A clearing simulation method considering congestion risk allocation

Abstract

The application discloses a method for clearing simulation considering congestion risk allocation, relates to the technical field of power system risk warning, and solves the problem of difficulty in accurately depicting congestion risk. The line congestion probability risk and internalization of the allocation are internalized into the node electricity price through an R-SCUC model and an R-SCED model, so that the simulation price signal can truly reflect the congestion risk under high uncertainty, accurate incentives are provided for relieving power transmission congestion by using distribution storage, a safer and more reasonable distribution storage is guided, the stability and safety of the power system are improved, and the development of clean energy is promoted.

Description

Technical Field

[0001] This invention relates to the field of power system risk alarm technology, and in particular to a clearing simulation method that takes into account the allocation of congestion risk. Background Technology

[0002] In the Beijing-Tianjin-Hebei power market, the proportion of wind and solar installed capacity continues to rise. To promote consumption and smooth fluctuations, renewable energy power plants are mostly equipped with energy storage in proportion. Currently, the mainstream technology for dynamic simulation of the entire power system operation process is based on security-constrained unit commitment (SCUC) and security-constrained economic dispatch (SCED), which calculates unit output plans and nodal marginal prices sequentially for selected typical days or extreme scenarios. This type of method treats wind and solar output as a deterministic curve, energy storage as a price taker charging and discharging according to preset rules, and nodal prices are obtained through decomposition of energy, network losses, and congestion components.

[0003] However, when calculating nodal prices, SCED only considers deterministic line overruns as the congestion component, lacking characterization of the probability of line power flow overruns and their allocation. Therefore, nodal prices cannot reflect the true value of congestion risk. In the simulation evaluation of distribution and storage to alleviate transmission section congestion, the price signal shows systematic bias.

[0004] Therefore, a clearing simulation method that takes into account the sharing of congestion risk is needed. Summary of the Invention

[0005] To address the problem that existing technologies struggle to accurately characterize congestion risk, this invention provides a clearing simulation method that considers congestion risk allocation. This method can accurately and realistically reflect congestion risk under high uncertainty, guiding safer and more rational power storage allocation, improving the stability and security of the power system, and promoting the development of clean energy. The specific technical solution is as follows: In a first aspect, embodiments of this application provide a clearing simulation method that takes into account the sharing of blocking risk, including: The network topology and hardware parameters of the target power grid, as well as the historical wind and solar power operation data of each renewable energy station in the target power grid, are obtained. This historical wind and solar power operation data is input into a wind and solar power output prediction model to obtain the predicted output trajectory of each renewable energy station under different weather scenarios. For each weather scenario, the corresponding predicted output trajectory is input into a day-ahead bidding model to obtain the bid volume-price curve output by the day-ahead bidding model. The day-ahead bidding model aims to maximize the expected return of the aggregate, and its constraints include a bid risk constraint based on conditional value of risk. The aggregate includes at least one of wind farms, solar farms, and energy storage equipment. The network topology, hardware parameters, and bid volume-price curve are then input into a risk-safety-safety-return model. A fully constrained unit combination R-SCUC model is used to obtain the next day's unit start-up, shutdown, and reserve capacity allocation plan output by the R-SCUC model. The constraints of the R-SCUC model include line power flow opportunity constraints to ensure that the probability of power flow exceeding limits due to wind and solar forecast errors does not exceed a given confidence level. This next day's unit start-up, shutdown, and reserve capacity allocation plan is then input into a risk-safety constrained economic dispatch R-SCED model to obtain the output of each node, the node price, and the congestion risk component of the node price. This congestion risk component is obtained based on the marginal contribution of each node's injection to the exposure of line power flow exceeding limits. A clearing simulation report is generated based on the R-SCED model output results corresponding to each meteorological scenario.

[0006] Secondly, embodiments of this application provide a clearing simulation system that takes into account the sharing of blocking risk, characterized in that the system includes: The acquisition module is used to acquire the network topology and hardware parameters of the target power grid, as well as the historical operation data of wind and solar power plants in the target power grid. The prediction module is used to input the historical operation data of the wind and solar power into the wind and solar power output prediction model to obtain the predicted power output trajectory of each of the new energy power stations under different meteorological scenarios. The day-ahead bidding module is used to input the corresponding predicted power output trajectory into the day-ahead bidding model for each weather scenario, and obtain the bid power-price curve output by the day-ahead bidding model. The day-ahead bidding model aims to maximize the expected revenue of the aggregate, and the constraints of the day-ahead bidding model include bidding risk constraints based on conditional value of risk. The aggregate includes at least one of wind farms, photovoltaic farms, and energy storage equipment. The unit combination module is used to input the network topology, hardware parameters and bid electricity-price curve into the risk-security constraint unit combination R-SCUC model to obtain the next day's unit start-up and shutdown and reserve capacity allocation plan output by the R-SCUC model; wherein, the constraints of the R-SCUC model include line power flow opportunity constraints, which are used to ensure that the probability of power flow exceeding the limit caused by wind and solar forecast errors does not exceed a given confidence level. The economic dispatch module is used to input the next day's unit start-up and shutdown and reserve capacity allocation plan into the risk-safety constraint economic dispatch R-SCED model to obtain the output of each node, the node price, and the congestion risk component of the node price output by the R-SCED model; wherein, the congestion risk component is obtained based on the marginal contribution of each node to the exposure of line power flow exceedance risk. The report generation module is used to generate a clearing simulation report based on the output results of the R-SCED model corresponding to each meteorological scenario.

[0007] Thirdly, embodiments of this application provide a computing device, including: a memory for storing a program; and a processor for loading the program to execute the method as described in the first aspect.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the method described in the first aspect.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: by internalizing the probability risk and allocation of line congestion into the nodal price through the R-SCUC model and the R-SCED model, the simulated price signal can truly reflect the congestion risk under high uncertainty, provide accurate incentives for using distribution and storage to alleviate grid transmission congestion, guide safer and more reasonable distribution and storage, improve the stability and security of the power system, and promote the development of clean energy. Attached Figure Description

[0010] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0011] Figure 1 A flowchart illustrating a clearing simulation method that takes into account the sharing of blocking risk, provided for an embodiment of this application; Figure 2 A system architecture diagram of a clearing simulation system that takes into account the sharing of blocking risk is provided for an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of the present 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 the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0014] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0015] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0016] To address the difficulty of accurately characterizing congestion risk in traditional technologies, this invention provides a clearing simulation method that takes into account the allocation of congestion risk. This method can accurately and realistically reflect congestion risk under high uncertainty, guide safer and more reasonable power storage allocation, improve the stability and security of the power system, and promote the development of clean energy.

[0017] Please see Figure 1 , Figure 1 This application provides a flowchart illustrating a clearing simulation method that considers congestion risk allocation, applied to a computing device; as shown below. Figure 1 As shown, the method includes: Step 101: The computing device obtains the network topology and hardware parameters of the target power grid, as well as the historical operation data of wind and solar power plants in the target power grid.

[0018] The computing device can collect the network topology and hardware parameters of the target power grid from public websites or dedicated databases to perform power system simulation, which serves as the basis for subsequent clearing simulation.

[0019] Specifically, the obtained target power grid network topology includes all bus numbers in the power system, line connection relationships, and the resistance, reactance, ground susceptance, thermal stability limit, and power transmission limit of each line, as well as the power transmission limit of critical sections. The thermal stability limit refers to the long-term allowable current carrying capacity of a line; critical sections are the set of lines connecting different areas of the power system and undertaking important power transmission, and their stability is directly related to the safe operation of the power grid.

[0020] Specifically, the unit parameters need to cover the maximum and minimum technical output, upward and downward ramp rates, minimum continuous operating time and minimum downtime of all thermal, gas, and hydropower units, as well as the piecewise linearized power generation cost function coefficients.

[0021] For wind farms and photovoltaic power plants, the computing equipment can obtain the historical operation data of wind and solar power from the corresponding database. Preferably, the historical operation data of wind and solar power includes at least one full year of historical operation data for the site.

[0022] Specifically, the historical operation data of wind and solar power includes the active power output sequence of each new energy power station every 15 minutes, as well as high-resolution numerical weather forecast data for the same period, such as meteorological variables such as wind speed, wind direction, temperature, irradiance, and cloud cover.

[0023] The computing equipment can also obtain the energy storage allocation parameters of each energy storage power station in the power system. These parameters include the rated charging and discharging power, rated capacity, overall charging and discharging efficiency, upper and lower limits of the allowable state of charge (SOC), and whether the energy storage power station has rapid response capabilities.

[0024] Step 102: The computing device inputs the historical wind and solar power operation data into the wind and solar power output prediction model to obtain the predicted power output trajectory of each new energy power station under different meteorological scenarios.

[0025] Among them, the wind and solar power output prediction model is used to predict the power output of wind power stations and photovoltaic power stations under different meteorological conditions and at different times. Specifically, the wind and solar power output prediction model can output deterministic point prediction power output, or it can output the probability distribution of power output.

[0026] Preferably, the wind and solar power output prediction model is a dynamic conditional variance time series regression model, which is used to express wind and solar power output as the product of the historical mean, standard deviation and standardized residual under the corresponding meteorological conditions.

[0027] The expression for this wind and solar power output prediction model may include: ; in, The historical average output at time t is obtained by mapping nonlinear functions such as gradient boosting regression or neural networks based on weather forecast data for the same period, reflecting the average impact of meteorological factors on output. The standard deviation of output at time t is used to characterize the time-varying magnitude of prediction uncertainty. It can be obtained by modeling the absolute value or square of historical prediction errors through another regression model. It can be the standardized random residual at time t, that is, a white noise sequence with a mean of zero and a variance of 1.

[0028] Furthermore, the actual wind and solar power output prediction error exhibits significant temporal persistence, meaning that prediction errors in adjacent time periods are often correlated. Moreover, this correlation structure varies across different meteorological models. Therefore, this method further refines the standardized residuals. The joint distribution is further modeled to capture its temporal dependence.

[0029] Preferably, the calculation process of the standardized residual includes: constructing a Gaussian Copula model on the initial standardized residual to capture its temporal dependence, and obtaining a correlation coefficient matrix that reflects the correlation degree of the initial standardized residual between different time periods; generating a Gaussian sample based on the correlation coefficient matrix of the corresponding meteorological conditions through Cholesky decomposition; and obtaining the standardized residual based on the Gaussian sample through inverse probability transformation.

[0030] The initial standardized residual represents the prediction error for different time periods in the historical wind and solar operation data under the corresponding meteorological conditions.

[0031] The computing device can denot the initial standardized residual vector for the T time periods of the whole day as follows: Then, using probability integral transformation, the initial standardized residuals of each time period are mapped to a uniform distribution, and the uniform variables are transformed into latent Gaussian variables through the inverse function of the standard normal distribution.

[0032] Thus, the randomness of vector ε can be indirectly described by a Gaussian vector z with a covariance matrix Σ, where the elements of matrix Σ... This reflects the degree of linear correlation between the standardized residuals of time period i and time period j. After the obtained standardized residuals are incorporated into the wind and solar power output prediction model, the power output trajectory predicted by the model can correspond to meteorological scenarios with temporal dependent structures, resulting in higher accuracy.

[0033] Specifically, the computing device can divide daily data into typical weather scenarios based on historical meteorological records, such as: strong winds and sunny days, moderate winds and cloudy days, low winds and overcast / rainy days, strong radiation and sunny days, and weak radiation and rainy days. For each weather scenario, its frequency of occurrence is statistically analyzed, and the correlation coefficient matrix of the standardized residual vector under that condition is calculated. , where k represents the meteorological condition index. The temporal fluctuation characteristics of prediction errors may differ under different weather processes; modeling them separately can more realistically restore the error structure.

[0034] In generating a random scene covering the entire next day, the meteorological condition k to which the scene belongs can first be randomly selected based on the season or a preset pattern transition probability, and then a correlation coefficient matrix can be generated. Find a T-dimensional Gaussian vector z; then perform Cholesky decomposition on z and calculate the lower triangular matrix. Make Next, a vector η composed of T independent standard normal random numbers is generated, and then the operation is performed. Obtain Gaussian samples with target temporal correlation .

[0035] Then, the Gaussian samples were processed one by one using the standard normal distribution function and the inverse marginal distribution function of the corresponding time period. Transform back to standardized residuals with actual marginal distribution .

[0036] Finally, the historical average for this period and standard deviation Substitute and combine the standardized residuals under different meteorological conditions. By using random sampling results, the predicted power output trajectory under different meteorological scenarios can be obtained.

[0037] For different power stations, each station independently calculates its predicted power output trajectory.

[0038] Compared with existing technologies, the embodiments of this application can generate random scenarios containing rich meteorological modal information and real time-series evolution patterns by constructing a Gaussian Copula model, Cholesky decomposition, and then randomly sampling the standardized residuals under different meteorological conditions. These scenarios cover various possible combinations of outputs from normal to extreme meteorological processes, thereby providing complete input for subsequent models and improving overall accuracy.

[0039] Step 103: For each weather scenario, the calculation device inputs the corresponding predicted power output trajectory into the day-ahead bidding model to obtain the bidding power-price curve output by the day-ahead bidding model.

[0040] The bidding model for that day aimed to maximize the expected return of the aggregate.

[0041] Specifically, this aggregate includes market participants from at least one of the following: wind farms, photovoltaic power plants, and energy storage facilities. Understandably, different renewable energy plants and energy storage facilities may belong to different investors. For the sake of geographical aggregation and cluster management, these plants and energy storage facilities are divided into multiple independent aggregate units to participate in the market. Subsequent steps will use the independent bid curves of these aggregates as market prices, and conduct unified clearing on the same network topology. The cleared electricity volume for each aggregate after winning the bid and the nodal price for each node will be calculated, conforming to the actual operation of the electricity market of "individual bidding, unified clearing."

[0042] The computing device can extract samples of day-ahead electricity prices or system marginal electricity prices for weather days similar to the current weather scenario, based on historical spot market data, and construct a set denoted as set S, consisting of multiple electricity price scenarios. Each electricity price scenario s has a corresponding probability weight. And the sum of all weights equals 1; for scenario s, it includes the predicted electricity price for each time period t of the next day. The electricity price can be the price at the node where the aggregate is located or the unified price of the system.

[0043] These electricity price scenarios retain the temporal volatility, peak-valley characteristics, and peak risk of historical electricity prices, forming the input of random variables in the optimization process. While the modeling method itself can draw upon existing scenario generation techniques, it provides a foundation for subsequent risk measurement.

[0044] The objective function expression of the current day bidding model may include: ; Where t is the time period number and T is the total number of time periods throughout the day; The planned electricity sales for the day before time period t. and These represent the actual discharge power and charging power of the energy storage during time period t in scenario s, respectively. Let t represent the power of wind and solar curtailment during time period t under scenario s.

[0045] Specifically, since the total output of new energy power plants is the sum of wind power and solar power output, curtailment may occur when the total output exceeds the day-ahead plan and energy storage charging demand. Therefore, in each scenario and time period, the aggregated net on-grid power is... Multiplying this by the electricity price gives the revenue. A weighted average yields the expected revenue.

[0046] Among them, the constraints of the current bidding model include the timing coupling constraint of energy storage state of charge, the limit constraint of charging and discharging power, and the power balance constraint.

[0047] Specifically, the state of charge (SOC) timing coupling constraint of energy storage refers to the fact that the change in energy storage charge between adjacent time periods conforms to the physical relationship, and the SOC is always within the allowable range.

[0048] Specifically, the charging and discharging power limit constraint means that the charging and discharging power shall not exceed the rated power.

[0049] Specifically, the power balance constraint means that for each time period t and scenario s, the total output of wind and solar power must be equal to the day-ahead bid amount plus the net charging amount of energy storage plus the abandoned power.

[0050] Preferably, the constraints of the day-ahead bidding model also include a bidding risk constraint based on conditional value at risk (CVaR); the expression for this bidding risk constraint includes: ; ; ; in, Confidence level The maximum possible loss quantile; In scenario s, when the total revenue is less than The difference in time; This represents the minimum acceptable expected return level.

[0051] In this model, an auxiliary variable representing the maximum potential loss at a given confidence level is introduced, and constraints are used to ensure that the expected return is lower than that of the auxiliary variable is not lower than a preset level. This allows the risk aversion of the aggregate to be reflected in the output bidding curve.

[0052] By incorporating bidding risk constraints into the model, the entire model can be treated as a linear programming problem, which can be efficiently solved using mature commercial solvers. The solution yields the planned electricity sales per day. Then, for each time period t, the computing device can use duality theory to extract the marginal value of the electricity consumption during that time period from the dual variables of the power balance constraint, thus forming the bidding electricity consumption-electricity price curve.

[0053] The bidding curve generated by CVaR risk constraints dynamically responds to the combined risks of wind and solar power output characteristics and electricity price uncertainty in a given scenario, enabling energy storage to proactively adjust its day-ahead bidding strategy to avoid potentially high-loss periods, reflecting risk-responsive bidding behavior.

[0054] Step 104: The computing device inputs the network topology, the hardware parameters, and the bid electricity-price curve into the risk-security constrained unit commitment (R-SCUC) model to obtain the next day's unit start-up and shutdown and reserve capacity allocation plan output by the R-SCUC model.

[0055] The computing equipment can obtain the predicted power output trajectory of each station obtained in the aforementioned steps, as well as the bidding curve. Based on the quotations of all market participants and the physical constraints of the system, it can formulate the start-up and shutdown plan of the units for the next day and reserve the reserve capacity to ensure that the system is economically optimal under the predicted conditions, while maintaining a safe power flow level in the face of wind and solar uncertainties.

[0056] The objective function of the R-SCUC model is to minimize the operating cost of all online units during the entire scheduling cycle. At the same time, it considers the operational risks that may be caused by line power flow exceeding the limit, introduces the line limit exceeding risk penalty cost, and internalizes the congestion risk.

[0057] Specifically, the expression for the objective function may include: + ; Where N is the total number of generating units and T is the total number of time periods. This represents the start / stop status of unit i during time period t, with a value of 1 indicating operation and 0 indicating shutdown. Let i be the cost of a single start-up of unit i, when =1 and This fee is incurred when the value is 0; Downtime costs are incurred when a unit transitions from operation to shutdown. Let g be the output value of unit i in the g-th output segment of time period t. G represents the marginal cost corresponding to this segment. L represents the total number of segments. M represents the total number of lines. M is a sufficiently large penalty coefficient, the value of which should be much higher than the marginal cost of power generation. This represents the power flow deficit or over-limit value of transmission line l within the risk tolerance range of time period t.

[0058] A sufficiently large penalty coefficient M is used to ensure that limit violations are avoided in optimization, and only a very small number of limit violations are allowed when the line capacity is indeed limited and the adjustment cost is extremely high.

[0059] The constraints of the R-SCUC model include active power balance constraints, unit output upper and lower limit constraints, ramping constraints, minimum operation and downtime constraints, system reserve capacity constraints, and energy storage operation constraints.

[0060] Specifically, the active power balance constraint means that at each time period t, the sum of the output of all generating units, the net discharge of energy storage, and the predicted output of each new energy source must equal the total system load minus network losses; the load data comes from the overall network load forecast. The energy storage operation constraint is the energy storage-related constraint in the day-ahead bidding model mentioned above, which will not be elaborated here.

[0061] Preferably, the constraints of the R-SCUC model also include a line power flow opportunity constraint, used to ensure that the probability of power flow exceeding the limit due to wind-solar prediction errors does not exceed a given confidence level. The expression for this line power flow opportunity constraint includes: ; ; in, The sensitivity factor for injecting power into the bus node of line l. It is the sum of the power output of all units and new energy sources in the bus at node t during time period. This is a preset baseline value representing the sum of the output of all units and new energy sources in the bus at node t during time period t. The load of the bus at node t during time period t; Represents fluctuations in all sets of uncertainty U. Within, the value of the power flow increment of line l under the worst-case scenario; For the thermal stability limit of the line, This is an auxiliary variable for exceeding the limit penalty.

[0062] By utilizing the sensitivity factor of node injection on line power flow and the uncertainty boundary of prediction error of each new energy power station, the maximum positive and negative increments of line power flow under the worst fluctuation are calculated. Non-negative relaxation variables are introduced into the power flow constraints to absorb potential over-limits, and a penalty coefficient is used in the objective function to penalize the relaxation variables, thereby coupling unit start-up and shutdown and reserve allocation decisions with congestion risk management.

[0063] Understandably, it is necessary to constrain both the maximum possible forward power flow and the maximum possible reverse power flow simultaneously. However, because the absolute value function is not linearizable and both positive and negative worst-case scenarios are handled within a single constraint, it cannot be solved using standard dual theory or analytical methods. Therefore, the above constraint contains two expressions.

[0064] Thus, the R-SCUC model is transformed into a mixed-integer linear programming problem, with decision variables including unit start-up and shutdown status, output segment values ​​of each unit, reserve allocation, and potential line overload variables. By penalizing potential line overload variables in the objective function and reflecting the worst-case fluctuation scenario in the constraints, the R-SCUC model tends to reserve more ramp-up and reserve capacity when optimizing unit combination, in order to mitigate power flow fluctuations at high-sensitivity nodes and strictly limit the probability of congestion below the confidence level. The unit start-up and shutdown plan is not only based on economic ranking but also inherently includes considerations of avoiding overload in potentially risky sections.

[0065] The computing device can solve this mixed-integer linear programming problem to obtain the start-up and shutdown status of all units the next day, the output plan, and the allocation plan of various reserve capacities.

[0066] Step 105: The computing device inputs the next day's unit start-up and shutdown and standby capacity allocation plan into the risk-security constrained economic dispatch (R-SCED) model to obtain the output of each node, the node price, and the congestion risk component of the node price output by the R-SCED model.

[0067] The objective function of the R-SCED model is to minimize the sum of generation cost and congestion risk penalty cost. Its expression can include: ; Where N is the number of online units, Let i be the cost function of unit i. The active power output of unit i is a continuous decision variable. In the second term, L represents the total number of power lines. is the penalty coefficient for exceeding the limit for line l. This refers to the potential surge amplitude that line l may experience even after accounting for uncertainties. In this way, the computing device can unify fuel costs and congestion risk into a single value dimension, forming a congestion risk price signal.

[0068] The constraints of the R-SCED model include active power balance constraints, unit output upper and lower limit constraints, ramp-up constraints, and energy storage operation constraints. These are conventional constraints and will not be elaborated here.

[0069] Preferably, the constraints of the R-SCED model also include risk-aware line power flow constraints, the expressions of which include: ; ; ; in, The sensitivity factor for injecting power into the bus node of line l. This is a preset baseline value representing the sum of the output of all units and new energy sources in the node bus. For the load of the node bus, This represents the ground-state power flow calculated using the predicted baseline value; For the thermal stability limit of the line, This is an auxiliary variable for exceeding the limit penalty. It represents the absolute value of the power flow increment of line l under the worst-case scenario, under all possible wind and light fluctuations. This represents the maximum value of the power output fluctuation of the wind power station. Let l be the sensitivity factor of line l to the node where wind farm w is located. This represents the maximum value of the power output fluctuation of the photovoltaic power station. is the sensitivity factor of line l to the node where the photovoltaic field PV is located.

[0070] Specifically, the expression for the above constraint means that, regardless of the fluctuations in wind and solar power, as long as the fluctuations do not exceed the preset uncertainty set range, the maximum possible forward or reverse power flow of line l is the ground state power flow plus or minus the ground state power flow. . As a slack variable, it allows the constraints to be slightly exceeded at the cost of extremely high penalties, thereby ensuring that the model has feasible solutions in all scenarios and preventing the model from failing to clear due to excessive conservatism.

[0071] After solving the R-SCED model, the computing equipment can obtain the optimal unit output and the dual variables (shadow prices) of all constraints. The nodal marginal price is obtained through the nodal decomposition of the power balance constraints. Under the DC optimal power flow framework, for each node j, its nodal price... It can be recovered from the constrained Lagrange multipliers: ; in, The shadow price is the active power balance constraint of the system, representing the marginal cost of providing an additional unit of electricity. It is the energy component in the nodal price and is uniform across the entire system. The marginal cost multiplier for network loss is... This term represents the rate of change in total network loss caused by the change in node j; it is the network loss component. and These are the shadow prices for the forward and reverse deterministic power flow constraints of line l, respectively. For line l to node j, this is the sensitivity factor, which corresponds to the congestion price caused by the physical line capacity limit; For the above risk constraints and The shadow price of the associated slack constraint reflects the marginal cost of increasing the risk tolerance by one unit. The sensitivity of the risk limit of line l to node j can be calculated as equivalent to the physical sensitivity of the line. This product is the congestion risk component, which quantifies the marginal contribution value of the injection increment of node j to the risk of line l exceeding the limit.

[0072] When increased injection at a node worsens the power flow risk of a line, that node will have a positive congestion risk price premium; conversely, if node injection can alleviate the risk, a negative risk component may occur, i.e., a lower electricity price. This approach overcomes the deficiency in existing technologies where node electricity prices do not reflect the true value of congestion risk.

[0073] After the computing device calculates the node electricity price of each node, which has a complete structure including four components: energy, network loss, congestion, and congestion risk, it outputs it together with the unit output, energy storage charging and discharging plan, line power flow, and risk indicators.

[0074] Step 106: The computing device generates a clearing simulation report based on the output results of the R-SCED model corresponding to each meteorological scenario.

[0075] The computing device can perform weighted summation of the same data items in the output results based on the weights corresponding to each meteorological scenario to obtain comprehensive clearing simulation data, and then analyze the clearing simulation data to obtain a clearing simulation report; or it can use the output results of the R-SCED model corresponding to each meteorological scenario as simulation data for one day in a year to generate a periodic simulation data for one month (720 hours) or a whole year (8760 hours), and then perform analysis based on the periodic simulation data.

[0076] Preferably, the computing device can calculate the risk proportion based on the congestion risk component and the node electricity price; assess the risk severity based on the line over-limit relaxation variable in the output of the R-SCED model; calculate the incremental renewable energy consumption based on the actual wind and solar power curtailment in the system output of the R-SCED model; and generate the clearing simulation report based on the risk proportion, the risk severity, and the incremental renewable energy consumption.

[0077] The study statistically analyzed the nodal electricity price series to measure the effectiveness of the power allocation and storage scheme in mitigating electricity price volatility and levels. A higher risk proportion indicates that the congestion risk caused by uncertainty is more fully reflected in the electricity price. If the power allocation and storage scheme can reduce this proportion, it verifies its role in mitigating congestion risk.

[0078] Among them, the computing device can define the risk severity index of line l. Then, for the same system, we can compare the scenarios with and without storage allocation. Total value, if high-risk lines are allocated after storage The significant decrease indicates that energy storage alters the power flow distribution through charging and discharging regulation, mitigating the power flow impact caused by uncertainties and thus quantifying the degree of congestion mitigation.

[0079] Among them, the total absorption rate index can be defined for computing devices as follows: By comparing the absorption rates with and without energy storage schemes, the difference represents the incremental energy absorption brought about by energy storage.

[0080] In this embodiment, the line congestion probability risk and its allocation are internalized into the nodal price through the R-SCUC model and the R-SCED model, so that the simulated price signal can truly reflect the congestion risk under high uncertainty. This provides accurate incentives for using distribution and storage to alleviate grid transmission congestion, guides safer and more reasonable distribution and storage, improves the stability and security of the power system, and promotes the development of clean energy.

[0081] The method provided in the embodiments of this application has been described above. The system provided in the embodiments of this application will be described below.

[0082] Please see Figure 2 , Figure 2 A system architecture diagram for clearing simulation that takes into account blocking risk sharing is provided for embodiments of this application, such as... Figure 2 As shown, the system 20 includes: The acquisition module 201 is used to acquire the network topology and hardware parameters of the target power grid, as well as the historical wind and solar operation data of each new energy power station in the target power grid. Prediction module 202 is used to input the historical wind and solar power operation data into the wind and solar power output prediction model to obtain the predicted power output trajectory of each new energy station under different meteorological scenarios. The day-ahead bidding module 203 is used to input the corresponding predicted power output trajectory into the day-ahead bidding model for each weather scenario, and obtain the bidding power-price curve output by the day-ahead bidding model; wherein, the day-ahead bidding model aims to maximize the expected revenue of the aggregate, and the constraints of the day-ahead bidding model include bidding risk constraints based on conditional value of risk; the aggregate includes at least one of wind farms, photovoltaic farms and energy storage equipment; The unit combination module 204 is used to input the network topology, the hardware parameters and the bid electricity-price curve into the R-SCUC model to obtain the next day's unit start-up and shutdown and reserve capacity allocation plan output by the R-SCUC model; wherein, the constraints of the R-SCUC model include line power flow opportunity constraints, which are used to ensure that the probability of power flow exceeding the limit caused by wind and solar prediction errors does not exceed a given confidence level. The economic dispatch module 205 is used to input the unit start-up and shutdown and reserve capacity allocation plan for the next day into the R-SCED model to obtain the output of each node, the node price, and the congestion risk component of the node price output by the R-SCED model; wherein, the congestion risk component is obtained based on the marginal contribution of each node injection to the exposure of line power flow over-limit risk. The report generation module 206 is used to generate a clearing simulation report based on the output results of the R-SCED model corresponding to each meteorological scenario.

[0083] The clearing simulation that takes into account the sharing of blocking risk provided in this application embodiment can be understood by referring to the relevant content in the foregoing method embodiment section, and will not be repeated here.

[0084] like Figure 3 As shown, Figure 3 This is a schematic diagram of a possible logical structure of a computing device provided in an embodiment of this application. The computing device 30 includes a processor 301, a communication interface 302, a memory 303, and a bus 304. The processor 301, the communication interface 302, and the memory 303 are interconnected via the bus 304. In an embodiment of this application, the processor 301 is used to control and manage the operation of the computing device 30. For example, the processor 301 is used to execute... Figure 1 The steps in the embodiments and / or other processes used in the techniques described herein. Communication interface 302 is used to support communication by computing device 30. Memory 303 is used to store program code and data of computing device 30.

[0085] The processor 301 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc. The bus 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0086] In another embodiment of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the above-described... Figure 1 The method described in the embodiments.

[0087] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0088] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0089] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0090] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0091] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0092] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or 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, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0093] 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A clearing simulation method considering congestion risk allocation, characterized in that, The method includes: Obtain the network topology and hardware parameters of the target power grid, as well as the historical wind and solar operation data of each new energy power station in the target power grid; Inputting the historical wind and solar power operation data into the wind and solar power output prediction model, the predicted power output trajectory of each of the new energy power stations under different meteorological scenarios is obtained. For each meteorological scenario, the corresponding predicted power output trajectory is input into the day-ahead bidding model to obtain the bid power-price curve output by the day-ahead bidding model; wherein, the day-ahead bidding model aims to maximize the expected revenue of the aggregate, and the constraints of the day-ahead bidding model include bidding risk constraints based on conditional value at risk; the aggregate includes at least one of wind farms, photovoltaic farms, and energy storage equipment; The network topology, hardware parameters, and bid electricity-price curve are input into the risk-safety constraint unit combination R-SCUC model to obtain the next day's unit start-up and shutdown and reserve capacity allocation plan output by the R-SCUC model; wherein, the constraints of the R-SCUC model include line power flow opportunity constraints, which are used to ensure that the probability of power flow exceeding the limit caused by wind and solar forecast errors does not exceed a given confidence level. The next day's unit start-up and shutdown and reserve capacity allocation plan is input into the Risk-Safety Constraint Economic Dispatch (R-SCED) model to obtain the output of each node, the node price, and the congestion risk component of the node price output by the R-SCED model; wherein, the congestion risk component is obtained based on the marginal contribution of each node injection to the exposure of line power flow exceedance risk. Based on the output results of the R-SCED model corresponding to each meteorological scenario, a clearing simulation report is generated.

2. The method according to claim 1, characterized in that, The wind and solar power output prediction model is a dynamic conditional variance time series regression model, which is used to express wind and solar power output as the product of the historical mean, standard deviation and standardized residual under the corresponding meteorological conditions; wherein, the historical mean is obtained based on the historical wind and solar operation data through gradient boosting regression or neural network mapping.

3. The method according to claim 2, characterized in that, The calculation process of the standardized residual includes: A Gaussian Copula model is constructed on the initial standardized residuals to capture their temporal dependence, resulting in a correlation coefficient matrix that reflects the degree of correlation between the initial standardized residuals at different time periods; wherein, the initial standardized residuals are the prediction errors of different time periods in the historical wind and solar operation data under the corresponding meteorological conditions; Gaussian samples are generated based on the correlation coefficient matrix of the corresponding meteorological conditions through Cholesky decomposition. Based on the Gaussian sample, the standardized residual is obtained through inverse probability transformation.

4. The method according to any one of claims 1-3, characterized in that, The expression for the bidding risk constraint includes: ； ； ； Where s represents the electricity price scenario, and S represents the set of electricity price scenarios. Confidence level The maximum possible loss quantile; In scenario s, when the total revenue is less than The difference in time; t is the time period number, T is the total number of time periods in a day; For the predicted clearing electricity price in scenario s during time period t, The planned electricity sales for the day before time period t. and These represent the actual discharge power and charging power of the energy storage during time period t in scenario s, respectively. The power of wind and solar curtailment in time period t under scenario s; Let be the probability weights for scenario s. This represents the minimum acceptable expected return level.

5. The method according to any one of claims 1-3, characterized in that, The expressions for the line power flow opportunity constraints include: ； ； in, The sensitivity factor for injecting power into the bus node of line l. It is the sum of the power output of all units and new energy sources in the bus at node t during time period. This is a preset baseline value representing the sum of the output of all units and new energy sources in the bus at node t during time period t. The load of the bus at node t during time period t; Represents fluctuations in all sets of uncertainty U. Within, the value of the power flow increment of line l under the worst-case scenario; For the thermal stability limit of the line, This is an auxiliary variable for the penalty of exceeding the limit.

6. The method according to any one of claims 1-3, characterized in that, The allocation method for the blocking risk component is as follows: The shadow price associated with the slack variable in the risk constraint of the R-SCED model is multiplied by the node sensitivity coefficient of the corresponding line to obtain the congestion risk component; the shadow price is used to reflect the marginal cost incurred by the system to control the potential overruns caused by wind and solar uncertainties within the risk tolerance range.

7. The method according to any one of claims 1-3, characterized in that, The clearing simulation report is generated based on the output results of the R-SCED model corresponding to each meteorological scenario, including: The risk weight is calculated based on the congestion risk component and the node electricity price. The severity of the risk is assessed based on the line limit relaxation variable in the output of the R-SCED model. Based on the actual wind and solar curtailment power in the system output of the R-SCED model, calculate the incremental renewable energy consumption. Based on the risk proportion, the risk severity, and the incremental amount of new energy consumption, the clearing simulation report is generated.

8. A clearing simulation system that takes into account the sharing of congestion risk, characterized in that, The system includes: The acquisition module is used to acquire the network topology and hardware parameters of the target power grid, as well as the historical wind and solar operation data of each new energy power station in the target power grid. The prediction module is used to input the historical wind and solar power operation data into the wind and solar power output prediction model to obtain the predicted power output trajectory of each of the new energy power stations under different meteorological scenarios. The day-ahead bidding module is used to input the corresponding predicted power output trajectory into the day-ahead bidding model for each of the aforementioned meteorological scenarios, and obtain the bid power-price curve output by the day-ahead bidding model; wherein, the day-ahead bidding model aims to maximize the expected revenue of the aggregate, and the constraints of the day-ahead bidding model include bidding risk constraints based on conditional value of risk; the aggregate includes at least one of wind farms, photovoltaic farms, and energy storage equipment; The unit combination module is used to input the network topology, the hardware parameters, and the bid electricity-price curve into the risk-security constraint unit combination R-SCUC model to obtain the next day's unit start-up and shutdown and reserve capacity allocation plan output by the R-SCUC model; wherein, the constraints of the R-SCUC model include line power flow opportunity constraints, which are used to ensure that the probability of power flow exceeding the limit caused by wind and solar forecast errors does not exceed a given confidence level. The economic dispatch module is used to input the next day's unit start-up and shutdown and reserve capacity allocation plan into the risk-safety constraint economic dispatch R-SCED model to obtain the output of each node, the node price, and the congestion risk component of the node price output by the R-SCED model; wherein, the congestion risk component is obtained based on the marginal contribution of each node to the exposure of line power flow over-limit risk. The report generation module is used to generate a clearing simulation report based on the output results of the R-SCED model corresponding to each meteorological scenario.

9. A computing device, characterized in that, include: Memory, used to store programs; A processor for loading the program to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method of any one of claims 1-7.

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