generating power of the generating unit $j$ in the receiving-end area at time $t$ in scenario $a$, $P_{w_j,t}^a$ is the generating power of the wind farm $w_j$ in the receiving-end area at time $t$ in scenario $a$, $P_{p_j,t}^a$ is the generating power of the PV power plant $p_j$ in the receiving-end area at time $t$ in scenario $a$, $P_{l,t}$ is the transmission power of the DC channel $l$ at time $t$, $P_{mj,t}$ is the demand power of the load node $m_j$ in the receiving-end area at time $t$, $MT$ is the number of load nodes in the receiving-end area, $L$ is the number of DC channels, $NT$ is the number of generating units in the receiving-end area, $WT$ is the number of wind farms in the receiving-end area, and $PT$ is the number of PV power plants in the receiving-end area;

[0156] The electricity trading constraint of the DC channel is shown in the following formula:

[0157]

[0158] Among them, $T$ is the model simulation time, and $Q$ l $Q_l$ is the trading electricity volume of the DC channel $l$ during the entire simulation time.

[0159] S101-3-3, the inequality constraints include: DC channel output constraint, power adjustment amplitude constraint, power unidirectional adjustment constraint, DC line transmission power adjustment time constraint, DC line transmission power adjustment times constraint, thermal power unit power constraint, thermal power unit power ramp constraint, unit start-stop time constraint, and new energy generation potential constraint;

[0160] The DC channel output constraint is shown in the following formula:

[0161]

[0162] Among them, $P_{l,\max}$ is the maximum output limit of the transmission power of the DC channel $l$, $P_{l,\min}$ is the minimum output limit of the transmission power of the DC channel $l$;

[0163] The power adjustment amplitude constraint is shown in the following formula:

[0164]

[0165] Among them, is the maximum upward adjustment amplitude per unit time of the transmission power of the DC line l at time t, is the maximum downward adjustment amplitude per unit time of the transmission power of the DC line l at time t, is the state variable of the transmission power of the DC line l upward at time t, is the state variable of the downward adjustment of the transmission power of the DC line l at time t, where 1 represents adjustment and 0 represents no adjustment;

[0166] The one-way power adjustment constraint is shown as the following formula:

[0167]

[0168] The adjustment time constraint of the DC line transmission power is shown as the following formula:

[0169]

[0170] Among them, TL l is the minimum stable time after the adjustment of the transmission power of the DC line l, is the state variable of the upward adjustment of the transmission power of the DC line l at time li, is the state variable of the downward adjustment of the transmission power of the DC line l at time li;

[0171] The adjustment times constraint of the DC line transmission power is shown as the following formula:

[0172]

[0173] Among them, NL l is the maximum number of times of the adjustment of the transmission power of the DC line l;

[0174] The power constraint of the thermal power unit is shown as the following formula:

[0175]

[0176]

[0177] Among them, is the maximum output limit of the unit i in the sending area, is the minimum output limit of the unit i in the sending area; is the maximum output limit of the unit j in the receiving area, is the minimum output limit of the unit j in the receiving area;

[0178] The power ramp constraint of the thermal power unit is shown as the following formula:

[0179]

[0180]

[0181] Among them, is the upward ramp limit of the sending - end area unit i at time t, is the downward ramp limit of the sending - end area unit i at time t, is the upward ramp limit of the receiving - end area unit j at time t, is the downward ramp limit of the receiving - end area unit j at time t;

[0182] The unit start - up and shut - down time constraints are shown as follows:

[0183]

[0184]

[0185]

[0186]

[0187] Among them, TSO i is the minimum shut - down time of the sending - end area unit i, and TOO i is the minimum start - up time of the sending - end area unit i; TST j is the minimum shut - down time of the receiving - end area unit j, and TOT j is the minimum start - up time of the receiving - end area unit j;

[0188] The new - energy power generation potential constraints are shown as follows:

[0189]

[0190] Among them, is the actual output of the wind farm wi in the sending - end area at time t in scenario a, is the actual output of the PV power station pi in the sending - end area at time t in scenario a, is the actual output of the wind farm wj in the receiving - end area at time t in scenario a, is the actual output of the PV power station pj in the receiving - end area at time t in scenario a, is the theoretical power generation of the wind farm wi in the sending - end area at time t in scenario a; is the theoretical power generation of the PV power station pi in the sending - end area at time t in scenario a; is the theoretical power generation of the wind farm wj in the receiving - end area at time t in scenario a; is the theoretical power generation of the PV power station pj in the receiving - end area at time t in scenario a.

[0191] Example 2:

[0192] Based on the same inventive concept, the present invention also provides a new energy cross-region consumption system based on scenario analysis, as Figure 4 shown, the system includes:

[0193] An optimization module, configured to input the historical prediction error data of new energy in the sending region and the receiving region, the predicted power generation curve of new energy, and the new energy load curve into the UHV DC cross-region consumption model, so as to obtain the start-stop optimization plan of the units in the two regions and the DC line transmission optimization plan that meet the goal of maximizing the expected value of the cross-region new energy consumption electricity;

[0194] A processing module, configured to execute the start-stop optimization plan of the units in the two regions and the DC line transmission optimization plan, so as to realize the consumption of new energy in the two regions.

[0195] Among them, the UHV DC cross-region consumption model includes: a new energy prediction scenario set unit, an objective function unit, and a constraint condition unit;

[0196] The new energy prediction scenario set unit is configured to obtain a new energy prediction scenario set based on the historical prediction error data of new energy in the sending region and the receiving region and the predicted new energy curve;

[0197] The objective function unit is configured to determine an objective function for maximizing the expected value of the cross-region new energy consumption electricity according to the operating data of the thermal power units in the sending region and the receiving region and the new energy prediction scenario;

[0198] The constraint condition unit is configured to determine the optimization constraint conditions of the objective function according to the operating data of the thermal power units in the sending region and the receiving region, the new energy prediction scenario, and the new energy load curve.

[0199] Among them, the optimization module includes: an input unit, an optimization unit, and an output unit;

[0200] The input unit is configured to input the historical prediction error data of new energy in the sending region and the receiving region, the predicted power generation curve of new energy, and the new energy load curve into the new energy prediction scenario set unit of the UHV DC cross-region consumption model, so as to obtain a new energy prediction scenario set;

[0201] The optimization unit is configured to input the new energy prediction scenario set data into the objective function unit and the constraint condition unit of the UHV DC cross-region consumption model, so as to obtain the start-stop optimization plan of the units in the two regions and the DC line transmission optimization plan that meet the constraint conditions and meet the requirements of the objective function;

[0202] The output unit is configured to output the start-stop optimization plan of the units in the two regions and the DC line transmission optimization plan obtained by the optimization unit to the processing module for execution.

[0203] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take 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.) that contain computer-usable program code.

[0204] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, 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 devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0205] 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, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0206] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0207] The above are only the embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention pending approval.

Claims

1. A new energy cross-regional consumption method based on scenario analysis, characterized in that Including: Respectively input the new energy historical prediction error data, new energy predicted power generation curve, and new energy load curve of the two regional power grids into the pre-constructed UHV DC cross-regional consumption model to obtain the optimized plans for the start-stop of each regional unit and the transmission of DC lines that meet the maximum expected value of cross-regional new energy consumption electricity; Execute the optimized plans for the start-stop of each regional unit and the transmission of DC lines to achieve the cross-regional consumption of new energy; Among them, the two regions include: a sending-end region and a receiving-end region; The pre-constructed UHV DC cross-regional consumption model includes: a new energy prediction scenario set, an objective function with the maximum expected value of cross-regional system new energy consumption electricity, and objective function optimization constraints; The construction of the UHV DC cross-regional consumption model includes: Respectively based on the new energy historical prediction error data of the sending-end regional power grid and the receiving-end regional power grid, obtain the new energy prediction error expectation sequences of each region; According to the new energy predicted power generation curves of each region and the new energy prediction error expectation sequences of each region, obtain a new energy prediction scenario set based on the ARMA model; According to the operating data of thermal power units and new energy prediction scenarios of the sending-end regional power grid and the receiving-end regional power grid, determine the objective function with the maximum expected value of cross-regional system new energy consumption electricity; According to the operating data of thermal power units, new energy prediction scenarios, and new energy load curves of the sending-end regional power grid and the receiving-end regional power grid, determine the objective function optimization constraints; Input the data of the new energy prediction scenario set into the objective function with the maximum expected value of cross-regional system new energy consumption electricity, and combine the objective function optimization constraints to obtain the optimized plans for the start-stop of each regional unit and the transmission of DC lines that meet the constraint conditions and meet the requirements of the objective function; The ARMA is: Autoregressive Moving Average Model; The obtaining of the new energy prediction scenario set based on the ARMA model according to the new energy predicted power generation curves of each region and the new energy prediction error expectation sequences of each region includes: According to the new energy predicted power generation curves of each region and the new energy prediction error expectation sequences of each region, obtain the new energy prediction basic scenario sets of each region based on the ARMA model; Use scenario reduction technology to respectively reduce the scenarios of the new energy basic scenario sets of each region to obtain the new energy prediction scenario sets of each region; Pair the scenarios in the new energy prediction scenario sets of each region two by two to obtain a new energy prediction scenario set describing the future output of new energy prediction in the two regions.

2. The new energy cross-regional consumption method based on scenario analysis according to claim 1, wherein The respectively obtaining of the new energy prediction error expectation sequences of each region based on the new energy historical prediction error data of the sending-end regional power grid and the receiving-end regional power grid includes: Respectively select the new energy prediction historical error data under the same weather conditions in the historical time period with the same prediction time period as the sending-end regional power grid and the receiving-end regional power grid; Based on the selected new energy prediction historical error data, calculate the new energy prediction error expectation values at each time point to obtain the new energy prediction error expectation sequences of each region in the prediction time period.

3. The new energy cross-regional consumption method based on scenario analysis according to claim 1, characterized in that The respectively obtaining of the new energy prediction basic scenario sets of each region based on the ARMA model according to the new energy predicted power generation curves of each region and the new energy prediction error expectation sequences of each region includes: Solve the ARMA model parameters using the least squares method; Respectively select the Gaussian white noise samples of the new energy prediction error expectation sequences with equal occurrence probabilities in each region, and substitute the Gaussian white noise samples and the model parameters in each region into the ARMA model formula to obtain the new energy prediction error sequences in each region; Correct the new energy predicted power generation curves in each region according to the new energy prediction error sequences in each region to obtain multiple corrected new energy predicted power generation curves in each region; The multiple corrected new energy predicted power generation curves in each region and the occurrence probabilities of each predicted power generation curve constitute the new energy prediction basic scenario set in each region.

4. The new energy cross-region consumption method based on scenario analysis according to claim 1, characterized in that Use the scenario reduction technology to respectively reduce the scenarios in the new energy basic scenario set in each region to obtain the new energy prediction scenario set in each region, including: S1: Calculate the number of scenarios in the new energy prediction basic scenario set in each region respectively, and reset the initial value of the occurrence probability of all scenarios in the new energy prediction basic scenario set in each region to the reciprocal of the number of scenarios in each region; S2: Record the initial value of the remaining number of scenarios in each region as the number of scenarios in the new energy prediction basic scenario set in each region, and set the initial value of the number of deleted scenarios in each region to zero; S3: Calculate the distance between every two new energy prediction scenarios in the new energy prediction basic scenario set in each region to form the scenario symmetric matrix in each region; S4: Calculate the sum of the elements in each row of the scenario symmetric matrix in each region to obtain the column matrix representing the sum of the distances from all scenarios in the basic scenario set in each region to other scenarios; S5: Multiply each element of the column matrix in each region by the occurrence probability of each scenario in each region to obtain the scenario probability column matrix in each region; S6: Select the scenario corresponding to the minimum value in the scenario probability column matrix in each region as the deleted scenario in each region, subtract one from the remaining number of scenarios in each region, and add one to the number of deleted scenarios in each region; S7: In the scenario symmetric matrix in each region, determine the non-deleted scenario corresponding to the column where the non-zero minimum value is located in the row where the deleted scenario is located according to step S6 to obtain the non-deleted scenario closest to the deleted scenario; S8: Add the occurrence probability of the deleted scenario to the non-deleted scenario closest to it; S9: Judge whether the remaining number of scenarios in each region meets the set scenario number requirement. If not, return to step S3. Otherwise, output the remaining scenarios and the occurrence probabilities of the remaining scenarios in each region to obtain the new energy prediction scenario set in each region.

5. The method for cross-region consumption of new energy based on scenario analysis according to claim 4, wherein, The calculation formula for the distance between every two new energy prediction scenarios is as follows: Among them, is the distance between and is the value of the scenario at time period t, is the value of the scenario at time period t.

6. The method for cross-regional consumption of new energy based on scenario analysis according to claim 1, wherein The objective function for maximizing the expected value of the cross-regional system's new energy consumption electricity is shown as the following formula: Among them, is the model simulation time, is the simulation time resolution, S is the number of new energy day-ahead prediction scenarios, is the new energy prediction scenario a probability, is the total output of the wind and solar power plants in the two regions at scenario a time, t is the actual output of the wind farm in the sending-end region at scenario a time, t is the actual output of the PV power station in the sending-end region at scenario a time, t is the actual output of the wind farm in the receiving-end region at scenario a time, t is the actual output of the PV power station in the receiving-end region at scenario a time, t is the number of wind farms in the sending-end region of the DC line, is the number of PV power stations in the sending-end region of the DC line, is the number of wind farms in the receiving-end region of the DC line, is the number of PV power stations in the receiving-end region of the DC line.​​​​​ 7. The method for cross-regional consumption of new energy based on scenario analysis according to claim 1, wherein The optimization constraint conditions of the objective function include: constraints between scenarios and equality constraints and inequality constraints under each scenario; Among them, the equality constraints include: power balance constraints in the sending region, power balance constraints in the receiving region, and DC channel electricity trading constraints; The inequality constraints include: DC channel output constraint, power adjustment amplitude constraint, unidirectional power adjustment constraint, DC line transmission power adjustment time constraint, DC line transmission power adjustment frequency constraint, thermal power unit power constraint, thermal power unit power ramp constraint, unit start-stop time constraint, and new energy generation potential constraint.

8. The method for cross-regional consumption of new energy based on scenario analysis according to claim 7, characterized in that The constraints between scenarios are shown in the following formula: wherein, is the number of units in the cross-regional power grid, 、 are any two scenarios in the new energy prediction scenario set; is the scenario in which the unit k is in the start-stop state at t ; is the scenario in which the unit k is in the start-stop state at t ; is the scenario in which the unit k has a power generation output at t ; is the scenario in which the unit k has a power generation output at t-1 ; is the upward ramp rate of the unit k ; is the downward ramp rate of the unit k ; 9. The method for cross-regional consumption of new energy based on scenario analysis according to claim 7, characterized in that The power balance constraint of the sending region is shown in the following formula: Among them, is the sending-end area unit a in the scenario i at t start-stop state, is the sending-end area unit a in the scenario i at t generated power, is the sending-end area wind farm a in the scenario at t generated power, is the sending-end area PV power station a in the scenario at t generated power, is the DC channel l at t transmission power, is the sending-end area load node mi at t demand power, is the number of sending-end area load nodes, L is the number of DC channels, is the number of sending-end area units, is the number of sending-end area wind farms, is the number of sending-end area PV power stations; The power balance constraint of the receiving region is shown in the following formula: Among them, is the start-stop status of the receiving-end area units a in the scenario j at t ; is the generated power of the receiving-end area units a in the scenario j at t ; is the generated power of the receiving-end area wind farms a in the scenario at t ; is the generated power of the receiving-end area PV power plants a in the scenario at t ; is the transmission power of the DC channel l at t ; is the demand power of the receiving-end area load nodes mj at t ; is the number of receiving-end area load nodes, L is the number of DC channels, is the number of receiving-end area units, is the number of receiving-end area wind farms, is the number of receiving-end area PV power plants; The electricity trading constraint of the DC channel is shown in the following formula: Among them, T is the model simulation time, is the DC channel l and is the transaction power during the entire simulation time.

10. The method for cross-regional consumption of new energy based on scenario analysis according to claim 7, characterized in that The DC channel output constraint is shown in the following formula: Among them, is the DC channel l maximum output limit of the transmitted power, is the DC channel l minimum output limit of the transmitted power; The power adjustment amplitude constraint is shown in the following formula: Among them, is the DC line l at t the maximum upward adjustment amplitude per unit time of the transmission power at the moment, is the DC line l at t the maximum downward adjustment amplitude per unit time of the transmission power at the moment, is the DC line l at t the state variable of the upward transmission power at the moment, is the DC line l at t the state variable of the downward adjustment of the transmission power at the moment, where 1 represents adjustment and 0 represents non-adjustment; The unidirectional power adjustment constraint is shown in the following formula: ； The DC line transmission power adjustment time constraint is shown in the following formula: Among them, is the minimum stable time after the transmission power of the DC line l is adjusted, is the state variable when the transmission power of the DC line l at li is adjusted upward, is the state variable when the transmission power of the DC line l at li is adjusted downward; The DC line transmission power adjustment frequency constraint is shown in the following formula: Among them, is the DC line l maximum number of times for adjusting transmission power; The thermal power unit power constraint is shown in the following formula: Among them, is the maximum output limit of the sending-end area units i ; ， is the minimum output limit of the sending-end area units i ; is the maximum output limit of the receiving-end area units j ; ， is the minimum output limit of the receiving-end area units j ; The thermal power unit power ramp constraint is shown in the following formula: Among them, is the upward ramping limit of the sending-end area units i at t time, is the downward ramping limit of the sending-end area units i at t time, is the upward ramping limit of the receiving-end area units j at t time, is the downward ramping limit of the receiving-end area units j at t time; The unit start-stop time constraint is shown in the following formula: Among them, is the minimum shutdown time of the sending-end area units i , is the minimum startup time of the sending-end area units i ; is the minimum shutdown time of the receiving-end area units j , is the minimum startup time of the receiving-end area units j ; The new energy generation potential constraint is shown in the following formula: Among them, is the wind farm in the sending-end area in the scenario a at t the actual output power, is the PV power station in the sending-end area in the scenario a at t the actual output power, is the wind farm in the receiving-end area in the scenario a at t the actual output power, is the PV power station in the receiving-end area in the scenario a at t the actual output power, is the theoretical power generation of the wind farm in the sending-end area a in the scenario wi at t ; is the theoretical power generation of the PV power station in the sending-end area a in the scenario pi at t ; is the theoretical power generation of the wind farm in the receiving-end area a in the scenario wj at t ; is the theoretical power generation of the PV power station in the receiving-end area a in the scenario pj at t ; 11. A new energy cross-regional consumption system based on scenario analysis for implementing the method as described in claim 1, characterized in that, including: An optimization module, configured to input the new energy historical prediction error data, new energy predicted generation curve, and new energy load curve of the sending region and the receiving region into the UHV DC cross-regional consumption model, and obtain the optimization plan for unit start-stop and DC line external transmission in the two regions that meets the goal of maximizing the expected value of cross-regional new energy consumption; A processing module, configured to execute the optimization plan for unit start-stop and DC line transmission in the two regions to achieve cross-regional new energy consumption.

12. A new energy cross-regional consumption system based on scenario analysis according to claim 11, characterized in that, The UHV DC cross-regional consumption model includes: a new energy prediction scenario set unit, an objective function unit, and a constraint condition unit; The new energy prediction scenario set unit is configured to obtain a new energy prediction scenario set based on the new energy historical prediction error data and new energy prediction curve of the sending region and the receiving region; The objective function unit is configured to determine an objective function for maximizing the expected value of cross-regional system new energy consumption according to the thermal power unit operation data and new energy prediction scenarios of the sending region and the receiving region; The constraint condition unit is configured to determine the optimization constraint conditions of the objective function according to the thermal power unit operation data, new energy prediction scenarios, and new energy load curves of the sending region and the receiving region.

13. A new energy cross-regional consumption system based on scenario analysis according to claim 11, characterized in that, The optimization module includes: an input unit, an optimization unit, and an output unit; The input unit is configured to input the new energy historical prediction error data, new energy predicted generation curve, and new energy load curve of the sending region and the receiving region into the new energy prediction scenario set unit of the UHV DC cross-regional consumption model to obtain a new energy prediction scenario set; The optimization unit is configured to input the new energy prediction scenario set data into the objective function unit and the constraint condition unit of the UHV DC cross-regional consumption model to obtain the optimization plan for unit start-stop and DC line external transmission in the two regions that meets the constraint conditions and the requirements of the objective function. The output unit is configured to output the optimized unit start-stop and DC line external transmission optimization plan obtained by the optimization unit to the processing module for execution.

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