Variable cost calculation method and system for power distribution network operation cost evaluation

Abstract

The application provides a variable cost calculation method and system for distribution network operation cost evaluation, and relates to the technical field of power system automation.The method comprises the following steps: obtaining basic data of distributed photovoltaic integration into a distribution network within a preset period; determining a network loss incremental cost based on a photovoltaic output curve, user time-sharing load, distribution network time-sharing electricity price and distribution network line parameters; determining a carbon emission cost based on the photovoltaic output curve, carbon trading price and carbon tax standard; determining a backup capacity cost based on the photovoltaic output curve, user time-sharing load and backup power supply parameters; and taking the sum of the network loss incremental cost, the carbon emission cost and the backup capacity cost as the variable cost of the distributed photovoltaic integration into the distribution network.The variable cost calculation method and system for distribution network operation cost evaluation provided by the application realizes accurate calculation of the variable cost generated by the distributed photovoltaic integration into the distribution network.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, and in particular to a variable cost calculation method and system for evaluating the operating costs of distribution networks. Background Technology

[0002] The variable cost of a distribution network refers to the dynamically changing operating costs incurred when distributed photovoltaic (PV) systems are integrated into the distribution network. It is used to reflect the operating efficiency and economy of the distribution network system and mainly includes incremental network loss costs, reserve capacity costs, carbon emission costs (or benefits), and the cost of absorbing high-proportion grid connections.

[0003] Existing technologies mainly use the following methods to calculate the variable cost of distribution network operation: (1) Calculate and derive an isolated model for a single variable cost. This calculation method results in a relatively one-sided calculation result and cannot reflect the actual operating burden of the distribution network system; (2) Calculate the variable cost using deterministic methods (such as static calculation based on historical averages) or simplified probabilistic models (such as assuming that the output error follows a normal distribution). This calculation method cannot reflect the output change of photovoltaics in different scenarios, resulting in a large calculation error; (3) Calculate the variable cost by designing a calculation model based on low-penetration photovoltaic scenarios. However, it does not consider the impact of problems such as "reverse power flow", "voltage over-limit", and "multi-voltage level coordination" on the variable cost under high-proportion grid connection, resulting in the calculation result deviating from reality and having low accuracy. Summary of the Invention

[0004] This invention provides a variable cost calculation method and system for evaluating the operating costs of distribution networks, which solves the technical problem of inaccurate calculation of variable costs generated by distributed photovoltaic power generation connected to the distribution network in the prior art, and realizes accurate evaluation of the operating costs of distribution networks.

[0005] This invention provides a variable cost calculation method for evaluating the operating costs of a power distribution network, comprising the following steps: Acquire basic data on distributed photovoltaic (PV) power generation connected to the distribution network within a preset period; the basic data includes PV output curves, user time-of-use load, distribution network time-of-use electricity price, distribution network line parameters, carbon trading price, carbon tax standard, and backup power parameters; the backup power parameters include backup power capacity and backup power cost. Based on the photovoltaic output curve, the user time-of-use load, the distribution network time-of-use electricity price, and the distribution network line parameters, the incremental cost of network loss is determined; The carbon emission cost is determined based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard. Based on the photovoltaic output curve, the user time-of-use load, and the backup power parameters, determine the backup capacity cost; The sum of the incremental cost of grid loss, the cost of carbon emissions, and the cost of reserve capacity is taken as the variable cost of integrating the distributed photovoltaic system into the distribution network.

[0006] According to the present invention, a variable cost calculation method for evaluating the operating costs of a distribution network includes determining the incremental cost of network losses based on the photovoltaic output curve, the user time-of-use load, the time-of-use electricity price of the distribution network, and the distribution network line parameters. Based on the photovoltaic output curve, the active and reactive power outputs of the photovoltaic distributed power source at the grid-connected node are determined. Based on the active power output, the reactive power output, the user time-of-use load, and the distribution network line parameters, determine the change in network loss; The incremental cost of network loss is determined based on the time-of-use electricity price of the distribution network and the change in network loss.

[0007] According to the variable cost calculation method for power distribution network operation cost assessment provided by the present invention, the step of determining carbon emission costs based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard includes: Based on the photovoltaic output curve, the carbon flow rate of the photovoltaic distributed power source at the grid-connected node is determined; The carbon emission cost is determined based on the carbon flow rate, the carbon trading price, and the carbon tax standard.

[0008] According to the present invention, a variable cost calculation method for evaluating the operating costs of a distribution network, wherein determining the standby capacity cost based on the photovoltaic output curve, the user time-of-use load, and the standby power parameters includes: Based on the normal distribution and the user's time-sharing load, determine the first probability density function corresponding to the load prediction error; Based on the Beta distribution and the photovoltaic power output curve, the second probability density function corresponding to the photovoltaic prediction error is determined. A risk quantification model is constructed based on the first probability density function and the second probability density function; Based on the aforementioned risk quantification model and preset risk conditions, a rotating reserve capacity model is constructed; Based on the backup power parameters and the spinning reserve capacity model, the backup capacity cost is obtained.

[0009] According to the present invention, a variable cost calculation method for evaluating the operating costs of a distribution network is provided, wherein the risk quantification model is used to quantify the operating risk of the distribution network; the operating risk is determined based on the backup power capacity.

[0010] According to the present invention, a variable cost calculation method for evaluating the operating costs of a power distribution network is provided, the method further comprising: Anomalies are removed from the basic data using anomaly detection and interpolation algorithms.

[0011] The present invention also provides a variable cost calculation system for evaluating the operating costs of a power distribution network, comprising the following modules: The data acquisition module is used to acquire basic data before and after the distributed photovoltaic system is connected to the distribution network within a preset period. The basic data includes photovoltaic output curves, user time-of-use loads, distribution network time-of-use electricity prices, distribution network line parameters, carbon trading prices, carbon tax standards, and backup power parameters. The backup power parameters include backup power capacity and backup power cost. The incremental network loss cost calculation module is used to determine the incremental network loss cost based on the photovoltaic output curve, the user's time-of-use load, the time-of-use electricity price of the distribution network, and the parameters of the distribution network lines. The carbon emission cost calculation module is used to determine the carbon emission cost based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard. The backup capacity cost calculation module is used to determine the backup capacity cost based on the photovoltaic output curve, the user time-of-use load, and the backup power parameters. The variable cost calculation module is used to take the sum of the incremental network loss cost, the carbon emission cost, and the reserve capacity cost as the variable cost of integrating the distributed photovoltaic power into the distribution network.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the variable cost calculation method for power distribution network operation cost assessment as described above.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the variable cost calculation method for power distribution network operation cost assessment as described above.

[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the variable cost calculation method for power distribution network operation cost assessment as described above.

[0015] The present invention provides a variable cost calculation method and system for evaluating the operating costs of distribution networks. This method acquires basic data on distributed photovoltaic (PV) power generation connected to the distribution network within a preset period. This basic data includes PV output curves, user time-of-use loads, distribution network time-of-use tariffs, distribution network line parameters, carbon trading prices, carbon tax standards, and standby power parameters. The standby power parameters include standby power capacity and standby power cost. This integrates PV output, load, and tariff parameters, providing a reliable data foundation for subsequent cost calculations and reducing deviations caused by missing or abnormal data. Based on the PV output curves, user time-of-use loads, distribution network time-of-use tariffs, and distribution network line parameters, the incremental cost of network losses is determined, thereby quantifying the changes in network losses caused by PV grid connection and accurately reflecting these changes in real-time tariffs. Incremental grid loss cost; based on photovoltaic output curves, carbon trading prices, and carbon tax standards, determine carbon emission costs to assess the carbon emission reduction benefits of photovoltaic clean energy replacing traditional thermal power, and accurately reflect carbon emission costs by combining carbon market parameters; based on photovoltaic output curves, user time-of-use load, and backup power parameters, determine backup capacity costs to accurately calculate backup capacity costs considering the randomness and intermittency of photovoltaic output; use the sum of incremental grid loss cost, carbon emission cost, and backup capacity cost as the variable cost of integrating distributed photovoltaic into the distribution network, thereby integrating incremental grid loss cost, carbon emission cost, and backup capacity cost to achieve accurate calculation of the variable costs generated by integrating distributed photovoltaic into the distribution network, providing reliable data for grid dispatch, electricity pricing, and photovoltaic acceptance capacity assessment. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the variable cost calculation method for power distribution network operation cost assessment provided by the present invention.

[0018] Figure 2 This is a schematic diagram of variable cost data visualization provided by the present invention.

[0019] Figure 3 This is a schematic diagram of the variable cost calculation system for power distribution network operation cost assessment provided by the present invention.

[0020] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] The following is combined with Figures 1 to 4 This invention describes a variable cost calculation method and system for evaluating the operating costs of power distribution networks.

[0023] Figure 1 This is a flowchart illustrating the variable cost calculation method for power distribution network operation cost assessment provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps: Step 101: Obtain basic data on distributed photovoltaic power generation connected to the distribution network within a preset period; the basic data includes photovoltaic power output curves, user time-of-use loads, distribution network time-of-use electricity prices, distribution network line parameters, carbon trading prices, carbon tax standards, and backup power parameters; the backup power parameters include backup power capacity and backup power cost. Specifically, basic data on distributed photovoltaic (PV) power grid integration within a preset period is collected. This basic data includes PV output curves, user time-of-use load, time-of-use electricity prices in the distribution network, distribution network line parameters, carbon trading prices, carbon tax standards, and backup power parameters. Backup power parameters include backup power capacity and backup power cost. The preset period can be 30 days, 50 days, or 60 days, etc., and this embodiment of the invention does not limit this period.

[0024] Furthermore, after acquiring the basic data before and after the distributed photovoltaic system is connected to the distribution network within a preset period, the method further includes: Anomalies are removed from the basic data using anomaly detection and interpolation algorithms.

[0025] Specifically, the basic data is cleaned and corrected by matching the data volume with that of typical scenarios. For example, anomaly detection and interpolation algorithms are used to remove outliers and smooth fluctuations in the basic data, thereby ensuring the accuracy of subsequent model inputs.

[0026] For example, in one embodiment, during summer photovoltaic power output monitoring in a certain area, the system collects hourly photovoltaic power output curves and residential load data, detects outliers (such as sudden changes in values ​​caused by sensor failures) using a box plot algorithm, and fills in missing time periods using linear interpolation to make the data smoothly adapt to the measurement needs of sunny day scenarios.

[0027] For example, in another embodiment, for extreme weather days (such as typhoon days), the system automatically identifies abnormal data where photovoltaic output drops by 90%, and performs interpolation correction by combining historical weather patterns to ensure the matching of load and output data under cloudy and rainy conditions and avoid data distortion.

[0028] Step 102: Determine the incremental cost of network loss based on the photovoltaic output curve, the user's time-of-use load, the time-of-use electricity price of the distribution network, and the parameters of the distribution network lines; Furthermore, determining the incremental cost of network loss based on the photovoltaic output curve, the user's time-of-use load, the distribution network's time-of-use electricity price, and the distribution network line parameters includes: Based on the photovoltaic output curve, the active and reactive power outputs of the photovoltaic distributed power source at the grid-connected node are determined. Based on the active power output, the reactive power output, the user time-of-use load, and the distribution network line parameters, determine the change in network loss; The incremental cost of network loss is determined based on the time-of-use electricity price of the distribution network and the change in network loss.

[0029] Specifically, photovoltaic (PV) grid connection alters the transmission path and power flow direction of electrical energy, leading to increased local voltage and current fluctuations, which in turn causes changes in grid losses. Therefore, the incremental grid loss cost is the additional expense caused by the increase in grid losses due to PV grid connection, such as unreasonable grid connection points or backflow of power flow.

[0030] The embodiments of this invention calculate the incremental cost of grid loss based on the increase or decrease in grid loss before and after photovoltaic grid connection. The calculation expression is as follows: In the formula, m Indicates the number of days in the preset period; t Indicates time; ei ( t ) represents the real-time electricity price, which can be determined based on the time-of-use pricing of the distribution network; Δ P locz This represents the increase or decrease in grid loss before and after photovoltaic grid connection, i.e., the change in grid loss. The calculation expression is as follows: In the formula, U m,1 Indicates the first photovoltaic grid connection m The node voltage amplitude of each node; U m,2 Indicates the first day after photovoltaic grid connection m The node voltage amplitude of each node; They represent the first The active and reactive loads of each node (or its corresponding load) can be determined based on the user's time-of-use load. This indicates the active and reactive power output of the photovoltaic distributed power source at the grid connection point. The active power output is determined by the photovoltaic power output curve (which can be directly read from the historical data record of the power system, and is a curve showing how the photovoltaic active power changes over time), while the reactive power output is set according to the power factor of the inverter. For the first The equivalent resistance of a distribution network line can be determined based on the relevant parameters of the distribution network line; n represents the total number of nodes.

[0031] Among them, for the first For each distribution network node, its active load is the sum of the time-of-use active loads of all users under that node. Reactive load can be determined by the corresponding power factor or reactive power metering data. The calculation expression is as follows: In the formula, This represents the power factor. The embodiments of this invention determine the incremental cost of network losses based on photovoltaic active / reactive power output and power flow calculations. This accurately quantifies the actual impact of bidirectional power flow changes caused by photovoltaic access on distribution network line losses, overcoming the limitations of traditional unidirectional power flow models. Therefore, it can accurately reflect the impact of bidirectional power flow on line losses and accurately calculate the incremental cost of network losses.

[0032] Step 103: Determine the carbon emission cost based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard; Furthermore, determining the carbon emission cost based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard includes: Based on the photovoltaic output curve, the carbon flow rate of the photovoltaic distributed power source at the grid-connected node is determined; The carbon emission cost is determined based on the carbon flow rate, the carbon trading price, and the carbon tax standard.

[0033] Specifically, the carbon emission source of the power distribution network is the gas generated during the power generation process of thermal power generating units. In this embodiment of the invention, carbon emission intensity is defined as the carbon emission generated per unit of electricity produced. The carbon emission intensity indices vary among different types of generating units. For distributed photovoltaic grid-connected power generation, its carbon emission intensity is almost zero. However, the grid connection of these clean energy units affects both system network losses and the corresponding carbon flow distribution; therefore, it is necessary to consider the impact of distributed photovoltaic grid connection on the carbon flow distribution model. The carbon emission intensity of thermal power generating units is affected by various factors. Based on the operating state of the thermal power generating unit at any given time, the expression for calculating the coal consumption rate is: In the formula, ai , bi c i All are thermal power generating units i The power generation consumption characteristic coefficient; For thermal power generating units i Standard coal consumption rate (g / (kW·h)); This is the coal consumption rate correction factor. For thermal power generating units Those who have made contributions The values ​​of are shown in Table 1.

[0034] Table 1 Relationship between the value and the unit's operating status Carbon emission intensity of thermal power generating units E Gi The calculation expression is: In the formula, For thermal power generating units i Carbon capture rate; The carbon content of the coal; Carbon oxidation rate; The value is the molar mass of carbon, taken as 12 g / mol; The value is 44 g / mol, representing the molar mass of carbon dioxide.

[0035] The above equation represents the relationship between the output of thermal power generating units and carbon emission intensity. Based on the bidirectional power flow tracking method that takes into account the correction of the equivalent model of grid losses, the active power of the load nodes with photovoltaic correction can be established separately. Distribution network branches active power The relationship between the active power output of each thermal power generating unit and the node net load, where the nodal net load is the sum of the power contributed by each thermal power generating unit, is expressed as follows: In the formula, For nodes Active load, For nodes The active power output of grid-connected photovoltaic systems, For nodes The total active power passing through, For the first The equivalent active power output of a thermal power generating unit. The inverse matrix elements of the power allocation matrix represent thermal power generating units. The output reaches the node through the network. The allocation coefficient is denoted by n, where n represents the total number of nodes.

[0036] Based on the aforementioned two-way power flow tracing method, the output of each thermal power unit can be allocated to the load nodes / branches, and then multiplied by the unit's carbon emission intensity to obtain the carbon flow rate at each location. The calculation expression is as follows: In the formula, Represents the power allocation matrix The inverse matrix, the first line, number The elements of the column are used to represent the first... The active power output of the thermal power generating unit is transmitted to the first unit via the distribution network. The distribution coefficient of active power of each load node.

[0037] Photovoltaic-corrected active power at load nodes Active power of distribution network branches The formula for calculating the carbon flow rate corresponding to each thermal power generating unit, taking into account network losses, is as follows: In the formula, Load nodes for photovoltaic correction i The carbon flow rate represents the carbon emissions equivalent to the total active power consumption per unit hour at the thermal power generating unit level, expressed in units of [unit value missing]. t h ; branch road i - t The carbon flow rate represents the carbon emissions corresponding to the active power flowing through the branch per unit hour. For thermal power generating units k Carbon flow rate corresponding to the network loss portion. This indicates the carbon emission intensity of the Nth thermal power generating unit; This represents the equivalent active power output correction for the nth thermal power generating unit due to the consideration of distribution network losses.

[0038] The formula for calculating carbon emission costs is: In the formula, For nodes carbon flow rate, C a , C b These are the tax levied per unit of carbon emissions (which can be determined according to carbon tax standards) and the carbon trading price.

[0039] The embodiments of the present invention calculate the carbon flow rate based on the photovoltaic grid-connected node, realizing the accurate and traceable monetization and quantification of the carbon emission reduction benefits generated by photovoltaic power generation replacing traditional thermal power, thereby improving the accuracy of carbon emission cost calculation.

[0040] Step 104: Determine the backup capacity cost based on the photovoltaic output curve, the user time-of-use load, and the backup power parameters; Furthermore, determining the standby capacity cost based on the photovoltaic output curve, the user's time-of-use load, and the standby power parameters includes: Based on the normal distribution and the user's time-sharing load, determine the first probability density function corresponding to the load prediction error; Based on the Beta distribution and the photovoltaic power output curve, the second probability density function corresponding to the photovoltaic prediction error is determined. A risk quantification model is constructed based on the first probability density function and the second probability density function; Based on the aforementioned risk quantification model and preset risk conditions, a rotating reserve capacity model is constructed; Based on the backup power parameters and the spinning reserve capacity model, the backup capacity cost is obtained.

[0041] Furthermore, the risk quantification model is used to quantify the operational risk of the distribution network; the operational risk is determined based on the backup power capacity.

[0042] Specifically, reserve capacity cost is an incremental cost incurred by the power system due to the increased demand for reserve capacity caused by the uncertainty and intermittency of distributed photovoltaic (PV) power generation. Under high-proportion PV integration, the net load volatility of the power system increases significantly, especially on short-term timescales. Changes in solar radiation can cause a sharp drop in PV output within minutes, creating sudden pressure on the power system's frequency regulation and peak-shaving resources. To ensure power supply security and power balance, power system dispatching agencies need to allocate more flexible resources, such as pumped storage stations, gas turbines, fast-start / stop thermal power units, energy storage systems, or rely on the ancillary services market to procure reserve capacity. Since these flexible resources themselves have high fixed investment and operation and maintenance costs, their "idle guarantee" function translates into corresponding reserve capacity cost expenditures.

[0043] From the perspective of power system operation, as photovoltaic penetration increases to a certain level, the output of traditional low-cost baseload power sources is squeezed. System dispatch must introduce additional flexibility constraints, such as N-1 safety constraints, ramp-up rate limits, and start-up and shutdown costs. This causes the dispatch scheme originally based on marginal cost optimization to no longer be optimal, and the power system needs to activate non-economical standby units at a higher cost, increasing operating costs. In addition, due to the uncertainty of photovoltaics, the power system also needs to reserve greater capacity margins in both day-ahead and real-time markets to cope with the risks brought about by forecast deviations. Therefore, the cost of standby capacity is ultimately reflected in the system in the form of capacity market payments, power reserve compensation, and ancillary service fees, and is then transmitted to the user side or market participants through the transmission and distribution pricing mechanism, placing higher demands on the economy and flexibility of the entire power system.

[0044] However, due to the inherent randomness and intermittency of photovoltaics, and the inadequacy of existing new energy output prediction methods, large-scale photovoltaic grid connection will pose severe challenges to the peak shaving and active power balance of the power system. Therefore, it is necessary to configure a certain capacity of spinning reserve to ensure the safe and stable operation of the power system. However, the traditional deterministic method for determining the spinning reserve capacity can no longer meet the operational needs of power systems containing photovoltaics.

[0045] Due to the uncertainty of large-scale renewable energy output, it is difficult to determine the spinning reserve capacity of the power system. Considering the error in renewable energy output and unit failure outages, this invention proposes a spinning reserve capacity model with photovoltaic grid connection in the power market environment. Through Monte Carlo simulation, the system spinning reserve capacity under different renewable energy penetration rates is analyzed and the correlation between the two is found. By analyzing the impact of photovoltaic output and load on spinning reserve capacity under risk levels, the correlation between spinning reserve capacity and system operating costs under different confidence levels is quantitatively studied using the conditional value at risk (CVaR) method.

[0046] Existing methods for risk assessment mainly include mean-variance, value at risk (VaR), and conditional value at risk. Mean-variance cannot be tailored to the decision-maker's risk preferences and often fails to meet the assumption that returns follow a normal distribution. Due to its non-additive nature and tail risk, VaR yields different conclusions depending on the parameters chosen. CVaR, referring to the conditional mean of losses exceeding VaR, reflects the average potential loss that may be incurred when losses exceed the VaR threshold, thus better representing the potential value at risk. Therefore, this invention constructs a risk quantification model based on CVaR to quantify the operational risks of distribution networks or power systems.

[0047] The process of constructing the risk quantification model includes: Assumption Let be the decision vector, where It is a random vector. The continuous probability distribution function is , ) is the profit / loss function, and .

[0048] At a given risk level When CVaR is used, it can be expressed as: By introducing functions Calculate the CVaR value, i.e.: in, These represent mathematical variables and serve as auxiliary decision-making variables in risk quantification models. for By applying random vectors Historical data or Monte Carlo simulations can be used to obtain the integer part of the formula.

[0049] Assumption , , , If it is sample data, then The estimated value can be expressed as: because For about Continuous convex functions, therefore, by... By minimizing it, we can calculate CVaR, that is: In grid-connected photovoltaic (PV) power systems, uncertainties mainly include the uncertainty of user load demand and the uncertainty of PV output. This invention, based on a normal distribution, determines a first probability density function corresponding to the load forecasting error to characterize the uncertainty of user load demand; and based on a Beta distribution, determines a second probability density function corresponding to the PV forecasting error to characterize the uncertainty of PV output. The first and second probability density functions are used to stochastically model the load forecasting error and the PV forecasting error, respectively, and generate random sample data accordingly, serving as random vectors in the risk quantification model. Thus, a risk quantification model is obtained. This is to achieve the quantitative calculation of the Value at Risk (CVaR) in photovoltaic grid-connected power systems, specifically including: Description of user load demand uncertainty: Based on the statistical characteristics of user time-of-use load data, it can be assumed that the user load prediction error follows a normal distribution with a mean of 0. The first probability density function corresponding to the load prediction error is then... It can be represented as: In the formula, For system load in Load forecasting error for a given period Load forecasting error exist Standard deviation over a period of time.

[0050] Uncertainty description of photovoltaic power output: Based on the photovoltaic power output curve data, the Beta distribution is used to model the photovoltaic power output error. The Beta distribution is a continuous distribution defined on the interval [0, 1], which is suitable for modeling normalized power prediction error.

[0051] Considering the diurnal periodicity and strong correlation with weather, photovoltaic power output error modeling based on the Beta distribution is more accurate. Photovoltaic power output has a clearly defined physical boundary, and its range is always within [0, P]. pv max Within the designated area, and with no output at night.

[0052] The second probability density function corresponding to the photovoltaic prediction error is as follows: in, Normalized value for photovoltaic power output; It is a Beta function; α and β are both shape parameters of the distribution, and both are greater than 0; The standard deviation of the actual photovoltaic prediction error at time t (with dimensions). denoted as the standard deviation of the normalized photovoltaic prediction error at time t (dimensionless). and All are shape parameters of a Beta distribution; This indicates the maximum output of the photovoltaic power source.

[0053] The optimal parameters α and β can be obtained by performing maximum likelihood estimation (MLE) or least squares fitting on historical photovoltaic prediction and measured values.

[0054] For example, in one embodiment, for a photovoltaic system under typical sunny weather conditions, α=2.5 and β=3.0 can better fit the actual distribution of strong sunlight output that is slightly higher around noon and lower in the morning and evening.

[0055] Furthermore, considering the reality of zero photovoltaic output at night, a piecewise approach can be used in the modeling, meaning the second probability density function can be further expressed as: In the formula, δ(x) represents the unit impulse function (output is always 0); To predict the probability of zero output due to complete shading or sudden shading; ( ) is the Beta distribution density function.

[0056] Using the Beta distribution model can more accurately describe the skewed distribution and boundary conditions of photovoltaic power output, thereby improving the ability of the reserve capacity model to handle prediction errors and thus enhancing the economy and safety of system operation.

[0057] Assuming that load forecasting errors are completely uncorrelated with photovoltaic output errors, then the standard deviation of the actual system error... It can be represented as: In the formula, This indicates the load forecasting error.

[0058] When the Beta distribution parameter is large (greater than a certain preset threshold) or approximately normal, the system error can be considered as a normal distribution. Therefore, the actual system error in a power system containing photovoltaics... The probability density function is: When a photovoltaic (PV) grid-connected system is in operation, the changes in system costs caused by the impact of PV grid connection on the output / price of other generating units need to be considered not only the operating costs of thermal power units but also the environmental costs caused by the pollution from thermal power units, i.e., pollution costs. Furthermore, the spinning reserve costs provided by conventional units and expected outage costs will also be included in the overall system cost, thus incorporating reserve capacity costs. It can be represented as: In the formula: Cost of spare capacity; These are the operating and pollution discharge costs of thermal power units. For spinning reserve costs; The expected cost of a power outage; This indicates that the battery level is below the expected value. This refers to the number of thermal power units. The operating cycle of the unit They are thermal power generating units Operating cost coefficient; For thermal power generating units exist The state at any given moment, when At that time, it indicates a thermal power generating unit. If the thermal power generating unit is not scheduled to operate, If it is already planned to be put into operation, then it needs to be based on the thermal power generating units. Fault downtime rate Determine For thermal power generating units exist Startup cost at any time For thermal power generating units exist Environmental compensation costs for the period, in units of 10,000 yuan. , For thermal power generating units Sewage discharge characteristic coefficient The first The thermal power generator unit is rotated up and down for standby; The unit loss of load is the value; Δp is the output shortfall caused by the shutdown of the thermal power generating unit; M r Let be the system's rotating reserve capacity at time t; Indicates at time Active power output of thermal power generating units; Indicates the first Taiwan thermal power generating unit at time Provided spinning reserve capacity; Indicates the system at time 10:00 The active power deficit is caused by uncertainties such as the shutdown of thermal power generating units or forecast errors.

[0059] Spinning reserve capacity is closely related to the overall system cost, and different spinning reserve capacities also affect the power balance of power systems with photovoltaic (PV) integration. Due to the randomness of PV output, the obtained system spinning reserve capacity can only be valid with a certain degree of confidence. Therefore, based on the aforementioned risk quantification model, a risk-considered spinning reserve capacity model for grid-connected power systems with PV integration was established, incorporating the aforementioned reserve capacity cost... Further defined as a loss function characterizing reserve capacity, the expression is as follows: Based on the above loss function, with the preset risk condition being the rate of return... In this case, a spinning reserve capacity model considering the corresponding risks can be derived, as shown in the following expression: In the formula: As an auxiliary variable; , , These are the electricity load, the electricity price, and the duration of the electricity load, respectively.

[0060] Based on the above embodiments, a risk quantification model is used to comprehensively consider both load forecasting error and photovoltaic output forecasting error, to address the aforementioned... By solving for the value of , we can obtain the value of the given rate of return. Under certain conditions, the minimum reserve capacity required for the power system to maintain safe operation even in extremely adverse scenarios. Then, based on the minimum standby capacity, combined with standby power supply parameters (such as the unit capacity compensation price or capacity service price of standby power supply) and the configuration period or operating cycle of standby capacity, the cost is calculated to obtain the standby capacity cost that the power system needs to bear to cope with the uncertainty of photovoltaic output and load.

[0061] It should be noted that the above solution process must at least satisfy the following constraints: power balance constraint, thermal power generator output constraint, thermal power generator operating time constraint, thermal power generator ramp rate constraint, and spinning reserve constraint. Specifically, these include: i) The power balance constraint of the power system is expressed as: In the formula, N represents the number of thermal power generating units. The actual load at time t. This represents the photovoltaic output at time t.

[0062] ii) The output constraint of thermal power generating units is expressed as: In the formula, These represent the minimum and maximum output of thermal power generating unit i at time t, respectively.

[0063] iii) The operating time constraint of thermal power generating units is expressed as: In the formula, These are the operating time and minimum operating time of thermal power generating unit i, respectively. These represent the outage time and minimum outage time of thermal power generating unit i, respectively.

[0064] iv) The gradient rate constraint for thermal power generating units is expressed as: In the formula, Indicates the first Downhill climbing speed limit for thermal power generating units. Indicates the first The uphill climbing speed limit of the thermal power generating unit This represents the electrical load of thermal power generating unit i at time t. This represents the electrical load of thermal power generating unit i at time t-1.

[0065] v) The rotational spare constraint is expressed as: Assume the forecast ranges for photovoltaic power and load demand at time t are respectively and Then, the on-spinning reserve capacity required by the photovoltaic power system at time t. , Lower rotating spare capacity They can be represented as: In the formula, This indicates the active power output of the photovoltaic system at time t. This represents the load power at time t.

[0066] The power system's spin-up reserve constraint at time t Lower rotation spare constraint They can be represented as follows: In the formula, Indicates the first Taiwan thermal power generating unit at time Maximum permissible output Indicates the first Taiwan thermal power generating unit at time The minimum stable output, Indicates the first The maximum uphill climbing capacity that a coal-fired power generating unit can provide in adjacent time periods.

[0067] Due to the randomness and uncertainty of photovoltaic power output, based on the chance constraint method, the probabilistic constraint on the rotation of the power system's reserve capacity at time t is derived, namely: In the formula, P r Let δ1 and δ2 be probabilities, respectively, and both be confidence levels. Indicates the first Taiwan thermal power generating unit at time Spinning spare capacity that can be increased Indicates the first Taiwan thermal power generating unit at time Spinning reserve capacity that can be reduced This represents the state variables of a thermal power generating unit.

[0068] This invention describes load forecasting error using a normal distribution and photovoltaic forecasting error using a Beta distribution. It also constructs a spinning reserve capacity model by combining a CVaR risk quantification model, thereby improving the accuracy of reserve capacity cost calculation in scenarios with uncertain photovoltaic output.

[0069] Step 105: The sum of the incremental network loss cost, the carbon emission cost, and the reserve capacity cost is used as the variable cost of integrating the distributed photovoltaic system into the distribution network.

[0070] Specifically, the sum of incremental grid loss cost, carbon emission cost, and reserve capacity cost is used as the variable cost of integrating distributed photovoltaic power into the distribution network.

[0071] Based on the above embodiments, by establishing a typical scenario library (sunny day, cloudy day, extreme weather day, peak load period, off-peak period, etc.) through simulation, the variable cost can be simulated and calculated under different photovoltaic penetration rates and grid operation conditions, which can further verify the accuracy of the calculation results.

[0072] The core benchmark scenario (denoted as S0) is named "Traditional Distribution Network Benchmark Operation." Its initial purpose is to provide a unified baseline for incremental cost calculation—simulating the operation of a traditional distribution network without distributed photovoltaic (PV) access, thus providing a reference for cost comparisons in subsequent PV-enabled scenarios. From an operational perspective, this scenario adopts a pure load power supply mode, where all power demand is supplied by the upstream grid and traditional centralized power plants. The distribution network exhibits typical unidirectional power flow characteristics (power flow direction from the grid side to the load side), and there are no issues such as bidirectional power flow, voltage fluctuations, or output uncertainty caused by distributed PV access. Specifically, the main characteristics of this scenario can be broken down into four aspects: First, power flow characteristics, characterized by traditional unidirectional power flow from substations to various load nodes; second, voltage stability, with voltage regulation based on traditional load characteristics, unaffected by the additional influence of PV inverters; third, reserve requirements, only considering traditional reserve capacity requirements caused by load forecasting deviations and equipment failures; and fourth, network loss and carbon emission characteristics, maintaining the basic network loss level (without the impact of power flow changes caused by PV access) and the carbon emission benchmark of complete reliance on traditional energy sources for power supply.

[0073] In terms of simulation input parameter settings, this scenario explicitly sets the installed capacity of both distributed photovoltaic and centralized photovoltaic to 0MW; the load characteristics are consistent with the load time-series curves of other photovoltaic access scenarios to ensure that the interference of load variables on cost comparison is minimized; thermal power output is automatically calculated through power balance constraints, while wind power and hydropower maintain their original output levels; the electricity price system maintains the benchmark electricity price level to avoid the impact of price fluctuations on cost calculation.

[0074] Based on the scenario-based research, variable costs were calculated by setting up "1 basic scenario + 9 typical application scenarios" according to the two major operation modes of "node mode" and "regional network scenario". (1) Node mode: The goal is to prioritize the consumption of photovoltaic power by local loads, reduce the amount of electricity purchased by the upper-level grid and the grid loss, and the amount of back-feeding power is small, so the impact on the upper-level network is limited. Representative scenarios include self-consumption by industrial and commercial enterprises, distributed access by residents and optimization of direct photovoltaic and energy storage supply. (2) Regional network scenario: The photovoltaic scale is large and the access range is wide, which has a significant impact on the power flow, voltage stability and reserve demand of the upper-level grid and neighboring areas. Representative scenarios include peak back-feeding, reserve pressure under continuous rainy weather, protection coordination for sudden output changes and power transmission at multiple voltage levels. The scenarios and simulation parameters are shown in Table 2.

[0075] Table 2 Typical Scenarios Description Table In the calculation result output stage, the embodiments of the present invention will visualize the real-time monitoring data and the variable cost calculation results, and obtain the variable cost data of 30 days of simulation calculation.

[0076] Figure 2 This is a schematic diagram of variable cost data visualization provided by the present invention, such as... Figure 2 As shown, the incremental costs for typical scenarios are illustrated, specifically including incremental costs of network losses, incremental costs of carbon emissions (i.e., carbon emission costs), and incremental costs of standby (i.e., standby capacity costs). Comparing the variable costs across scenarios, the total variable cost of the S1–S3 high-proportion distributed photovoltaic (PV) grid connection scenarios is significantly lower than that of the R1–R6 scenarios, and the variable cost per unit of electricity also shows a significant decrease. This aligns with the basic principle that PV replaces thermal power, leading to reduced system fuel consumption and lower carbon emissions. In terms of cost composition, the S-type scenarios have lower proportions of network loss costs and carbon emission costs, and the standby capacity cost is relatively low, indicating that under conditions of high PV output-load matching and reasonable grid connection layout, the pressure on system operating costs can be effectively reduced. Conversely, the R-type scenarios, due to frequent adjustments in standby unit output, lead to increased standby capacity costs and network loss costs. This verifies that the variable cost calculation method provided in this invention for assessing distribution network operating costs can accurately calculate variable costs, thereby accurately reflecting the differences in cost composition under different grid connection modes.

[0077] The present invention provides a variable cost calculation method for evaluating the operating costs of distribution networks. This method acquires basic data on distributed photovoltaic (PV) power generation connected to the distribution network within a preset period. This basic data includes PV output curves, user time-of-use loads, distribution network time-of-use tariffs, distribution network line parameters, carbon trading prices, carbon tax standards, and standby power parameters. The standby power parameters include standby capacity and standby cost. This integrates PV output, load, and tariff parameters, providing a reliable data foundation for subsequent cost calculations and reducing deviations caused by missing or abnormal data. Based on the PV output curves, user time-of-use loads, distribution network time-of-use tariffs, and distribution network line parameters, the incremental cost of network losses is determined, thereby quantifying the changes in network losses caused by PV grid connection and accurately reflecting network losses in conjunction with real-time tariffs. Incremental costs: Based on photovoltaic output curves, carbon trading prices, and carbon tax standards, carbon emission costs are determined to assess the carbon emission reduction benefits of photovoltaic clean energy replacing traditional thermal power. Combined with carbon market parameters, carbon emission costs are accurately reflected. Based on photovoltaic output curves, user time-of-use loads, and backup power parameters, backup capacity costs are determined to accurately calculate backup capacity costs, taking into account the randomness and intermittency of photovoltaic output. The sum of incremental grid loss costs, carbon emission costs, and backup capacity costs is used as the variable cost of integrating distributed photovoltaics into the distribution network. This integrates incremental grid loss costs, carbon emission costs, and backup capacity costs to accurately calculate the variable costs generated by integrating distributed photovoltaics into the distribution network, providing reliable data for grid dispatching, electricity pricing, and photovoltaic acceptance capacity assessment.

[0078] The variable cost calculation system for distribution network operation cost assessment provided by the present invention is described below. The variable cost calculation system for distribution network operation cost assessment described below can be referred to in correspondence with the variable cost calculation method for distribution network operation cost assessment described above.

[0079] Figure 3 This is a schematic diagram of the variable cost calculation system for power distribution network operation cost assessment provided by the present invention, as shown in the figure. Figure 3 As shown. This embodiment of the invention provides a variable cost calculation system for assessing the operating costs of a distribution network, comprising a data acquisition module 301, a network loss incremental cost calculation module 302, a carbon emission cost calculation module 303, a reserve capacity cost calculation module 304, and a variable cost calculation module, wherein: The data acquisition module 301 is used to acquire basic data of distributed photovoltaic (PV) grid connection within a preset period. The basic data includes PV output curves, user time-of-use loads, distribution network time-of-use prices, distribution network line parameters, grid loss changes, carbon trading prices, carbon tax standards, and backup power parameters. The backup power parameters include backup power capacity and backup power cost. The grid loss incremental cost calculation module 302 is used to determine the grid loss incremental cost based on the PV output curves, user time-of-use loads, distribution network time-of-use prices, and distribution network line parameters. The carbon emission cost calculation module 303 is used to determine the carbon emission cost based on the PV output curves, carbon trading prices, and carbon tax standards. The backup capacity cost calculation module 304 is used to determine the backup capacity cost based on the PV output curves, user time-of-use loads, and backup power parameters. The variable cost calculation module 305 uses the sum of the grid loss incremental cost, the carbon emission cost, and the backup capacity cost as the variable cost of the distributed PV grid connection.

[0080] The variable cost calculation system for power distribution network operation cost assessment provided by this invention acquires basic data on distributed photovoltaic (PV) grid connection within a preset period. This basic data includes PV output curves, user time-of-use loads, distribution network time-of-use tariffs, distribution network line parameters, carbon trading prices, carbon tax standards, and standby power parameters. The standby power parameters include standby power capacity and standby power cost. This integrates PV output, load, and tariff parameters, providing a reliable data foundation for subsequent cost calculations and reducing deviations caused by missing or abnormal data. Based on the PV output curves, user time-of-use loads, distribution network time-of-use tariffs, and distribution network line parameters, the system determines the incremental cost of network losses, thereby quantifying the changes in network losses caused by PV grid connection and accurately reflecting network losses in conjunction with real-time tariffs. Incremental costs: Based on photovoltaic output curves, carbon trading prices, and carbon tax standards, carbon emission costs are determined to assess the carbon emission reduction benefits of photovoltaic clean energy replacing traditional thermal power. Combined with carbon market parameters, carbon emission costs are accurately reflected. Based on photovoltaic output curves, user time-of-use loads, and backup power parameters, backup capacity costs are determined to accurately calculate backup capacity costs, taking into account the randomness and intermittency of photovoltaic output. The sum of incremental grid loss costs, carbon emission costs, and backup capacity costs is used as the variable cost of integrating distributed photovoltaics into the distribution network. This integrates incremental grid loss costs, carbon emission costs, and backup capacity costs to accurately calculate the variable costs generated by integrating distributed photovoltaics into the distribution network, providing reliable data for grid dispatching, electricity pricing, and photovoltaic acceptance capacity assessment.

[0081] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a variable cost calculation method for assessing the operating costs of the power distribution network. This method includes: Acquire basic data on distributed photovoltaic (PV) power generation connected to the distribution network within a preset period; the basic data includes PV output curves, user time-of-use load, distribution network time-of-use electricity price, distribution network line parameters, carbon trading price, carbon tax standard, and backup power parameters; the backup power parameters include backup power capacity and backup power cost. Based on the photovoltaic output curve, the user time-of-use load, the distribution network time-of-use electricity price, and the distribution network line parameters, the incremental cost of network loss is determined; The carbon emission cost is determined based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard. Based on the photovoltaic output curve, the user time-of-use load, and the backup power parameters, determine the backup capacity cost; The sum of the incremental cost of grid loss, the cost of carbon emissions, and the cost of reserve capacity is taken as the variable cost of integrating the distributed photovoltaic system into the distribution network.

[0082] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is able to execute the variable cost calculation method for power distribution network operation cost assessment provided by the above methods, the method comprising: Acquire basic data on distributed photovoltaic (PV) power generation connected to the distribution network within a preset period; the basic data includes PV output curves, user time-of-use load, distribution network time-of-use electricity price, distribution network line parameters, carbon trading price, carbon tax standard, and backup power parameters; the backup power parameters include backup power capacity and backup power cost. Based on the photovoltaic output curve, the user time-of-use load, the distribution network time-of-use electricity price, and the distribution network line parameters, the incremental cost of network loss is determined; The carbon emission cost is determined based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard. Based on the photovoltaic output curve, the user time-of-use load, and the backup power parameters, determine the backup capacity cost; The sum of the incremental cost of grid loss, the cost of carbon emissions, and the cost of reserve capacity is taken as the variable cost of integrating the distributed photovoltaic system into the distribution network.

[0084] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a variable cost calculation method for power distribution network operation cost assessment provided by the methods described above, the method comprising: Acquire basic data on distributed photovoltaic (PV) power generation connected to the distribution network within a preset period; the basic data includes PV output curves, user time-of-use load, distribution network time-of-use electricity price, distribution network line parameters, carbon trading price, carbon tax standard, and backup power parameters; the backup power parameters include backup power capacity and backup power cost. Based on the photovoltaic output curve, the user time-of-use load, the distribution network time-of-use electricity price, and the distribution network line parameters, the incremental cost of network loss is determined; The carbon emission cost is determined based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard. Based on the photovoltaic output curve, the user time-of-use load, and the backup power parameters, determine the backup capacity cost; The sum of the incremental cost of grid loss, the cost of carbon emissions, and the cost of reserve capacity is taken as the variable cost of integrating the distributed photovoltaic system into the distribution network.

[0085] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0087] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0088] In this application's embodiments, "determine B based on A" means that factor A must be considered when determining B. It is not limited to "B can be determined based solely on A," but should also include: "determine B based on A and C," "determine B based on A, C, and E," "determine C based on A, and further determine B based on C," etc. Additionally, it can include using A as a condition for determining B, for example, "when A meets the first condition, determine B using the first method"; another example, "when A meets the second condition, determine B," etc.; another example, "when A meets the third condition, determine B based on the first parameter," etc. Of course, it can also be a condition where A is a factor in determining B, for example, "when A meets the first condition, determine C using the first method, and further determine B based on C," etc.

[0089] It should also be noted that the terms "target," "first," and "second" in this invention are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, without limiting the number of objects; for example, the first object can be one or more.

[0090] In this invention, the term "multiple" refers to two or more, and other quantifiers are similar.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A variable cost calculation method for evaluating the operating costs of a power distribution network, characterized in that, include: Acquire basic data on distributed photovoltaic (PV) power generation connected to the distribution network within a preset period; the basic data includes PV output curves, user time-of-use load, distribution network time-of-use electricity price, distribution network line parameters, carbon trading price, carbon tax standard, and backup power parameters; the backup power parameters include backup power capacity and backup power cost. Based on the photovoltaic output curve, the user time-of-use load, the distribution network time-of-use electricity price, and the distribution network line parameters, the incremental cost of network loss is determined; The carbon emission cost is determined based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard. Based on the photovoltaic output curve, the user time-of-use load, and the backup power parameters, determine the backup capacity cost; The sum of the incremental cost of grid loss, the cost of carbon emissions, and the cost of reserve capacity is taken as the variable cost of integrating the distributed photovoltaic system into the distribution network.

2. The variable cost calculation method for power distribution network operation cost assessment according to claim 1, characterized in that, The determination of incremental network loss cost based on the photovoltaic output curve, the user's time-of-use load, the distribution network's time-of-use electricity price, and the distribution network line parameters includes: Based on the photovoltaic output curve, the active and reactive power outputs of the photovoltaic distributed power source at the grid-connected node are determined. Based on the active power output, the reactive power output, the user time-of-use load, and the distribution network line parameters, determine the change in network loss; The incremental cost of network loss is determined based on the time-of-use electricity price of the distribution network and the change in network loss.

3. The variable cost calculation method for power distribution network operation cost assessment according to claim 2, characterized in that, The determination of carbon emission costs based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard includes: Based on the photovoltaic output curve, the carbon flow rate of the photovoltaic distributed power source at the grid-connected node is determined; The carbon emission cost is determined based on the carbon flow rate, the carbon trading price, and the carbon tax standard.

4. The variable cost calculation method for power distribution network operation cost assessment according to claim 1, characterized in that, The determination of backup capacity cost based on the photovoltaic output curve, the user's time-of-use load, and the backup power parameters includes: Based on the normal distribution and the user's time-sharing load, determine the first probability density function corresponding to the load prediction error; Based on the Beta distribution and the photovoltaic power output curve, the second probability density function corresponding to the photovoltaic prediction error is determined. A risk quantification model is constructed based on the first probability density function and the second probability density function; Based on the aforementioned risk quantification model and preset risk conditions, a rotating reserve capacity model is constructed; Based on the backup power parameters and the spinning reserve capacity model, the backup capacity cost is obtained.

5. The variable cost calculation method for power distribution network operation cost assessment according to claim 4, characterized in that, The risk quantification model is used to quantify the operational risk of the distribution network; the operational risk is determined based on the backup power capacity.

6. The variable cost calculation method for power distribution network operation cost assessment according to claim 1, characterized in that, After acquiring the basic data before and after the distributed photovoltaic power generation is connected to the distribution network within a preset period, the method further includes: Anomalies are removed from the basic data using anomaly detection and interpolation algorithms.

7. A variable cost calculation system for evaluating the operating costs of a power distribution network, characterized in that, include: The data acquisition module is used to acquire basic data before and after the distributed photovoltaic system is connected to the distribution network within a preset period. The basic data includes photovoltaic output curves, user time-of-use loads, distribution network time-of-use electricity prices, distribution network line parameters, carbon trading prices, carbon tax standards, and backup power parameters. The backup power parameters include backup power capacity and backup power cost. The incremental network loss cost calculation module is used to determine the incremental network loss cost based on the photovoltaic output curve, the user's time-of-use load, the time-of-use electricity price of the distribution network, and the parameters of the distribution network lines. The carbon emission cost calculation module is used to determine the carbon emission cost based on the photovoltaic output curve, the carbon trading price, and the carbon tax standard. The backup capacity cost calculation module is used to determine the backup capacity cost based on the photovoltaic output curve, the user time-of-use load, and the backup power parameters. The variable cost calculation module is used to take the sum of the incremental network loss cost, the carbon emission cost, and the reserve capacity cost as the variable cost of integrating the distributed photovoltaic power into the distribution network.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the variable cost calculation method for power distribution network operation cost assessment as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the variable cost calculation method for power distribution network operation cost assessment as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the variable cost calculation method for power distribution network operation cost assessment as described in any one of claims 1 to 6.

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