Power cross-region configuration optimization method, device and equipment based on implicit decision method

Abstract

The application discloses a power cross-regional configuration optimization method, device and equipment based on an implicit decision method, and the method establishes new energy and load uncertainty models of each region; uses the implicit decision method to construct an uncertainty key scene set and an unexpected constraint condition according to the new energy and load uncertainty models of each region; and establishes a power cross-regional optimization model according to the uncertainty key scene set and the unexpected constraint condition, so as to determine the optimal configuration of the cross-regional power system. The method can realize the joint and coordinated optimization of flexible resources, new energy and direct-current channels of the cross-regional power system, fully excavate the flexibility potential of the cross-regional power system, reduce the overall economic cost of the system, and ensure the unexpectedness and all-scene feasibility.

Description

Technical Field

[0001] This invention belongs to the field of smart new energy power grids, and in particular, it relates to a method, device and equipment for optimizing cross-regional power allocation based on implicit decision-making. Background Technology

[0002] The inverse distribution of renewable energy resources and electricity demand dictates that inter-regional power systems with long-distance, large-capacity DC transmission channels are the primary means of meeting the renewable energy consumption needs of western and northern regions. With the continuous increase in the grid-connected scale of high-proportion renewable energy bases, the intermittent and random characteristics of renewable energy power generation have brought challenges to inter-regional power systems, such as difficulties in peak shaving and low channel utilization efficiency. Against this backdrop, ample flexible adjustment resources have become a necessary condition for supporting the consumption of renewable energy through inter-regional power systems. Fully utilizing the flexible adjustment capabilities of DC transmission channels and configuring diverse flexible resources such as energy storage, demand-side management, and flexible thermal power can provide multiple support functions for the system, including smoothing renewable energy output, improving transmission service flexibility, and enhancing channel utilization efficiency. This can effectively alleviate a series of problems caused by the inter-provincial and inter-regional consumption of high-proportion renewable energy.

[0003] Under the current organizational model of cross-regional power system dispatching in my country, higher-level dispatching agencies manually formulate transmission plans for DC transmission lines at various time periods based on the predicted power generation and demand of each region, and issue these plans as tie-line plans to lower-level dispatching agencies. Lower-level dispatching agencies then optimize their own unit plans within their respective regions using the tie-line plans as marginal conditions. However, this hierarchical dispatching model fails to achieve coordinated optimization between the sending and receiving end systems and DC transmission, thus failing to fully realize the benefits of cross-regional system coordination and optimization. Furthermore, this model does not consider the uncertainties in renewable energy and load forecasting, and the dispatching plans cannot guarantee feasibility across all scenarios. In actual operation, severe wind and solar curtailment may occur, significantly reducing the cross-regional absorption of renewable energy. Therefore, it is urgent to establish an effective and specific cross-regional power system optimization model to achieve joint coordinated optimization of cross-regional power systems and mitigate the impact of uncertainties on cross-regional power systems.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of the present invention, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a method, apparatus, and equipment for cross-regional power allocation optimization based on implicit decision-making. Based on the uncertainty model of new energy sources and loads in each region, this invention uses implicit decision-making to construct a set of key scenarios and unpredictable constraints, establishes a cross-regional power optimization model, realizes the optimized allocation of flexible resources and new energy sources in cross-regional power systems, and ensures the unpredictability of the solution and the feasibility of all scenarios.

[0006] The objective of this invention is achieved through the following technical solution: a cross-regional power allocation optimization method based on implicit decision-making includes the following steps:

[0007] Step S100: Establish uncertainty models for new energy sources and loads in each region, where new energy sources include photovoltaic units and wind turbine units;

[0008] Step S200: Based on the uncertainty models of new energy and load in each region, an implicit decision-making method is used to construct a set of key uncertainty scenarios and unpredictable constraints. The set of key scenarios includes prediction scenarios, extreme ramping scenarios and peak scenarios. The unpredictable constraints include unpredictable constraints of thermal power, unpredictable constraints of energy storage and unpredictable constraints of DC channels.

[0009] Step S300: Based on the set of key uncertainties and unexpected constraints, establish a cross-regional power optimization model to determine the optimal configuration of the cross-regional power system.

[0010] In the method described, the uncertainty model for new energy sources and load is as follows:

[0011]

[0012]

[0013] in, Represents the Cartesian product of the set of uncertainties at all times. Let represent the uncertain set at time t, where t represents time and T represents the total number of time intervals. T Represents the transpose of a vector. Let 'a' represent a time set and 'a' represent a region. P represents a set of regions. t a,pv P t a,w P t a,l Let represent the uncertain power of the photovoltaic units, wind turbines, and load in region a at time t, respectively, and let pv, w, and l represent the photovoltaic units, wind turbines, and load, respectively. Let v represent the lower and upper bounds of the uncertainty in region a at time t, respectively, where v represents the type of uncertainty source.

[0014] In the method described, the prediction scenario is:

[0015]

[0016] Where BS represents the set of prediction scenarios. A matrix of predicted values ​​representing uncertainty. N represents the predicted value of uncertainty v in region a at time t. a N represents the total number of regions. v Indicates the number of categories of uncertainty sources;

[0017] Extreme climbing scenarios are as follows:

[0018] ERS = {OS, ES}

[0019]

[0020]

[0021] Wherein, ERS represents the set of extreme hill climbing scenarios, OS represents the set of scenarios where the uncertainty value reaches its maximum in odd-numbered periods and its minimum in even-numbered periods, and ES represents the set of scenarios where the uncertainty value reaches its minimum in odd-numbered periods and its maximum in even-numbered periods. A numerical matrix representing uncertainty in an OS scenario. This represents the OS scenario value of uncertainty v in region a at time t. This represents a numerical matrix representing uncertainty in the ES scenario. The uncertainty v in region a is represented by the ES scenario value at time t.

[0022] The vertex scene is:

[0023]

[0024]

[0025]

[0026] Where SVS represents the set of vertex scenes, This represents the numerical matrix of the scene at the nth vertex. N represents the SVS scenario value of uncertainty v in region a at time t. SVS The number of vertex scenes is represented by ∑(·), which represents the summation function.

[0027] In the method described, the unexpected constraint of thermal power is:

[0028]

[0029]

[0030]

[0031] Where Δt represents the time interval between two adjacent time periods, and i represents the key scenario. Represents the set of key scenarios, β a C represents the minimum thermal power output coefficient of region a. a,g P represents the installed capacity of thermal power plants in region a. t a,g,min P t a,g,max Let represent the minimum and maximum safety boundaries of the thermal power output in region a, respectively. R represents the output of the thermal power plant in region a at time t in scenario i. a,g This represents the maximum ramp rate of thermal power plants in region a.

[0032] Unexpected constraints on energy storage are:

[0033]

[0034]

[0035]

[0036] Where, α a C represents the maximum depth of discharge of energy stored in region a. a,sto This represents the installed energy storage capacity of region a. Let represent the minimum and maximum safety boundaries of the energy storage capacity in region a at time t, respectively. η represents the amount of energy stored in region a at time t in scenario i. a,d η a,c μ represents the maximum discharge rate coefficient and the maximum charge rate coefficient of energy storage in region a, respectively. a,d μ a,c These represent the discharge efficiency and charging efficiency of energy storage in region a, respectively.

[0037] Unintended constraints of the DC channel are:

[0038]

[0039] in, Let represent the minimum and maximum transmission power of the k-th DC channel, respectively. Let represent the minimum and maximum safety boundaries of the power transmitted by the k-th DC channel, respectively. v represents the transmission power of the k-th DC channel in scenario i at time t. k,t A 0-1 variable representing whether the power transmitted by the k-th DC channel is adjusted at time t. This represents the maximum ramp rate of the k-th DC channel.

[0040] In the method described, the cross-regional power optimization model is as follows:

[0041] Objective function:

[0042]

[0043] Where, ρ i Let F represent the probability of the i-th critical scenario occurring. sto (·), F pv (·), F w (·) represent the investment cost functions for energy storage, photovoltaic, and wind power, respectively. g F represents the operating cost function of thermal power plants. ls C represents the demand-side response cost function. a ,sto C a,pv C a,w These represent the installed capacity of energy storage, photovoltaic, and wind power in region a, respectively. This represents the output of the thermal power plant in region a at time t in scenario i. This represents the power reduction of the controllable load in region a at time t in scenario i.

[0044] The constraints include: power supply and demand balance constraints in each region under key scenarios, operation constraints of thermal power units in each region under key scenarios, operation constraints of energy storage in each region under key scenarios, demand-side response operation constraints in each region under key scenarios, operation constraints of DC channels under key scenarios, and unexpected constraints.

[0045] In the method described, the cross-regional power optimization model can optimize the DC channel trading volume.

[0046] An apparatus for implementing the method includes,

[0047] The first modeling unit is configured to establish uncertainty models of new energy sources and loads in various regions, where new energy sources include photovoltaic units and wind turbine units;

[0048] The second modeling unit is connected to the first modeling unit to construct a set of key uncertainty scenarios and unpredictable constraints based on the uncertainty models of new energy sources and loads in each region using implicit decision-making methods. The set of key scenarios includes prediction scenarios, extreme ramping scenarios, and peak scenarios. The unpredictable constraints include unpredictable constraints of thermal power, unpredictable constraints of energy storage, and unpredictable constraints of DC channels.

[0049] The third modeling unit is connected to the second modeling unit to establish a cross-regional power optimization model based on the set of uncertain key scenarios and unexpected constraints.

[0050] In the device described, the first modeling unit, the second modeling unit, and the third modeling unit include a central processing unit.

[0051] A storage device stores multiple instructions suitable for loading and execution by a processor, the instructions including:

[0052] Establish uncertainty models for new energy sources and loads in various regions, where new energy sources include photovoltaic units and wind turbine units;

[0053] Based on the uncertainty models of new energy sources and loads in various regions, an implicit decision-making method is used to construct a set of key uncertainty scenarios and unpredictable constraints. The set of key scenarios includes prediction scenarios, extreme ramping scenarios, and peak scenarios. The unpredictable constraints include unpredictable constraints for thermal power, unpredictable constraints for energy storage, and unpredictable constraints for DC transmission channels.

[0054] Based on the set of key uncertainties and unforeseen constraints, a cross-regional power optimization model is established to determine the optimal configuration of the cross-regional power system.

[0055] A computer device, comprising:

[0056] Memory, used to store computer instructions;

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

[0058] This invention utilizes implicit decision-making to construct a set of key scenarios and unforeseen constraints, ensuring the unpredictability and feasibility of the optimization results across all scenarios. This avoids the severe wind and solar curtailment problems that may exist during actual operation, effectively mitigating the impact of uncertainty on cross-regional power systems. It enables joint coordinated optimization of flexibility resources, new energy sources, and DC transmission lines in cross-regional power systems, determining the optimal allocation of flexibility resources and new energy sources, optimizing DC transmission line scheduling plans, fully tapping the flexibility potential of cross-regional power systems, and improving the cross-regional new energy absorption capacity. It also enables optimization of DC transmission line trading volume, significantly reducing the overall economic cost of the system compared to existing trading plans. This method has broad application prospects in cross-regional power system optimization models, including: optimization of cross-regional system new energy absorption boundaries, absorption of cross-regional system new energy uncertainty, cross-regional system investment planning, optimization of cross-regional system tie-line trading volume, and optimization of the overall allocation and operation of cross-regional system flexibility resources.

[0059] The above description is merely an overview of the technical solution of the present invention. In order to make the technical means of the present invention clearer and more understandable, so that those skilled in the art can implement it according to the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more obvious and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0060] Various other advantages and benefits of the present invention will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0061] In the attached diagram:

[0062] Figure 1 This is a flowchart of a cross-regional power allocation optimization method based on implicit decision-making provided in this disclosure;

[0063] Figure 2 This is a schematic diagram of a cross-regional power system according to an embodiment of this disclosure;

[0064] Figure 3 This is a schematic diagram of the optimal solution for DC channel trading volume in an embodiment of this disclosure;

[0065] Figure 4 This is the total cost over 10 years at the sending and receiving ends calculated by the power cross-regional configuration optimization method under different DC channel operating modes in this embodiment of the disclosure;

[0066] Figure 5 This is a timing logic diagram of the rescheduling decision-making process based on implicit decision-making in this embodiment of the disclosure.

[0067] The present invention will be further explained below with reference to the accompanying drawings and embodiments. Detailed Implementation

[0068] The following will refer to the appendix. Figures 1 to 5 Specific embodiments of the invention will be described in more detail below. While specific embodiments of the invention are shown in the accompanying drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0069] 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 the invention; however, these descriptions are for the purpose of understanding the general principles of the specification and are not intended to limit the scope of the invention. The scope of protection of this invention is determined by the appended claims.

[0070] To facilitate understanding of the embodiments of the present invention, 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 the present invention.

[0071] The power cross-regional allocation optimization method based on implicit decision-making includes,

[0072] S100. Establish uncertainty models for new energy sources and loads in each region, where new energy sources include photovoltaic units and wind turbine units.

[0073] In this step, it is assumed that the load and the output of new energy sources are independent of each other in each time stage, that is, the inter-time coupling characteristics of uncertainty are not considered. Then the uncertainty model of new energy sources and load is expressed as follows:

[0074]

[0075]

[0076] in, Represents the Cartesian product of the set of uncertainties at all times. Let represent the uncertain set at time t, where t represents time and T represents the total number of time intervals. T Represents the transpose of a vector. Let 'a' represent a time set and 'a' represent a region. Represents a set of regions and s and r represent the sending and receiving regions, respectively, and P t a,pv P t a,w P t a,l Let represent the uncertain power of the photovoltaic units, wind turbines, and load in region a at time t, respectively, and let pv, w, and l represent the photovoltaic units, wind turbines, and load, respectively. Let v represent the lower and upper bounds of the uncertainty in region a at time t, respectively, where v represents the type of uncertainty source. Equation (1) defines the set of uncertainties over the entire time period, i.e., the set of uncertainties over the entire time period is equal to the Cartesian product of the sets of uncertainties at each time point. Equation (2) defines the set of uncertainties at each time point. The boundary of the load uncertainty set can be directly obtained from the extreme values ​​of historical load data or the boundary of the prediction confidence interval. In this embodiment, the boundary of the prediction confidence interval with a 95% confidence level is used as the boundary of the load uncertainty set, which can be expressed as:

[0077]

[0078] in, P t a,l , This represents the boundary of the confidence interval for load forecasting with a 95% confidence level. Unlike load, the boundary of the uncertainty set for renewable energy changes with its installed capacity. Therefore, the boundary of the uncertainty set for renewable energy can be expressed as:

[0079]

[0080] in, This represents the boundary of the new energy prediction confidence interval with a 95% confidence level, under a unit installed capacity of new energy v. (C) a,v This represents the installed capacity of new energy source v in region a.

[0081] S200. Based on the uncertainty models of new energy sources and loads in each region described in step S100, use the implicit decision-making method to construct a set of key uncertainty scenarios and unexpected constraints.

[0082] In this step, the set of key uncertainty scenarios includes the following three scenarios for new energy sources and loads:

[0083] First, the predicted scenario can be represented as:

[0084]

[0085] Where BS represents the set of prediction scenarios. A matrix of predicted values ​​representing uncertainty. N represents the predicted value of uncertainty v in region a at time t. a N represents the total number of regions. v This indicates the number of uncertainty source categories. The load forecasting scenario can be obtained from historical load data. In this embodiment, the expected value of historical data is used as the forecasting scenario, which can be expressed as:

[0086]

[0087] Among them, P t a,l Let E(·) represent the historical load data of region a at time t, and E(·) represent the expectation function. Unlike load, the predicted value of renewable energy will change with its installed capacity. Therefore, the predicted value of renewable energy can be expressed as:

[0088]

[0089] in, This represents the historical data of new energy v per unit installed capacity in region a at time t.

[0090] Secondly, extreme uphill scenarios can be represented as:

[0091] ERS = {OS, ES}(8)

[0092]

[0093]

[0094] Wherein, ERS represents the set of extreme hill climbing scenarios, OS represents the set of scenarios where the uncertainty value reaches its maximum in odd-numbered periods and its minimum in even-numbered periods, and ES represents the set of scenarios where the uncertainty value reaches its minimum in odd-numbered periods and its maximum in even-numbered periods. A numerical matrix representing uncertainty in an OS scenario. This represents the OS scenario value of uncertainty v in region a at time t. This represents a numerical matrix representing uncertainty in the ES scenario. Let v represent the ES scenario value of region a at time t.

[0095] Third, the vertex scene can be represented as:

[0096]

[0097]

[0098]

[0099] Where SVS represents the set of vertex scenes, This represents the numerical matrix of the scene at the nth vertex. N represents the SVS scenario value of uncertainty v in region a at time t. SVS The number of vertex scenes is represented by ∑(·), and the summation function is denoted by ∑(·). At this point, the set of uncertain key scenes is complete.

[0100] Below, based on the aforementioned set of key scenarios, unexpected constraints are constructed, specifically including the following three types of constraints:

[0101] Firstly, unforeseen constraints on thermal power:

[0102]

[0103]

[0104]

[0105] Where Δt represents the time interval between two adjacent time periods, and i represents the key scenario. Let t represent the set of key scenarios, and t represent time. Denotes the time set, β a C represents the minimum thermal power output coefficient of region a. a,g P represents the installed capacity of thermal power plants in region a. t a,g,min P t a,g,max Let represent the minimum and maximum safety boundaries of the thermal power output in region a, respectively. R represents the output of the thermal power plant in region a at time t in scenario i. a,g This represents the maximum ramp rate of thermal power plants in region a.

[0106] Secondly, the unforeseen constraints of energy storage:

[0107]

[0108]

[0109]

[0110] Where, α a C represents the maximum depth of discharge of energy stored in region a. a,sto This represents the installed energy storage capacity of region a. Let represent the minimum and maximum safety boundaries of the energy storage capacity in region a at time t, respectively. η represents the amount of energy stored in region a at time t in scenario i. a,d η a,c μ represents the maximum discharge rate coefficient and the maximum charge rate coefficient of energy storage in region a, respectively. a,d μ a,c These represent the discharge efficiency and charging efficiency of energy storage in region a, respectively.

[0111] Third, unintended constraints of the DC channel:

[0112]

[0113]

[0114]

[0115] in, P dc , P represents the minimum and maximum transmission power of the DC channel, respectively. t dc,min P t dc,max These represent the minimum and maximum safety boundaries for the power transmitted through the DC channel, respectively. v represents the power transmitted by the DC channel in scenario i at time t. t R is a 0-1 variable representing whether the DC channel transmission power is adjusted at time t. dc This indicates the maximum ramp rate of the DC channel.

[0116] In summary, [P] t a,g,min ,P t a,g,max ], and [P] t dc,min ,P t dc,max ] represent the implicit decisions for thermal power, energy storage, and DC channels, respectively. Implicit decisions are key to ensuring the unpredictability of the solution and its feasibility across all scenarios.

[0117] S300. Based on the set of key uncertainties and unforeseen constraints described in step S200, establish a cross-regional power optimization model to determine the optimal configuration of the cross-regional power system.

[0118] In this step, the cross-regional power optimization model aims to minimize investment and operating costs while taking into account the uncertainties of new energy sources and loads. It jointly optimizes cross-regional power systems, fully explores the potential for cross-regional flexibility, finds the optimal configuration mode for flexible resources and new energy sources, and ensures the unpredictability of the solution and its feasibility across all scenarios.

[0119] The objective of the cross-regional power optimization model is to minimize the investment costs of energy storage and wind / solar turbine units, as well as the fuel costs and demand-side response costs of traditional thermal power generation. The specific objective function is expressed as follows:

[0120]

[0121] Where, ρ i Let F represent the probability of the i-th critical scenario occurring. sto (·), F pv(·), F w (·) represent the investment cost functions for energy storage, photovoltaic, and wind power, respectively. g F represents the operating cost function of thermal power plants. ls C represents the demand-side response cost function. a ,sto C a,pv C a,w These represent the installed capacity of energy storage, photovoltaic, and wind power in region a, respectively. This represents the output of the thermal power plant in region a at time t in scenario i. This represents the power reduction of the controllable load in region a at time t in scenario i.

[0122] Below, we will model the power supply and demand balance constraints, thermal power unit operation constraints, energy storage operation constraints, demand-side response operation constraints, and DC transmission channel operation constraints in key scenarios of cross-regional power systems:

[0123] 1. Power supply and demand balance constraints in different regions under key scenarios

[0124]

[0125]

[0126] in, These represent the power outputs of traditional thermal power units at the sending and receiving ends, respectively. These represent the energy storage discharge rates at the sending and receiving ends, respectively. These represent the energy storage charging rates at the sending and receiving ends, respectively. Indicates the power transmitted through the DC channel. These represent the photovoltaic and wind power outputs at the sending end, respectively. Let represent the power of large-scale conventional loads and flexible loads at the receiving end, respectively. Equation (23) represents the power supply and demand balance constraint in the sending-end region, and Equation (24) represents the power supply and demand balance constraint in the receiving-end region.

[0127] 2. Operational constraints of thermal power units in various regions under key scenarios

[0128]

[0129]

[0130] Where Δt represents the time interval between two adjacent time periods, β a C represents the minimum thermal power output coefficient of region a. a,g This represents the installed capacity of thermal power plants in region a. R represents the output of the thermal power plant in region a at time t in scenario i.a,g Equation (25) represents the maximum ramp rate of thermal power plants in region a. Equation (26) represents the output limit constraint of thermal power units in each region.

[0131] 3. Constraints on Energy Storage Operation in Different Regions under Key Scenarios

[0132]

[0133]

[0134]

[0135]

[0136]

[0137] Where, α a C represents the maximum depth of discharge of energy stored in region a. a,sto This represents the installed energy storage capacity of region a. η represents the amount of energy stored in region a at time t in scenario i. a,d η a,c μ represents the maximum discharge rate coefficient and the maximum charge rate coefficient of energy storage in region a, respectively. a,d μ a,c Let represent the discharge efficiency and charging efficiency of energy storage in region a, respectively. Equation (27) represents the energy storage capacity update constraint for each region. Equation (28) represents the energy storage capacity limit constraint for each region. Equation (29) represents the energy storage charging rate constraint for each region. Equation (30) represents the energy storage discharge rate constraint for each region. Equation (31) represents the constraint that the energy storage capacity in each region is equal at the beginning and end of the scheduling cycle.

[0138] 4. Demand-side response operational constraints in different regions under key scenarios

[0139]

[0140] in, β represents the power of the flexible load at the receiving end. r This indicates the percentage of the maximum flexible load at the receiving end. This represents the load forecast at the receiving end. The above formula represents the demand-side response limit constraints for each region.

[0141] 5. Operational constraints of DC channels in critical scenarios

[0142]

[0143]

[0144]

[0145]

[0146]

[0147] in, P dc , These represent the minimum and maximum transmission power of the DC channel, respectively. v represents the power transmitted by the DC channel in scenario i at time t. t R is a 0-1 variable representing whether the DC channel transmission power is adjusted at time t. dc T represents the maximum ramp rate of the DC channel. M Let X represent the minimum stabilization time of the DC channel after adjustment, X represent the maximum number of adjustments of the DC channel, and Q represent the amount of electricity transmitted by the DC channel. Equation (33) represents the constraint on the rate of change of the DC channel's transmission power. Equation (34) represents the limit constraint on the DC channel's transmission power. Equation (35) represents the constraint on the minimum stabilization time after the DC channel's transmission power adjustment. Equation (36) represents the limit constraint on the number of DC channel transmission power adjustments. Equation (37) represents the constraint on the amount of electricity traded by the DC channel.

[0148] It should be noted that, in the DC channel operation constraints under the aforementioned critical scenarios, besides v t Besides being a decision variable, the DC channel trading volume Q is also a decision variable. For example... Figure 3 As shown, the power transmission capacity of the DC channel is affected by cost factors in each region. Ideally, Figure 3 The intersection point is the optimal point for transmitting electricity.

[0149] At this point, the cross-regional power optimization model has been established and can be summarized as follows:

[0150]

[0151] The proposed cross-regional power optimization model is a mixed-integer programming (MIP) problem, which can be solved directly by a commercial solver to obtain the allocation results of flexible resources and new energy sources in cross-regional power systems.

[0152] 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: Matlab 2019b, solver: Gurobi 9.1.2.

[0153] This implementation uses historical wind and solar load data from a sending-end and receiving province in my country to extract typical 24-hour daily curves for four seasons for simulation testing, totaling 96 time periods with a time resolution of 1 hour. The peak load in the receiving region is set at 107GW based on actual data from 2020. The investment costs for energy storage, photovoltaic units, and wind turbines are set at $385 / kWh, $534 / kW, and $877 / kW, respectively. This implementation considers a 10-year planning timescale; therefore, the investment cost of each unit is first converted to an annual average investment cost using the equivalent annuity method, and then averaged to obtain the daily average investment cost. Fuel costs and flexible load compensation costs are set at $0.045 / kWh and $0.1 / kW, respectively. In the objective function, the weight of the prediction scenario is set at 0.6, and the weights of the other key scenarios are all 0.04. The technical characteristic parameters of traditional thermal power units, energy storage, flexible loads, and DC channels in the inter-regional power system are shown in Table 1.

[0154] Table 1 Technical characteristics parameters of cross-regional power systems in the embodiments

[0155]

[0156] To verify the effectiveness of the proposed cross-regional power allocation optimization method, a comparative analysis was conducted on the following three DC channel operation modes:

[0157] Mode 1: The DC channel transmission power is set according to current engineering practice.

[0158] Mode 2: The DC channel power is obtained through optimization, but there is no rescheduling action.

[0159] Mode 3: The DC channel power is obtained through optimization and has a rescheduling action.

[0160] To ensure fairness in the comparison, the DC transmission power contracts for the three modes are derived from current engineering practices. Under the three DC channel operation modes, the total 10-year planning cost calculated using the aforementioned power inter-regional allocation optimization method is as follows: Figure 4As shown, the total cost calculated under Mode 3 is the lowest, at $334.45 billion. This is because Mode 3 of the DC channel operation provides the system with cross-regional flexibility. When uncertainties occur, the DC channel transmission power can be adjusted, i.e., rescheduled actions can be taken. Furthermore, energy storage, thermal power, and flexible loads in the receiving region can also take rescheduled actions to absorb changes in the DC channel transmission power. Therefore, the system leverages its cross-regional flexibility to absorb uncertainties, improving system efficiency and thus reducing overall costs. The total cost calculated under Mode 2 is $335.28 billion, $830 million higher than Mode 1. This is because the DC channel in Mode 2 lacks reschedulable capabilities, resulting in a loss of cross-regional flexibility. However, because Mode 2 uses optimized DC channel transmission power, the calculated total cost is lower than that of Mode 1. These results demonstrate that the proposed cross-regional power allocation optimization method can reduce the overall system cost by leveraging cross-regional flexibility and optimizing DC channel transmission power.

[0161] Table 2 shows the installed capacity of photovoltaic, wind power, and energy storage calculated by the power cross-regional allocation optimization method under three DC channel operation modes. In Mode 1, the installed capacity of energy storage is 21.2 GWh (14.3 GWh + 6.9 GWh = 21.2 GWh), and the installed capacity of new energy is 6.3 GW, all of which are photovoltaic units. In Mode 2, the installed capacity of energy storage is 9.1 GWh (3.0 GWh + 6.1 GWh = 9.1 GWh), and the installed capacity of new energy is 4.6 GW (4.5 GW + 0.1 GW = 4.6 GW). Compared to Mode 1, the installed capacity of energy storage in Mode 2 decreases by 12.1 GWh. This is because the optimized DC channel transmission power in Mode 2 alleviates the channel congestion problem, significantly reducing the demand for energy storage. Figure 4 As shown, this significantly reduced overall costs. However, the substantial decrease in energy storage capacity also impacted the installed capacity of new energy sources, resulting in a reduction of 1.7 GW. In Mode 3, the installed capacity of energy storage is 11 GWh (2.5 GWh + 8.5 GWh = 11 GWh), and the installed capacity of new energy sources is 5.6 GW (3.0 GW + 2.6 GW = 5.6 GW). Compared to Mode 2, Mode 3 saw an increase of 1.9 GWh in energy storage capacity and 1 GW in new energy capacity. Figure 4 As shown, the total cost of Mode 3 is reduced. This indicates that the cross-regional flexibility of Mode 3 can increase the installed capacity of new energy sources while ensuring economic benefits.

[0162] Table 2. Wind, Solar and Storage Capacity of Cross-Regional Power Systems under Three DC Channel Operation Modes

[0163]

[0164] The following discussion focuses on another key aspect of this disclosure: the impact of optimizing DC transmission power contracts on interregional systems. To this end, a comparative analysis was conducted using different DC transmission power contracts. Table 3 shows the 10-year cost results calculated by the aforementioned interregional power allocation optimization method under these two DC transmission power contract formulation methods. The table shows that, under current engineering practice, the total 10-year cost of the sending and receiving end grids is US$334.45 billion. However, the total 10-year cost of the sending and receiving end grids using the method of this disclosure is US$332.44 billion. Therefore, compared to current engineering practice, the method of this disclosure can save US$2.01 billion (334.45 billion - 332.44 billion = US$2.01 billion) in total 10-year costs for both sending and receiving ends. These results demonstrate that the DC transmission power optimization method of this disclosure can significantly reduce the overall cost of interregional systems.

[0165] Table 3. 10-Year Cost Calculation Results under Different DC Transmission Channel Power Contracts

[0166]

[0167] Finally, to verify the feasibility of the proposed cross-regional power allocation optimization method, 500 wind and solar load scenarios were used to validate the results. The installed capacity of wind and solar turbines was obtained using the proposed method, while the wind and solar load scenarios were sampled within the predicted confidence interval using the Monte Carlo sampling method. The results show that none of the 500 wind and solar load scenarios exhibited wind or solar curtailment or large-scale shelving of conventional loads, confirming the full-scenario feasibility of the proposed cross-regional power allocation optimization method. It should be noted that in practical engineering applications, the implicit decisions calculated by the proposed method can be used as marginal conditions. Based on the realized uncertainty values, an Economic Dispatch (ED) problem can be solved to obtain the rescheduling actions of the cross-regional system through rolling optimization, such as... Figure 5 As shown. This ensures the unexpectedness of the solution. Therefore, the proposed cross-regional power allocation optimization method can simultaneously guarantee the unexpectedness of the solution and its feasibility across all scenarios.

[0168] In another embodiment, this disclosure also provides a power cross-regional allocation optimization device based on implicit decision-making, comprising:

[0169] The first modeling unit establishes uncertainty models for new energy sources and loads in various regions, where new energy sources include photovoltaic units and wind turbine units;

[0170] The second modeling unit, based on the uncertainty models of new energy sources and loads in each region established by the first modeling unit, uses implicit decision-making to construct a set of key uncertainty scenarios and unpredictable constraints. The set of key scenarios includes prediction scenarios, extreme ramping scenarios, and peak scenarios. The unpredictable constraints include unpredictable constraints of thermal power, unpredictable constraints of energy storage, and unpredictable constraints of DC channels.

[0171] The third modeling unit establishes a cross-regional power optimization model based on the set of key uncertainties and unforeseen constraints built by the second modeling unit, in order to determine the optimal configuration of the cross-regional power system.

[0172] In another embodiment, this disclosure also provides a storage device storing a plurality of instructions adapted to be loaded and executed by a processor:

[0173] Establish uncertainty models for new energy sources and loads in various regions, where new energy sources include photovoltaic units and wind turbine units;

[0174] Based on the uncertainty models of new energy sources and loads in each region, an implicit decision-making method is used to construct a set of key uncertainty scenarios and unpredictable constraints. The set of key scenarios includes prediction scenarios, extreme ramping scenarios, and peak scenarios. The unpredictable constraints include unpredictable constraints for thermal power, unpredictable constraints for energy storage, and unpredictable constraints for DC transmission channels.

[0175] Based on the aforementioned set of key uncertainties and unexpected constraints, a cross-regional power optimization model is established using implicit decision-making to determine the optimal configuration of the cross-regional power system.

[0176] In another embodiment, this disclosure also provides a computer device, including:

[0177] Memory, used to store computer instructions;

[0178] A processor is used to execute computer instructions to implement a cross-regional power allocation optimization method based on implicit decision-making.

[0179] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.

Claims

1. A method for optimizing cross-regional power allocation based on implicit decision-making, the method comprising the following steps: Step S100: Establish uncertainty models for new energy sources and loads in each region, where new energy sources include photovoltaic units and wind turbine units; Step S200: Based on the uncertainty models of new energy and load in each region, an implicit decision-making method is used to construct a set of key uncertainty scenarios and unpredictable constraints. The set of key scenarios includes prediction scenarios, extreme ramping scenarios and peak scenarios. The unpredictable constraints include unpredictable constraints of thermal power, unpredictable constraints of energy storage and unpredictable constraints of DC channels. Step S300: Based on the set of key uncertainty scenarios and unexpected constraints, establish a cross-regional power optimization model to determine the optimal configuration of the cross-regional power system. The cross-regional power optimization model is as follows: Objective function: ， in, Indicates the first The probability of occurrence of key scenarios. , , Let these represent the investment cost functions for energy storage, photovoltaics, and wind power, respectively. This represents the operating cost function of thermal power plants. This represents the demand-side response cost function. , , Representing regions The installed capacity of energy storage, photovoltaic and wind power, Indicates the area thermal power in time Effort output in the scenario Indicates the area Controllable load in time Power reduction in the scenario; The constraints include: power supply and demand balance constraints in each region under key scenarios, operation constraints of thermal power units in each region under key scenarios, operation constraints of energy storage in each region under key scenarios, demand-side response operation constraints in each region under key scenarios, operation constraints of DC channels under key scenarios, and unexpected constraints.

2. The method according to claim 1, wherein, The uncertainty model for the new energy source and load is as follows: ， ， in, Represents the Cartesian product of the set of uncertainties at all times. Indicates in An uncertain set of moments Indicates time, Indicates the total number of time periods. Represents the transpose of a vector. Represents a time set, Indicates a region. Represents a set of regions. , , Representing regions Photovoltaic units, wind turbines and loads in Uncertainty power at time, , , These represent photovoltaic units, wind turbine units, and loads, respectively. , They represent Regional uncertainty exist The lower and upper bounds of the uncertainty of time. Indicates the type of source of uncertainty.

3. The method according to claim 2, wherein, The predicted scenario is: ， in, Represents the set of prediction scenarios. A matrix of predicted values ​​representing uncertainty. express Regional uncertainty exist Predicted value at time, Indicates the total number of regions. Indicates the number of categories of uncertainty sources; Extreme climbing scenarios are as follows: ， ， ， in, This represents a set of extreme hill-climbing scenarios. This represents the set of scenarios where the uncertainty value reaches its maximum in odd-numbered periods and its minimum in even-numbered periods. This represents the set of scenarios where the uncertainty value is minimized in odd-numbered periods and maximized in even-numbered periods. Indicating uncertainty in Numerical matrix in the scene, express Regional uncertainty exist Moment Scene value selection Indicating uncertainty in Numerical matrix in the scene, express Regional uncertainty exist Moment Scenario-based value selection; The vertex scene is: ， ， ， in, Represents the set of vertex scenes. Indicates the first Numerical matrix of a vertex scene, express Regional uncertainty exist Moment Scenario value selection Indicates the number of vertex scenes. This represents the summation function.

4. The method according to claim 1, wherein, Unexpected constraints for thermal power plants are: ， ， ， in, This indicates the time interval between two adjacent time periods. Indicates key scenarios, Represents a set of key scenarios. Indicates the area The minimum output coefficient of thermal power plants, Indicates the area thermal power installed capacity, , Representing regions The minimum and maximum safety boundaries of thermal power output. Indicates the area thermal power in time Effort output in the scenario Indicates the area The maximum ramp rate of thermal power plants; Represents a set of regions. Represents a time set, Unexpected constraints on energy storage are: ， ， ， in, Indicates the region The maximum depth of discharge of the stored energy, Indicates the region The installed capacity of energy storage , Representing regions The energy storage capacity is Minimum and maximum safety boundaries at any given time. Indicates the region Energy storage time Battery life in different scenarios , Representing regions The maximum discharge rate coefficient and the maximum charge rate coefficient of energy storage. , Representing regions The discharge efficiency and charging efficiency of energy storage; Unintended constraints of the DC channel are: ， ， ， in, , They represent the first The minimum and maximum transmission power of each DC channel. , They represent the first The minimum and maximum safety boundaries for the power transmitted through a DC channel. Indicates the first DC channels in time Transmission power in the scenario Indicates the first DC channel power transmission in A 0-1 variable indicating whether an adjustment occurs at any given time. Indicates the first The maximum ramp rate of the DC channels, where K is the set of DC channels.

5. The method according to claim 1, wherein, The power cross-regional optimization model can optimize the trading volume of DC channels.

6. An apparatus for implementing the method according to any one of claims 1-5, characterized in that, It includes, The first modeling unit is configured to establish uncertainty models of new energy sources and loads in various regions, where new energy sources include photovoltaic units and wind turbine units; The second modeling unit is connected to the first modeling unit to construct a set of key uncertainty scenarios and unpredictable constraints based on the uncertainty models of new energy sources and loads in each region using implicit decision-making methods. The set of key scenarios includes prediction scenarios, extreme ramping scenarios, and peak scenarios. The unpredictable constraints include unpredictable constraints of thermal power, unpredictable constraints of energy storage, and unpredictable constraints of DC channels. The third modeling unit is connected to the second modeling unit to establish a cross-regional power optimization model based on the set of uncertain key scenarios and unexpected constraints.

7. The apparatus according to claim 6, wherein, The first modeling unit, the second modeling unit, and the third modeling unit include a central processing unit.

8. A storage device storing a plurality of instructions adapted for loading and execution by a processor, the instructions comprising: Establish uncertainty models for new energy sources and loads in various regions, where new energy sources include photovoltaic units and wind turbine units; Based on the uncertainty models of new energy sources and loads in various regions, an implicit decision-making method is used to construct a set of key uncertainty scenarios and unpredictable constraints. The set of key scenarios includes prediction scenarios, extreme ramping scenarios, and peak scenarios. The unpredictable constraints include unpredictable constraints for thermal power, unpredictable constraints for energy storage, and unpredictable constraints for DC transmission channels. Based on the aforementioned set of key uncertainty scenarios and unforeseen constraints, a cross-regional power optimization model is established to determine the optimal configuration of cross-regional power systems. The cross-regional power optimization model is as follows: Objective function: ， in, Indicates the first The probability of occurrence of key scenarios. , , Let these represent the investment cost functions for energy storage, photovoltaics, and wind power, respectively. This represents the operating cost function of thermal power plants. This represents the demand-side response cost function. , , Representing regions The installed capacity of energy storage, photovoltaic and wind power, Indicates the area thermal power in time Effort output in the scenario Indicates the area Controllable load in time Power reduction in the scenario; The constraints include: power supply and demand balance constraints in each region under key scenarios, operation constraints of thermal power units in each region under key scenarios, operation constraints of energy storage in each region under key scenarios, demand-side response operation constraints in each region under key scenarios, operation constraints of DC channels under key scenarios, and unexpected constraints.

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 1-5.

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