A method for cross-regional power grid flexibility resource allocation based on opportunity-constrained programming

Abstract

This disclosure reveals a method for allocating flexible resources in a cross-regional power grid based on opportunity-constrained programming, comprising: 1. obtaining physical operating parameters of DC channels, operating parameters of thermal power units, power generation cost data, historical data of renewable energy generation, and flexibility parameters of the cross-regional power system; 2. establishing a physical operating model of the cross-regional power system and an uncertainty model of renewable energy and load at the sending and receiving ends based on the parameters and data described in step 1; 3. establishing a two-stage opportunity-constrained model P1 for the flexible resource allocation of the cross-regional DC interconnected power system based on the physical operating model and the uncertainty model; 4. constructing a mixed-integer linear programming model P2 based on the model P1 to complete the flexible resource allocation of the cross-regional power grid.

Description

Technical Field

[0001] This disclosure belongs to the field of power system operation and dispatch automation technology, and specifically relates to a cross-regional power grid flexibility resource allocation method based on chance-constrained programming. Background Technology

[0002] With the continuous evolution of the components of my country's power system, power system planning faces increasingly severe challenges. Appropriately addressing these challenges is essential for ensuring optimized management, grid reliability, and flexibility in my country's power system. Against this backdrop, high-voltage direct current (HVDC) transmission technology will play a crucial role. Therefore, in grid planning and medium-to-long-term research, there is an urgent need to establish effective and specific optimization models for HVDC channel configuration and their corresponding solution methods.

[0003] The main technical challenges in optimizing DC transmission line configuration lie in modeling the uncertainties of new energy sources such as wind and solar power, as well as load demand, and planning the flexibility resources at both the sending and receiving ends, such as thermal power generation, energy storage, and demand-side response. Rational utilization of flexibility resources at both ends can significantly facilitate the integration of new energy sources; however, existing research rarely mentions or analyzes the role of flexibility resources in the integration of new energy sources through DC transmission lines. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this disclosure is to provide a method for flexible resource allocation in cross-regional power grids based on chance-constrained programming. This method is based on the original physical model of the cross-regional DC power grid system and uses a chance-constrained two-stage stochastic programming method to solve the model, thereby realizing the absorption of new energy in cross-regional interconnected power grids and minimizing investment and operating costs, and optimizing the current DC interconnection power transmission configuration mode in my country.

[0005] To achieve the above objectives, this disclosure provides the following technical solutions:

[0006] A cross-regional power grid flexibility resource allocation method based on opportunity-constrained programming includes the following steps:

[0007] S100: Obtain physical operating parameters of DC channels in cross-regional power systems, operating parameters of thermal power units, power generation cost data, historical data of new energy power generation, and flexibility parameters;

[0008] S200. Construct a physical operation model of the cross-regional power system and an uncertainty model of new energy sources and loads at the sending and receiving ends based on the parameters and data described in step S100.

[0009] S300. Based on the physical operation model and uncertainty model, construct a two-stage opportunity constraint model P1 for the flexible resource allocation of cross-regional DC interconnection power systems.

[0010] S400. Construct a mixed-integer linear programming model P2 based on the model P1 to complete the flexible resource allocation for cross-regional power grids.

[0011] Preferably, in step S200, the physical operation model includes traditional thermal power units, new energy units, energy storage and loads belonging to the sending-end power generation area, as well as traditional thermal power units, energy storage, large-scale conventional loads and flexible loads belonging to the receiving-end area.

[0012] Preferably, in step S200, the uncertainty model of the new energy source and load at the sending and receiving ends is expressed as follows:

[0013]

[0014]

[0015]

[0016] in, Let P represent the actual load and the predicted load at time t, respectively. t w P t wf P represents the actual and predicted wind power output at time t, respectively. t pv P t pvf Let represent the actual photovoltaic output and the predicted output at time t, respectively, and let A represent the sets of representations for the sending and receiving ends. These are mathematical symbols, representing any value; s represents the sending end; r represents the receiving end; t represents the optimized time period; l represents the load; w represents wind power; pv represents photovoltaic power; f represents the predicted value; ε represents the load. t A，l , ε t w , ε t pv Let represent Gaussian distribution functions with zero mean, and let σ be their standard deviations. t A，L , σ t w , σ t pv .

[0017] Preferably, in step S300, the two-stage opportunity constraint model P1 for the flexible resource allocation of the inter-regional DC interconnection power system is expressed as:

[0018]

[0019] Among them, C e This represents the cost coefficient for energy storage construction. This represents the cost coefficient for the flexible retrofitting of thermal power units. C represents the cost coefficient for operating thermal power units. rg C represents the cost coefficient for standby thermal power units. re This represents the cost coefficient for energy storage backup. Indicates the maximum energy storage capacity. This represents the decision variable for whether thermal power unit g should be retrofitted. This represents the output of thermal power unit g at time t. These represent the upward and downward standby states of thermal power unit g at time t, respectively. Let e ​​represent energy storage up and down as standby at time t, g represent thermal power unit, m represent thermal power unit flexibility retrofit, o represent thermal power unit operation, rg represent thermal power unit standby, re represent energy storage standby, A represent the set of sending and receiving ends, s represent sending end, r represent receiving end, up represent up standby, dn represent down standby, and N represent up standby. g This indicates the total number of thermal power units in the region.

[0020] Preferably, in step S400, the mixed-integer linear programming model P2 includes a unit aggregation model, which is expressed as:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] in, This represents the upper limit capacity of the aggregation unit at point A at time t-1. This represents the upper limit capacity of the aggregation unit at point A at time t. This indicates the number of generating units at end A. These represent the start-up and shutdown capacities of the aggregation unit at time t, respectively. Let represent the start-up and shutdown state variables of aggregation unit c at time t, respectively. Let represent the start-up and shutdown state variables of unit g at time t, respectively. These represent the output of the accelerator unit at times t-1 and t, respectively. These represent the upward and downward standby provided by the merging unit at time t, respectively. These represent the upward and downward reserves provided by the merging unit at time t-1, respectively, and τ represents a specific optimization period. These represent the minimum start-up and shutdown times of the polymerization unit, respectively. These represent the minimum and maximum output of the polymerization unit, respectively. These represent the maximum upward and downward reserve that the polymerization unit can provide, respectively. Δt represents the upward and downward ramp rates of the aggregation unit, τ represents a specific optimization period, c represents the aggregation unit, su represents the unit's start-up, sd represents the unit's shutdown, on represents the unit's minimum start-up, off represents the unit's minimum shutdown, s represents the sending end, r represents the receiving end, and up and dn represent the upward and downward standby, respectively.

[0037] Preferably, in step S400, the mixed-integer linear programming model P2 further includes an equivalent deterministic mixed-integer programming model, which is expressed as:

[0038]

[0039]

[0040]

[0041] Where, φ t -1 (k) represents the k-quantile of the cumulative distribution function of the normal distribution, α up α dn β represents the confidence level of the upward and downward flexibility opportunity constraints at the sending end, respectively. up ,β dnThese represent the confidence levels constrained by upward and downward flexibility opportunities, respectively. These represent the upward and downward standby provided by unit g at time t, respectively. These represent the upward and downward standby provided by unit g at time t, respectively. These represent the upward and downward backup provided by the sending-end energy storage at time t, respectively. These represent the upward and downward reserves provided by the receiving-end energy storage at time t, respectively. N represents the upward and downward standby capacity provided by the DC channel at time t, respectively. g This indicates the total number of thermal power units in the region.

[0042] Preferably, in step S400, the mixed-integer linear programming model P2 is represented as:

[0043]

[0044] Among them, C e This represents the cost coefficient for energy storage construction. Indicates the maximum energy storage capacity. This represents the cost coefficient for the flexible retrofitting of thermal power units. This represents the decision variable for whether thermal power unit g should be retrofitted. This represents the cost coefficient for operating thermal power units. C represents the output of thermal power unit g at time t. rg This represents the cost coefficient for standby thermal power units. C represents the upward and downward standby states of thermal power unit g at time t, respectively. re This represents the cost coefficient for energy storage backup.

[0045] This disclosure also provides a cross-regional power grid flexibility resource allocation device based on opportunity-constrained programming, comprising:

[0046] The acquisition module is used to acquire the physical operating parameters of the DC channel of the cross-regional power system, the operating parameters of thermal power units, the power generation cost data, the historical data of new energy power generation, and the flexibility parameters.

[0047] The first construction module is used to construct a physical operation model of the cross-regional power system and an uncertainty model of new energy sources and loads at the sending and receiving ends based on the parameters and data obtained by the acquisition module.

[0048] The second construction module is used to construct a two-stage opportunity constraint model P1 for the flexible resource allocation of the cross-regional DC interconnection power system based on the physical operation model and uncertainty model constructed by the first construction module.

[0049] The third construction module is used to construct a mixed-integer linear programming model P2 based on model P1, in order to complete the flexible resource allocation of cross-regional power grids.

[0050] This disclosure also provides a storage device storing a plurality of instructions adapted to be loaded and executed by a processor:

[0051] Obtain physical operating parameters of DC channels in cross-regional power systems, operating parameters of thermal power units, power generation cost data, historical data of new energy power generation, and flexibility parameters;

[0052] Based on parameters and data, a physical operation model of the cross-regional power system and an uncertainty model of renewable energy and load at the sending and receiving ends are constructed.

[0053] Based on the physical operation model and uncertainty model, a two-stage opportunity constraint model P1 for the flexible resource allocation of cross-regional DC interconnected power systems is constructed.

[0054] Based on the model P1, a mixed-integer linear programming model P2 is constructed to achieve flexible resource allocation for cross-regional power grids.

[0055] This disclosure also provides a computer device, including:

[0056] Memory, used to store computer instructions;

[0057] A processor is used to execute computer instructions to implement the methods described in any of the preceding methods.

[0058] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0059] 1. This disclosure provides a physical model for the power configuration of new energy consumption and transmission in two-region DC transmission channels that includes flexible resources, and proposes a corresponding optimization model for the power configuration of DC transmission channels at both the sending and receiving ends. This model can be used for the configuration of new energy installed capacity and the planning of energy storage flexibility resources, which helps to promote the consumption of new energy and has certain practical value for relevant departments such as power grid planning and construction.

[0060] 2. This disclosure provides a two-stage stochastic programming model based on chance constraints, which is used for the collaborative planning and operation of cross-regional interconnected power grids. Simultaneously, while addressing the uncertainties in renewable energy output and load forecasting, this model introduces chance constraints for flexible adaptation, effectively solving the problems of renewable energy absorption and minimizing investment and operating costs.

[0061] 3. This disclosure provides a series of methods to address the difficulty in efficiently solving the proposed two-stage stochastic programming model based on chance constraints, transforming the model into a deterministic mixed-integer programming model. First, a unit aggregation model is proposed to address the computational burden caused by excessive integer variables in the original problem. Second, an analytical method is proposed to transform chance constraints into equivalent deterministic constraints, converting the two-stage problem with chance constraints into a mixed-integer linear programming problem. These methods significantly improve the model's solution efficiency while ensuring the accuracy of the solution. Attached Figure Description

[0062] Figure 1 This is a flowchart of a cross-regional power grid flexibility resource allocation method based on opportunity-constrained programming provided in this disclosure;

[0063] Figure 2 This is a schematic diagram of a physical model for flexible resource allocation in a cross-regional DC interconnected power system, provided in this disclosure.

[0064] Figure 3 This is a schematic diagram of a multi-timescale decision-making framework model provided in this publication;

[0065] Figure 4 This is a schematic diagram of a mechanism provided in this disclosure for a DC channel to serve as a system backup provider and user;

[0066] Figure 5 This is a schematic diagram of a planned backup system that meets all possible climbing scenarios provided in this disclosure;

[0067] Figure 6 This is a schematic diagram of the probability distribution function of the relationship between system power generation and system load provided in this disclosure;

[0068] Figure 7 This is a schematic diagram of the output and adjustable range of the thermal power unit obtained by the aggregation model in the embodiments of this disclosure;

[0069] Figure 8 This is a schematic diagram of the output and adjustable range of the thermal power unit obtained from the non-polymer model in the embodiments of this disclosure;

[0070] Figure 9 This is a schematic diagram of the planned transmission power and adjustable range of the DC channel obtained from the aggregation model in the embodiments of this disclosure;

[0071] Figure 10 This is a schematic diagram of the planned transmission power and adjustable range of the DC channel obtained from the non-aggregation model in the embodiments of this disclosure;

[0072] Figure 11 This is a schematic diagram of the optimal energy storage capacity under different new energy levels in the embodiments of this disclosure;

[0073] Figure 12 This is a schematic diagram of the optimal energy storage capacity under different confidence levels in the embodiments of this disclosure. Detailed Implementation

[0074] The following will refer to the appendix. Figures 1 to 12 Specific embodiments of this disclosure are described in detail. While specific embodiments of this disclosure are shown in the accompanying drawings, it should be understood that this disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0075] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that different terms may be used to refer to the same component. This specification and claims do not distinguish components based on differences in terminology, but rather on differences in function. The terms "comprising" or "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising but not limited to." The following descriptions are preferred embodiments for carrying out this disclosure; however, these descriptions are for the purpose of understanding the general principles of the specification and are not intended to limit the scope of this disclosure. The scope of protection of this disclosure is determined by the appended claims.

[0076] To facilitate understanding of the embodiments of this disclosure, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. The accompanying drawings do not constitute a limitation on the embodiments of this disclosure.

[0077] In one embodiment, such as Figure 1 As shown, this disclosure provides a method for cross-regional power grid flexibility resource allocation based on opportunity-constrained programming, including the following steps:

[0078] S100: Obtain physical operating parameters of DC channels in cross-regional power systems, operating parameters of thermal power units, power generation cost data, historical data of new energy power generation, and flexibility parameters.

[0079] In this step, the physical operating parameters of the DC channel include: the upper limit of the DC channel's transmission power. and lower limit DC channel maximum uphill power R dc，up and downhill power R dc，dn Minimum adjustment time interval τ for DC channel power transmission dc Maximum daily adjustment frequency N for DC channels dc Daily planned transmission power Q of DC channel dc .

[0080] The operating parameters of the thermal power unit include: the upper limit of the unit's output. and lower limit Maximum uphill power of the unit and downhill power Minimum adjustment interval of unit output

[0081] The power system flexibility parameters include: a random variable model ε of the error between predicted load and predicted renewable energy output. t A，l , ε t w , ε t pv and model parameter σ t A，L , σ t w , σ t pv .

[0082] S200. Based on the parameters and data described in step S100, construct a physical operation model of the cross-regional power system and an uncertainty model of new energy sources and loads at the sending and receiving ends.

[0083] In this step, such as Figure 2 As shown, the physical model includes a sending-end power generation area and a receiving-end area. The sending-end power generation area includes traditional thermal power units, new energy units, energy storage, and loads. The receiving-end area includes traditional thermal power units, energy storage, and large-scale conventional and flexible loads.

[0084] In this step, it is assumed that the uncertain prediction errors of load and renewable energy output follow a normal distribution function. Furthermore, it is assumed that load and renewable energy output are independent at each time stage. The uncertainty model for renewable energy and load at both the sending and receiving ends is as follows:

[0085]

[0086]

[0087]

[0088] in, Let P represent the actual load and the predicted load at time t, respectively. t w P t wf P represents the actual and predicted wind power output at time t, respectively. t pv P t pvfLet represent the actual photovoltaic output and the predicted output at time t, respectively, and let A represent the sets of representations for the sending and receiving ends. These are mathematical symbols, representing any value; s represents the sending end; r represents the receiving end; t represents the optimized time period; l represents the load; w represents wind power; pv represents photovoltaic power; f represents the predicted value; ε represents the load. t A，l , ε t w , ε t pv Let represent Gaussian distribution functions with zero mean, and let σ be their standard deviations. t A，L , σ t w , σ t pv These three equations define the probability distribution functions of load and new energy output, and the distribution of uncertainty parameters can be expressed as:

[0089]

[0090]

[0091]

[0092] in, C represents the uncontrollable load in the predicted load. w C pv These represent the installed capacity of wind turbine units and photovoltaic units, respectively.

[0093] Secondly, the coordinated planning and operation of inter-regional interconnected power grids is an optimization problem that comprehensively considers reliability and economy. Furthermore, the predicted output of load and new energy sources is uncertain. Therefore, this disclosure establishes the following... Figure 3The disclosure presents a decision-making framework across different time scales. Within this framework, considering the economic performance of the planning results, this disclosure aims to minimize the investment costs of battery energy storage and thermal power unit retrofitting, as well as fuel and standby operating costs, under the predicted scenarios. Based on reliability requirements, the dispatch center plans sufficient thermal power units, energy storage, and DC interconnections as reserves to address uncertainties in sending-end and receiving-end loads and renewable energy output. In the inter-regional interconnected power grid modeled in this disclosure, the sending-end power system submits historical data on wind power, photovoltaic output, and load to the system dispatch center. Furthermore, the dispatch center can also obtain historical load data from the receiving-end power grid. Based on this historical data, the dispatch center can generate a base scenario for each season by averaging the data over a season. Specifically, the model is modeled as a two-stage optimization problem. In the planning stage, based on the information from the predicted data, the energy storage capacity and thermal power unit retrofitting schemes are determined. The transmission power of the high-voltage DC transmission channel should also be dispatched during this stage. However, there are errors between the predicted data and actual values. To address these prediction errors, the system dispatch center needs to dispatch available flexible resource reserves to ensure the reliability of the inter-regional power system during operation.

[0094] This disclosure discloses that reserves are provided by thermal power units, energy storage, and HVDC transmission channels. Thermal power units can provide upward (downward) reserves by increasing (decreasing) the output at the sending and receiving ends. Energy storage can supply upward (downward) reserves by increasing (decreasing) the discharge (charging) rate or decreasing (increasing) the charging (discharging) rate. Additionally, HVDC transmission channels can also serve as a form of reserve provision. If reserves are provided at the sending end, then the receiving end can similarly provide or utilize reserves. For example, when the actual net load at the sending end exceeds the forecast, the HVDC channel should supply upward reserves by reducing transmission power. The receiving-end power system operates in two ways: when the actual load exceeds the forecast, the HVDC channel acts as a reserve user; when the actual demand is less than the forecast, the HVDC transmission channel provides downward reserves, with the mechanism as follows: Figure 4 As shown. Figure 4 This is a schematic diagram of a DC channel acting as a backup provider or user. When the DC channel provides upward backup at the sending end, backup can also be provided or used at the receiving end. These represent the net load before and after the increase at the sending end, respectively. These represent the net load before and after the change at the receiving end, respectively. These represent the transmission power before and after the change in the DC channel. These represent the minimum and maximum transmission power of the DC channel, respectively. These represent the maximum downward and upward reserve that the DC channel can provide, r. t dc ，up This indicates the actual upward reserve provided by the DC channel at this time.

[0095] S300. Based on the physical operation model and uncertainty model, construct a two-stage opportunity constraint model P1 for the flexible resource allocation of cross-regional DC interconnection power systems.

[0096] This step involves establishing a two-stage opportunity-constrained planning model for the allocation of flexibility resources in inter-regional DC interconnected power systems. The aim is to minimize system investment and operating costs and maximize renewable energy absorption by identifying the optimal allocation patterns for flexibility resources within the system. The model comprises two phases: a planning phase and an operation phase. The planning phase involves scheduling the transmission power of the DC transmission channels, determining the capacity of energy storage at both the sending and receiving ends, and making decisions regarding the flexibility retrofitting of thermal power units. The operation phase allocates system reserves to address multiple uncertainties from both the generation and demand sides.

[0097] The objective of the two-stage planning problem is to minimize the investment costs of energy storage and flexible retrofitting of thermal power units, as well as the fuel costs and standby costs required for power generation. The specific objective function is determined as follows:

[0098]

[0099] Among them, C e This represents the cost coefficient for energy storage construction. This represents the cost coefficient for the flexible retrofitting of thermal power units. C represents the cost coefficient for operating thermal power units. rg C represents the cost coefficient for standby thermal power units. re This represents the cost coefficient for energy storage backup. Indicates the maximum energy storage capacity. This represents the decision variable for whether thermal power unit g should be retrofitted. This represents the output of thermal power unit g at time t. These represent the upward and downward standby states of thermal power unit g at time t, respectively. Let e ​​represent energy storage up and down as standby at time t, g represent thermal power unit, m represent thermal power unit flexibility retrofit, o represent thermal power unit operation, rg represent thermal power unit standby, re represent energy storage standby, A represent the set of sending and receiving ends, s represent sending end, r represent receiving end, up represent up standby, dn represent down standby, and N represent up standby. g This indicates the total number of thermal power units in the region.

[0100] Below, we will model the planning phase and the operation phase respectively.

[0101] 1. Planning Phase Modeling. To address the volatility and uncertainty of load demand and renewable energy, the allocation of flexibility resources is crucial during the planning phase. The goal of planning phase modeling is to determine the optimal energy storage capacity and the flexibility retrofit strategies for sending and receiving-end thermal power units. Furthermore, the optimal transmission power of the DC transmission line is determined at this stage. Objectives of the planning phase include energy storage investment costs and thermal power unit retrofit costs. Power system flexibility refers to the power system's ability to adapt to changes and uncertainties by deploying available resources within a given time scale. This disclosure considers system flexibility as a reserve of flexibility resources.

[0102] The model constructed in this stage also needs to meet constraints such as those at the sending and receiving ends and the DC tie line, as follows:

[0103] 1) Power balance constraint at the sending end:

[0104]

[0105] 2) Power balance constraint at the receiving end:

[0106]

[0107] Among them, P t s，c P t s，d P represents the charging and discharging power of the energy stored at the sending end at time t. t dc P represents the power transmitted by the DC channel at time t. t r，c P t r，d and represent the charging and discharging power of the energy stored at the receiving end at time t, respectively.

[0108] Traditional thermal power units lack flexibility, therefore unit retrofitting is introduced to increase their flexibility by refining minimum output and increasing the gradeability. The physical and operational constraints of thermal power units are introduced as follows:

[0109] 3) Output constraints of existing thermal power units:

[0110]

[0111] 4) Output constraints of the modified thermal power unit:

[0112]

[0113] 5) Ramp-up constraints for the unit:

[0114]

[0115]

[0116] 6) Minimum start-up and shutdown time constraints for the unit:

[0117]

[0118]

[0119] in, This indicates the on / off status of the unit at time t, with the unit starting up. Or the unit is shut down Indicates whether the unit has undergone flexibility modifications; the unit has undergone modifications. Or not modified Let represent the minimum output of unit g before and after the modification, respectively. This indicates the maximum output of the generator unit. These represent the upward ramp rate of unit g before and after the modification, respectively. These represent the downward ramp rate of unit g before and after the modification, respectively. These represent the minimum start-up and shutdown times of unit g, respectively.

[0120] In this disclosure, the maximum installed capacity of energy storage is used as a decision variable. Therefore, the depth of discharge and the maximum charge / discharge rate are set to be proportional to its capacity. The specific constraints related to energy storage are introduced as follows:

[0121] 7) Energy storage batteries cannot be charged and discharged simultaneously:

[0122]

[0123] 8) Limitations on the charging and discharging energy of energy storage:

[0124]

[0125]

[0126] 9) Energy storage limitations:

[0127]

[0128]

[0129] 10) Relationship between energy levels at the beginning and end of the planning period:

[0130]

[0131] 11) Relationship between depth of discharge, maximum charge / discharge rate, and maximum energy storage capacity:

[0132]

[0133]

[0134] in, These represent the charge and discharge state variables of energy storage, respectively. These represent the maximum charging and discharging power of the energy storage, Let η represent the stored energy at time t, i.e., the minimum stored energy, respectively. A，c η A，d These represent the charging and discharging efficiencies of energy storage, respectively. These represent the time interval and the entire planned time period, respectively. k1 represents the ratio of minimum energy storage capacity to maximum energy storage capacity, and k2 and k3 represent the ratio of maximum charging and discharging power to energy storage capacity, respectively.

[0135] 12) DC channel transmission power upper and lower limit constraints:

[0136]

[0137] 13) DC transmission power ramping constraint:

[0138]

[0139] 14) Minimum adjustment time constraint for DC channel:

[0140]

[0141] 15) Maximum number of adjustments allowed for the DC channel:

[0142]

[0143] 16) Constraints on DC channel power transmission trading plan:

[0144]

[0145] in, These represent the minimum / maximum transmission power of the DC channel, s t The state variable representing whether the DC channel power transmission is adjusted at time t is: Adjustment (s) t =1) or no adjustment (s) t =0), τ dc N represents the minimum settling time of the DC channel, T represents the total number of planned time periods, and N represents the minimum settling time of the DC channel. dc Q represents the maximum allowable number of adjustments for the DC channel. dc This indicates the total electricity volume to be traded through the planned DC channels.

[0146] 2. Operational Phase Modeling. This phase establishes an opportunity-constrained programming model that considers multiple uncertainties in wind power, solar power, and load demand. The aim of this phase is to schedule the flexibility of various power system devices to meet demand with a high probability, while minimizing operating costs, including generation fuel costs and reserve costs in the event of uncertainties.

[0147] This stage of modeling also requires the introduction of reserve constraints for thermal power units. As a traditional form of reserve, thermal power units can provide both upward and downward reserves for the power system to address forecast errors. Furthermore, flexibility upgrades for thermal power units can further increase flexibility. The specific introduction of reserve constraints for thermal power units is as follows:

[0148] 17) Consider the technical limitations and constraints of the unit's upward and downward standby:

[0149]

[0150]

[0151] 18) Considering the ramp-up constraint of conservative reserve scheduling:

[0152]

[0153]

[0154] The above constraints indicate that the planned reserve in adjacent time intervals ensures sufficient ramp-up capability to meet all possible scenarios, as illustrated in the diagram below. Figure 5 As shown.

[0155] 19) Maximum upward spare constraint:

[0156]

[0157] 20) Maximum downward spare constraint:

[0158]

[0159] in, These represent the maximum upward / downward reserve that unit g can provide, respectively.

[0160] Energy storage is one of the most rapidly developing flexible resources recently because it can respond quickly to disturbances. Due to its special characteristics, when the system has excess renewable energy output, the energy storage will charge; when the system's power generation cannot meet demand, the energy storage will discharge. The specific energy storage reserve constraints are introduced as follows:

[0161] 21) Maximum upward reserve constraint for energy storage at time t:

[0162]

[0163] 22) Maximum downward reserve constraint of energy storage at time t:

[0164]

[0165] 23) Conservative constraints on energy storage backup planning:

[0166]

[0167]

[0168] It should be noted that when thermal power units and high-voltage direct current transmission channels do not have sufficient upward reserves for the entire dispatch cycle within a day, energy storage should provide upward reserves in each stage to satisfy the first constraint in constraint 23), while the second constraint in constraint 23) corresponds to the lack of downward reserves for the entire dispatch cycle.

[0169] While high-voltage direct current (HVDC) transmission channels are not as flexible as thermal power units and energy storage, they can still provide some backup power. The relevant constraints of HVDC channels are introduced as follows:

[0170] 24) Includes upper and lower limits for planned transmission power with upward reserve:

[0171]

[0172] 25) Includes upper and lower limits for planned transmission power with downside reserve:

[0173]

[0174] 26) Includes conservative backup climbing constraints:

[0175]

[0176]

[0177] 27) Reserve power constraints for transmission along the channel:

[0178]

[0179]

[0180] Where, r t dc，dn r t dc，up These represent the downward and upward reserves provided by the DC channel at time t, respectively. M is a very large positive value, meaning that there is no limit to the ramp-up of the channel reserve.

[0181] In practice, due to the low probability of extreme scenarios, it is unnecessary to rely on reserves to meet net load in all situations. Therefore, it is possible that the system's planned flexibility supply may be insufficient to meet net load. Specifically, when the upward or downward flexibility supply is insufficient, load shedding or curtailment of renewable energy sources such as wind and solar power may occur. Figure 6 As shown. Figure 6 This paper presents the probability distribution function for meeting electricity demand at the power generation stage within the feasible region. When the power system lacks sufficient downward flexibility, there may be curtailment of wind and solar power on the left side of the distribution, while when the power system lacks sufficient upward flexibility, there may be load shedding on the right side. Therefore, this disclosure models the flexibility constraint as a chance constraint, as follows:

[0182] 28) Opportunity constraints of power shedding at the sending end and curtailment of renewable energy:

[0183]

[0184]

[0185] 29) Constrained by opportunities arising from load shedding and the abandonment of renewable energy sources.

[0186]

[0187]

[0188]

[0189]

[0190] Where, α up α dn β represents the confidence level of the upward and downward flexibility opportunity constraints at the sending end, respectively. up ,β dn These represent the confidence levels for being constrained by upward and downward flexibility opportunities, respectively.

[0191] It should be noted that the first constraint in constraint 28) means that when the planned upward reserve is greater than or equal to the random variation in network load, the joint probability must be greater than or equal to the confidence level α. up This means there is a high probability that planned reserves are greater than or equal to random variations in network load. Similarly, this interpretation can also be applied to the case of renewable energy reduction, i.e., the second constraint in constraint 28) and constraint 29).

[0192] S400. Construct a mixed-integer linear programming model P2 based on the model P1 to complete the flexible resource allocation for cross-regional power grids.

[0193] In this step, the model P1 proposed in step S400 is very difficult to solve. One reason is the computational burden of the large number of integer decision variables in the unit aggregation problem; another reason is the stochastic nature of load demand and renewable energy. To address this, this disclosure proposes a unit aggregation model to alleviate the computational burden. Furthermore, by using analytical methods, the chance constraints are transformed into equivalent deterministic constraints, and the two-stage problem with chance constraints is transformed into a mixed-integer linear programming problem.

[0194] 1. Unit Aggregation Modeling. In long-run planning problems, the large number of integer decision variables leads to significant computational overhead. Therefore, this disclosure proposes a method for aggregating similar units to reduce the number of integer variables in unit aggregation problems and improve solution efficiency. The unit aggregation model is modeled as follows:

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[0210] in, This represents the upper limit capacity of the aggregation unit at point A at time t-1. This represents the upper limit capacity of the aggregation unit at point A at time t. This indicates the number of generating units at end A. These represent the start-up and shutdown capacities of the aggregation unit at time t, respectively. Let represent the start-up and shutdown state variables of aggregation unit c at time t, respectively. Let represent the start-up and shutdown state variables of unit g at time t, respectively. These represent the output of the accelerator unit at times t-1 and t, respectively. These represent the upward and downward standby provided by the merging unit at time t, respectively. These represent the upward and downward reserves provided by the merging unit at time t-1, respectively, and τ represents a specific optimization period. These represent the minimum start-up and shutdown times of the polymerization unit, respectively. These represent the minimum and maximum output of the polymerization unit, respectively. These represent the maximum upward and downward reserve that the polymerization unit can provide, respectively. Δt represents the upward and downward ramp rates of the aggregation unit, τ represents a specific optimization period, c represents the aggregation unit, su represents the unit's start-up, sd represents the unit's shutdown, on represents the unit's minimum start-up, off represents the unit's minimum shutdown, s represents the sending end, r represents the receiving end, and up and dn represent the upward and downward standby, respectively.

[0211] It should be noted that relevant parameters of the polymerization unit, such as the unit's maximum or minimum output... Slope rate Minimum power on / off time All parameters are set to the weighted average of the original units, regarding the standby parameters. The average value is then used. Its specific introduction is as follows:

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[0220] 2. Modeling an Equivalent Deterministic Mixed Integer Programming Model. Due to the complex form of the cumulative distribution function of random variables, stochastic programming models with opportunity constraints are difficult to solve. Furthermore, solving this model using stochastic simulation methods is time-consuming and computationally intensive. However, analytical methods readily yield the inverse cumulative distribution of normally distributed random variables. Therefore, this disclosure applies analytical methods to transform model P1 into an equivalent deterministic model. The specific transformation process is as follows:

[0221] Let X t Y t They represent The probability distribution of the given information still satisfies a normal distribution because... Since they are mutually independent normal distributions, X t Y t It can be represented as:

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[0224] For a given confidence level 'a', it corresponds to a K. a Satisfying Pr{K a ≤X}=a, that is, K a =φ -1 (1-a). Accordingly, for a given confidence level b, it corresponds to a K. b Satisfying Pr{K a ≥Y}=b, that is, K b =φ -1 (b). Based on this principle, the opportunity constraints 28) and 29) in step S400 can be transformed into the following deterministic form:

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[0228] in, The k-quantile of the cumulative distribution function of a normal distribution, α up α dn β represents the confidence level of the upward and downward flexibility opportunity constraints at the sending end, respectively. up ,β dn These represent the confidence levels constrained by upward and downward flexibility opportunities, respectively. These represent the upward and downward standby provided by unit g at time t, respectively. These represent the upward and downward standby provided by unit g at time t, respectively. These represent the upward and downward backup provided by the sending-end energy storage at time t, respectively. These represent the upward and downward reserves provided by the receiving-end energy storage at time t, respectively. t dc,up r t dc,dn N represents the upward and downward standby capacity provided by the DC channel at time t, respectively. g This indicates the total number of thermal power units in the region.

[0229] Thus, after unit aggregation and opportunity constraint transformation, the two-stage model of network flexibility resource planning based on opportunity constraints can be transformed into a mixed-integer linear programming problem. Specifically, the cross-regional DC channel internet flexibility resource planning and operation model can be summarized as P2, as shown below:

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[0231] Among them, C e This represents the cost coefficient for energy storage construction. Indicates the maximum energy storage capacity. This represents the cost coefficient for the flexible retrofitting of thermal power units. This represents the decision variable for whether thermal power unit g should be retrofitted. This represents the cost coefficient for operating thermal power units. C represents the output of thermal power unit g at time t. rg This represents the cost coefficient for standby thermal power units. C represents the upward and downward standby states of thermal power unit g at time t, respectively. re This represents the cost coefficient for energy storage backup.

[0232] To better understand the technical solutions described in this disclosure, the following is based on... Figure 2 The two-region DC interconnection power grid shown was tested. The test environment was a computer: Intel(R) Xeon(R) Gold-5118 server, 2.30GHz, 256GB memory, programming language: C++, solver: Gurobi 9.1.2.

[0233] The interconnected power grid system consists of three generating units each at the sending and receiving ends of the grid. Detailed data for each unit is shown in Table 1, where parameter selection is indicated.

[0234] To avoid duplication, not all entries are listed in Table 1.

[0235] Table 1. Relevant data of thermal power units in the two-region interconnected power grid of the embodiment.

[0236]

[0237] The hourly electricity demand, wind power, and solar power generation data were modified from open-source data from Austria in 2016. These data were averaged and normalized for each season. The simulation timescale was set to one year, with 96 time periods, i.e., each season is represented by 24 periods, and the time resolution is one hour. The loads for the two regions were calculated proportionally based on peak loads of 1000 MW and 2500 MW.

[0238] In practice, load uncertainty is minimal, so only 10% of the predicted load fluctuates. The prediction error is then fitted to a normal distribution with a mean of 0 and a standard deviation of 20% of the load variation. Wind and solar power are proportionally converted to 1500MW and 1500MW of installed capacity, respectively. The prediction error for new energy power generation follows a normal distribution with a mean of 0 and a standard deviation of 2% of the installed capacity plus 20% of the predicted value. To promote the integration of new energy, the installed capacity of energy storage is set as a variable affecting economic efficiency. Therefore, the depth of discharge, the maximum and minimum values ​​of energy storage capacity are set as variables related to the installed capacity, with scaling factors of 0.9, 0.5, and 0.5, respectively. The initial state of charge is set to half the installed capacity, and the cycle efficiency is 0.9.

[0239] In addition, detailed parameters for the DC channel are shown in Table 2:

[0240] Table 2. DC Channel Related Data in Examples

[0241] Parameter meaning Value Transmission capacity 2000MW Slope rate 1000MW / h Minimum adjustment time interval 2 Maximum number of adjustments 4

[0242] To verify the effectiveness of the unit aggregation model, two simulation experiments were conducted in this embodiment. Experiment 1 included only two unit units, aggregating the sending-end unit and the receiving-end unit into a single unit. Experiment 2 treated each unit as an independent unit with its own integer variables and solved the unit aggregation problem.

[0243] Detailed information including model size and computation time is shown in Table 3:

[0244] Table 3. Results of the Unit Aggregate Model Validation Experiment

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[0246] Table 3 shows detailed information on the two simulation models, including model size and computation time. The second column displays the total number of constraints for both cases. Clearly, the non-aggregate model has approximately 1.5 times the number of constraints as the aggregate model, because each independent unit has many associated physical constraints. Regarding the total number of variables, the sum of continuous and integer variables is listed in the third column. Table 3 shows that the non-aggregate model has 4622 variables, while the aggregate model has 3854, indicating a significantly larger total number of variables in the non-aggregate model. Columns 4 and 5 represent the number of continuous and integer variables for the two models, respectively. The number of continuous variables is the same for both models, but the number of integer variables in the non-aggregate model is approximately twice that of the aggregate model, because the aggregate model has fewer integer variables related to unit combinations. The number of non-zero elements in the coefficient matrix is ​​listed in the sixth column, demonstrating that the aggregate model has higher memory utilization efficiency than the non-aggregate model. The last column shows the computation time for the two cases, indicating that the aggregated model saves about 20 times the time compared to the non-aggregated model. This is because the aggregated method greatly reduces the scale of the model's constraints and the number of variables, thus significantly improving the model's solution efficiency while ensuring the accuracy of the solution.

[0247] Figure 7 and Figure 8 The planned output and adjustable range of thermal power units obtained by the aggregation model and the non-aggregation model are given respectively. Figure 9 and Figure 10 The planned transmission power and adjustable range of the DC channel obtained by the non-aggregate model, which uses the aggregation model as a reference, are described in this embodiment. For example... Figure 7 and Figure 8 As shown, the planned output and adjustable range of thermal power units in the precise non-aggregate model are very similar to the results in the unit aggregation model proposed in this disclosure. Furthermore, the energy storage capacity at both the sending and receiving ends is a crucial factor in the planning problem of this disclosure. As shown in Table 4, the optimization capability of the aggregation model is almost identical to that of the non-aggregate model. By comparing the scheduling results of the two models, the proposed aggregation model guarantees effectiveness.

[0248] Table 4 shows the optimal energy storage capacity for the two models.

[0249] Model Sending-end energy storage capacity (MW) Receiving-end energy storage capacity (MW) Aggregation Model 2874 1408 Non-aggregational model 2887 1563

[0250] Another key point of this disclosure is discussed below, namely, the impact of DC channel flexibility on planned energy storage capacity in the case of a convergent model. In the experimental process for this part, this embodiment uses experimental results considering both using DC channels for backup and not using DC channels as backup.

[0251] Table 5 compares the optimal energy storage capacity and total cost under two scenarios. The optimal energy storage capacity of the sending-end grid considering DC channel backup is significantly smaller than that without DC channel backup, while the opposite is true for the receiving-end grid. This is because DC transmission channels, when considering DC channel backup, can collaboratively address uncertainties, whereas without considering DC channel backup, uncertainties at both the sending and receiving ends must be handled independently. Specifically, when DC channel backup is considered, the DC channel provides backup for the sending-end grid, but for the receiving end, it is a backup demander. In other words, compared to not considering DC channel backup, this mode allows the receiving-end grid to share the backup burden of the sending-end grid. Therefore, in the sending-end grid, the optimized energy storage capacity is 2874 MWh with DC channel backup and 12075 MWh without DC channel backup. Conversely, in the receiving-end grid, the optimized energy storage capacity is 1408 MWh and 420 MWh respectively. Furthermore, due to the lower investment cost of energy storage construction, the total planning cost considering DC channel backup is less than that without considering DC channel backup. Experiments have shown that when DC channels are used as a reserve flexibility resource in the power system, they can both facilitate the absorption of new energy sources and reduce the investment and operating costs of the power system. In summary, in the proposed model with good economic benefits, the flexibility of DC channels can share the adjustment pressure with other flexibility resources.

[0252] Table 5 Optimal Energy Storage Capacity and Total Cost under Two Scenarios

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[0254] Driven by environmental policies and economic development, the annual installed capacity of new energy sources is gradually increasing. This embodiment explores the impact of new energy installed capacity levels on the proposed planning method. Since the predicted output error of new energy sources is related to capacity, both fluctuations and output increase with the increase of new energy levels. The experimental simulations were conducted at five new energy installed capacity levels, with each level differing by 100MW of wind and solar power capacity.

[0255] Figure 11 This paper presents the optimal energy storage capacity at both the sending and receiving ends under five different renewable energy installed capacity levels. The results show that the energy storage capacity at the sending end increases with the increase in renewable energy installed capacity. Although all units have been upgraded and the DC transmission channel provides backup, flexibility remains insufficient. Therefore, more energy storage should be built to cope with the fluctuations in renewable energy output at the sending end. However, the optimized energy storage capacity at the receiving end shows little difference. This is because receiving-end energy storage only needs to address load uncertainties and a small portion of uncertainties from the sending end and the DC transmission channel. This indicates that with the future increase in renewable energy installed capacity, more investment should be made in energy storage to provide greater flexibility for inter-regional power systems.

[0256] Finally, to investigate the impact of confidence levels on the planning results, different α values ​​were used in the example test. up α dn ,β up ,β dn Values ​​were assigned. In a single experiment, these values ​​were all set to a uniform value, with an increment of 0.3 for each experiment. The results are as follows: Figure 12 As shown, the energy storage capacity at both the sending and receiving ends increases with the increase in confidence level. This is because the higher the confidence level, the wider the fluctuation range of the prediction errors at both the sending and receiving ends. Therefore, more flexible resources, such as larger energy storage capacity, are needed to avoid load shedding and the abandonment of new energy sources with a lower probability.

[0257] In another embodiment, this disclosure also provides a cross-regional power grid flexibility resource allocation device based on opportunity-constrained programming, comprising:

[0258] The acquisition module is used to acquire the physical operating parameters of the DC channel of the cross-regional power system, the operating parameters of thermal power units, the power generation cost data, the historical data of new energy power generation, and the flexibility parameters.

[0259] The first construction module is used to construct a physical operation model of the cross-regional power system and an uncertainty model of new energy sources and loads at the sending and receiving ends based on the parameters and data obtained by the acquisition module.

[0260] The second construction module is used to construct a two-stage opportunity constraint model P1 for the flexible resource allocation of the cross-regional DC interconnection power system based on the physical operation model and uncertainty model constructed by the first construction module.

[0261] The third construction module is used to construct a mixed-integer linear programming model P2 based on model P1, in order to complete the flexible resource allocation of cross-regional power grids.

[0262] In another embodiment, this disclosure also provides a storage device storing a plurality of instructions adapted to be loaded and executed by a processor:

[0263] Obtain physical operating parameters of DC channels in cross-regional power systems, operating parameters of thermal power units, power generation cost data, historical data of new energy power generation, and flexibility parameters;

[0264] Based on parameters and data, a physical operation model of the cross-regional power system and an uncertainty model of renewable energy and load at the sending and receiving ends are constructed.

[0265] Based on the physical operation model and uncertainty model, a two-stage opportunity constraint model P1 for the flexible resource allocation of cross-regional DC interconnected power systems is constructed.

[0266] Based on the model P1, a mixed-integer linear programming model P2 is constructed to achieve flexible resource allocation for cross-regional power grids.

[0267] In another embodiment, this disclosure also provides a computer device, including:

[0268] Memory, used to store computer instructions;

[0269] A processor is used to execute computer instructions to implement the above-described cross-regional power grid flexibility resource allocation method based on chance-constrained programming.

[0270] The technical solutions provided by this disclosure have been described in detail above with reference to specific embodiments. However, the descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this disclosure. For those skilled in the art, there will be changes in specific implementation methods and application scope based on the ideas of this disclosure. Therefore, the content of this specification should not be construed as a limitation of this disclosure.

Claims

1. A method for cross-regional power grid flexibility resource allocation based on opportunity-constrained programming, comprising the following steps: S100: Obtain physical operating parameters of DC channels in cross-regional power systems, operating parameters of thermal power units, power generation cost data, historical data of new energy power generation, and flexibility parameters; S200. Construct a physical operation model of the cross-regional power system and an uncertainty model of new energy sources and loads at the sending and receiving ends based on the parameters and data described in step S100. S300. Based on the physical operation model and uncertainty model, construct a two-stage opportunity constraint model P1 for the flexible resource allocation of cross-regional DC interconnection power systems. S400. Construct a mixed-integer linear programming model P2 based on the model P1 to complete the flexible resource allocation for cross-regional power grids; In step S400, the mixed-integer linear programming model P2 is represented as: ,in, This represents the cost coefficient for energy storage construction. Indicates the maximum energy storage capacity. This represents the cost coefficient for the flexible retrofitting of thermal power units. Indicates thermal power unit The decision variable for whether or not to carry out the renovation. This represents the cost coefficient for operating thermal power units. Indicates thermal power unit exist Constant effort This represents the cost coefficient for standby thermal power units. They represent thermal power units exist Always ready to move upwards and downwards. This represents the cost coefficient for energy storage backup. They represent energy storage The terms "upward and downward standby" are used to indicate the following: "e" represents energy storage, "g" represents thermal power unit, "m" represents thermal power unit flexibility modification, "o" represents thermal power unit operation, "rg" represents thermal power unit standby, and "re" represents energy storage standby. Represents the set of sender and receiver. Indicates the sending end. This indicates the receiving end; up indicates upward backup; dn indicates downward backup. This indicates the total number of thermal power units in the region.

2. The method according to claim 1, wherein, In step S200, the physical operation model includes traditional thermal power units, new energy units, energy storage and loads belonging to the sending-end power generation area, as well as traditional thermal power units, energy storage, large-scale conventional loads and flexible loads belonging to the receiving-end area.

3. The method according to claim 1, wherein, In step S200, the uncertainty model of the new energy source and load at the sending and receiving ends is expressed as follows: ， ， ， in, They represent Actual load and predicted load at any given time They represent Actual wind power output and predicted output at any given time They represent Actual photovoltaic output and predicted output at any given time Represents the set of sender and receiver. It is a mathematical symbol that represents any. Indicates the sending end. Indicates the receiving end, The 'l' represents the optimization period, 'w' represents wind power, 'pv' represents solar power, and 'f' represents the predicted value. Let represent Gaussian distribution functions with zero mean, and their standard deviations be respectively. .

4. The method according to claim 1, wherein, In step S300, the two-stage opportunity constraint model P1 for the flexible resource allocation of the inter-regional DC interconnection power system is expressed as: ,in, This represents the cost coefficient for energy storage construction. This represents the cost coefficient for the flexible retrofitting of thermal power units. This represents the cost coefficient for operating thermal power units. This represents the cost coefficient for standby thermal power units. This represents the cost coefficient for energy storage backup. Indicates the maximum energy storage capacity. Indicates thermal power unit The decision variable for whether or not to carry out the renovation. Indicates thermal power unit exist Constant effort They represent thermal power units exist Always ready to move upwards and downwards. They represent energy storage The terms "upward and downward standby" are used to indicate the following: "e" represents energy storage, "g" represents thermal power unit, "m" represents thermal power unit flexibility modification, "o" represents thermal power unit operation, "rg" represents thermal power unit standby, and "re" represents energy storage standby. The set of representations for the sender and receiver. Indicates the sending end. This indicates the receiving end; up indicates upward backup; dn indicates downward backup. This indicates the total number of thermal power units in the region.

5. The method according to claim 1, wherein, In step S400, the mixed-integer linear programming model P2 includes a unit aggregation model, which is expressed as: ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， in, Indicates in Always The upper limit capacity of the end-of-line aggregation unit, Indicates in Always The upper limit capacity of the end-of-line aggregation unit, Indicates in The number of units at the terminal They represent in Real-time on / off capacity of the aggregation unit They represent in The startup and shutdown status variables of unit c are aggregated at all times. They represent in Time crew Power-on and power-off status variables, They represent in The output of the unit is constantly being aggregated. They represent in The up and down backup provided by the time-integrated unit. They represent in The up and down backup provided by the time-integrated unit. This indicates a specific optimization period. These represent the minimum start-up and shutdown times of the polymerization unit, respectively. These represent the minimum and maximum output of the polymerization unit, respectively. These represent the maximum upward and downward reserve that the polymerization unit can provide, respectively. These represent the upward and downward ramp rates of the polymerization unit, respectively. Indicates time resolution, 'c' represents the aggregation unit, 'su' represents the unit's start-up, 'sd' represents the unit's shutdown, 'on' represents the minimum start-up time of the unit, and 'off' represents the minimum shutdown time of the unit. Indicates the sending end. Indicates the receiving end, These represent the up and down switches, respectively.

6. The method according to claim 1, wherein, In step S400, the mixed-integer linear programming model P2 further includes an equivalent deterministic mixed-integer programming model, which is expressed as: ， ， ， ， ， ， in, The cumulative distribution function of the normal distribution -Quantities, These represent the confidence levels of the upward and downward flexibility opportunity constraints at the sending end, respectively. These represent the confidence levels constrained by upward and downward flexibility opportunities, respectively. They represent the generating units. At any moment Provided for both upward and downward backup. They represent the generating units. At any moment Provided for both upward and downward backup. These represent the time intervals of the sending-end energy storage. Provided for both upward and downward backup. These represent the receiving-end energy storage time. Provided for both upward and downward backup. These represent the DC channel at time [time]. Provided for both upward and downward backup. This indicates the total number of thermal power units in the region.

7. A cross-regional power grid flexibility resource allocation device based on opportunity-constrained programming, comprising: The acquisition module is used to acquire the physical operating parameters of the DC channel of the cross-regional power system, the operating parameters of thermal power units, the power generation cost data, the historical data of new energy power generation, and the flexibility parameters. The first construction module is used to construct a physical operation model of the cross-regional power system and an uncertainty model of new energy sources and loads at the sending and receiving ends based on the parameters and data obtained by the acquisition module. The second construction module is used to construct a two-stage opportunity constraint model P1 for the flexible resource allocation of the cross-regional DC interconnection power system based on the physical operation model and uncertainty model constructed by the first construction module. The third construction module is used to construct a mixed-integer linear programming model P2 based on model P1, so as to complete the flexible resource allocation of cross-regional power grids; The mixed-integer linear programming model P2 is represented as follows: ,in, This represents the cost coefficient for energy storage construction. Indicates the maximum energy storage capacity. This represents the cost coefficient for the flexible retrofitting of thermal power units. Indicates thermal power unit The decision variable for whether or not to carry out the renovation. This represents the cost coefficient for operating thermal power units. Indicates thermal power unit exist Constant effort This represents the cost coefficient for standby thermal power units. They represent thermal power units exist Always ready to move upwards and downwards. This represents the cost coefficient for energy storage backup. They represent energy storage The terms "upward and downward standby" are used to indicate the following: "e" represents energy storage, "g" represents thermal power unit, "m" represents thermal power unit flexibility modification, "o" represents thermal power unit operation, "rg" represents thermal power unit standby, and "re" represents energy storage standby. Represents the set of sender and receiver. Indicates the sending end. This indicates the receiving end; up indicates upward backup; dn indicates downward backup. This indicates the total number of thermal power units in the region.

8. A storage device storing a plurality of instructions adapted for loading and execution by a processor: Obtain physical operating parameters of DC channels in cross-regional power systems, operating parameters of thermal power units, power generation cost data, historical data of new energy power generation, and flexibility parameters; Based on flexibility parameters and data, a physical operation model of the cross-regional power system and an uncertainty model of renewable energy and load at the sending and receiving ends are constructed. Based on the physical operation model and uncertainty model, a two-stage opportunity constraint model P1 for the flexible resource allocation of cross-regional DC interconnected power systems is constructed. Based on the model P1, a mixed-integer linear programming model P2 is constructed to achieve flexible resource allocation for cross-regional power grids; in, The mixed-integer linear programming model P2 is represented as follows: ,in, This represents the cost coefficient for energy storage construction. Indicates the maximum energy storage capacity. This represents the cost coefficient for the flexible retrofitting of thermal power units. Indicates thermal power unit The decision variable for whether or not to carry out the renovation. This represents the cost coefficient for operating thermal power units. Indicates thermal power unit exist Constant effort This represents the cost coefficient for standby thermal power units. They represent thermal power units exist Always ready to move upwards and downwards. This represents the cost coefficient for energy storage backup. They represent energy storage The terms "upward and downward standby" are used to indicate the following: "e" represents energy storage, "g" represents thermal power unit, "m" represents thermal power unit flexibility modification, "o" represents thermal power unit operation, "rg" represents thermal power unit standby, and "re" represents energy storage standby. Represents the set of sender and receiver. Indicates the sending end. This indicates the receiving end; up indicates upward backup; dn indicates downward backup. This indicates the total number of thermal power units in the region.

9. A computer device, comprising: Memory, used to store computer instructions; A processor for executing computer instructions to implement the method of any one of claims 1-6.

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