Multi-direct-current power transmission line power optimization method and device giving consideration to supply guarantee and consumption

Abstract

The invention provides a multi-direct-current power transmission line power optimization method and device giving consideration to supply guarantee and consumption. The method comprises the steps of carrying out initial configuration, simulating source load characteristics of a transmitting end and a receiving end, proposing a load loss probability and a renewable energy power abandoning rate index, constructing a multi-objective function for multi-loop direct current power optimization, constructing a multi-loop direct current line safety constraint, constructing a positive and negative rotation standby calling constraint, solving by a solver and outputting a result. The invention specifically provides a cross-regional multi-direct-current transmission line power model under the background of high-proportion new energy, provides a Monte Carlo method for simulating load characteristics of a transmitting end and a receiving end and fluctuation characteristics of new energy, provides a load loss probability and a renewable energy power abandoning rate index, and further realizes multi-target collaborative optimization of the multi-direct-current transmission line. The method can achieve the modeling of a power model of the cross-regional multi-DC transmission line under the background of high-proportion new energy, helps to achieve the day-ahead optimal scheduling of a multi-DC line power grid, is large in engineering application potential, will effectively support the optimal scheduling of a multi-circuit DC line, and provides a basic guarantee for the safe and economical operation of a system.

Description

Technical Field

[0001] The present application relates to the field of power system operation, and particularly to a power optimization method and device for multiple DC transmission lines that balance power supply and consumption. Background Art

[0002] Facing the continuous growth of energy demand and the increasingly prominent environmental protection pressure, China has put forward the goal of achieving a renewable energy supply ratio of more than 60% by 2050. Vigorously developing new energy represented by wind power and photovoltaic power is an important means to achieve this goal. However, the intermittency and uncertainty of new energy output also pose a series of challenges to the safe and stable operation of the power system. DC tie lines have the characteristics of flexible control. Making full use of the adjustment ability of DC tie lines will greatly promote the consumption of new energy in a larger space range. Therefore, it is urgent to propose a new optimization mode for the operation mode of UHV DC tie lines aiming at improving the new energy consumption ability in combination with the actual operation of China's power grid, and establish a corresponding mathematical model and efficient solution method.

[0003] Under the current dispatching operation mode in China, the formulation of the day-ahead active power dispatching plan for inter-regional UHV DC tie lines still largely depends on manual work. Based on considering the annual trading plan of inter-regional UHV DC tie lines, the load curve characteristics and peak shaving requirements of the power grids at both ends of the tie line, the operation characteristics of the DC tie line, and the basic peak shaving rate requirements of the DC tie line, the active power plan of the UHV DC tie line is formulated by relying on manual experience, and little attention is paid to the power and electricity balance of the power grids at both ends of the DC tie line. During the day, the operation mode of the inter-regional UHV DC tie line is to track the fixed planned curve of the day-ahead, and the fast and flexible adjustment ability and peak shaving potential of the DC tie line are not fully utilized.

[0004] Under the background of building a new power system with new energy as the main body, the installed capacity of new energy on the power supply side has increased rapidly, the load on the user side shows diverse changes, and the power system faces many challenges. Summary of the Invention

[0005] The present application aims to solve at least one of the technical problems in the related technologies to a certain extent.

[0006] To this end, the first object of the present application is to propose a power optimization method for multiple DC transmission lines that balance power supply and consumption.

[0007] The second object of the present application is to propose a power optimization device for multiple DC transmission lines that balance power supply and consumption.

[0008] The third object of the present application is to propose an electronic device.

[0009] The fourth object of the present application is to propose a computer-readable storage medium.

[0010] The fifth object of this application is to propose a computer program product.

[0011] To achieve the above object, an embodiment of the first aspect of this application proposes a multi - DC transmission line power optimization method that takes into account both power supply guarantee and consumption, including the following steps:

[0012] Based on the power grid topology structure and transmission requirements, read the power network parameter data and cost and constraint information, and establish a power system model;

[0013] Calculate the prediction deviations of load, wind power, and light intensity, as well as the output of wind power and photovoltaic power, and use Monte Carlo sampling to calculate the loss - of - load probability and the probability of renewable energy curtailment and verify;

[0014] Based on the verification results of the loss - of - load probability and the probability of renewable energy curtailment, construct a multi - objective function for multi - circuit DC power optimization, and set the penalty terms for generation cost, generator start - stop cost, reserve cost, transmission cost, loss - of - load probability, and the probability of renewable energy curtailment on the premise of meeting the power grid security constraints;

[0015] Construct the safety constraints for multi - circuit DC lines, and set the DC line power flow constraints, system reliability constraints, and system load balance constraints; and construct the positive and negative spinning reserve call constraints, and set the regional minimum reserve requirements, node reserve limits, and the balance constraints between regional reserve and DC transmission reserve;

[0016] Based on the multi - objective function, the safety constraints for multi - circuit DC lines, and the constructed positive and negative spinning reserve call constraints, use a solver to solve the power system model to obtain the optimal power distribution plan.

[0017] Optionally, the step of reading the power network parameter data and cost and constraint information and establishing a power system model includes:

[0018] Read the power network parameter data, where the power network parameter data includes the network topology connection relationship, the position and parameters of DC transmission lines, transformer turns ratio parameters, the resistance matrix, susceptance matrix of the power grid, and the network parameter base value;

[0019] Read the cost and constraint information, where the cost and constraint information includes the generator ramp - up capacity of the existing network model, the fastest continuous start - stop duration, the generator generation cost function, the generator start - stop cost, and the DC tie - line power transmission cost;

[0020] Set the restrictive constraints for DC transmission power and configure the constraint requirements for multi - circuit DC transmission power across regions. The constraint requirements include the upper limit of the number of constraints, the parameter form of the constraint matrix, and the regulation of the direction of the constraint inequality sign, and read the initialized restrictive constraint set to ensure that the dispatching center can receive and execute the constraints through a unified interface.

[0021] Optionally, the calculated load, wind power and light intensity prediction deviations, and wind power and photovoltaic power outputs include:

[0022] Calculating a load prediction error based on load prediction data, where the load prediction error is defined as:

[0023]

[0024] where, represents the load prediction value, ΔP D represents the load prediction deviation, and the load prediction deviation ΔP D follows a normal distribution with a mean of zero, and its variance satisfies:

[0025]

[0026] where the load prediction deviation ΔP D has a probability density function of:

[0027]

[0028] Based on wind speed prediction data, calculating a wind speed prediction error, where the wind speed prediction deviation Δv follows a normal distribution with a mean of zero; assuming the predicted wind speed the relationship between the actual wind speed v and the wind speed prediction deviation Δv is then the probability density function of the wind speed prediction deviation is:

[0029]

[0030] where, σ v is the variance of Δv, and τ is the wind speed variance coefficient, satisfying:

[0031]

[0032] Calculating wind power output based on the actual wind speed, where the wind power output is a piecewise function of the actual wind speed, and the expression is:

[0033]

[0034] where, v i is the fan startup wind speed, v o is the fan cut-out wind speed, v N is the rated wind speed, p N is the rated output of the fan, and k1 and k2 are fitting parameters of the wind power output;

[0035] Based on light intensity prediction data, calculating a light intensity prediction error, where the light intensity prediction deviation Δs follows a normal distribution with a mean of zero; assuming the predicted light intensity The relationship between the actual light intensity s and the light intensity prediction deviation Δs is Then the probability density function of the light intensity prediction deviation is:

[0036]

[0037] where σ s is the variance of Δs, and μ is the photovoltaic variance coefficient, satisfying:

[0038]

[0039] Based on the actual light intensity, the photovoltaic output is calculated. The photovoltaic output is a linear function of the light intensity, and the expression is:

[0040] P S = sη PV S

[0041] where η PV is the photovoltaic conversion efficiency, and S is the total area of the photovoltaic modules.

[0042] Optionally, the calculation of the loss-of-load probability and the curtailment probability of renewable energy using Monte Carlo sampling and verification includes:

[0043] Calculating the loss-of-load probability of region k The calculation method is:

[0044]

[0045] where is the loss-of-load probability density function, is the random variable load deviation ΔP of region k D , the photovoltaic and wind power outputs P s,t and P w,t are linear combinations, is the positive spinning reserve of region k at time t, and p i,t is the generator output at node i at time t;

[0046] Using Monte Carlo sampling to replace the double integral to calculate the loss-of-load expectation The expression is:

[0047]

[0048] where l, m, and n are the Monte Carlo sampling times of load, photovoltaic, and wind power, is the loss-of-load amount for each sampling, and the formula is:

[0049]

[0050] Based on the auxiliary function Calculating the loss-of-load probability The formula is:

[0051]

[0052] Wherein, indicates whether a loss-of-load occurs in the scenario for each sampling result. The formula is:

[0053]

[0054] Wherein, is the loss-of-load amount in the scenario for each sampling result;

[0055] Calculating the curtailment probability of renewable energy in region k Its calculation method is:

[0056]

[0057] Wherein, is the curtailment probability density function of renewable energy, is the random variable load deviation ΔP in region k D , the output of photovoltaic and wind power P s,t and P w,t is a linear combination of, is the negative spinning reserve in region k at time t, p i,t is the generator output at node i at time t;

[0058] Using Monte Carlo sampling to replace the double integral, the expected value of the curtailment amount of renewable energy Its expression is:

[0059]

[0060] Wherein, l, m, and n are the Monte Carlo sampling times of load, photovoltaic, and wind power, is the loss-of-load amount for each sampling. The formula is:

[0061]

[0062] Based on the auxiliary function Calculating the curtailment probability of renewable energy The formula is:

[0063]

[0064] Wherein, indicates whether curtailment of renewable energy occurs in the scenario for each sampling result. The formula is:

[0065]

[0066] Wherein, Let 0 indicate that there is no curtailment of renewable energy.

[0067] Optionally, based on the verification results of the loss-of-load probability and the curtailment probability of renewable energy, a multi-objective function for optimizing the multi-circuit DC power is constructed. On the premise of meeting the grid security constraints, the power generation cost, generator start-stop cost, reserve cost, transmission cost, and penalty terms for the loss-of-load probability and the curtailment probability of renewable energy are set, including:

[0068] Construct a power generation cost function, the expression of which is:

[0069]

[0070] where a i , b i and c i are the coefficients of the quadratic function of the power generation cost of generator i; and are the start-stop costs of generator i;

[0071] Construct a capacity cost function, the expression of which is:

[0072]

[0073] where are the positive and negative spinning reserve cost coefficients of generator i respectively;

[0074] Construct a penalty term function, the expression of which is:

[0075]

[0076] where is a penalty function, which calculates the penalty value for the loss-of-load probability and the curtailment rate of renewable energy in each region at time t, and λ L and λ R are the penalty coefficients respectively;

[0077] Construct a multi-objective function for optimizing the multi-circuit DC power, the formula of which is:

[0078]

[0079] where is the power generation cost function, is the generator start-stop cost function, is the reserve cost function, is the transmission cost function of the DC line ij, ψ is the penalty function for the loss-of-load probability and the curtailment probability of renewable energy, β i,t is the generator startup process flag at node i at time t, and γ i,t is the generator shutdown process flag at node i at time t; and are the positive and negative spinning reserves of the generator at node i during time period t, respectively; are the power transmission amount and the positive and negative spinning reserves sent from node i to node j of the DC line ij during time period t, respectively.

[0080] Optionally, the construction of the multi-circuit DC line security constraints, and setting the DC line power flow constraints, system reliability constraints and system load balance constraints, include:

[0081] Describing the power of the multi-circuit DC transmission line by using the virtual generator modeling method, and the power transmission amount of each DC line is represented by the sum of the on-off states of several virtual generators, expressed as:

[0082]

[0083] Among them, represents the maximum transmission power of the DC line ij; T u,dc and T d,dc are the fastest continuous start-stop and stop-start times of the virtual generator, respectively; p s is the capacity of a single virtual generator of the DC tie line; represents the on-off state flag of the virtual generator s of the DC line ij during time period t; represent the start-up and shutdown process flags of the virtual generator s of the DC line ij during time period t, respectively; t end is the total number of hours of operation simulation;

[0084] Setting the DC power transmission constraint to ensure that the power dispatching of the DC line is within the allowable range, and using to represent the power flow direction of the DC line ij, expressed as:

[0085]

[0086] Among them are the forward and reverse power transmission flags of the DC line ij at time t, respectively;

[0087] Setting the DC line power flow adjustment constraint to ensure the stability of the power flow direction adjustment, satisfying:

[0088]

[0089] Among them, T +,dc and T -,dc are the fastest direction change times of the DC line, respectively;

[0090] Constructing the loss-of-load probability partition reliability constraint, expressed as:

[0091]

[0092] Among them, is the maximum tolerable load shedding probability of region k at time t;

[0093] Construct the reliability constraint of the renewable energy curtailment probability, expressed as:

[0094]

[0095] Among them, is the maximum tolerable renewable energy curtailment probability of region k at time t;

[0096] Construct the system day-ahead load balance constraint, expressed as:

[0097]

[0098] Among them, is the predicted value of the system's wind power output at time t; is the predicted value of the system's photovoltaic power output at time t; is the predicted value of the system's load at time t; are respectively the predicted values of the wind power output, photovoltaic power output and load of region k at time t; N k is the node set of region k, L DC is the set of DC lines composed of the start and end node groups (i, j) of the DC lines;

[0099] Construct the system power flow capacity constraint, expressed as:

[0100]

[0101] Among them, is the AC line capacity vector; S GSDF is the GSDF matrix; D t , are respectively the system power output, load and DC power flow vectors;

[0102] Construct the generator ramp rate constraint, expressed as:

[0103]

[0104] Among them, and are respectively the upward and downward ramp rate coefficients of the generator at node i; P i g respectively represent the maximum and minimum power outputs of the generator at node i when it is in operation; δ i,t represents the on / off state flag of the generator at node i at time t, and this flag is related to the on state β i,t , the off state flag γ i,t as:

[0105] δ i,t -δ i,t-1 ≤β i,t ≤1

[0106] δ i,t-1 -δ i,t ≤γ i,t ≤1

[0107] Construct the output upper and lower limit constraints, expressed as:

[0108]

[0109] Wherein, P i g 、 are respectively the output upper and lower limits of the generator at node i;

[0110] Construct the continuous start-up and shutdown constraints, expressed as:

[0111]

[0112] Wherein, are respectively the fastest continuous start-up and shutdown, shutdown and start-up times of the generator at node i.

[0113] Optionally, the construction of positive and negative spinning reserve call constraints, setting the minimum reserve demand of the region, the node reserve limit and the balance constraint between the regional reserve and the DC transmission reserve, includes:

[0114] Construct the minimum reserve constraint within the region, expressed as:

[0115]

[0116] Where L u 、W u 、S u are respectively the load, wind power, and photovoltaic uncertainty coefficients of the minimum positive reserve, and L d 、W d 、S u are respectively the load, wind power, and photovoltaic uncertainty coefficients of the minimum negative reserve. The minimum values of positive and negative reserves are affected by uncertainty factors;

[0117] Construct the node reserve constraint, expressed as:

[0118]

[0119] Construct the constraint between the regional reserve and the DC line transmission reserve, expressed as:

[0120]

[0121] Wherein, is the total positive and negative spinning reserve of region k at time t.

[0122] To achieve the above object, an embodiment of the second aspect of the present application proposes a multi - DC transmission line power optimization device that takes into account power supply guarantee and consumption, including:

[0123] A reading module, configured to read power network parameter data and cost and constraint information based on the power grid topology structure and transmission requirements, and establish a power system model;

[0124] A calculation module, configured to calculate the prediction deviations of load, wind power, and light intensity, and the output of wind power and photovoltaic power, and use Monte Carlo sampling to calculate the loss - of - load probability and the probability of renewable energy curtailment and verify them;

[0125] An objective construction module, configured to construct a multi - objective function for multi - circuit DC power optimization based on the verification results of the loss - of - load probability and the probability of renewable energy curtailment, and set penalty terms considering generation cost, generator start - stop cost, reserve cost, transmission cost, and loss - of - load probability and the probability of renewable energy curtailment on the premise of meeting the power grid security constraints;

[0126] A constraint construction module, configured to construct the safety constraints of multi - circuit DC lines, and set DC line power flow constraints, system reliability constraints, and system load balance constraints; and construct positive and negative spinning reserve call constraints, and set regional minimum reserve requirements, node reserve limits, and regional reserve and DC transmission reserve balance constraints;

[0127] A solution module, configured to solve the power system model by using a solver based on the multi - objective function, the safety constraints of multi - circuit DC lines, and the constructed positive and negative spinning reserve call constraints, and obtain an optimal power distribution scheme.

[0128] To achieve the above object, an embodiment of the third aspect of the present application proposes an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0129] The memory stores computer - executable instructions;

[0130] The processor executes the computer - executable instructions stored in the memory to implement the method according to any one of the above - mentioned first aspects.

[0131] To achieve the above object, an embodiment of the fourth aspect of the present application proposes a computer - readable storage medium, in which computer - executable instructions are stored, and when the computer - executable instructions are executed by a processor, they are used to implement the method according to any one of the above - mentioned first aspects.

[0132] To achieve the above object, an embodiment of the fifth aspect of the present application proposes a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method according to any one of the above - mentioned first aspects.

[0133] The technical solutions provided by the embodiments of the present application at least bring the following beneficial effects:

[0134] Specifically, a power model for multi - region multi - HVDC transmission lines under the background of a high proportion of new energy is proposed. A Monte Carlo method is proposed to simulate the load characteristics at the sending and receiving ends and the fluctuation characteristics of new energy. Indices such as the loss - of - load probability and the renewable - energy curtailment rate are proposed. Furthermore, the multi - objective collaborative optimization of multi - HVDC transmission lines is realized. The present application can establish a power model for multi - region multi - HVDC transmission lines under the background of a high proportion of new energy, help achieve the day - ahead optimal scheduling of the multi - HVDC grid, has great potential for engineering applications, will effectively support the optimal scheduling of multiple HVDC lines, and provide a basic guarantee for the safe and economic operation of the system.

[0135] The additional aspects and advantages of the present application will be partly given in the following description, partly will become obvious from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0136] The above - mentioned and / or additional aspects and advantages of the present application will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0137] Figure 1 is a schematic flowchart of a power optimization method for multi - HVDC transmission lines that takes into account power supply guarantee and consumption

[0138] Figure 2 is a schematic structural diagram of a device for a power optimization method for multi - HVDC transmission lines that takes into account power supply guarantee and consumption. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0139] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application, and should not be construed as limiting the present application.

[0140] In view of the problems existing in the current power system, the embodiments of the present application provide a power optimization method for multi - HVDC transmission lines that takes into account power supply guarantee and consumption. From the theoretical and application perspectives, a scientific scheduling and coordination operation mechanism for multiple outgoing HVDC tie - lines is studied and proposed to improve the operation mode of multiple outgoing UHV DCs, consider safety and stability operation constraints such as the loss - of - load probability, and improve the level of joint consumption of new energy in multiple regions. Figure 1 is a schematic flowchart of a power optimization method for multi - HVDC transmission lines that takes into account power supply guarantee and consumption. As Figure 1 shown, the method includes the following steps:

[0141] Step 101: Based on the power grid topology structure and transmission requirements, read the power network parameter data and cost and constraint information, and establish a power system model.

[0142] In the embodiment of the present application, in step 101, based on the topology structure and transmission requirements of the power grid, by reading the power network parameter data and cost and constraint information, a power system model required for optimization is established. This step is the basis for power system scheduling and optimization, that is, this step executes the process of carrying out the initial configuration.

[0143] Specifically, this step includes the following sub-steps:

[0144] First, the embodiment of the present application reads the parameter data of the power network to ensure that the physical structure and operating characteristics of the power grid can be accurately described. The topological connection relationship of the power grid is the first data content read. Through this information, the system determines the connection mode between each node and line in the power network, so as to calculate the power flow path and analyze the operating state of the power grid. At the same time, the position and parameters of the DC transmission line also need to be read, including the resistance, admittance, capacity, etc. of the line, to ensure that the electrical characteristics of each line are accurately modeled. The transformation ratio parameters of the transformer also need to be read. These parameters help to determine the voltage conversion ratio of each transformer in the power grid and ensure the accurate calculation of the power grid voltage level. The resistance matrix, susceptance matrix and network parameter base value of the power grid are also necessary inputs. These data provide the basic electrical characteristics of the power grid and ensure the reliability of subsequent optimization calculations.

[0145] Next, the embodiment of the present application reads the cost and constraint information. This information includes the ramp rate capacity of the generators in the existing power grid model, the fastest continuous start-stop duration, the power generation cost function and start-stop cost of the generators, and the power transmission cost of the DC tie lines, etc. These cost information will be used for cost calculation and decision-making in the subsequent optimization process. At the same time, the restrictive constraints of the DC transmission power need to be considered. These constraint conditions can avoid the infeasibility problems of the day-ahead DC transmission plan and power generation plan. To ensure that the constraint conditions can be transmitted to the dispatching center through a unified interface, it is necessary to stipulate the configuration requirements of the cross-regional multi-circuit DC transmission power constraints, including the upper limit of the number of constraints, the form of the main matrix parameters of the constraints, the direction regulations of the inequality signs, etc. In addition, the initialized restrictive constraint set will also be read here.

[0146] On this basis, a power system model is established, providing accurate data support and constraint conditions for subsequent optimization calculations.

[0147] Step 102: Calculate the prediction deviations of load, wind power and light intensity and the output of wind power and photovoltaic power.

[0148] In the embodiment of the present application, this step is used to simulate the source-load characteristics at the sending and receiving ends, which is divided into two parts: simulating the load fluctuation characteristics and simulating the output power fluctuation characteristics of wind power and photovoltaic power.

[0149] It should be noted that the output power fluctuation characteristics of photovoltaic power do not need to be limited to the normal distribution. Weibull distribution, Beta distribution, etc. can be used. Here, the normal distribution is used as an example, but the present application is not limited thereto.

[0150] The following is a detailed description thereof.

[0151] (1) Simulating the load fluctuation characteristics.

[0152] In the embodiment of the present application, first, the load prediction error is calculated according to the load prediction data. The load prediction error is defined as:

[0153]

[0154] Among them, represents the load prediction value, and ΔP D represents the load prediction deviation. The load prediction deviation ΔP D obeys the normal distribution with a mean of zero, and its variance satisfies:

[0155]

[0156] Among them, the load prediction deviation ΔP D has a probability density function of:

[0157]

[0158] (1) Simulating the output power fluctuation characteristics of wind power and photovoltaic power.

[0159] Similarly, the wind speed prediction deviation can also be expressed as the normal distribution with a mean of 0. Assuming the predicted wind speed the relationship between the actual wind speed v and the wind speed prediction deviation Δv is Then the probability density function of the wind speed prediction deviation is:

[0160]

[0161] Among them, σ v is the variance of Δv, and τ is the wind speed variance coefficient, satisfying:

[0162]

[0163] Finally, in the embodiment of the present application, the wind power output is calculated based on the actual wind speed. The wind power output is a piecewise function of the actual wind speed, and the expression is:

[0164]

[0165] Among them, v i is the wind turbine start-up wind speed, v o is the wind turbine cut-out wind speed, v N is the rated wind speed, p N is the rated output of the wind turbine, and k1 and k2 are the fitting parameters of the wind power output.

[0166] Similarly, the light intensity can also be expressed as a normal distribution with a mean of 0. Based on the light intensity prediction data in the embodiments of the present application, the light intensity prediction error is calculated, and the light intensity prediction deviation Δs follows a normal distribution with a mean of zero.

[0167] Assume the predicted light intensity The relationship between the actual light intensity s and the light intensity prediction deviation Δs is Then the probability density function of the light intensity prediction deviation is:

[0168]

[0169] Among them, δ s is the variance of Δs, and μ is the photovoltaic variance coefficient, satisfying:

[0170]

[0171] Furthermore, the present application calculates the photovoltaic output based on the actual light intensity. The photovoltaic output is a linear function of the light intensity, and the expression is:

[0172] P S = sη PV S

[0173] Among them, η PV is the photovoltaic conversion efficiency, and S is the total area of the photovoltaic modules.

[0174] Step 103: Calculate the loss-of-load probability and the renewable energy curtailment probability by Monte Carlo sampling and verify.

[0175] In the embodiments of the present application, step 103 involves proposing the loss-of-load probability and the renewable energy curtailment rate indicators. This step is divided into two parts: generating and verifying the loss-of-load probability indicator and generating and verifying the renewable energy curtailment probability.

[0176] The following is a detailed description thereof.

[0177] (1) Generate and verify the loss-of-load probability indicator.

[0178] In the embodiments of the present application, first calculate the loss-of-load probability of region k The calculation method is:

[0179]

[0180] wherein, is the loss-of-load probability density function, is the random variable load deviation ΔP of region k D , the output powers P of photovoltaic and wind power s,t and P w,t are linear combinations, is the positive spinning reserve of region k at time t, and p i,t is the generator output at node i at time t.

[0181] Furthermore, the embodiment of the present application uses Monte Carlo sampling to replace the multiple integral to calculate the expected loss of load and its expression is:

[0182]

[0183] wherein, l, m, and n are the Monte Carlo sampling times of load, photovoltaic, and wind power, is the amount of load loss for each sampling, and the formula is:

[0184]

[0185] Similarly, the loss-of-load probability can be calculated based on the auxiliary function and the formula is:

[0186]

[0187] wherein, is whether the load loss occurs in the scenario for each sampling result, and the formula is:

[0188]

[0189] wherein, is the amount of load loss in the scenario for each sampling result.

[0190] (2) Generate and verify the probability of renewable energy curtailment.

[0191] In the embodiment of the present application, the steps of generating and verifying the probability of renewable energy curtailment are similar to those of generating and verifying the loss-of-load probability index.

[0192] First, calculate the probability of renewable energy curtailment of region k and its calculation method is:

[0193]

[0194] wherein, is the probability density function of renewable energy curtailment, is the random variable load deviation ΔP of region k D, the output P of photovoltaic and wind power s,t and P w,t are linearly combined, is the negative spinning reserve of area k at time t, and p i,t is the generator output at node i at time t.

[0195] Similarly, using Monte Carlo sampling to replace the double integral, the expected curtailment of renewable energy Its expression is:

[0196]

[0197] where l, m, and n are the Monte Carlo sampling times of load, photovoltaic, and wind power, is the load shedding amount for each sampling, and the formula is:

[0198]

[0199] Similarly, the curtailment probability of renewable energy can be calculated based on the auxiliary function The formula is: The formula is:

[0200]

[0201] where, is whether renewable energy curtailment occurs in the scenario for each sampling result, and the formula is:

[0202]

[0203] where, Taking 0 means that no renewable energy curtailment has occurred.

[0204] Step 104, based on the verification results of the load shedding probability and the curtailment probability of renewable energy, construct a multi-objective function for multi-circuit DC power optimization. On the premise of meeting the grid security constraints, design the penalty terms of generation cost, generator start-stop cost, reserve cost, transmission cost, load shedding probability, and curtailment probability of renewable energy.

[0205] In the embodiment of the present application, if the verification shows that no renewable energy curtailment has occurred and no load shedding has occurred in the scenario for each sampling result, this means that the system is operating stably and meets the expected economic and reliability goals. In this case, the subsequent steps can be continued, entering the optimization process for optimal power distribution. If the verification shows that renewable energy curtailment has occurred or load shedding has occurred in the scenario for some sampling results, it indicates that the system may be unstable or unable to meet the load demand under the current configuration, and further adjustment and optimization are required. At this time, the optimization objectives can be adjusted according to these adverse results (such as increasing the reserve capacity, reducing the curtailment amount, etc.), and then enter the subsequent steps.

[0206] This step involves constructing a multi-objective function for multi-circuit DC power optimization. The constructed multi-objective function for multi-circuit DC power optimization can be expressed as:

[0207]

[0208] Wherein, is the power generation cost function, is the generator start-stop cost function, is the reserve cost function, is the transmission cost function of DC line ij, ψ is the penalty function of load shedding probability and renewable energy curtailment probability, β i,t is the generator on-process flag at node i in time period t, γ i,t is the generator off-process flag at node i in time period t; and are the positive and negative spinning reserves of the generator at node i in time period t respectively; are the transmitted power and positive and negative spinning reserves of DC line ij from node i to node j in time period t respectively.

[0209] It should be noted that multiple calculation processes are involved in the process of constructing the objective function, and the following provides a detailed description of it.

[0210] (1) Construct the power generation cost function, and the expression is:

[0211]

[0212] Wherein, a i , b i and c i are the coefficients of the quadratic function of the power generation cost of generator i; and are the start-stop costs of generator i.

[0213] (2) Construct the capacity cost function, and the expression is:

[0214]

[0215] Wherein, are the positive and negative spinning reserve cost coefficients of generator i respectively;

[0216] (3) Construct the penalty term function, and the expression is:

[0217]

[0218] Wherein, is the penalty function, which calculates the penalty value for the load shedding probability and renewable energy curtailment rate of each region in time period t, λ L and λR are the penalty coefficients respectively.

[0219] Step 105: Construct the security constraints for multiple HVDC lines, and set the DC line power flow constraint, system reliability constraint, and system load balance constraint.

[0220] In the embodiment of the present application, Step 105 involves constructing the security constraints for multiple HVDC lines, which are divided into two parts: constructing the multiple HVDC power model and constructing the system constraints.

[0221] The following is a detailed description thereof.

[0222] (1) Construct the multiple HVDC power model.

[0223] It should be noted that in actual dispatching, as an inter-regional transmission line, the HVDC line has many dispatching constraints. To ensure the safe and efficient operation of the power grid and facilitate dispatching, the dispatching flexibility of the actual HVDC line is relatively low. To meet the medium- and long-term contracts and day-ahead dispatching plans, the power of the HVDC line is often adjusted only a few times a day.

[0224] Therefore, in the embodiment of the present application, the virtual generator modeling method is adopted to describe the power of multiple HVDC transmission lines. The transmitted power of each HVDC line is represented by the sum of the on-off states of several virtual generators, expressed as:

[0225]

[0226] Where represents the maximum transmission power of the HVDC line ij; T u,dc and T d,dc are the fastest continuous on-off and off-on times of the virtual generator respectively; p s is the capacity of a single virtual generator of the DC tie line; represents the on-off state flag of the virtual generator s of the HVDC line ij at time t; represent the on and off process flags of the virtual generator s of the HVDC line ij at time t respectively; t end is the total number of hours of operation simulation;

[0227] At the same time, set the DC power transmission constraint to ensure that the power dispatching of the HVDC line is within the allowable range. Use to represent the power flow direction of the DC line ij, expressed as:

[0228]

[0229] Where are the forward and reverse power transmission flags of the DC line ij at time t respectively;

[0230] Furthermore, the embodiment of the present application sets the DC line power flow adjustment constraint to ensure the stability of the power flow direction adjustment, satisfying:

[0231]

[0232] wherein, T +,dc , T -,dc are respectively the fastest direction-changing times of the DC line.

[0233] (2) Construct system constraints.

[0234] It can be understood that the system constraints include multiple parts, including system reliability constraints, system day-ahead load balance constraints, system power flow capacity constraints, generator ramp constraints, upper and lower limits of output constraints, continuous start-up and shutdown constraints, etc.

[0235] In the embodiment of the present application, the system reliability constraints are first constructed, which include two parts: the loss-of-load probability partition reliability constraint and the renewable energy curtailment probability reliability constraint.

[0236] Among them, the loss-of-load probability partition reliability constraint is expressed as:

[0237]

[0238] wherein, is the maximum tolerable loss-of-load probability of region k at time t;

[0239] The renewable energy curtailment probability reliability constraint is expressed as:

[0240]

[0241] wherein, is the maximum tolerable renewable energy curtailment probability of region k at time t.

[0242] In the embodiment of the present application, the system day-ahead load balance constraint is further constructed, expressed as:

[0243]

[0244] wherein, is the predicted value of the system's wind power output at time t; is the predicted value of the system's photovoltaic power output at time t; is the predicted value of the system's load at time t; are respectively the predicted values of the wind power output, photovoltaic power output and load of region k at time t; N k is the node set of region k, and L DC is the set of DC lines composed of the start and end node groups (i, j) of the DC lines.

[0245] Meanwhile, construct the system power flow capacity constraint, expressed as:

[0246]

[0247] Wherein, is the AC line capacity vector; S GSDF is the GSDF matrix; D t , are the system output, load, and DC power flow vectors respectively.

[0248] Meanwhile, construct the generator ramp rate constraint, expressed as:

[0249]

[0250] Wherein, and are the upward and downward ramp rate coefficients of the generator at node i respectively; P i g represent the maximum and minimum outputs of the generator at node i when it is in operation; δ i,t represents the on-off state flag of the generator at node i at time t, and this flag has the following relationship with the on state flag β i,t , and the off state flag γ i,t :

[0251] δ i,t -δ i,t-1 ≤β i,t ≤1

[0252] δ i,t-1 -δ i,t ≤γ i,t ≤1

[0253] Meanwhile, construct the upper and lower output limits constraint, expressed as:

[0254]

[0255] Wherein, P i g , are the upper and lower output limits of the generator at node i respectively.

[0256] Meanwhile, construct the continuous on-off constraint, expressed as:

[0257]

[0258] Wherein, are the fastest continuous on-off and off-on times of the generator at node i respectively.

[0259] Step 106: Construct positive and negative rotating reserve call constraints, and set the minimum regional reserve requirement, node reserve limit, and the balance constraint between regional reserve and DC transmission reserve.

[0260] In the embodiment of the present application, this step involves constructing positive and negative rotating reserve call constraints, and the specific steps are as follows:

[0261] First, the embodiment of the present application constructs the minimum reserve constraint within the region, which is expressed as:

[0262]

[0263]

[0264] where L u , W u , S u are the load, wind power, and PV uncertainty factors of the minimum positive reserve respectively, and L d , W d , S u are the load, wind power, and PV uncertainty factors of the minimum negative reserve respectively. The minimum values of positive and negative reserves are affected by uncertainty factors.

[0265] At the same time, the embodiment of the present application constructs the node reserve constraint, which is expressed as:

[0266]

[0267] At the same time, the embodiment of the present application constructs the constraint between regional reserve and DC line transmission reserve, which is expressed as:

[0268]

[0269] where, is the total positive and negative rotating reserve of region k at time t.

[0270] Step 107: Based on the multi-objective function, the safety constraints of multiple DC lines, and the constructed positive and negative rotating reserve call constraints, use a solver to solve the power system model to obtain the optimal power distribution plan.

[0271] In this step, the embodiment of the present application uses a solver to solve the power system model, uploads the multi-objective function and constraint equations to the dispatching center, and at the same time uploads the solution results to support its day-ahead dispatching plan, comprehensively considering practical problems such as system security, dispatching operation complexity, and difficulty in formulating transmission plans. All previous steps will generate operation logs during execution. This step will comprehensively organize various results and log records in the above calculation process, and then store them in the historical log backup library as required for future verification.

[0272] So far, the entire process of power optimization for multiple DC transmission lines that takes into account both the loss-of-load probability and the utilization rate of renewable energy has ended.

[0273] To implement the above embodiments, the present application also proposes a power optimization device for multiple DC transmission lines that takes into account both power supply guarantee and consumption. Figure 2 It is a structural schematic diagram of a power optimization device for multiple DC transmission lines that takes into account both power supply guarantee and consumption provided by the embodiments of the present application. As Figure 2 shown, the device includes:

[0274] A reading module 100, configured to read power network parameter data and cost and constraint information based on the power grid topology structure and transmission requirements, and establish a power system model;

[0275] A calculation module 200, configured to calculate the prediction deviations of load, wind power, and light intensity, and the output of wind power and photovoltaic power, and use Monte Carlo sampling to calculate the loss-of-load probability and the probability of renewable energy curtailment and verify them;

[0276] A target construction module 300, configured to construct a multi-objective function for optimizing the power of multiple DC lines based on the verification results of the loss-of-load probability and the probability of renewable energy curtailment, and set the power generation cost, generator start-stop cost, reserve cost, transmission cost, and penalty terms for the loss-of-load probability and the probability of renewable energy curtailment on the premise of meeting the power grid security constraints;

[0277] A constraint construction module 400, configured to construct the safety constraints of multiple DC lines, and set the DC line power flow constraints, system reliability constraints, and system load balance constraints; and construct the positive and negative spinning reserve call constraints, and set the regional minimum reserve requirements, node reserve limits, and regional reserve and DC transmission reserve balance constraints;

[0278] A solution module 500, configured to solve the power system model by using a solver based on the multi-objective function, the safety constraints of multiple DC lines, and the constructed positive and negative spinning reserve call constraints, and obtain the optimal power distribution plan.

[0279] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0280] To implement the above embodiments, the present application also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0281] To implement the above embodiments, the present application also provides a computer-readable storage medium storing computer-executable instructions, which when executed by a processor are used to implement the methods provided in the foregoing embodiments.

[0282] To implement the above embodiments, the present application also provides a computer program product including a computer program, which when executed by a processor implements the methods provided in the foregoing embodiments.

[0283] The collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0284] It should be noted that personal information from users should be collected for legal and reasonable purposes and not shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the user, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization including authorizing the relevant user information before the user uses the function. In addition, any necessary steps should be taken to safeguard and protect access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0285] The present application anticipates providing embodiments in which users can selectively block the use or access of personal information data. That is, the present disclosure anticipates providing hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of the user.

[0286] In the description of the foregoing embodiments, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0287] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0288] Any process or method description represented in a flowchart or described otherwise herein may be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of the present application includes additional implementations, in which functions may be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed. This should be understood by those skilled in the technical field to which the embodiments of the present application belong.

[0289] The logic and / or steps represented in a flowchart or described otherwise herein, for example, may be considered as an ordered list of executable instructions for implementing a logical function, and may be specifically implemented in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or otherwise appropriate processing if necessary, and then stored in a computer memory.

[0290] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0291] Those of ordinary skill in the art can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0292] In addition, in each embodiment of the present application, each functional unit can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0293] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

[0294] It should be understood that various forms of processes shown above can be used, reordering, adding, or deleting steps. For example, the steps described in the present application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present application can be achieved. No limitation is imposed herein.

[0295] The above specific embodiments do not constitute a limitation to the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for optimizing the power of multiple DC transmission lines, characterized in that, Including the following steps: Based on the power grid topology structure and transmission requirements, read the power network parameter data and cost and constraint information, and establish a power system model; Calculate the prediction deviations of load, wind power, and light intensity, and the output of wind power and photovoltaic power, and use Monte Carlo sampling to calculate the loss of load probability and the probability of renewable energy curtailment and verify; Based on the verification results of the loss of load probability and the probability of renewable energy curtailment, construct a multi-objective function for multi-circuit DC power optimization, and set the generation cost, generator start-stop cost, reserve cost, transmission cost, and penalty terms for the loss of load probability and the probability of renewable energy curtailment on the premise of meeting the power grid security constraints; Construct the safety constraints of multi-circuit DC lines, and set the DC line power flow constraints, system reliability constraints, and system load balance constraints; and construct the positive and negative spinning reserve call constraints, and set the regional minimum reserve requirements, node reserve limits, and regional reserve and DC transmission reserve balance constraints; Based on the multi-objective function, the safety constraints of multi-circuit DC lines, and the constructed positive and negative spinning reserve call constraints, use a solver to solve the power system model to obtain the optimal power distribution plan.

2. The method according to claim 1, characterized in that, The step of reading the power network parameter data and cost and constraint information and establishing a power system model includes: Read the power network parameter data, and the power network parameter data includes the network topology connection relationship, the position and parameters of DC transmission lines, the transformer turns ratio parameters, the resistance matrix, the susceptance matrix of the power grid, and the network parameter base value; Read the cost and constraint information, and the cost and constraint information includes the generator ramp rate, the fastest continuous start-stop duration, the generator generation cost function, the generator start-stop cost, and the DC tie line power transmission cost of the existing network model; Set the restrictive constraints of DC transmission power and configure the constraint requirements for multi-circuit DC transmission power across regions. The constraint requirements include the upper limit of the number of constraints, the parameter form of the constraint matrix, and the provisions of the constraint inequality direction, and read the initialized restrictive constraint set to ensure that the dispatching center can receive and execute the constraints through a unified interface.

3. The method according to claim 2, wherein The step of calculating the prediction deviations of load, wind power, and light intensity, and the output of wind power and photovoltaic power includes: Calculate the load prediction error according to the load prediction data, and the load prediction error is defined as: Among them, represents the load prediction value, ΔP D represents the load prediction deviation, and the load prediction deviation ΔP D obeys a normal distribution with a mean of zero, and its variance satisfies: where the load forecasting deviation ΔP D has a probability density function as follows: Based on the wind speed prediction data, calculate the wind speed prediction error, and the wind speed prediction deviation Δv follows a normal distribution with a mean of zero; assume the predicted wind speed The relationship between the actual wind speed v and the wind speed prediction deviation Δv is Then the probability density function of the wind speed prediction deviation is: where, σ v is the variance of Δv, τ is the wind speed variance coefficient, and they satisfy: Calculate the wind power output based on the actual wind speed, and the wind power output is a piecewise function of the actual wind speed, and the expression is: Among them, v i is the fan startup wind speed, v o is the fan cut-out wind speed, v N is the rated wind speed, p N is the rated output of the fan, and k1 and k2 are the fitting parameters of the wind power output; Based on the predicted light intensity data, calculate the predicted light intensity error, and the predicted light intensity deviation Δs follows a normal distribution with a mean of zero; assume the predicted light intensity The relationship between the actual light intensity s and the predicted light intensity deviation Δs is Then the probability density function of the predicted light intensity deviation is:[[]]END]] where σ s is the variance of Δs, and μ is the photovoltaic variance coefficient, satisfying: Calculate the photovoltaic output based on the actual light intensity, and the photovoltaic output is a linear function of the light intensity, and the expression is: P S = sη PV S Among them, η PV is the photovoltaic conversion efficiency, and S is the total area of the photovoltaic module.

4. The method according to claim 3, wherein The step of using Monte Carlo sampling to calculate the loss of load probability and the probability of renewable energy curtailment and verify includes: Calculate the loss-of-load probability of area k The calculation method is as follows: Among them, is the loss-of-load probability density function, is the random variable load deviation ΔP of area k D , the output of photovoltaic and wind power P s,t and P w,t is a linear combination of is the positive spinning reserve of area k at time t, p i,t is the generator output at node i at time t; Use Monte Carlo sampling to replace the double integral and calculate the expected load shedding Its expression is as follows: where l, m, and n are the Monte Carlo sampling times of load, photovoltaic, and wind power, which is the load shedding amount for each sampling, and the formula is: Based on the auxiliary function Calculate the loss of load probability The formula is as follows: Among them, For whether a load shedding occurs in the scenario under each sampling result, the formula is: Among them, is the load shedding amount of the scenario under each sampling result; Calculate the probability of renewable energy curtailment in region k The calculation method is as follows: Among them, is the probability density function of renewable energy curtailment, is the random variable load deviation ΔP in region k D , the output of photovoltaic and wind power P s,t and P w,t is a linear combination of is the negative spinning reserve in region k at time t, p i,t is the generator output at node i at time t; Using Monte Carlo sampling to replace the double integral, the expected value of the curtailment of renewable energy Its expression is as follows: where l, m, and n are the Monte Carlo sampling times of load, photovoltaic, and wind power, which is the amount of load loss for each sampling, and the formula is: Based on an auxiliary function Calculate the curtailment probability of renewable energy The formula is as follows: Among them, It is whether renewable energy is curtailed in the scenario for each sampling result, and the formula is: Among them, Taking 0 indicates that there is no curtailment of renewable energy.

5. The method according to claim 4, characterized in that, Based on the verification results of the loss of load probability and the probability of renewable energy curtailment, construct a multi-objective function for multi-circuit DC power optimization, and set the generation cost, generator start-stop cost, reserve cost, transmission cost, and penalty terms for the loss of load probability and the probability of renewable energy curtailment on the premise of meeting the power grid security constraints, including: Construct a generation cost function, and the expression is: Among them, a i , b i and c i are the coefficients of the quadratic function of the power generation cost of generator i; and are the start-stop costs of generator i; Construct a capacity cost function, and the expression is: Among them, are the positive and negative spinning reserve cost coefficients of generator i, respectively; Construct a penalty term function, and the expression is: Among them, is a penalty function that calculates the penalty value for the loss-of-load probability and the renewable energy curtailment rate in each region during period t, and λ L and λ R are the penalty coefficients respectively; Construct a multi-objective function for multi-circuit DC power optimization, and the formula is: Among them, is the power generation cost function, is the generator start-up and shut-down cost function, is the reserve cost function, is the transmission cost function of DC line ij, ψ is the penalty function of loss-of-load probability and renewable energy curtailment probability, β i,t is the generator start-up process flag at node i in period t, γ i,t is the generator shut-down process flag at node i in period t; and are the positive and negative spinning reserves of the generator at node i in period t respectively; are the transmitted power and positive and negative spinning reserves of DC line ij from node i to node j in period t respectively.

6. The method according to claim 5, wherein Construct the security constraints of multiple HVDC lines, and set the DC line power flow constraints, system reliability constraints, and system load balance constraints, including: Use the virtual generator modeling method to describe the power of multiple HVDC transmission lines. The transmitted power of each DC line is represented by the sum of the on / off states of several virtual generators, expressed as: Among them, represents the maximum transmission power of DC line ij; T u,dc , T d,dc are the fastest continuous start-up and shut-down times, shut-down and start-up times of the virtual generator respectively; p s is the capacity of a single virtual generator of the DC tie line; represents the start-up and shut-down status flag of virtual generator s of DC line ij at time period t; represent the start-up and shut-down process flags of virtual generator s of DC line ij at time period t respectively; t end is the total number of hours of operation simulation; Set the DC power transfer constraint to ensure that the power scheduling of the DC line is within the allowable range. Use to represent the power flow direction of the DC line ij, which is expressed as: wherein are respectively the forward and reverse power transmission signs of the DC line ij at time t; Set the DC line power flow adjustment constraints to ensure the stability of the power flow direction adjustment, satisfying: Among them, T +,dc and T -,dc are the fastest changing direction times of the DC line respectively; Construct the load shedding probability partition reliability constraints, expressed as: Among them, is the maximum tolerable load shedding probability of area k at time t; Construct the renewable energy curtailment probability reliability constraints, expressed as: Among them, is the maximum tolerable curtailment probability of renewable energy in region k at time t; Construct the system day-ahead load balance constraints, expressed as: Among them, is the predicted value of the wind power output of the system at time t; is the predicted value of the PV power output of the system at time t; is the predicted value of the load of the system at time t; are respectively the predicted values of the wind power output, PV power output and load in area k at time t; N k is the node set of area k, L DC is the set of DC lines composed of the start and end node groups (i, j) of the DC lines; Construct the system power flow capacity constraints, expressed as: Among them, is the AC line capacity vector; S GSDF is the GSDF matrix; D t and are the system output, load, and DC power flow vectors, respectively; Construct the generator ramping constraints, expressed as: Among them, and are the upward and downward ramping factors of the generator at node i, respectively; P i g represent the maximum and minimum output powers of the generator at node i when it is in operation, respectively; δ i,t represents the on / off state flag of the generator at node i in time period t, and this flag is related to the on state β i,t and the off state flag γ i,t as follows: δ i,t -δ i,t-1 ≤β i,t ≤1 δ i,t-1 -δ i,t ≤γ i,t ≤1 Construct the upper and lower output limits constraints, expressed as: wherein, P i g and are respectively the upper and lower limits of the output of the generator at node i; Construct the continuous on / off constraints, expressed as: Among them, are the fastest consecutive start-up and shut-down, shut-down and start-up times of the generator at node i, respectively.

7. The method according to claim 6, wherein The construction of the positive and negative spinning reserve call constraints, setting the regional minimum reserve demand, node reserve limits, and regional reserve and HVDC transmission reserve balance constraints, including: Construct the minimum reserve constraints within the region, expressed as: where L u , W u , S u are the uncertainty factors of load, wind power, and photovoltaic for the lowest positive reserve respectively, and L d , W d , S u are the uncertainty factors of load, wind power, and photovoltaic for the lowest negative reserve respectively. The minimum value of positive and negative reserves is affected by uncertainty factors; Construct the node reserve constraints, expressed as: Construct the constraints of regional reserve and DC line transmission reserve, expressed as: Among them, is the total positive and negative rotational reserve of area k at time t.

8. A multi - HVDC transmission line power optimization device that takes into account both power supply guarantee and power consumption, characterized in that Include: A reading module for reading the power network parameter data and cost and constraint information based on the power grid topology structure and transmission requirements, and establishing a power system model; A calculation module for calculating the load, wind power, and light intensity prediction deviations, as well as the wind power and photovoltaic output, and using Monte Carlo sampling to calculate the load shedding probability and renewable energy curtailment probability and perform verification; An objective construction module for constructing a multi-objective function for optimizing the power of multiple HVDC lines based on the verification results of the load shedding probability and renewable energy curtailment probability, and setting the generation cost, generator start / stop cost, reserve cost, transmission cost, and penalty terms for the load shedding probability and renewable energy curtailment probability on the premise of satisfying the power grid security constraints; A constraint construction module for constructing the security constraints of multiple HVDC lines, and setting the DC line power flow constraints, system reliability constraints, and system load balance constraints; and constructing the positive and negative spinning reserve call constraints, setting the regional minimum reserve demand, node reserve limits, and regional reserve and HVDC transmission reserve balance constraints; A solution module for solving the power system model using a solver based on the multi-objective function, the security constraints of multiple HVDC lines, and the constructed positive and negative spinning reserve call constraints to obtain the optimal power distribution plan.

9. An electronic device, characterized in that, Include: A processor, and a memory communicatively connected to the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by the processor, they are used to implement the method according to any one of claims 1-7.

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