Novel power system scheduling method and system based on wind-solar uncertain set and carbon flow

Abstract

The invention discloses a novel power system scheduling method and system based on a wind and light uncertainty set and carbon flow, and the system comprises a wind and light uncertainty set module which is used for obtaining the historical data of a power grid load, photovoltaic output and wind power output, and determining the wind and light uncertainty set under the residual vector norm bounded constraint based on a vector autoregression model; integrating the wind and light uncertainty set into an adaptive robust optimization framework, and adopting Camp; carrying out CG algorithm iterative solution to obtain a wind-solar optimal uncertainty set; the scheduling scheme module is used for determining a scheduling scheme according to the wind and light optimal uncertainty set; the scheduling scheme is optimized by taking low-carbon benefit maximization of the scheduling scheme as a target; according to an error between the optimized scheduling scheme and actual wind and light output, updating a wind and light optimal uncertainty set under a residual vector norm bounded constraint; and obtaining a final scheduling scheme based on the updated wind and light optimal uncertainty set. The technical problem of difficulty in cooperative processing of renewable energy uncertainty and system low-carbon benefit fine evaluation is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy power system scheduling and control, and particularly relates to a new power system scheduling method and system based on wind and light uncertain sets and carbon flow. BACKGROUND

[0002] The present application relates to the field of new energy power system scheduling and control, and particularly relates to a new power system model and scheduling method introducing new uncertain sets and carbon flow.

[0003] In the prior art, the research on such an integrated system mainly develops along two paths. Firstly, in terms of dealing with uncertainty, the random optimization method represented by scenario analysis method is widely used. By generating and reducing a series of typical wind and light output scenarios, this method can convert the uncertainty problem into a deterministic optimization problem under multiple scenarios for solving, thereby improving the robustness of the scheduling scheme. Secondly, in terms of low-carbon benefit evaluation, carbon emission flow theory is introduced to quantify the carbon emission responsibility of each node. However, such models often take the minimization of total system operation cost as the main goal, although reducing abandoned wind and light can reduce carbon emissions to a certain extent, they lack a fine quantification and evaluation mechanism of the low-carbon performance of the system and a model research on the uncertainty of renewable resources such as wind and light, resulting in an overly conservative model that is difficult to achieve effective coordination between economy and low carbon in scheduling decisions.

[0004] At present, existing methods either focus on carbon flow analysis under deterministic conditions and ignore volatility, or focus on uncertainty optimization and lack fine low-carbon evaluation indicators, resulting in the inability to achieve optimal coordination between economy and low carbon in scheduling decisions; there is still a significant technical gap in the research on the deep integration of fine carbon emission flow analysis and optimization scheduling considering uncertainty. Existing technologies cannot accurately capture the random characteristics of renewable energy and quantify the specific impact of scheduling strategies on the carbon emission flow of the whole system under different scenarios in a unified framework. This leads to the fact that scheduling decisions often have to choose between the two, making it difficult to maximize the low-carbon potential of the integrated system in a real fluctuating environment while ensuring the economic and reliable operation of the system. In addition, traditional static uncertainty modeling methods cannot capture the time autocorrelation of wind and solar output, increasing the economic loss caused by excessive conservatism due to the addition of scenarios that do not match reality. Traditional dynamic uncertain sets based on AR models cannot accommodate multiple different time series models due to their simple model, resulting in poor applicability and accuracy. SUMMARY

[0005] To address the shortcomings of existing technologies, a novel power system model and scheduling method based on new uncertain sets and carbon flows are introduced. This model also considers the stochastic volatility of renewable energy and the refined tracking of carbon emissions, thereby synergistically optimizing the economic and low-carbon benefits of cascaded hydro-wind-solar-storage integrated systems. This solves the technical problem that existing scheduling models for cascaded hydro-wind-solar-storage integrated systems struggle to coordinate the handling of renewable energy uncertainties and the refined assessment of system low-carbon benefits.

[0006] The present invention adopts the following technical solution.

[0007] This invention proposes a novel power system dispatching method based on wind and solar uncertainty sets and carbon flow, comprising: Step 1: Obtain historical data on grid load, photovoltaic output, and wind power output. Based on the vector autoregression model, determine the wind and solar uncertainty set under the bounded constraint of the residual vector norm. Step 2: Integrate the wind and solar uncertainty set into the adaptive robust optimization framework, and use the C&CG algorithm to iteratively solve the problem to obtain the optimal wind and solar uncertainty set; Step 3: Determine the scheduling scheme based on the optimal uncertainty set of wind and solar power; optimize the scheduling scheme with the goal of maximizing the low-carbon benefits of the scheduling scheme. Step 4: Based on the error between the optimized scheduling scheme and the actual output of wind and solar power, update the optimal uncertainty set of wind and solar power under the bounded constraint of the residual vector norm; based on the updated optimal uncertainty set of wind and solar power, obtain the final scheduling scheme.

[0008] Step 1 includes: establishing a vector autoregressive model for grid load, photovoltaic output, and wind power output; using historical data of grid load, photovoltaic output, and wind power output, and based on the vector autoregressive model, obtaining an uncertain data sequence of grid load, photovoltaic output, and wind power output within the total time period; obtaining the residual vector of the uncertain data sequence; and using the uncertain data sequence that satisfies the bounded constraint of the residual vector norm as the wind and solar uncertain set.

[0009] The mathematical expression for the order VAR model is: (1) (2) (3) In the formula, For a moment - The power grid load, For a moment - Photovoltaic power output, For a moment - Wind power output; , , are seasonal pattern coefficients corresponding to grid load demand, photovoltaic output, and wind power output at time , , , are noises corresponding to grid load demand, photovoltaic output, and wind power output at time , , , are correlation coefficient matrices of grid load, photovoltaic output, and wind power output with grid load at hours ago; , , are correlation coefficient matrices of grid load, photovoltaic output, and wind power output with photovoltaic output at hours ago; , , are correlation coefficient matrices of grid load, photovoltaic output, and wind power output with wind power output at hours ago; grid load at time ≥ 0, photovoltaic output at time ≥ 0, wind power output at time ≥ 0, , , is the number of times, representing the total period. ,

[0010] The residual vector of the uncertain data sequence is determined based on the VAR model of order , as shown in the following formula:

[0011] In the formula, is the residual vector.

[0012] The residual norm bounded constraint includes: L2 norm bounded constraint of the residual vector, L norm bounded constraint of the residual vector, and L1 norm bounded constraint of the residual vector; as shown in the following formula:

[0013]

[0014]

[0015] In the formula, , are L2 norm and its threshold value, respectively, ,​ L respectively Norm and its threshold , Let L1 norm and its threshold be defined.

[0016] A prediction error sequence is obtained by performing a one-step prediction using historical data and a VAR model, with the maximum absolute value of the prediction error being used. quantile as L Norm threshold, the prediction error sequence within the rolling window is a subsequence, with The threshold for L2 norm is defined as the condition that all subsequences have an L2 norm less than a certain threshold, and the threshold for L1 norm is defined as the maximum value of the L1 norm of all subsequences. , where is the confidence level.

[0017] Step 2 includes: In the adaptive robust optimization framework, when using the C&CG algorithm for iterative solution, adding scenarios that cause economic losses to the system to the main problem to obtain the optimal uncertainty set of wind and solar power. Among them, offline clustering based on historical data is used to generate wind and solar power output scenarios, the system economic loss index of each scenario is calculated, the scenarios are clustered according to the economic loss index, and the scenarios that are no more than a set distance threshold from the cluster center are identified as the scenarios that cause system economic losses.

[0018] Step 3 includes: establishing a scheduling optimization model with the objective function of minimizing the expected total operating cost under the system's flexibility and stability constraints; and solving the scheduling optimization model based on the optimal uncertainty set of wind and solar power to determine the scheduling scheme.

[0019] Step 3 also includes: The low-carbon benefits of scheduling schemes are determined based on carbon emission flow theory, including: calculating the branch carbon flow rate, which characterizes the carbon emission rate of line transmission, and calculating the nodal carbon potential, which characterizes the carbon emission intensity of nodal electricity consumption. Specifically, the active power and reactive power commands of each branch in the scheduling scheme are used to calculate the node. carbon potential As shown in the following formula:

[0020] In the formula, For inflow node branch road Active carbon flow rate refers to the amount of carbon dioxide emitted per hour for a unit of active power transmitted in a power system. For inflow node branch road The reactive carbon flow rate refers to the amount of carbon dioxide emitted per hour corresponding to the transmission of a unit of reactive power in a power system. , are active power instruction and reactive power instruction on branch in the dispatching scheme respectively; is the conversion coefficient of reactive contribution carbon potential, which functions as an equivalent to convert the reactive contribution carbon potential into equivalent active contribution carbon potential; is the set of all branches flowing into node .

[0021] Step 4 comprises: The optimized dispatching scheme comprises the optimized photovoltaic output instruction and wind power output instruction; the error sequence is formed by the error of the photovoltaic output instruction and the actual photovoltaic output and the error of the wind power output instruction and the actual wind power output; The L norm threshold value is the quantile of the absolute value of the maximum error, the error sequence in the rolling window is a subsequence, the L2 norm of the subsequence is less than the threshold value as the L2 norm threshold value, and the L1 norm maximum value of all subsequence is the L1 norm threshold value, is the confidence level; When updating, if each norm threshold value is reduced, steps 1 to 3 are repeatedly executed; when each norm threshold value is no longer reduced or reaches a preset iteration number, the iteration is ended, and the updated wind-solar optimal uncertainty set is output. The application further provides a novel power system dispatching system based on a wind-solar uncertainty set and carbon flow, comprising: A wind-solar uncertainty set module is used to acquire historical data of power grid load, photovoltaic output and wind power output, determine the wind-solar uncertainty set under the constraint of residual vector norm bound based on a vector autoregressive model, integrate the wind-solar uncertainty set into an adaptive robust optimization framework, and obtain the wind-solar optimal uncertainty set by iterative solution with a C&CG algorithm.

[0022] A dispatching scheme module is used to determine the dispatching scheme according to the wind-solar optimal uncertainty set, optimize the dispatching scheme with the maximum low-carbon benefit of the dispatching scheme as the target, update the wind-solar optimal uncertainty set under the constraint of residual vector norm bound according to the error of the optimized dispatching scheme and the actual wind-solar output, and obtain the final dispatching scheme based on the updated wind-solar optimal uncertainty set. The application is also a terminal comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0023] The application is also a computer readable storage medium, which stores a computer program; the program is executed by the processor to realize the steps of the method.

[0024] The application is also a computer readable storage medium, which stores a computer program; the program is executed by the processor to realize the steps of the method.

[0025] ​The beneficial effects of this invention, compared with the prior art, include at least the following: This invention proposes a novel power system model and scheduling method that introduces a new uncertainty set and carbon flow. First, this invention captures the temporal autocorrelation of wind and solar energy output by constructing a novel dynamic uncertainty set for photovoltaic and wind power. Compared with traditional static methods, this best describes the output behavior of wind and solar energy, reducing supply scenarios and minimizing economic losses caused by excessive conservatism. Finally, carbon emission flow theory is applied to the post-evaluation of uncertain scheduling results. Indicators such as nodal carbon potential are used to intuitively quantify the low-carbon value of the scheduling scheme in mitigating fluctuations and promoting the consumption of new energy sources. Computational results demonstrate that the method proposed in this invention can significantly improve the system's flexibility in responding to renewable energy fluctuations, effectively reducing carbon emissions while lowering total operating costs, and providing effective technical support for the low-carbon economic operation of high-proportion renewable energy power systems. Attached Figure Description

[0026] Figure 1 This is a flowchart of a novel power system dispatching method based on wind and solar uncertainty sets and carbon flow proposed in this invention; Figure 2 The wind and solar power output time series in the embodiments of the present invention and the wind and solar uncertainty set (VAR-Based uncertainty set) based on the VAR model proposed in the present invention. Figure 3 The residual sequences (photovoltaic residuals and wind power residuals) and VAR prediction intervals (photovoltaic residual prediction intervals and wind power residual prediction intervals) in the embodiments of the present invention are shown. Figure 4 This is the waveform of wind-solar residual cross-correlation in an embodiment of the present invention. Detailed Implementation

[0027] 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 of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0028] This invention proposes a novel power system dispatching method based on wind and solar uncertainty sets and carbon flow. Under a unified optimization framework, the method first uses a novel dynamic uncertainty set to model the uncertainty of wind and solar power output, then constructs a low-carbon economic dispatching model with the goal of minimizing the expected total cost, and finally applies an improved carbon emission flow theory to quantitatively evaluate the low-carbon benefits of the optimization results.

[0029] like Figure 1As shown, the method includes: Step 1: Obtain historical data on grid load, photovoltaic output, and wind power output. Based on the vector autoregression model, determine the wind and solar uncertainty set under the bounded constraint of the residual vector norm.

[0030] Specifically, step 1 includes: Step 1.1: Establish a vector autoregressive model for grid load, photovoltaic power output, and wind power output; For a three-variable system including grid load, photovoltaic output, and wind power output, the core of the vector autoregression (VAR) model is to treat each variable in the system as a function of the lagged values ​​of all variables, thereby simultaneously obtaining the autocorrelation and cross-correlation of grid load, photovoltaic output, and wind power output. The mathematical expression for the order VAR model is: (1) (2) (3) In the formula, For a moment - The power grid load, For a moment - Photovoltaic power output, For a moment - Wind power output; , , They are time points The seasonality model coefficients corresponding to the grid load demand, photovoltaic output, and wind power output are determined by fitting typical daily curves in the example. , , They are time points The noise corresponding to the grid load demand, photovoltaic output, and wind power output is used in the embodiment. The residuals of the grid load demand, photovoltaic output, and wind power output are used as noise to represent random fluctuations that the model cannot explain. The residuals are random sequences with zero mean and a certain covariance structure. , , They are respectively Correlation matrix of grid load, photovoltaic output, wind power output and grid load 1 hour ago; , , They are respectively Correlation matrix of grid load, photovoltaic output, wind power output and photovoltaic output up to 1 hour ago; , , They are respectively The correlation coefficient matrix of grid load, photovoltaic output, wind power output, and wind power output one hour ago; time period power grid load ≥0, time Photovoltaic output ≥0, time wind power output ≥0, , The number of moments represents the total time period; Step 1.2: Using historical data on grid load, photovoltaic output, and wind power output, and based on the VAR model, obtain the uncertain data sequence of grid load, photovoltaic output, and wind power output within the total time period; Renewable energy output exhibits strong temporal and spatial autocorrelation. Traditional uncertainty sets, which are intervals based on predicted values ​​± deviations, are completely unable to capture this dynamic structure. The VAR model itself is a model specifically designed to capture the autocorrelation and cross-correlation of multivariate time series. By using the VAR model, based on the degree and direction of the influence of historical data on current data, the inherent dynamic laws and spatiotemporal correlations of grid load, photovoltaic output, and wind power output are captured. The VAR model becomes the framework for obtaining the uncertainty set of wind and solar power.

[0031] This invention utilizes historical data on grid load demand, photovoltaic output, and wind power output to perform structural estimation on a VAR model. The correlation coefficient matrix accurately quantifies the degree and direction of the influence of historical data on current data. In the embodiments, according to... The correlation coefficient matrix is ​​determined by the marginal effect of historical data from hours ago on the current data. The VAR model learns the correlation coefficient matrix from historical data and determines the seasonal pattern coefficients by fitting typical daily curves. Essentially, it encodes the periodic physical laws of wind and solar energy, such as meteorological data, thus embedding physical laws into the VAR model. This makes the uncertain data sequences of grid load, photovoltaic output, and wind power output output by the VAR model real, and the scenarios obtained based on such uncertain data sequences are consistent with reality.

[0032] Step 1.3: Obtain the residual vector of the uncertain data sequence; the uncertain data sequence that satisfies the bounded constraint of the residual vector norm is taken as the wind and solar uncertainty set. ; based on The VAR model of order 1 determines the residual vector of an uncertain data sequence, as shown in the following equation:

[0033] In the formula, It is the residual vector; , , They are time points The noise corresponding to grid load demand, photovoltaic output, and wind power output; , The number of moments represents the total time period; The bounded constraints of the residual norm include: the bounded constraints of the L2 norm of the residual vector, and the bounded constraints of the L2 norm of the residual vector. Norm bounded constraints, L1 norm bounded constraints of residual vectors; As shown in the following formula:

[0034]

[0035]

[0036] In the formula, , These are the L2 norm and its threshold, respectively. , L respectively Norm and its threshold , L1 norm and its threshold; A prediction error sequence is obtained by performing a one-step prediction using historical data and a VAR model, with the maximum absolute value of the prediction error being used. quantile as L Norm threshold, the prediction error sequence within the rolling window is a subsequence, with The threshold for L2 norm is defined as the condition that all subsequences have an L2 norm less than a certain threshold, and the threshold for L1 norm is defined as the maximum value of the L1 norm of all subsequences. Confidence level; The residuals are the true random shocks that the VAR model cannot explain. Different norm bounded constraints filter random shocks in different ways. Among them, the L2 norm bounded constraint filters the total disturbance of random shocks over the total time period. Norm bounded constraints filter random shocks at each moment within the total time period, while L1 norm bounded constraints filter the sum of the absolute values ​​of random shocks at all moments within the total time period. Traditional scenario methods rely on Monte Carlo simulations or kernel density estimation to generate hundreds to thousands of scenarios to cover the probability distribution and correlation of wind and solar power output. For example, capturing a 95% confidence interval might require 1000 scenarios. The above filtering optimizes and controls the scope of scenarios. For instance, when L2 norm bounded constraints and / or L1 norm bounded constraints are not satisfied, catastrophic scenarios that, although real, have an extremely low probability of occurrence can be excluded from uncertain data sequences. The existence of such scenarios would lead to scheduling schemes sacrificing economy for safety. When the norm bounded constraint is not satisfied, scenarios that exist in reality but have instantaneous changes can be excluded from the uncertain data sequence. Traditional uncertain sets contain such scenarios because there is no correlation between data and constraints at different times. Therefore, the wind and light uncertain set obtained by this invention based on the residual vector norm bounded constraint effectively preserves real high-risk scenarios. The robust optimization decision implemented based on the wind and light uncertain set can achieve a much better balance between safety and economy than traditional methods.

[0037] It is a key control parameter; increase it. This will expand the uncertainty set, making the optimization results more conservative to cope with more extreme cases; adjusting... This would tighten the uncertainty set, resulting in a more aggressive and lower-cost solution, but potentially lacking robustness. In the example, a one-step prediction is performed using historical data and a VAR model to obtain the prediction error, with the 95th percentile of the absolute value of the prediction error used as L. Norm thresholds are used, with the prediction error within the rolling window as a subsequence. The L2 norm threshold is set when the L2 norm of 95% of the subsequences is less than a certain value, and the L1 norm threshold is set when the maximum L1 norm of all subsequences is the maximum value. By setting and periodically updating each norm threshold, the landscape uncertainty set is a set of scenes that effectively reduces the scene and ensures that a certain confidence level (95%) of the historical data points fall within the path range generated by the uncertainty set. This has multiple advantages over traditional scene generation methods.

[0038] The wind and solar uncertainty set generation method proposed in this invention is a novel dynamic uncertainty set for photovoltaic and wind power. It can effectively capture the time autocorrelation of wind and solar energy output. Compared with traditional static methods, it can best describe the output behavior of wind and solar energy, reduce supply scenarios, and reduce economic losses caused by excessive conservatism. Furthermore, within the path range generated by the novel dynamic uncertainty set, the set of uncertainty scenarios can be defined using only the upper and lower bounds of the path.

[0039] The structure of a VAR model is related to two parameters: the number of variables and the maximum lag order. In this example, the VAR model is used as a modeling framework, and the least squares (OLS) method is used to estimate the structure of the VAR model. The choice of lag order depends on information criteria (such as AIC and BIC) to balance model complexity and fitting effect, which is a non-restrictive and better choice.

[0040] Step 2: Integrate the wind and solar uncertainty set into the adaptive robust optimization framework, and use the C&CG algorithm to iteratively solve the problem to obtain the optimal wind and solar uncertainty set; Uncertain parameters are integrated into the Adaptive Robust Optimization (ARO) framework, and the C&CG algorithm is used for iterative solution. Scenarios causing economic losses to the system are continuously added to the main problem, ultimately converging to a solution applicable to all defined... The optimal solution is robust to all uncertainties within it; In this embodiment, offline clustering is performed based on historical data or Monte Carlo simulations to generate a large number of wind and solar power output scenarios. Economic loss indicators for these scenarios are calculated, including but not limited to: load shedding costs and wind / solar curtailment costs. Scenarios are clustered according to these economic loss indicators, with scenarios whose distance from the cluster center is no greater than a set distance threshold considered as causing system economic losses. This set distance threshold is determined manually based on the number of scenarios to be added to the main problem. In the C&CG iteration, the subproblem no longer seeks the worst-case scenario from a continuous set, but instead selects representative scenarios (such as the highest-cost scenario in the cluster) from various economic impact clusters and adds them to the main problem. This significantly reduces the number of scenarios to be added to the main problem and ensures that the added scenarios are representative of different economic damage modes, avoiding redundancy, accelerating convergence, and directly generating the optimal uncertainty set for wind and solar power based on economic loss constraints, further reducing supply scenarios and minimizing economic losses caused by excessive conservatism.

[0041] Step 3: Determine the scheduling scheme based on the optimal uncertainty set of wind and solar power; optimize the scheduling scheme with the goal of maximizing the low-carbon benefits of the scheduling scheme.

[0042] Specifically, under the constraints of system flexibility and stability, a scheduling optimization model is established with the objective function of minimizing the expected total operating cost; based on the optimal uncertainty set of wind and solar power, the scheduling optimization model is solved to determine the scheduling scheme.

[0043] The system's flexibility constraints include, but are not limited to, constraints on conventional unit operation and constraints on renewable energy and energy storage; the system's stability constraints include, but are not limited to, constraints on inertia and frequency control; in this embodiment, the total system inertia is not less than the critical inertia level, and the system meets the minimum inertia requirement to prevent frequency collapse; wherein, the critical inertia level CIL is determined by the rate of change of frequency ROCOF; the inertia constraint scheduling is set as follows: if the total system inertia at the current moment is less than the critical inertia level, then the output of synchronous units is increased; asynchronous resources (such as wind power, photovoltaics, batteries, fuel cells) do not provide inertia; In this embodiment, a hypercube constraint plane is constructed using flexibility and stability constraints, and the optimization scheduling is performed with the objective function of minimizing the total operating cost.

[0044] Specifically, when quantitatively evaluating the low-carbon benefits of the optimal scheduling scheme based on carbon emission flow theory, carbon emission responsibility is virtually attached to the active power flow of the power grid. Key evaluation indicators are calculated and analyzed, including: "node carbon potential" to characterize the carbon emission intensity of node electricity consumption and "branch carbon flow rate" to characterize the carbon emission rate of line transmission. Through these indicators, the contribution of the integrated system to reducing the overall carbon intensity of the power grid under different renewable energy output scenarios is quantitatively evaluated.

[0045] Using the active power and reactive power commands of each branch in the scheduling scheme, the node is calculated. carbon potential (Unit: kgCO2 / kWh), as shown in the following formula: (5) In the formula, For inflow node branch road Active carbon flow rate (unit: kgCO2 / h) refers to the amount of carbon dioxide emitted per hour corresponding to the transmission of a unit of active power in a power system. For inflow node branch road The reactive carbon flow rate (unit: kgCO2 / h) refers to the amount of carbon dioxide emitted per hour corresponding to the transmission of a unit of reactive power in a power system. , Branches in the scheduling scheme Active power command and reactive power command; The conversion factor for reactive carbon potential is used to convert reactive carbon potential into equivalent active carbon potential. In the examples, it is manually calibrated, typically within the range of 0.1-0.2 (unit: ...). ); For all inflow nodes A set of branch paths; carbon flow rate of the branch in this invention Defined as the flow of active or reactive power through a branch per unit time. The carbon emissions are calculated by considering the reactive carbon potential contribution when determining the nodal carbon potential, which is used to characterize the nodal carbon emissions. The carbon emissions on the generation side corresponding to each unit of electrical energy consumed. By analyzing these indicators, the improvement effect of the integrated system on the carbon intensity distribution of the power grid can be quantitatively assessed under different scenarios.

[0046] Step 4: Based on the error between the optimized scheduling scheme and the actual output of wind and solar power, update the optimal uncertainty set of wind and solar power under the bounded constraint of the residual vector norm; based on the updated optimal uncertainty set of wind and solar power, obtain the final scheduling scheme.

[0047] The optimized scheduling scheme includes optimized photovoltaic power output commands and wind power output commands; and an error sequence is formed by the error between the photovoltaic power output command and the actual photovoltaic power output, and the error between the wind power output command and the actual wind power output. With the absolute value of the maximum error quantile as L Norm threshold, the error sequence within the rolling window is a subsequence, with The threshold for L2 norm is defined as the condition that all subsequences have an L2 norm less than a certain threshold, and the threshold for L1 norm is defined as the maximum value of the L1 norm of all subsequences. Confidence level; During the update, if the norm thresholds decrease, steps 1 to 3 are repeated; the iteration ends when the norm thresholds no longer decrease or the preset number of iterations is reached, and the updated optimal uncertainty set of the landscape is output.

[0048] The norm threshold has reached its minimum value when it no longer decreases, which narrows the scene to the optimal range and overcomes the scene redundancy problem caused by conservatism. Ideally, all norm thresholds have reached their minimum values. To avoid excessive iteration and wasted computing resources caused by non-convergence, the number of iterations is set based on running experience.

[0049] Figure 2 The wind and solar power output time series is compared with the wind and solar uncertainty set (VAR-Based uncertainty set) proposed in this invention. Figure 2 The paper demonstrates the actual fluctuations in wind and solar power output (actual photovoltaic output and actual wind power output) and uses an advanced VAR model to provide a dynamic, joint prediction uncertainty range (photovoltaic uncertainty set and wind power uncertainty set). Figure 3 The residual sequences (photovoltaic residuals and wind power residuals) and VAR prediction intervals (photovoltaic residual prediction intervals and wind power residual prediction intervals) are used to diagnose the effectiveness of the prediction model and quantify the statistical characteristics of the prediction error. Figure 4The waveform of the wind-solar residual cross-correlation proves that the uncertainties of wind power and photovoltaics are not independent, but have a close temporal linkage with a lag of ±24 hours.

[0050] Simulation experiments were conducted using the method proposed in this invention. In the embodiments, Scenario 1 only considers the grid connection of a cascaded hydro-wind-solar power generation system. Its optimized output results show that thermal power units undertake the main base load and peak-shaving tasks, while hydropower is limited by inflow and reservoir capacity, resulting in limited regulation capacity. Wind and solar power output is uncertain, and wind curtailment is likely to occur during peak wind power generation periods at night. Scenario 2, based on the cascaded hydro-wind-solar power generation system, configures a battery energy storage power station for the integrated system. Its optimized output results show that battery storage stores excess wind power during off-peak periods (such as 0-9 am) and discharges it during peak periods (such as after 5 pm), effectively reducing the peak-shaving pressure and output level of thermal power units and reducing wind curtailment. Scenario 3, based on the cascaded hydro-wind-solar power generation system, configures a pumped storage power station for the integrated system. Its optimized output results show that the pumped storage power station plays a similar role to battery energy storage but with larger capacity and a longer regulation cycle. It absorbs redundant power on a large scale at night and provides stable output during daytime peak periods, realizing large-scale spatiotemporal energy transfer.

[0051] Table 1. Comparison of Key Economic and Low-Carbon Indicators for Optimized Scheduling under Three Different System Configuration Scenarios

[0052] Table 1 compares the key economic and low-carbon indicators for three different system configuration scenarios. The data shows that compared to Scenario 1, Scenario 2 and Scenario 3 significantly reduce total cost, carbon emissions, and wind and solar power curtailment. Scenario 3, in particular, exhibits the lowest total cost (RMB 509,900), the lowest carbon emissions (6,589.24 tons), and the lowest power curtailment (216.45 MWh), demonstrating the best overall economic and environmental benefits. This fully demonstrates that the optimized scheduling method proposed in this invention can effectively coordinate various resources and significantly improve the system's flexibility, economy, and low-carbon performance through integrated energy storage.

[0053] This invention also proposes a novel power system dispatching system based on wind and solar uncertainty sets and carbon flow, comprising: The wind and solar uncertainty set module is used to acquire historical data on grid load, photovoltaic output, and wind power output. Based on the vector autoregression model, the wind and solar uncertainty set is determined under the bounded constraint of the residual vector norm. The wind and solar uncertainty set is integrated into the adaptive robust optimization framework, and the C&CG algorithm is used to iteratively solve the problem to obtain the optimal wind and solar uncertainty set. The scheduling scheme module is used to determine the scheduling scheme based on the optimal uncertainty set of wind and solar power; optimize the scheduling scheme with the goal of maximizing the low-carbon benefits of the scheduling scheme; update the optimal uncertainty set of wind and solar power under the bounded constraint of the residual vector norm based on the error between the optimized scheduling scheme and the actual output of wind and solar power; and obtain the final scheduling scheme based on the updated optimal uncertainty set of wind and solar power.

[0054] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0055] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0056] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0057] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0058] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A novel power system dispatching method based on wind and solar uncertainty sets and carbon flow, characterized in that, include: Step 1: Obtain historical data on grid load, photovoltaic output, and wind power output. Based on the vector autoregression model, determine the wind and solar uncertainty set under the bounded constraint of the residual vector norm. Step 2: Integrate the wind and solar uncertainty set into the adaptive robust optimization framework, and use the C&CG algorithm to iteratively solve the problem to obtain the optimal wind and solar uncertainty set; Step 3: Determine the scheduling scheme based on the optimal uncertainty set of wind and solar power; The goal is to optimize the scheduling scheme to maximize its low-carbon benefits. Step 4: Based on the error between the optimized scheduling scheme and the actual output of wind and solar power, update the optimal uncertainty set of wind and solar power under the bounded constraint of the residual vector norm; based on the updated optimal uncertainty set of wind and solar power, obtain the final scheduling scheme.

2. The novel power system dispatching method based on wind and solar uncertainty sets and carbon flow according to claim 1, characterized in that, Step 1 includes: establishing a vector autoregressive model for grid load, photovoltaic output, and wind power output; using historical data of grid load, photovoltaic output, and wind power output, and based on the vector autoregressive model, obtaining an uncertain data sequence of grid load, photovoltaic output, and wind power output within the total time period; obtaining the residual vector of the uncertain data sequence; and using the uncertain data sequence that satisfies the bounded constraint of the residual vector norm as the wind and solar uncertain set.

3. The novel power system dispatching method based on wind and solar uncertainty sets and carbon flow according to claim 2, characterized in that, The mathematical expression for the order VAR model is: In the formula, For a moment - The power grid load, For a moment - Photovoltaic power output, For a moment - Wind power output; , , They are time points The seasonality model coefficients corresponding to grid load demand, photovoltaic output, and wind power output; , , They are time points The noise corresponding to grid load demand, photovoltaic output, and wind power output; , , They are respectively Correlation matrix of grid load, photovoltaic output, wind power output and grid load 1 hour ago; , , They are respectively Correlation matrix of grid load, photovoltaic output, wind power output and photovoltaic output up to 1 hour ago; , , They are respectively The correlation coefficient matrix of grid load, photovoltaic output, wind power output, and wind power output one hour ago; time period power grid load ≥0, time Photovoltaic output ≥0, time wind power output ≥0, , The number of moments represents the total time period.

4. The novel power system dispatching method based on wind and solar uncertainty sets and carbon flow according to claim 3, characterized in that, based on The VAR model of order 1 determines the residual vector of an uncertain data sequence, as shown in the following equation: In the formula, This is the residual vector.

5. The novel power system dispatching method based on wind and solar uncertainty sets and carbon flow according to claim 4, characterized in that, The bounded constraints of the residual norm include: the bounded constraints of the L2 norm of the residual vector, and the bounded constraints of the L2 norm of the residual vector. Norm bounded constraints and L1 norm bounded constraints of the residual vector; as shown in the following equation: In the formula, , These are the L2 norm and its threshold, respectively. , L respectively Norm and its threshold , Let L1 norm and its threshold be defined.

6. The novel power system dispatching method based on wind and solar uncertainty sets and carbon flow according to claim 5, characterized in that, A one-step prediction sequence is obtained by using historical data and a VAR model, with the maximum absolute value of the prediction error as the criterion. quantile as L Norm threshold, the prediction error sequence within the rolling window is a subsequence, with The threshold for L2 norm is defined as the condition that all subsequences have an L2 norm less than a certain threshold, and the threshold for L1 norm is defined as the maximum value of the L1 norm of all subsequences. , where is the confidence level.

7. The novel power system dispatching method based on wind and solar uncertainty sets and carbon flow according to claim 1, characterized in that, Step 2 includes: In the adaptive robust optimization framework, when using the C&CG algorithm for iterative solution, adding scenarios that cause economic losses to the system to the main problem to obtain the optimal uncertainty set of wind and solar power; Among them, offline clustering based on historical data is used to generate wind and solar power output scenarios, the system economic loss index of each scenario is calculated, the scenarios are clustered according to the economic loss index, and the scenarios that are no more than a set distance threshold from the cluster center are identified as the scenarios that cause system economic losses.

8. The novel power system dispatching method based on wind and solar uncertainty sets and carbon flow according to claim 1, characterized in that, Step 3 includes: establishing a scheduling optimization model with the objective function of minimizing the expected total operating cost under the system's flexibility and stability constraints; and solving the scheduling optimization model based on the optimal uncertainty set of wind and solar power to determine the scheduling scheme.

9. The novel power system dispatching method based on wind and solar uncertainty sets and carbon flow according to claim 1, characterized in that, Step 3 also includes: The low-carbon benefits of scheduling schemes are determined based on carbon emission flow theory, including: calculating the branch carbon flow rate, which characterizes the carbon emission rate of line transmission, and calculating the nodal carbon potential, which characterizes the carbon emission intensity of nodal electricity consumption. In the scheduling scheme, the sum of active power on all branches flowing into the node is converted into the first electrical energy contributing to the carbon potential, and the sum of reactive power on all branches flowing into the node is converted into the second electrical energy contributing to the carbon potential. The ratio of the sum of carbon flow rates on all branches flowing into the node to the sum of the first and second electrical energies is used as the node carbon potential.

10. The novel power system dispatching method based on wind and solar uncertainty sets and carbon flow according to claim 1, characterized in that, Step 4 includes: The optimized scheduling scheme includes optimized photovoltaic power output commands and wind power output commands; and an error sequence is formed by the error between the photovoltaic power output command and the actual photovoltaic power output, and the error between the wind power output command and the actual wind power output. With the absolute value of the maximum error quantile as L Norm threshold, the error sequence within the rolling window is a subsequence, with The threshold for L2 norm is defined as the condition that all subsequences have an L2 norm less than a certain threshold, and the threshold for L1 norm is defined as the maximum value of the L1 norm of all subsequences. Confidence level; During the update, if the norm thresholds decrease, steps 1 to 3 are repeated; the iteration ends when the norm thresholds no longer decrease or the preset number of iterations is reached, and the updated optimal uncertainty set of the landscape is output.

11. A novel power system dispatching system based on wind and solar uncertain sets and carbon flow, characterized in that, include: The wind and solar uncertainty set module is used to acquire historical data on grid load, photovoltaic output, and wind power output. Based on the vector autoregression model, the wind and solar uncertainty set is determined under the bounded constraint of the residual vector norm. The wind and solar uncertainty set is integrated into the adaptive robust optimization framework, and the C&CG algorithm is used to iteratively solve the problem to obtain the optimal wind and solar uncertainty set. The scheduling scheme module is used to determine the scheduling scheme based on the optimal uncertainty set of wind and solar power; optimize the scheduling scheme with the goal of maximizing the low-carbon benefits of the scheduling scheme; update the optimal uncertainty set of wind and solar power under the bounded constraint of the residual vector norm based on the error between the optimized scheduling scheme and the actual output of wind and solar power; and obtain the final scheduling scheme based on the updated optimal uncertainty set of wind and solar power.

12. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-10.

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